**Table 3.3** Master geometries

**Fig. 3.13** Point cloud of **a** Master1 and **b** Master3

ison returns the number of points of the slave that are matched/not matched on the master, together with information about the error (maximum, mean, minimum, and standard deviation). These statistics are further elaborated by a matching algorithm to identify the best fit among the master geometry, i.e. *Master1*, *Master2*, *Master3* for the three experiments in Table 3.4.

The digital model of the physical pallet can be obtained as soon as all its faces are identified. Then, the part program of the whole pallet can be automatically generated and tested using the simulation software Vericut (Fig. 3.14) to identify possible interferences between the tools (and the machine) and the fixture, thus providing a verification for the pallet configuration under study. The simulation was successful for experiments shown in Table 3.4. An interference between the tool and the baseplates was correctly identified for another set of experiments where the baseplates were rotated by 90°, thus labelling the configuration as infeasible.

**Table 3.4** Results of the experiments

**Fig. 3.14** CNC simulation of the new pallet configuration in Vericut©

#### **3.6 Conclusions and Future Research**

This chapter presented an integrated approach with methods and tools to support the configuration, planning and execution activities in flexible manufacturing systems, while exploiting zero-point fixtures to support fast pallet (re)configuration.

The interoperability and knowledge integration issues have been addressed with a Factory Data Model based on semantic web technologies. The proposed inspection system consists of a laser scanner and verifies the correctness of the mounted pallet in the shop floor by comparing the scanned elements with the information included in the semantic model. Finally, non-linear process plan technologies have been exploited to support the generation of modular process plans and management policies have been used to take advantage of the degrees of freedom given by zero-point fixtures.

More details about the developed methods and tools have already been presented in conference papers [24, 43, 44] and contributions in journals and books [12, 26, 28, 32, 34, 35]. The methodologies and the developed prototypes have been tested on realistic industrial cases, thus demonstrating the feasibility and potential of the approach. The Pro2ReFix prototype (Sect. 3.5.1) was presented at BI-MU 2016 exhibition in Milan and attracted interest from various machine tool and system producers. The acquired knowledge and developed tools form the basis to expand the partner capabilities to start new industrial and research collaborations.

Future developments aim at reducing the limitations of the proposed methods and tools. Regarding pallet inspection, possible variations of the ideal geometry (e.g. due to allowable differences in part shape and positioning) should be considered. In addition, a broader analysis of the geometric and functional characteristics of the various elements in relations to the potential mounting errors should be performed to provide more precise information to the operator (e.g. wrong part mounted, no part mounted, fixture not in correct position). Based on the new functionalities provided by the software tools, also extensions to the current version of the Factory Data Model will be needed to properly support interoperability.

**Acknowledgements** This work has been funded by the Italian Ministry of Education, Universities and Research (MIUR) under the Flagship Project "Factories of the Future—Italy" (Progetto Bandiera "La Fabbrica del Futuro") [45], Sottoprogetto 2, research projects Pro2Evo (Product and Process Co-Evolution Management via Modular Pallet configuration) and Pro2ReFix (Product and Process Co-Evolution Management via Reconfigurable Fixtures).

The authors thank the companies Cembre S.p.A. Italia, Schunk Intec S.r.l., and MCM Machining Centers Manufacturing S.p.A. for the useful suggestions and discussions. In addition, Cembre S.p.A. made available their product and production data and Schunk provided the modular pallet for the prototype realization. Particular thanks go to all the colleagues at CNR and POLIMI participating in the definition and development of the work presented in this chapter.

#### **References**

**Open Access** This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this book are included in the book's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Chapter 4 Knowledge Based Modules for Adaptive Distributed Control Systems**

**Andrea Ballarino, Alessandro Brusaferri, Amedeo Cesta, Guido Chizzoli, Ivan Cibrario Bertolotti, Luca Durante, Andrea Orlandini, Riccardo Rasconi, Stefano Spinelli and Adriano Valenzano**

**Abstract** Modern automation systems are asked to provide a step change toward flexibility and reconfigurability to cope with increasing demand for fast changing and highly fragmented production—which is more and more characterising the manufacturing sector. This reflects in the transition from traditional hierarchical and centralised control architecture to adaptive distributed control systems, being the latter capable of exploiting also knowledge-based strategies toward collaborating behaviours. The chapter intends to investigate such topics, by outlining major challenges and proposing a possible approach toward their solution, founded on autonomous, self-declaring, knowledge-based and heterarchically collaborating control modules. The benefits of the proposed approach are discussed and demonstrated in the field of re-manufacturing of electronic components, with specific reference to a pilot plant for the integrated End-Of-Life management of mechatronic products.

#### **4.1 Scientific and Industrial Motivations**

Since several years, the manufacturing industry has been facing a number of technological and production challenges related to the increasing variability of mix and demand of products driven by a short product life cycle. Nevertheless, the growing industrial demand for increased levels of reconfigurability—having impact both in production process automation, supervision and data collection and aggregation systems—is not properly fulfilled, mainly due to the lack of widely accepted solutions

CNR-STIIMA, Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Milan, Italy

e-mail<EMAIL_ADDRESS>
A. Cesta · A. Orlandini · R. Rasconi CNR-ISTC, Istituto di Scienze e Tecnologie della Cognizione, Rome, Italy

I. C. Bertolotti · L. Durante · A. Valenzano

CNR-IEIIT, Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, Turin, Italy

A. Ballarino (B) · A. Brusaferri · G. Chizzoli · S. Spinelli

and integrated reference models. In fact, implemented solutions are today typically characterized by rigid hierarchical structures, organized into strictly coupled layers.

Centralized PLCs (Programmable Logic Controller) architectures are often deployed, integrating the overall control logic of the production line for coordinating the execution of single mechatronic devices and machines. Such monolithic approach represents one of the major obstacles to achieve short-time reconfigurations. In fact, without a proper modularization and standardization of the control application entities, adaptations of automation system behaviour—to properly support required changes of factory production assets—result to be strongly time consuming for control system engineers and system integrators. Furthermore, the lack of modularization and standardization critically impacts on readability, portability and integration of single control modules across different applications, thus preventing the capitalization and re-use of company specific know-how on production process.

Further to this, modifications or extensions within the programs of the production line PLC possibly leads to the introduction of code errors, thus requiring extensive validation of complete automation system before deployment. Nevertheless, such activity is fundamental to avoid unpredicted deviations of the process execution, which could cause damages to devices/machines or, more critically, to human operators. As a result, existing industrial control systems are often very inefficient in facing the ever increasing demand for flexibility, expandability, agility and reconfigurability, which are typical requirements of advanced manufacturing system solutions [1, 2].

The previous requirements are fundamental to ensure a modern approach to efficient manufacturing, whose major competitiveness pillars rely upon: high value added goods, knowledge intensive production processes, and efficient and safe operating environments. This extremely articulated context is a very promising and appealing opportunity for countries heavily involved in the production of capital goods, machine tools and sophisticated solutions for manufacturing systems. A key capability of the control system is to evolve over time in order to anticipate and persistently adapt the control logics, functions and architectures to the evolving production scenarios. Therefore, flexibility in control infrastructure shall be prosecuted at the level of single control unit to leverage the hardware reconfigurability by exposing a set of (automation) functions/tasks that the single machine or device can execute to support production objectives. This latter aspect could effectively help in transforming monolithic and hardcoded solutions toward service oriented approaches. Yet this transformation cannot be fully tackled without considering that the execution of single task or function on a machine could either address the interaction with physical actuators, or imply a synchronization problem between different devices. Therefore, a real-time software infrastructure and communication framework shall be developed to properly support reliability within industrial automation field.

This chapter addresses the challenges related to the conception and development of next generation control systems, with reference to their application in Reconfigurable Manufacturing Systems (RMSs), as a viable solution while addressing varying production conditions within the manufacturing industry. The proposed approach, named *Generic Evolutionary Control Knowledge*-*based mOdule* (GECKO), aims at developing a flexible distributed control infrastructure, based on the interactive cooperation of control modules. GECKO enables each device (e.g. end-effector, complex machine equipment, integrated cell or even system) to evolve from a stand-alone, rigidly and hierarchically managed condition to an autonomous, self-declaring, hierarchically interacting and collaborating scenario. GECKO modules are conceived to detect and interpret the production environment characteristics and to adapt its capabilities on the basis of specific requirements by automatically accomplishing local and global objectives.

In consideration of aforementioned topics, the next sections will present the relevant state of the art (Sect. 4.2) and then the context and reference problem (Sect. 4.3). The overall approach toward an adaptive control infrastructure is first described (Sect. 4.4), and then discussed with particular reference to its realization via the GECKO solution (Sect. 4.5). A specific focus on Reconfigurable Transportation Systems (RTSs) will be kept as target application for proposed GECKO solution (Sect. 4.6), since RTSs can play a relevant role in embedding manufacturing systems with the capability of adapting their structure and functionalities to the evolving needs of production processes. Finally, the conclusions are presented in Sect. 4.7.

#### **4.2 State of the Art**

Traditional control systems based on hierarchical and centralized control structures hold good performance in terms of productivity over a limited and specific range of conditions. Nonetheless, such large monolithic software packages typically need relevant modifications of the control code to cope with any sort of (even minimal) system adaptation and reconfiguration. As a result, they are very inefficient to face the current requirements of flexibility, expansibility, agility and re-configurability required by advanced manufacturing system solutions.

When considering the existing scientific and industrial practices to evolve from current centralised approaches, three major topics shall be addressed, namely interoperable and pluggable (mostly defined as *plug and play*, P&P) control solutions, design of automatically reconfigurable control solutions as well as implementation of optimization strategies and learning mechanisms in the control.

In the field of P&P solutions, a number of EU projects [3–5] and scientific papers [6, 7] propose advanced solutions mostly related to specific devices (such as robots) or to specific enablers (such as the communication layers). The industrial manufacturing practice limits the usage of P&P concepts to very basic applications mostly related to the connection of devices preliminarily connected to a communication network, mainly disregarding the phases of automatic discovery and capability assessment, as well as of dynamic cooperation. Yet control entities—conceived to control mechatronic devices so that the former can be automatically run as soon as the device is physically plugged in the system dorsal—still represent an objective to be achieved. This basically implies addressing a number of challenging automation solutions under specific perspectives such as:

Regarding the first topic, the conception and deployment of reconfigurable solutions have mostly concentrated on the mechanical aspects of the devices, leading to the realization of reconfigurability enablers targeting several applications, from machine tools and robots, to transportation systems and fixturing systems based on standard interfaces and flexible CNC [12]. Challenging industrial applications of these reconfigurability concepts [13] have been realized by machine tool builders (e.g. Panasonic, Zevac, Mori Seki), by providers of robotic solutions (e.g. Kuka, Mitsubishi, Robotnik) and by providers of modular equipment (e.g. Festo and Flexlink). These automation reconfigurability options basically consist in a set of predefined optional add-ons or a set of deterministic capabilities that are acquired since the beginning, thus endowing the resources with very high flexibility instead of reconfiguration enablers [14].

As already mentioned, the majority of automation applications is characterised by traditional centralized control architecture, with consequent sensible limitations in the achievement of agile adaptation to run-time production changes. Moreover, such limitations become even more critical when the complexity of the process to be controlled and the functionalities to be automated increase.

The challenging aspect in the reconfigurability concept consists in its concurrent applicability to the control with regard both to logic and physical aspects. Logic reconfigurations of the control should stem from the need to modify the control functions and strategies in response to a production change (e.g. new product), events altering pieces of equipment (e.g. resource failures or abnormal behaviours) or revised production goals (e.g. minimization of energy consumption vs. idle times). On the other side, physical reconfigurations imply that the control physical device and the related control software interact with a modified control system, where new entities have been integrated or dismissed. This firstly requires a more complex reconfiguration process that goes beyond the control logic features and deals with physical interconnections, communications and bindings. Beside this, a new functional-oriented architecture enabling modular automation is needed to realize the encapsulation and (re-) use of self-contained functions and/or automation tasks as services.

SOA approaches can help to fulfil these requirements [15]. SOA is meant to provide services through a set of black box components exposing interfaces based on communication services [16]. In the field of modular control systems, single modules can be represented by such black box components, aimed at accomplishing functions by encapsulating services. The OASIS reference model was published for SOA, containing the definition of a service as a "…mechanism to enable access to one or more capabilities…" [17]. Services exposed by an entity are meant to be used by other entities. On top of this, the European research and development project named SOCRADES1 addressed a novel manufacturing paradigm deeply based on SOA, and particularly on web services [18]. According to SOCRADES framework the intelligence of the manufacturing system is implemented by an agent-based approach embedded in smart devices. These devices collaboratively act at the same hierarchical level by using web services as an interface for mutual communication [19]. The autonomous units therefore operate cooperatively, each being intelligent and proactive. In addition to the more general SOA approaches described, Mendes et al. [20] proposed an approach for the implementation of service-oriented control in process industry based on adoption of high level Petri nets.

The separation of control functionalities into services supporting process control and intelligence can be found in [21]. The proposed hierarchical structure is similar to the one described in ISA 106 [22]. In [23] a SOA implementation approach based on the adoption of function blocks [24] is introduced. Basic or atomic services are developed as encapsulated elements, to be invoked either directly by request-response messaging or indirectly, when contained in other complex high level services. Furthermore, both actuators and sensors signals require the access to field data. Such access is also implemented by using services. While mutual connectivity between all devices is required, services do not need to be allocated to particular hardware. Function block types deployed according to [24] are used as service types. Connections between function blocks realize the messaging mechanism, and therefore, the communication between corresponding services. Communication contains message types and parameters, where the former are achieved by event connections and the latter by data connections. Therefore, the introduced approach decomposes complex manufacturing tasks to the very basic level. Each basic task is implemented as an atomic service. Atomic services can be combined into aggregated ones to achieve more complex functionalities: the resulting set of basic and aggregated services can be orchestrated by a central coordinator or by autonomous choreography [23]. A high control level can invoke a service by messaging and the invoked service can invoke downstream subservices in a specific order.

When referring to the second of the mentioned topics (i.e. optimization strategies and learning mechanisms), the goal of the control optimization process traditionally pertains to a number of aspects related to the products quality to be achieved, the technologies to be exploited, along with the physical equipment usage over time.

<sup>1</sup>http://www.socrades.net.

Coherently with the traditional way of modelling the factory as a number of linked layers, these optimization paths are handled at the Production Management and Manufacturing Execution System (MES) levels by the scientific and industrial communities [25–31]. In this view, the system logic control is conceived with a very little awareness of the shop-floor and limited options to perform dynamic control adaptations. This is evident also when focussing on part routing problems in manufacturing transportation systems, where context recognition and optimization issues become crucial. Existing control solutions are based on centralized/hierarchical control structures that offer good performance but they require a big effort to implement, maintain or reconfigure control applications when a high-level of flexibility is needed. Thus, classical approaches usually do not meet requirements of manufacturing systems in terms of flexibility, expansibility or reconfigurability. Current R&D efforts focus on the development of novel control systems characterized by distributed intelligence, robustness and adaptation to the changes in the environment and exogenous factors [32]. The multi-agent paradigm aims at addressing control objectives by introducing modularity, decentralization, autonomy, scalability and re-usability as main features. Even if the definition of agent concept is neither unique nor shared [33], its most important properties are the autonomy, intelligence, adaptation and cooperation [34].

Although Artificial Intelligence (AI) based approaches have been considered as local enablers in some RMSs [35], the design of control models considering all the possible failures and changing situations remains a crucial problem. Moreover, relevant structural modifications in an agent configuration entail a re-design of the control strategies, which is hard to manage on the fly. In manufacturing, knowledge-based approaches exploiting ontologies have been applied to increase flexibility in modelling and planning of mechatronic devices [36], resources in collaborative environments [37], automation and control systems [38], and to manage information of distinct types [39]. In these cases, planning specifications, if considered, are fixed and neither automatically generated nor subsequently adapted. In robotics, ontologies have been more widely exploited in the knowledge framework OMRKF [40], KnowRob [41], ORO [42, 43]. Nevertheless, none of the previous approaches addresses the issue of dynamically adapting the planning models. Other AI approaches based on Answer Set Programming have been proposed [44, 45].

From the software infrastructure point of view, the use of a real-time operating system (RTOS) as an execution framework in control applications is now becoming more and more popular with respect to full-custom designs, due to its clear advantages in terms of software development time and cost. RTOS can be divided into two main categories according to their application programming interface (API) that directly affects application development and portability:

Among RTOS solutions, the POSIX interface is to be preferred when aiming at portable software and firmware suitable for long-term reuse and maintainability, because it is backed up by an international standard. As an additional benefit, it is widespread and well-known to programmers. Focusing on Linux, this option is made even more appealing by the increasing maturity of real-time extensions for the mainstream kernel, like the preemption Real-Time patch (Preempt\_RT),2 Real-Time Application Interface patch (RTAI),3 and Xen-based virtualization support.<sup>4</sup>

POSIX interface is also in line with International Standards of the International Electrotechnical Commission (IEC) IEC 61158 and IEC 61784 which enable interoperability between devices of different manufacturers. Three types of industrial networks are currently available: Fieldbuses, Real-Time Ethernet Networks (RTE) and wireless networks. Moreover, these networks may be used in a combined, resulting in hybrid systems [48–50]. In addressing the specific choice for each application, a key requirement to be considered is the capability to guarantee traffic separation, a critical feature needed to avoid interference between traffic flows with different timing requirements.

#### **4.3 Problem Statement**

A new approach to the design of automation system is required to address the aforementioned requirements and challenges, while exploiting advanced engineering practices for control modularization and distribution. Novel integrated platforms for automation system development, supporting distributed control system engineering, represent a fundamental enabling technology, where openness, interoperability and compliancy with international industrial standards shall be considered as fundamental features to be guaranteed.

The adoption of a distributed automation approach can help to increase the reconfigurability and robustness of the automation system. Traditional centralized and hierarchical architectures should be replaced by new modular control solutions that can better support a fast integration of new functional components and run-time adaptation of the system to the dynamic change of production demand and requirements. The modularization concept enables the organization of the control solution into software entities, encapsulated within standard interfaces, that can be directly connected to functional components of the system to be controlled, so that the re-usability of the applications can be enhanced.

The following core requirements must be tackled by a novel approach based on distributed automation:

• *Interoperable and pluggable control solution*. Compared to the existing solutions, control entities shall be conceived to control mechatronic devices so that the for-

<sup>2</sup>https://rt.wiki.kernel.org/index.php/Main\_Page.

<sup>3</sup>https://www.rtai.org/.

<sup>4</sup>https://www.xenproject.org/.

mer can be automatically run as soon as the device is physically plugged in the system dorsal. This P&P feature enables the automatic recognition and configuration process of the control modules; moreover, the interoperability characteristics should allow applying this procedure to every resource type and/or tasks, thus ensuring the possibility to interact with all the resources of the shop-floor.

Nevertheless, this design methodology requires an advanced development environment, capable of supporting the design of the control code according to international standards—for compliancy and interoperability—and of implementing runtime information exchange among different modules of the distributed control systems—to achieve global goal by means of real time collaborative strategies.

#### **4.4 Proposed Approach**

The proposed approach, named *Generic Evolutionary Control Knowledge*-*based mOdule* (GECKO), consists in a control infrastructure based on an interactive cooperation of control modules to cope with the requirements defined in the previous section. The GECKO approach enables the single devices—from end-effector, complex machine equipment up to the integrated cell and system—to evolve from stand alone, rigidly and hierarchically managed components into autonomous, selfdeclaring, heterarchically interacting and collaborating components. GECKO is conceived to detect and interpret the production environment features and adapt its capabilities on the basis of the specific requirements by automatically accomplishing local and global objectives. Under this perspective, opposite to pre-determined production system architecture, GECKO presents an adapting behaviour resulting from an advanced reconfiguration mechanism together with a form of intelligence and knowledge enabling the recognition of the products, events and other entities operating in the shop-floor.

GECKO is designed as a complex control architecture consisting of a number of cooperating software components:

These components provide functionalities that are integrated through a software infrastructure enabling the persistent internal communication. This infrastructure constitutes the physical and logic foundations to enable the joint functioning of all the GECKO modules. The production environment would consequently result in a community of GECKO entities encapsulated in the pieces of equipment that communicate, cooperate and negotiate to achieve the production goals.

#### **4.5 Developed Methodologies and Tools**

While further describing the software solutions developed for the GECKO approach, the attention will be focused on Reconfigurable Transportation Systems (RTSs) as target application, in consideration of their modular nature in implementing alternative inbound logistic systems' configurations. The transportation modules of a RTS are designed as mechatronic devices equipped with standard interfaces that allow modules to connect each other and embed logic controllers for actuators/motors and sensors. A central problem of RTSs is the Online Part Routing Problem (OPRP) addressing the formalization and synchronization of RTS mechatronic modules to transport all the parts in the manufacturing system according to their destinations [51]. A solution for the OPRP must ensure that all the parts are properly worked, the routings must be collision-free and routings must be efficient. RTS therefore represent a suitable bench-mark for proposed GECKO solution.

Since openness, interoperability and international industrial standards compliancy are fundamental requirements to be guaranteed, the hardware architecture for the deployment of GECKO control module is based on embedded industrial PC systems.

With reference to the operating system, as a foundation for the design choices, three different approaches to real-time execution support have been evaluated. Standalone RTOS, Linux plus RT Patch, and Xen-based virtual machines have been compared with respect to the following metrics: performance and determinism of execution at the operating system level, performance and determinism of real-time network communication, coexistence with non real-time applications, language and execution environment support, and communication among software modules. Linux and RT Patch have been chosen as base execution framework for the GECKO control module.

The GECKO components, as designed for the RTS case, are represented in Fig. 4.1 and will be described in the following subsections.

#### *4.5.1 Communication Component*

The communication architecture for GECKO modules has to accomplish two different tasks: data exchange with sensors/actuators distributed on the shop floor and communication between GECKO modules. The first task represents a typical feature of the networks deployed at the lowest levels of factory automation systems and is characterized by the fast and timely transmission of limited amounts of data, requiring both real-time and deterministic behaviours of the underlying networks. The second task serves to the purpose of coordinating modules activity and is typically characterized by higher traffic volume and more relaxed timing constraints than in the previous case. Considering the aforementioned traffic types, the best choice for the GECKO modules network is represented by Real-time Ethernet networks. Traffic separation, a critical feature needed to avoid interference between traffic flows

#### 4 Knowledge Based Modules for Adaptive Distributed Control Systems 93

**Fig. 4.1** GECKO module components

with different timing requirements, can be achieved in two ways: a single physical network, using a time-division multiple access (TDMA) method, and a physical traffic segregation that requires GECKO modules to be equipped with two network interfaces.

With reference to the communication protocol to access field I/O, Modbus was chosen, since the end-to-end round-trip delays proved to be compatible with execution times.

The implementation of the prototype (see Fig. 4.2) targeted the microcontrollers typical of low-cost embedded and industrial applications, namely the NXP LPC1768 and LPC2468, and required the configuration and integration of existing code5 (light grey blocks in Fig. 4.2), specifically (i) a custom-built Modbus TCP node, leveraging the FreeRTOS RTOS; (ii) the lwIP TCP/IP protocol stack; (iii) an open-source Modbus TCP protocol stack (for slave nodes) and a commercial Modbus TCP protocol stack (for masters). Moreover, it was necessary to develop dedicated modules of code from scratch (white blocks in Fig. 4.2), better described in the following:

<sup>5</sup>https://www.freemodbus.org/.

**Fig. 4.2** GECKO communication and software infrastructure prototype

#### *4.5.2 Control Component*

IEC 61499 modelling standard has been adopted to develop a modular control system. According to the standard, the function block (FB) represents a unit of control software that is associated with a hardware component in the controlled system. It is therefore aimed at defining the control logic for each basic functional component of the system, meant as a self-contained module. Each function block exposes: (i) an event type input request corresponding to each task the module is capable of performing (i.e. automation function); (ii) a data type input for each configuration parameter related to the execution of module tasks; (iii) an event type output to acknowledge the execution of each module task (including un-nominal and failure conditions) and to request task execution towards downstream modules; (iv) a data type output for each variable representing the module internal state and tasks execution [52]. The execution of the control logic is regulated by the Execution Control Chart (ECC): ECC is basically a Moore automaton, consisting of states, event-conditioned transitions and actions, as well as a set of algorithms, associated with the ECC states, to be executed during specific operating conditions.

The development of IEC 61499 compliant control application takes place into a softPLC environment, named IsaGRAF,6 natively supporting the IEC 61499 stan-

<sup>6</sup>http://www.isagraf.com/index.htm.

**Fig. 4.3** IEC61499 based distributed control of an electronic component remanufacturing line consisting of conveyors and specialised machines

dard. As a result, a structured modular design of the overall system can be addressed through composition, i.e. by aggregating simpler mechatronic devices in a recursive way, from the level of *atomic* devices, such as sensors/actuators, till more complex equipment. In fact, by IEC 61499 reference model, a complex control application is defined by connecting FBs, as well as encapsulating basic function blocks into aggregated (composite) function blocks.

Figure 4.3 shows the distributed control engineering framework schema based on IEC 61499.

In terms of internal connections with other GECKO components, the control component includes an OPC-UA server, linked to the IEC 61499 run-time system, for exposing (as software services) the capabilities provided by the controller, thus supporting dynamic discovery, assessment, configuration and interoperability. The address space nodes are structured according to an object oriented approach based on OPC-UA. A remarkable element to be mentioned is that the IEC 61499 modelling of the control code results in a consequent identification and *clustering* of data/parameters/variable as inputs/outputs of the FB, being therefore propaedeutic to their handling according to object oriented data exchange standards. Furthermore, function driven control development is intuitively oriented and absolutely prone to the integration with Service Oriented Computing, therefore resulting in a promising hybrid paradigm.

#### *4.5.3 Capability Assessment Component*

The large amount of information linked to the capabilities exposed by various control components as well as to the technological process has led to the creation of the so called Knowledge Manager, a dedicated component capable of managing and assessing such information. The Knowledge Manager is based on a suited ontology that models the general knowledge of manufacturing environments, classifying relevant information in three distinct contexts (i.e. Global, Local and Internal) and considering a Taxonomy of Functions which classifies the set of functions the GECKO modules can perform according to their effects in the environment [53]. The Knowledge Manager exploits the ontology to build and manage a Knowledge Base (KB) that represents an abstract description of the structure and the capabilities of the GECKO modules.

In terms of implementation, the Web Ontology Language (OWL), a well-known technology for semantic data modelling, has been exploited to represent the KB. The ontology editor Protégé [54] has been used for KB design and testing.

#### *4.5.4 Context Recognition Component*

Based on contents stored in the capability assessment component, a Rule-based Inference Engine analyses the KB information and infers the advanced functional capabilities the overall control module is actually able to perform by composing the atomic capabilities exposed by the control component. The result of such an inference is a *planning* model generated from the KB to be used by the optimization component for actually control the behaviour of the mechatronic device [55]. The planning model contains an abstraction of the device, the environment's parameters in which it operates, and all the relevant constraints necessary to guarantee physical consistency. In the specific case of RTS, the context to be recognised is the topology of the plant, which shall be maintained and/or updated dependently on reconfigurations and/or failure of single modules. Thanks to the information exchanged with other neighbouring modules, every module dynamically re-builds (and constantly keeps updated) a local map of the shop-floor topology. Each module acquires a complete layout of the RTS including the connections of all the modules [56].

In terms of implementation, the Knowledge Processing Mechanism relies on Ontology and RDF APIs and Inference API provided by the Apache Jena Software Library.<sup>7</sup>

<sup>7</sup>http://jena.apache.org.

#### *4.5.5 Optimization Component*

The optimization component determines the global optimization strategies as response to the plant specific optimization problem, therefore driving the control module in its evolution.

In the context of RTS, the routing problem—as the main problem to be solved—is addressed within the optimization component as a multi-agent problem where a set of agents interact and share information in order to transport pallets (or parts) to their final destination within a RTS. Specifically, a distributed auction-based algorithm for part routing has been developed that coordinates the transport modules [51]. Such algorithm, denominated Planning Framework, establishes the assignment (i.e. routing) of the parts to the most suitable agent (i.e. module) as a result of a negotiation process by executing an auction-based mechanism with an associated multi-objective function [56].

Further to this, a Supervision mechanism continuously checks the achievement of each task via a flexible and dynamic execution monitoring system. This system analyses the pallet routings to detect possible redundancies and deadlock situations. If necessary the Planning Framework is re-executed and the path updated. The same happens in case of unforeseen events, e.g. a RTS module failure [56].

The part routing problem has been implemented in Java SE 7 by developing a multi-agent framework within the JADE platform. Both Planning Framework and Supervision mechanisms use timeline-based technology and were framed by means of the EPSL architecture [57].

#### **4.6 Testing and Validation of Results**

#### *4.6.1 Industrial Case*

The De-Manufacturing Pilot Plant sited in the lab of CNR-STIIMA (ex CNR-ITIA) [58] is dedicated to the integrated End-Of-Life processing of mechatronic products (specifically, printed circuit board, or PCB) using modular technological solutions to process heterogeneous work pieces (i.e. the PCBs) while requiring limited hardware and software reconfigurations. The first goal of the plant is to repair the PCB via remanufacturing and, if this is not suitable or technically feasible, by recovering valuable components through disassembly. Eventually, the last option is to recover the raw material by shredding and separation.

The PCB enters the De-Manufacturing Pilot Plant by means of a robotized station and then it is mounted on a pallet and loaded on an automatic conveyor. Such conveyor transports PCBs to the workstations (see Fig. 4.4) that can execute the main operations: PCB circuit analysis (*Test* station) to identify possible failed components on the board; PCB rework (*Rework* station) to replace failed components; PCB or components that cannot be repaired for technical reason (e.g. overall circuit damage),

**Fig. 4.4** De-manufacturing process architecture: *RW* Rework, *T* Circuit Test, *R* Robotic disassembly, *W* material recovery cell

or which economic value does not justify remanufacturing are sent to a further cell (*Disassembly* station), and possibly to a shredding cell (*Material Recovery* station) aimed at disassembling the PCB and recovering precious raw materials, respectively.

If the PCB entering the system has an economic value justifying remanufacturing, then it is first sent to the PCB analysis process for identifying eventual corrupted components or board damages. If no unrecoverable damage is identified, the PCB is sent back to the first robot station and exits the system as reusable product. If the analysis identifies a not repairable failure on the board, then the PCB is sent to the material recovery cell. If the analysis identifies a failure on one (or more) components, then the PCB is sent to the rework process for substituting the components before undergoing again the PCB analysis for checking conditions. If the test is passed, then the PCB is moved to the first station, otherwise the loop is repeated (with a configurable number of iterations). As soon as the maximum number of iterations is reached, the PCB is sent to the material recovery process. Before such stage, if the PCB mounts valuable components, it is sent to the rework station for chips desoldering. If the PCB has not an economic value justifying remanufacturing, it is moved directly to the material recovery cell. In case some components of the PCB are valuable for reuse, or have to be removed before shredding for safety reason (i.e. hazardous material content), they are sent to the rework station to disassemble such components before shredding.

The overall conveyor system is composed of a set of mechatronic modules to provide the maximum flexibility and scalability. All modules have the same dimensions, are composed of three intermediate positions for pallet hosting, each of them supporting two main (straight) transfer services and in some cases cross module transfer services. From a mechatronic point of view, the main sensing and actuating elements

#### 4 Knowledge Based Modules for Adaptive Distributed Control Systems 99

**Fig. 4.5** RTS transportation module—Implemented control architecture

for each of the intermediate positions (and consequently for the whole module) are two-way motors—powering the belts for pallet transferring—indexing pins for pallet sequencing and proximity sensor for pallet detection. Each mechatronic module is mechanically, electronically and pneumatically self-contained, and designed so that connectors from one module to the neighbouring carry both power/air supply and Ethernet. Therefore, it is possible to dynamically design and deploy several configurations, depending on the particular needs of the transportation system and/or on the possible online events to occur during the production (e.g. integration of new machines in the line). As an example, a configuration can support forward and backward movements and one or more specific positions with right and left cross transfer capabilities.

#### *4.6.2 Experiments, Results and Analysis*

The GECKO control architecture has been validated within the De-Manufacturing plant described in Sect. 4.6.1 (hereinafter also referred to as *GECKO pilot plant*), by realizing a novel automation system for the RTS transportation modules. Following the GECKO approach, the control solution governing each module of the conveyor system has been re-designed to expose the dedicated services. Figure 4.5 summarizes the implemented control architecture for the RTS transportation modules of the demanufacturing plant.

Since the most critical timings of a control system often concern I/O operations, the first tests have been performed by evaluating the real-time communication performance of the runtime by means of a custom Modbus TCP slave, implemented as described in Sect. 4.5. The tests have been carried out considering different increasing computational loads designed to reproduce the characteristics of the other software components expected to run on the system in the real plant. Moreover, existing knowledge about the internals of the RT Patch extension of the Linux kernel has been leveraged to bring the system towards its worst case behaviour. During the experiments, a custom Modbus TCP slave collected traffic statistics for later analysis, which evidenced a significant performance improvement by limiting the worst-case amount of activation and communication jitter [59].

More specifically, FreeRTOS scheduling and synchronization overheads have been evaluated by considering three main aspects, all of them typical of embedded distributed applications: (i) task scheduling overhead upon semaphore synchronization; (ii) task scheduling overhead upon message queue (mailbox) synchronization; (iii) worst-case interference from the FreeRTOS timer interrupt handler.

The internal delays of lwIP have been evaluated by inserting a number of highresolution timestamping points into the protocol stack code, whose location has been determined by code analysis, and developing test code to generate the appropriate traffic patterns to be analysed. The experiments have been carried out using a frame size of 242 bytes, that is, close to the maximum frame size of Modbus TCP. The final part of the evaluation was by far the most complex one, because it involved, first of all, the implementation of a 2-node experimental testbed consisting of a Modbus TCP master/slave pair. Overall, the main outcome was the complete breakdown of Modbus TCP communication delays into simpler components, contributed by different software modules. For what concerns jitters, the most important discovery was to identify the role played by TCP acknowledgements, also depending on whether these acknowledgments are piggybacked onto other TCP segments or transmitted on their own.

An additional outcome has been a comprehensive comparison between the code execution performance of the two microcontrollers considered in the experiments. More specifically, besides the CPU clock speed, the two main contributing factors to the different performance of the two architectures are:

time is variable and depends on a multitude of factors, for instance, the length and likelihood of intervening refresh cycles, the current state of the memory controller state machine, and write buffer status. Due to the difficulty of calculating this factor analytically, its value is best derived from experimental data.

The next step aimed at verifying the real-time behaviour of the ISAGRAF IEC 61499. Functional tests have been conducted on a set of modules representative of the GECKO pilot plant, with runtime component hosted on the plant PLCs, so as to establish that this latter is not adversely affected by the concurrent execution of additional, interfering tasks on the same embedded control system. The output of such tests proved the operating system to be robust enough to be able to effectively isolate the IEC 61499 runtime component from the execution timing point of view.

The GECKO distributed control approach has been then tested both on the real plant and on a set of different simulated layouts to verify the effectiveness of the approach independently from the specific plant configuration. The design of experiment aims at (i) evaluating RTS throughput and parts lead time considering different layouts, (ii) comparing the features of the proposed control approach with respect to a well-defined benchmark case and (iii) assessing the benefits of the distributed approach when temporary and dynamic changes occur in the RTS. The observing horizon was set equal to a work shift (i.e. 8 h), while the rate of parts entering the system from the load station is kept constant. A complete and exhaustive description of such experiments can be found in [51].

Figure 4.6 shows the different RTS layouts used in simulated experiments. All layouts are composed of 20 transportation modules, where each module has a main movement direction and is equipped with cross-transfer devices (represented in the picture by the bold black segments) enabling the cross-transportation between adjacent transportation modules.Machines and I/O modules are represented, respectively, by red and green blocks.

The experiments have been carried out in two phases to (i) evaluate RTS throughput and parts lead time while considering different layouts (first phase), and (ii) assess the benefits of the distributed approach when changes occur in the RTS (second phase).

The experimental runs of the first phase proved the benefits associated with the distributed auction-based approach to support the design of RTS configurations. After the evaluation of the system throughput and the part lead time for the different RTS layouts, the system designer can select the most efficient configuration according to the production and technology requirements. Considering average throughput values and part lead time in each layout, the results of the experiments are reported in Table 4.1.

Figure 4.7 shows the trends of finished parts, i.e. the number of processed parts (*y*-axis) sampled every 30 min (*x*-axis). As shown in the figure, the RTS *Cross* configuration is the best topology among the tested ones, if the average throughput and part lead time are considered.

The experimental runs of the second phase targeted the capability of the distributed control approach to match temporary or permanent changes (e.g. module failures)

**Fig. 4.6** Examples of RTS layouts considered for the experiments: **a** RTS *A*, **b** RTS *B*, **c** RTS *Cross*, **d** RTS *Eight*, **e** RTS *Full*, **f** RTS *Ring*, **g** RTS *Star*

**Table 4.1** Average throughput values and part lead time of tested RTS layouts

of the RTS layout. A failure leads to a temporary unavailability of the corresponding module, thus triggering the generation of a new RTS layout corresponding to the new reality of the system, and allowing to resume the production of new routing policies until the original layout is (possibly) restored. The interested reader can find further details in [51].

Additional tests have been carried out to evaluate the knowledge processing mechanism and the capability of a single GECKO agent to build a consistent abstraction of the production context and synthesize timeline-based models accordingly. Experiments have been designed to evaluate—across different configurations—the inference time needed to build the knowledge base of an agent as well as the capability of updating such a knowledge every time that a physical change (i.e. a reconfiguration of the transportation module) occurs in the module. All the different physical

**Fig. 4.7** Production volumes associated to examples of RTS layouts

configurations of a transportation module have been considered, from unidirectional module (with no cross-transfer device) to module with three cross-transfer devices. These configurations are referred to as*simple*,*single*, *double* and *full*, respectively. A further possible configuration entails a different number of connected neighbouring modules. Clearly, the more complex scenario is the superimposed one with the highest number of cross-transfers (full configuration) and neighbours. In order to pursue such complex scenario, reconfiguration scenarios have been addressed, based on occurrence on different external events, particularly an increasing number (from 1 to 3) of transportation module neighbours momentarily unable to exchange pallets, as well as on occurrence of internal failures to a cross-transfer device engine to a specific port.

Figure 4.8 shows the results concerning the time needed to infer the knowledge base as a response to the new reconfigured scenario and the time needed to generate a corresponding timeline-based model. The experimental results prove the practical feasibility of the knowledge processing mechanism in increasingly complex configurations of a transportation module. The cost of such a reasoning mechanism has low impact on the performance of a module during its operations. Further details about the inference process and the experimental results can be found in [55].

#### **4.7 Conclusions and Future Research**

The proposed GECKO multi-agent distributed control approach [59] enables autonomous agents to dynamically recognize the working settings, identify their capabilities, and collaborate to control the production and support its reconfiguration [53, 55]. The proposed solution is capable of automatically dealing with structural

**Fig. 4.8** Knowledge base inference time and timeline-based model generation time

changes in the topology of the plant as well as the capabilities of the transportation modules without affecting the production, so that it is possible to cope with changes in the demand and/or unforeseen events/failures at shop floor level. Indeed, unlike classical centralized control approaches, the GECKO distributed control approach can dynamically optimize the paths that pallets follow, by reasoning online on the actual state of the plant. It is worth highlighting that this reasoning is performed without suspending the production.

The adoption of the GECKO approach for design and development of novel solutions to automation systems has been discussed with reference to the specific case study of a de-manufacturing plant, showing how such a decentralized control approach and the integration of planning and knowledge reasoning technologies can be beneficial to improve the efficiency and the flexibility of complex systems like RTSs [56].

The integration and (re)use of the control solutions is enhanced by the encapsulation of the control functionalities into a network of interconnected function blocks. Moreover, considering the development of the overall control system, the activity of the control engineer benefits from the availability of control modules of mechatronic devices, as single components, already tested and validated.

Distributed control system methodologies and prototype tools presented in this work are further extended towards a higher TRL (Technology Readiness Level) within the framework of two Horizon 2020 projects. In particular, the Daedalus project<sup>8</sup> deals with the extension of the modular and distributed control system platform, orienting towards the Industry 4.0 CPS (Cyber Physical System) paradigm. Moreover, the MAYA project<sup>9</sup> addresses the integration of the distributed automa-

<sup>8</sup>http://daedalus.iec61499.eu/index.php/en/.

<sup>9</sup>http://www.maya-euproject.com/.

tion system interoperability framework to support dynamic fitting of factory digital models with data gathered from the real plants, so to improve the reliability of the forecasting and optimizations.

**Acknowledgements** This work has been funded by the Italian Ministry of Education, Universities and Research (MIUR) under the Flagship Project "Factories of the Future—Italy" (Progetto Bandiera "La Fabbrica del Futuro") [60], Sottoprogetto 1, research project "Knowledge based modules for adaptive distributed control systems" (GECKO). The authors greatly acknowledge the industrial partners of the GECKO project, namely FF Falconi Snc, Magneti Marelli Spa and Tecnint HTE for the technical support to the conception of requirements, development ideas and validation scenarios. Authors from ISTC-CNR would like to thank their colleagues Stefano Borgo and Alessandro Umbrico for their valuable work during the project.

#### **References**

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## **Chapter 5 Highly Evolvable E-waste Recycling Technologies and Systems**

**Giacomo Copani, Nicoletta Picone, Marcello Colledani, Monica Pepe and Alessandro Tasora**

**Abstract** Materials recycling is a key process to close the loop of materials in the direction of circular economy. However, the variability of waste and the high volatility of the price of recovered materials are posing serious challenges to the current rigid design of mechanical recycling systems. This is particularly true for Waste Electric and Electronic Equipment (WEEE), whose volume is growing more than other waste streams in Europe due to the diffusion of electronic products and to their short technology cycles. This study is aimed at the development of new flexible recycling systems through the implementation of a Hyper Spectral Imaging system and a simulation model enabling the real-time characterisation of shredded particles and the dynamic optimisation of process parameters for efficient sorting. A hardware and software prototype was realised and tested at the De- and Re-manufacturing pilot plant of CNR-STIIMA. The positive economic impact of flexible recycling systems enabled by new technologies was assessed through scenario analysis.

#### **5.1 Scientific and Industrial Motivations**

De-manufacturing acquired high relevance in last few years due to increasing raw material costs and to laws in many countries aimed at improving material recycling rates and at limiting supply shortage risks for rare metals used in high-tech applications. While manufacturing transforms raw materials into products meeting the

G. Copani (B) · N. Picone

CNR-STIIMA, Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Milan, Italy

e-mail<EMAIL_ADDRESS>
M. Colledani Dipartimento di Meccanica, Politecnico di Milano, Milan, Italy

M. Pepe CNR-IREA, Istituto per il rilevamento Elettromagnetico dell'Ambiente, Milan, Italy

A. Tasora Dipartimento di Ingegneria e Architettura, Università di Parma, Parma, Italy

© The Author(s) 2019 T. Tolio et al. (eds.), *Factories of the Future,* https://doi.org/10.1007/978-3-319-94358-9\_5 customer requirements, de-manufacturing transforms post-consumer products into valuable materials meeting the customer requirements for secondary use. However, manufacturing and de-manufacturing objectives are strictly dependent. In fact, products that are currently manufactured will constitute the waste that will be treated by de-manufacturing system in the next years.

De-manufacturing can be defined as the set of processes, technologies, tools and knowledge-based methods to re-use, re-manufacture and recycle materials and components from End-of-Life products [1]. The de-manufacturing process includes the following steps:

According to recent studies, the major cause for losses of key metals (from 40 to 100% of material) in de-manufacturing process is the low efficiency of the mechanical pre-treatments step, while only marginal losses are due to the collection and to the downstream chemical end-processes. Therefore, the improvement of mechanical recycling technologies and systems is important for the recycling and high-tech product manufacturing sectors.

Currently, recyclers perform the mechanical pre-treatment of wastes, including metal and non-metal fraction, by:

In Europe, 85% of recycling companies are Small Medium Enterprises (SMEs). Due to the space and budget limitations, it is extensively challenging for these companies to operate dedicated treatment lines for mechanical shredding and separation processes for each specific product flow. Batch production and high utilization of a single or few recycling lines is a common solution. The process parameters are selected as a compromise between the different product types, thus leading to unoptimised performance. Moreover, in spite of the high variability of post-use products and material to be treated, currently available mechanical recycling systems are extremely rigid and they cannot be adapted to the different type of waste which is generated over time according to the technology cycle of products. Their design and technology does not allow to dynamically modify process parameters and material routing based on the specific type of mixture to be treated. In addition, the integration of new process technologies in the system is extremely time-consuming and expensive, since the rigid conveyor systems need to be re-arranged for the specific purpose with long ramp-up times. For these reasons, aligning recycling with the evolution of products and waste is a significant challenge. The system rigidity, coupled with the high variability in the input material composition and in the targeted output performance, ultimately causes many shortcomings of the process including:

Thus, the development of flexible and adaptable systems for the recycling industry is a promising avenue for the improvement of material recovery and for the adoption of recycled material in high-quality and high-value applications [2, 3]. The concepts of flexibility, re-configurability and adaptability have already been widely investigated in the scope of manufacturing systems [4–7], but they have been poorly addressed until now in the de-manufacturing area. Grounding on a solid knowledge base derived from manufacturing industry and getting inspiration from changeability enablers adopted in manufacturing processes, research activities should aim at the development of innovative technologies for supporting the transition towards smarter de- and re-manufacturing systems characterized by: (i) high adaptability to different waste to be treated and to changing market conditions, (ii) high automation level, (iii) availability and traceability of information, and (iv) intelligent decision algorithms based on data analytics and cyber-physical systems.

To this aim, a set of *hardware and software systems as well as business model applications* are required to be integrated, enabling a feedforward control to manage the system evolution leading to a more coherent matching between the fast dynamics and large variability of the End-of-Life product life-cycle and the system life-cycle, also called *co-evolution* [8]. Co-evolution is defined as the ability of a company to address engineering change according to context modifications, strategically and operationally, in order to gain and maintain competitive advantage [8]. The change propagation acts as a cause-effect wave across the various company domains, spanning from corporate strategy to plants and technologies. The co-evolution problem has been traditionally studied within the manufacturing industry (e.g. within the EU funded projects RobustPlanet,<sup>1</sup> RLW Navigator,<sup>2</sup> DEMAT3) but has never been addressed within the de-manufacturing sector.

This work investigates the implementation of the co-evolution principle for demanufacturing systems. New flexible and adaptive recycling systems are proposed while addressing the process technology and ICT development. Furthermore, new business and financial models are developed to enable the industrial uptake and largescale diffusion of the new processes by dynamically suggesting the best strategies to adopt and guaranteeing process and business dynamic adaptation to waste flows evolution over time.

The chapter is organised as follows. Section 5.2 presents the state of the art on flexible recycling systems. Section 5.3 outlines the research scope and approach, whereas Sect. 5.4 presents the different results of the research (i.e. hardware and software solutions, business model validation and prototypes). The results of industrial testing are discussed in Sect. 5.5 and, finally, the conclusions together with indications for future research are presented in Sect. 5.6.

#### **5.2 State of the Art**

The literature about recycling systems mainly concentrates on process planning [9] and economic assessment of the processes [10]. However, recent literature showed that in order to efficiently treat different material mixtures in the same mechanical material separation plant, the system itself should be designed to enable on-line modifications of the material flows [11]. Figure 5.1 shows how different routing configurations in the same system can bring to significantly different recovery and grade performance. In particular, treating multiple products would support coping with low revenues from material treatment, high variability of products and limited availability of specific post-use products, thus the capacity constraints [12, 13]. Therefore, the system architecture should be modified depending on what products are treated and which is the target recycling rate of the materials. If the system configuration and the process sequence is not modified when the mixture under processing changes, then the performance of the entire system drastically drops making the recovery process unsustainable for some products.

The volatility of material price and the high variability in composition of the input material represent considerable challenges for the sustainability of recycling systems due to their rigid designs. The major limitations in terms of flexibility of state of the art recycling technologies regard, among all, the two following aspects:

<sup>1</sup>ROBUSTPLANET—"Shock—robust Design of Plants and their Supply Chain Networks", FoF. NMP. 2013–9 Grant Agreement no 609087.

<sup>2</sup>FP7-2011-NMP-ICT-FoF EU Project, RLW Navigator "Remote Laser Welding System Navigator for Eco & Resilient Automotive Factories", Grant Agreement no 285051.

<sup>3</sup>DEMAT—"Dematerialised Manufacturing Systems: A new way to design, build, use and sell European Machine Tools", FP7. NMP. 2007–2013 Project ID 246020.

**Fig. 5.1** Optimal system configurations as a function of recovery (y-axis) and grade (x-axis) targets [11]

Within the research on recycling process modelling and control, separation process modelling has attracted interest from researchers, especially in the mineral technology area [14]. Physical models for particle trajectories in Corona Electrostatic Separation (CES) have been developed [15]. However, these models are always focused on single particle trajectories and do not support the modelling of two relevant causes determining the low performance of separation process, i.e. (i) particle impacts and interactions, and (ii) the presence of unliberated particles in the material flow. Discrete granular material flow models could handle these issues. Very recently, multi-particle and multi-body simulation models have been developed to analyse the dynamics of the impacts within recycling processes (Fig. 5.2) [16]. Although these models outperform existing ones to explain the process physics, they are relatively time consuming tools and hard to be used to support process optimization and control. Moreover, their validation in real settings is still ongoing.

Regarding hyperspectral imaging for metal and non-metallic materials, current methods to distinguish and characterise the composition of parts and shredded particles are based on off-line inspection, colour inspection systems, X-Ray, X-Ray-Backscatter or even spectroscopy over thermally stressed materials [17]. However, these methods are inaccurate with chromatic materials (mainly metals) or their adop-

**Fig. 5.2** Multi-particle CES simulation model

**Fig. 5.3** Application of hyperspectral imaging for identification of plastics products: **a** classified images, **b** related reference spectra [22]

tion is not feasible in industry where on-line inspection procedure is becoming extremely important [18–20]. As a matter of fact, although attempts have been made to extend this technology to metallic particles (e.g. EU project Sormen4), research contributions are mainly available for plastics particles (Fig. 5.3) [21, 22].

Commercial equipment for automatic optical sorting based on hyperspectral cameras can be found in the market. Although hyperspectral technologies can help characterizing the composition of metallic materials, current hyperspectral processing approaches involve poor characterization of the inherent variability of these materials and their visual appearance (optical texture, geometry, and pattern) of the particles

<sup>4</sup>SORMEN—Innovative Separation Method for Non Ferrous Metal Waste from Electric and Electronic Equipment (WEEE) based on Multi- and Hyperspectral Identification project, 6FP Horizontal Research Activities Involving SMES Co-Operative Research, 2006.

[23]. Besides this, the high amount of data that is inherent to hyperspectral imaging implies slow data processing rate, which is not acceptable for real-time recycling applications [24–26]. Thus, new real-time hyperspectral models enclosing texture, optical pattern, component variability and adaptability to improve the overall recycling process are necessary.

#### **5.3 Problem Statement and Research Approach**

An approach to deal with the product, process and system co-evolution problem for recycling systems needs to satisfy the following requirements:

The proposed approach consists of the following technological and ICT-based solutions (hardware and software) to meet the previous requirements:

The developed solutions were demonstrated in prototypal applications (Sect. 5.4.4) and the applicability of the approach was tested in a real industrial case (Sect. 5.5), i.e. the De- and Re-manufacturing Pilot Plant at CNR-STIIMA (ex CNR-ITIA). The pilot plant is a fully automated, modular and reconfigurable solution involving integrated technologies for the disassembly, testing, reworking, shredding and mechanical separation of mechatronic products. The facility supports industrial research and innovation with the main purpose of recovering both functionality (when possible) and target materials from End-of-Life products.

#### **5.4 Hardware, Software, Business Models and Prototypes**

#### *5.4.1 Hardware System*

The developed Hyper Spectral Imaging (HSI) system enables the in-line recognition and classification of shredded products in terms of mixture composition (percentage of metal and non-metal fractions, shape and dimensional distribution) [27], with the aim to adapt the configuration and the working parameters of the downstream separation processes. In particular, the system provides feedback to trigger the control model of CES (i.e. drum speed, electrode voltage, feed rate and output stream splitter position), according to the specific mixture under treatment.

From a technological point of view, the main challenge consisted in the development of a robust and flexible imaging system for the in-line characterization of the metal and non-metal fraction of waste. The underpinning knowledge regarding spectral signatures of pure materials is derived from end-member collections, with cross-check performed by Scanning Electron Microscope (SEM) measurements.

The information about composition and geometry of the particles in the mixture made available by the developed vision system is used to feed a multi-body simulation model and an optimization and control module to get optimal separation parameters and improve the configurability of the recycling system.

The implemented vision system includes three main elements: (1) the hyperspectral camera, (2) the illumination system and (3) the transportation system. The conveyor speed, the image frame rate and binning are mutually aligned for the effective performance of the system.

#### *5.4.2 Software System*

The software tool controlling the vision system was implemented ad hoc in Matlab. The group of scripts is able to automatically control the camera acquisition and characterize the mixture in terms of composition, geometry and dimensional distribution. The software tool sends then this information to the separator to optimize the sorting control parameters. In particular, the following steps have been coded in the script:

For the purpose of the simulation developments, granular flows involved in waste processing exhibit complex dynamical phenomena. Therefore, a custom simulation software based on multi-body dynamics was developed. Such a tool simulates the trajectory of thousands of processed particles, including mutual collision effects and their interaction with electric and magnetic fields in the CES and Eddy Current Separation (ECS) machines.

The large-scale nature of this type of simulation, along with the complexity of collision detection between heterogeneous shapes in three-dimensional space, advocated the adoption of innovative and efficient simulation algorithms backed by the theory of non-smooth dynamics, Measure Differential Inclusions (MDI) and Differential Variational Inequalities (DVI) [28]. The simulation software, available as open source C++ software, is written in C+language and is dynamically linked with the multi-physics ProjectChrono library, whose features have been customized to meet the needs of this work [29]. A simplified 2D version of the simulation tools has been developed in Matlab as well [30].

Two approaches were implemented to provide a source of virtual waste flow in the simulation tool. The first approach consisted in pre-processing a sample of waste flow using the hyper-spectral video camera, hence obtaining a set of faceted particle shapes and corresponding materials that are sorted and replicated continuously within the virtual inlet during the simulation. The second one consisted in the generation of particles as random samples from user-defined multivariate distributions; to this end, an innovative system based on a hierarchical description of the probability space was designed [31].

An add-in for the SolidWorks CAD, using C+language, was also designed to export coordinates, geometries, references and collision boundaries of the separating machines into a neutral file format to be processed by the simulator in a following phase. A further advancement came from the adoption of parallel computing hardware for accelerating the collision detection and the time integration [15].

#### *5.4.3 Business Model Validation*

Thanks to the new hardware and software technologies, the resulting flexible recycling systems are able to dynamically adapt the implemented recycling strategy to business conditions. This means that it will be possible not only to adapt process parameters to variations of volumes and characteristics of waste that will be required to treat over time, but also to select strategically—and source accordingly—the type of waste that should be treated to maximise the profit based on material pricing and availability of waste. However, a wider range of options and the uncertainty that is typical of the recycling context makes difficult to companies to fast decide what is the best strategy to be pursued. Thus, decision support tools able to indicate suitable business models and to predict economic performance under uncertain conditions are needed to dynamically address business operations and maximise profit.

A scenario analysis model was implemented for assessing the economic feasibility of the reconfigurable recycling system treating multiple waste product streams. The scenario analysis is capable of analysing possible future events by considering alternative probabilistic outcomes [32]. A base-case, optimistic and pessimistic scenario were defined and economic performances were assessed under each scenario calculating the Net Present Value (NPV) and the Pay Back Time (PBT).

Based on interviews to recycling companies and experts, two variables were selected as the main determinants of recycling economic performance: the relative volume of waste products to be treated in a time period (waste mix) and the market price of materials. Waste volume and mix may vary unpredictably due to fast products technology cycles, success (or failure) in regional tenders for treating specific waste over fixed time periods, new regulation, etc. Different types of waste contain different percentages of valuable materials and require different type of treatment processes which impact on costs. On the other hand, the market price of materials depends on their periodic demand and availability, thus determining the revenue that is made by selling recycled materials on the market. Scenarios were designed by making the hypothesis of average, favourable and unfavourable waste mixes according to their content of precious materials and of recycling cost. Such waste mixes were combined with three different levels of materials costs, considering positive and negative variations of prices from current levels (see Sect. 5.5.2).

#### *5.4.4 Prototypes*

A CPS platform was realised to integrate two workstations including *Vision System, Classification/Control Module* and *CES, Multi-Body Simulation Model and Optimization/Control Module* (Fig. 5.4).

The vision system workstation integrates an ImSpector V10E (Specim) working from 400 to 1000 nm, with a spectral sampling range from 0.78 to 6.27 nm/pixel, coupled with a CMOS sensor (1312 spatial×768 spectral pixels). The camera is equipped with an OLE 23 fore objective lens with a focal length of 23 mm and a FOV of 25.7°. The HSI sensing device is a push-broom system working as a line scan camera providing full contiguous spectral information for each pixel in the line illuminated by two opposite slots housing 3 halogen lamps each, which emit in the spectral range 380–2200 nm. The implementation of the HSI system deals with the need to recognize the materials on-line, enabling the recycler to perform a continuous quantitative characterisation of mixture composition, thus avoiding time-consuming off-line sampling material characterization procedures.

The separation workstation integrates a Corona Electrostatic Separator Hamos KWS. The quality of the separation process is influenced by five controllable parameters: (i) electrode voltage and position; (ii) drum speed; (iii) output stream splitters position; (iv) material feed rate; and (v) feeder vibration. However, due to the complexity of the process, non-controllable effects, such as particle-particle interactions and impacts among particles and machine, influence the efficiency of the pro**Fig. 5.4** Prototype for feed-forward control strategies: HSI workstation (right) and electrostatic separator (left). Implemented at the de- and re-manufacturing pilot plant of CNR-STIIMA (ex CNR-ITIA)

cess, providing contaminated output flows. For this reason, a multi-body simulation model of electrostatic separator process, considering particles attributes, trajectories, interaction and collision, has been developed. The whole system has been validated with experimental analyses, under different operational conditions (i.e. mixture type, machine working parameters).

Thanks to the integration between CES simulation model (that gives a prediction of particles trajectories) and the optimization and control modules, the optimal separation parameters are suggested to the operator for the mixture under treatment. A two-layer architecture integrated within the recycling process-chain supports the control, operation and reconfiguration of advanced multi-material de-manufacturing plants. This two-level approach is focused both on the process level and on the system level and considers the interconnections between physical and virtual entities (Fig. 5.5).

Data about the mixture (composition, geometrical attributes and dimensional distribution) gathered in real-time by HSI vision system are sent to the two-level modelling platform to enable the control of technologies and the reconfiguration of the recycling process chain according to the specific system and material state. A new software platform, controlled by the customized user interface shown in Fig. 5.6 has been designed and implemented to manage the information flow between software and hardware at system level. In this way, the whole process is automated and supervised. However, the operator can easily intervene in manual mode, if necessary.

The implementation of a CPS platform significantly improves the adaptability of the recycling system and the recycling rates. It avoids losses of high-value materials, improves the information about the quality of recycled material and, as a consequence, its market value.

**Fig. 5.5** Architecture of the CPS for online adaption of recycling processes

**Fig. 5.6** Graphical user interface for process control

The realised prototype demonstrates the benefits of the pursued integration approach in recycling systems, where high-cost and sophisticated control solutions would result inadequate due to technical (particles of different shape, size and composition) and economic reasons. The prototype represents a step towards the implementation of the HSI technology for a highly accurate classification of independent sets of wastes including metal and non-metal fraction undergoing mechanical treatment.

#### **5.5 Industrial Testing and Results**

#### *5.5.1 Industrial Case*

One of the most challenging modern recycling applications is the treatment of Waste from Electric and Electronic Equipment (WEEE) that represents the European waste with highest yearly average increase rate (5%). Such an increase was even higher in some European nations, e.g. in Italy the increase amounted to 30% in 2010 compared to the previous year (in terms of collected WEEE). Due to its material composition, WEEE represents an important source of recycled key-metals to be used for the production of advanced technological products. E-waste is a highly variable waste flow because of the wide range of products and material mixtures, which are in continue evolution due to fast technology cycles. For example, in small WEEEs (which in Italy are identified as "RAEE class 4"), Cathode Ray Tube (CRT) TVs, cellular phones and traditional HDD are rapidly being replaced by LCD/LED TVs, smart phones and SSD (Solid State Disks), respectively. Other new waste flows are rapidly entering the recycling streams, such as for example solar panels and tablet PCs. For the successful treatment of WEEE, besides being flexible, recycling systems must guarantee a high efficiency, since critical metals are present in small quantities (e.g. indium, palladium, ruthenium, gallium, tantalum, and platinum). For instance, indium is found in WEEE with an average weight of 39 mg in Notebooks, 79 mg in computer monitors and 254 mg in LCD TVs.

Considering these challenges, the scenario of WEEE was chosen as industrial case for the demonstration of the results of this research. In particular, Printed Circuit Boards (PCB) were chosen as reference WEEE products. PCBs are one of the key and most common components present in the majority of WEEEs. Critical and highvalue metals (such as copper, tin, nickel, gold and silver) account for 25–40% of their total weight composition. The rest of materials (60–75% in weight) are ceramics, plastics and glass fibres. PCBs, which are the major constituent of the obsolete and discarded electronic scrap, account approximately for 3–6% of the total WEEE mass [33, 34].

PCBs are evolving fast from the technological point of view (from large highgrade matrixes into small, compact and medium grade products). For this study, PCBs disassembled from End-of-Life household appliances (e.g. washing machines) have been used and a representative sample of different models has been selected to be processed (Fig. 5.7).

#### *5.5.2 Vision System and Material Separation*

The experiments have been carried out in a real life demonstrator including two workstations: the vision system and the separation machine (see Sect. 5.4.4).

**Fig. 5.7** Selected PCBs from EoL washing machines of different brands (**a**) and (**b**)

Two different sample sets have been prepared to: (i) train and test the HSI system and (ii) test the HSI system for the feed-forward control of separation processes. For both sets, the first part of the preparation procedure consisted of a two-stage shredding by using hammer and cutter mills, followed by sieving treatment. The liberated fraction (average diameter below 1 mm) was thus separated by the CES to obtain two fractions: conductive and non-conductive. For the HSI system training and validation, several metal and non-metal particles have been manually sorted from those output fractions according to their colours (conductive: yellowish, greyish or reddish; non-conductive: greenish), to be further analysed by using Scanner Electron Microscope (SEM).

An experiment was designed to assess the performance of the vision system for the mixture characterization. Fine metal particles with average size smaller than 2 mm were manually selected from shredded PCBs, sorted based on their colour and size, and placed on two different 5 cm×5 cm tiles in a 9 by 7 grid configuration (Fig. 5.8) [27].

The particles were also analysed with a scanning electron microscope (SEM) which provided information on their chemical composition. This information was used for the choice of training samples and for the accuracy assessment of classifications. The particles were classified according to the procedure, testing several different combinations of both classification algorithms and methods for the illumination compensation. The best results were achieved using algorithms which take into accounts spectral correlations through a covariance matrix, such as Mahalanobis Distance and Maximum Likelihood [35], and with the method for illumination compensation proposed by Stokman and Gevers [36]. The best classifications showed values of overall accuracy (OA) and kappa coefficient (KC) greater than 95% and 0.95, respectively.

Finally, the developed multi-body particle simulation tools were used to perform off-line optimization of the on-line control algorithms of the separation processes [37]. A virtual inlet was defined to generate a continuous flow of particles in the 3D simulated environment (given input statistical distribution about particle size, density, material and shape, according to the approach that presented in [31]). Gen-

**Fig. 5.8** True colours images of shredded PCBs of two selected metal fractions (**a**) and (**b**)

erated particles, in the order of tenths of thousands, underwent the simulated CES or ECS separation and finally the performance of the separation was evaluated by computing the statistical distribution of the ballistic separation in a post-processing stage, similarly to a Montecarlo method. Results of the processed multibody simulations, namely the probability density functions of the lengths of the trajectories for different materials, were discussed in detail in [28, 29] where the comparison against experimental data (sampled by hand using sieves and bins) shows that the multibody model can be used as a digital twin of the real separation process. The simulation of even five seconds of separation might involve large amounts of particles, thus leading to a computational bottleneck that, in the presented scenario, required up to one hour of computation. Therefore new classes of DVI solvers were used to improve the computational efficiency [38–40]. In addition, GPU parallel computing was adopted as discussed in [15].

#### *5.5.3 Business Sustainability*

The business sustainability of new flexible recycling technology (with the capacity of 400 kt/year) was simulated considering multiple waste flow that can be optimally treated in different time periods.

Besides PCBs, other selected waste flows were cellular phone, non-hazardous LED lamps and incandescent lamps. Considering material value, within the three types of product, PCB and cellular phone are characterised by higher material value compared to lamps.

The following scenarios were set for waste mix:

The following scenarios were set for materials price:

The combination of the possible outcomes leads to nine global scenarios. A hypothetic recycling company already operating in the market was taken in consideration to assess the acquisition of the new flexible technologies for business expansion. Investment costs, operation costs and performance parameters (such as system capability, lead time, grade, etc.) were estimated through experimental tests carried out at the CNR-STIIMA (ex CNR-ITIA) pilot plant [41] equipped with the new technologies and relying on data from recycling literature.

Table 5.1 reports the results of the Scenario Analysis for the described industrial case.

As it can be noticed, under the hypotheses of the study, the investment in new flexible technologies able to efficiently treat multiple waste streams appears sustainable in all scenarios (NPV at 10 years ranges from 7,457,343 euro in the pessimistic scenario to 13,502,507 euro in the optimistic one) and is repaid after the first year of operation. These results confirm the high business potential of flexible recycling plants.

**Table 5.1** Scenario analysis of the business model validation

#### **5.6 Conclusions and Future Research**

Circular Economy represents an innovative paradigm for sustainable manufacturing [42]. Relying on technology and business model innovation, the Circular Economy concept aims at maximising the use of products and materials. Recycling is one of the strategies that can be pursued in circular economy, together with re-use and remanufacturing. Through recycling, critical materials and key-metals can be recovered from post-use products when they can not be re-used or remanufactured.

Recycling systems are nowadays very rigid in their design and they do not allow dynamic adaptation of process parameters to different types of waste. For this reason, the performance of recycling plants is often sub-optimal. With the goal of developing a new generation of flexible recycling systems, new hardware and software solutions were developed in this study for the dynamic adaptation of process parameters to different type of waste. The Hyper Spectral Imaging (HSI) system and the software tool for the control of the sorting parameters based on a multi-body simulation model were demonstrated in a prototype at the De- and Re-manufacturing pilot plant of CNR-STIIMA (ex CNR-ITIA).

Business sustainability of flexible recycling plants based on innovative technical solutions was assessed as well. The selected industrial scenario is the recycling of WEEE, which poses significant challenges because of the high content of critical and key-metals and of the variability generated by rapid technology cycles. A scenario analysis model was developed to preliminary assess the economic sustainability of flexible technologies under the uncertainty deriving from the variability of future waste mixes and the turbulence of material prices. Under the hypotheses of the analysis, the investment in flexible technologies appears sustainable and very promising in all the simulated industrial scenarios, confirming the high business potential of flexible recycling technologies.

The conclusions of this research are aligned with some studies on other waste streams (e.g. in the recycling of construction and demolition waste [43] and of vehicle waste [44]). Results of the scenario analysis support the strategic importance of establishing reconfigurable recycling system due to their higher profitability, which should be achieved through the increase of quality of recovered materials [44].

This study has some limitations. Firstly, developed technologies were tested with a limited number of waste types and the wider capability of real-time adaptation to rapidly-changing waste flows was not extensively tested. Secondly, the economic assessment relied on data coming from literature and on the estimation of some costs derived from experimental tests and pilot infrastructure investment, which could differ from real industrial environment conditions. Further details on this research are reported in [31, 45–47].

This study provided first prototype results towards the introduction of flexible recycling systems in industry through HSI technology for the classification of independent sets of WEEE with high level of accuracy. Future technology developments will be aimed at:

In addition, the business assessment of new technologies will be improved through the introduction of more accurate cost and revenue structures based on a wider set of experiments and the introduction of a higher number of industrial scenarios.

**Acknowledgements** This work has been funded by the Italian Ministry of Education, University and Research (MIUR) under the Flagship Project "Factories of the Future—Italy" (Progetto Bandiera "La Fabbrica del Futuro" [48], research projects "WEEEReflex – Highly Evolvable Ewaste Recycling Technologies and Systems" and "WEEE ReFlex CPS—Cyber-Physical System (CPS) for reconfigurable e-waste recycling processes". Authors are grateful to Dr. Sarah Behnam for her support to the finalisation of the research and of this publication.

#### **References**

**Open Access** This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

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## Part III Sustainable Factory

## **Chapter 6 Innovative and Sustainable Production of Biopolymers**

**Simona Ortelli, Anna Luisa Costa, Cristian Torri, Chiara Samorì, Paola Galletti, Claudia Vineis, Alessio Varesano, Luca Bonura and Giacomo Bianchi**

**Abstract** This work aims to develop methodologies and tools to support the design and management of sustainable processes for the production of biodegradable polyhydroxyalcanoates (PHAs) biopolymers. PHAs are linear polyesters produced in nature by bacteria through aerobic fermentation of many carbon sources, completely biodegradable and biocompatible. We carried out a study inherent to the advancement of an innovative, cost-effective and environmentally sustainable technology for isolating PHAs from bacteria mixed cultures by combining: (a) innovative cells' pre-treatments and polymer purification's strategy by means of TiO2/UV or Ag0 nanostructured materials; (b) polymer extraction through a green and safe system directly applicable to bacterial cultures, which combines the advantages of solvent extraction and these of dissolution of the non-PHAs cellular matrix through surfactants; (c) monitoring and control tools for process energy and efficiency management. The outcomes put the basis for the design and subsequent building of a working pilot system for the production of completely biodegradable and biocompatible PHAs. The efficiency can be improved and the investments and operating costs can be decreased thanks to the optimization of the production process with the introduction of safe and cheap PHAs extraction route without use of toxic and harmful chemicals and the integration of monitoring and automation tools. The engineering and integration of nano-TiO2 phase within textile fibres and their use as photocatalytic active media for bacteria pre- and post-treatment of waste water added a new opportunity for improving process efficiency and sustainability.

S. Ortelli (B) · A. L. Costa CNR-ISTEC, Istituto di scienza e tecnologia dei materiali ceramici, Faenza, RA, Italy e-mail<EMAIL_ADDRESS>
C. Torri · C. Samorì · P. Galletti Dipartimento di Chimica, Università di Bologna, Campus di Ravenna, Ravenna, Italy

C. Vineis · A. Varesano CNR-ISMAC, Istituto per lo studio delle macromolecole, Biella, Italy

L. Bonura · G. Bianchi CNR-STIIMA, Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Milan, Italy

© The Author(s) 2019 T. Tolio et al. (eds.), *Factories of the Future,* https://doi.org/10.1007/978-3-319-94358-9\_6

#### **6.1 Scientific and Industrial Motivations**

Plastic materials from non-renewable feedstock (e.g. petroleum) have a crucial and undeniable role in our everyday life. However, the chemically engineered durability and the slow rate of biodegradation of most of the fully fossil-based plastics [e.g. polyethylene (PE), polyethylene terephthalate (PET), polypropylene (PP), polyvinyl chloride (PVC)], enable these synthetic polymers to withstand the ocean environment and terrestrial ecosystems for several years, thus affecting organisms at multiple trophic levels [1]. Polyhydroxyalkanoates (PHAs) are bio-based biodegradable polyesters produced by aerobic bacteria from various carbon sources. According to the carbon source, different monomers are produced, whose combination provides different chemo-physical properties. Thus, PHA can have properties ranging from polypropylene to polyethylene, being at the same time biodegradable and bio-based. This brings the interest towards the application of PHAs as packaging films and containers, biodegradable carriers for controlled chemical and/or drug release, disposable items, surgical pins and sutures, wound dressings, and bone replacements [2]. With respect to other bioplastics which are already commercially available and currently produced on industrial scale, e.g. polylactic acid (PLA) or starch-based polymers (Mater-Bi®), PHAs are still not widely used because of the high production costs, more expensive than usual petrochemical plastics (~5–6 vs. 0.5–2 \$/kg) [3]. The high costs' production is mainly due to a combination of three factors: (i) absolutely sterile operation conditions are necessary for pure microbial fermentation; (ii) the use of expensive substrates as carbon source which represents approximately 40% of total production costs; (iii) the extraction and purification process for obtaining PHAs. Thus, the commercialization of PHAs is now limited to applications with high added value. Given the need to reduce costs, a multilevel approach to tackle all bottlenecks is mandatory to develop more cost-effective processes for PHAs production. In principle, the use of microbial mixed cultures (which do not require sterility) combined with the utilization of low value substrates, as agro-industrial waste and byproducts, and with safe and cheap extraction and purification methodologies would allow lower investments and operating costs for the whole production process.

This work aimed to develop a new, cost-effective, and environmentally sustainable technology for isolating PHAs from bacteria mixed cultures, by combining:

The current technology limitations are tackled by optimizing the main process steps: microbial fermentation for PHAs purification, biomass pre-treatment, PHAs extraction and purification. An effort to implement the whole process was done by improving the monitoring and automation tools. Finally, on the basis of the results obtained by the process optimization, the study of a scaled-up prototype was performed leading to the realization of PHAs production semi-pilot plant as well as the construction of photocatalytic reactors for bioreactor wastewater purification with up to 100 L capacity.

#### **6.2 State of the Art**

Polyhydroxyalkanoates (PHAs) are bio-based and biodegradable polyesters produced by aerobic bacteria from various carbon sources. These polymers have tuneable elastomeric/thermoplastic properties according to the monomer composition. Despite the high potential and the efforts to put forward the development of cost effective fermentative systems, the PHA production costs remain considerably high (~5–6 \$/kg), thus hampering the exploitation of these biopolymers as commodity materials [4]. The main limitations could be removed by:

Mixed microbial population (MMCs) can be exploited for producing PHAs in a cheaper and more sustainable way [5]. In fact, compared to single strains (e.g. *Cupriavidus necator* or genetically modified *Escherichia coli*), MMCs do not require sterile conditions: this drastically reduces equipment costs and allows the exploitation of many cheaper substrates (e.g. wastes) thanks to the wider metabolic potential of MMCs in comparison to single strains such as waste biomass (e.g. fermented molasses), wastewater and pyrolysis products. Currently, in the literature there are few publications on PHAs recovery from MMCs, since the research effort is mainly focused on the set-up of efficient and sustainable cultivation systems. Little information is available on the characterization of PHAs extracted from MMCs and, above all, on the efficiency of the polymer recovery in the extraction step [6]. As it is well described in the literature for single strain cultures, obtaining the polymer from bacteria through a series of downstream steps (e.g. microbial biomass pre-treatment, polymer extraction, and post-treatment purification), could be challenging [7]. Indeed, it is reported that the downstream costs can represent up to 50% of the total production cost because non-recyclable chemicals/materials and high energy demands are required. However, since MMCs are more resistant than single strains, the extraction/purification of PHA can become challenging. The protocols reported in the literature [8, 9] for the extraction of PHA from MMCs include (i) an optional cell pre-treatment under acidic conditions followed by a solvent extraction (e.g. with acetone or chlorinated compounds) to dissolve the polymer or (ii) a treatment with additives that break microbial membranes enabling the release of the polymer inside the cells. The use of organic solvents helps in extracting high molecular weight PHAs but with a low yield (18–30%), whereas strong oxidants decrease the molecular weight but provide high extraction yields (close to 100%). Feed-back control strategies have already been studied for fed-batch systems, mainly to determine on-line when to feed the next pulse of substrate in the production reactor. Continuous feeding is also possible but it is used most successfully with single substrates and high cell densities [10]. Whiffin et al. [10] have proposed an algorithm that alternates continuous feeding and famine periods in order to prevent acetate accumulation and allow shorter feedback control intervals. The dissolved oxygen (DO) is closely related to the substrate concentration so that when the substrate is exhausted and oxygen consumption decreases, the DO level increases rapidly, suggesting to exploit the DO signal to control pulse feeding in the fed-batch [11, 12]. Westerberg [12] showed that inhibition of the biomass by the stored polymer is an important factor reducing the substrate and oxygen uptake rates and modelled the DO response to repeated substrate pulses. Another recent approach determines when to feed a pulse of substrate by using it as a control variable to regulate the pH [13]. Popova [14] designed a Kalman filter to estimate unmeasured variables in a mixed culture system, based on measurements of the substrate concentration. Vargas et al. [15] have explored the use of a model-based control scheme to enhance the productivity of polyhydroxyalkanoate (PHA) production in a mixed culture two-stage system. Oehmen et al. [16] operated a three-stage bioreactor with anaerobic fermentation, culture selection and PHA accumulation. Some adopted sensory systems, e.g. on-line gas chromatography in [17], strongly affect plant economic sustainability. In this work, a plant supervisor and controller has been developed exploiting the actual trend toward intelligent and interconnected production systems that integrate low cost distribute processing.

#### **6.3 Problem Statement and Proposed Approach**

A systematic analysis and consequent improvement strategies of the PHAs production process is needed to reduce costs and limit the use of hazardous solvents for extraction and purification technologies. The main actions to enhance sustainability and cost-effectiveness are schematised in Fig. 6.1 and better described in the remaining part of this section.

Wastes such as starch, cellulose and hemicellulose deriving materials, plant oils, molasses, whey, and various industrial by-products have been proposed as potentially useful and cheap carbon sources. As a matter of fact, a wide range of agriculture and forestry wastes can be converted into carbon sources for microbial fermentation if coupled with suitable pre-treatments like thermochemical conversions (e.g. pyrolysis or hydrothermal liquefaction) or acidogenic fermentation steps. These specific pre-treatments allow to convert substrates which could not be directly utilized by

**Fig. 6.1** Sustainability tools proposed and developed

microorganisms to produce PHAs, into PHAs chemical building blocks (volatile fatty acids, VFA), with a consequent abatement of the costs related to the carbon source and the accessibility to a widespread pool of substrates without any competition with food resources.

As regards microbial fermentation for PHA production, a feast (carbon excess) and famine (substrate exhaustion) approach was followed for growing MMCs. During the feast phase, bacteria able to accumulate PHA are selected since during the famine phase PHA can be exploited as energy and carbon source.

Highly skilled workforce for monitoring the system state and solving possible problems are required to ensure the achievement of satisfactory yield within an ecologically stable process. For this reason, a key aspect is to develop a complete monitoring of the process variables (physical and biological) with programmable logic controller that can readily react to the change observed (e.g. natural oscillation of the bacteria population, bulking problems, clogging) providing feedback in completely automated way and without the intervention of skilled labour. Therefore, the design and manufacturing of a compact all in one system able to convert a specific waste into ready-to-use bioplastic has been conceived. Another aspect investigated is the substitution of standard equipment series (usual approach of chemical plant) with one that can be tuned to provide multi-purpose function (e.g. bacteria growth, harvesting and extraction in the same apparatus), in order to reduce the cost (per unit of output).

As for the biomass pre-treatment, the challenge is the achievement of satisfactory yield in the PHAs extraction process despite the higher resistance of MMC than pure culture. For this reason, after the fermentation step, the bacterial broth is usually pretreated by means of a freezing/heating approach or salt/alkaline treatment, aiming at denaturing genetic material and proteins, and thus enhancing the cell disruption and polymer recovery.

The novelty, at this point, is the application of a nanoparticles (NPs)-based pretreatment step to weaken bacterial membranes without degrading the polymer stored inside the cells, exploiting photocatalytic TiO2 NPs and antibacterial Ag0 NPs. This pre-treatment requires lower energy consumption than the traditional approaches (freezing and heating) and a lower consumption of chemicals by using supported recyclable nanomaterials.

The PHAs extraction from cultures can be done by adopting a solvent extraction approach (mainly with chlorinated solvents, highly effective in solubilizing PHA granules) or a cellular-digestion approach thus breaking cellular constituents apart with additives. The final polymer purity is higher when solvents are used, however both approaches present some environmental/economic issues such as toxicity and non-recyclability. A new and green extraction process with the aim of increasing the sustainability of the entire process was designed through: (i) an abatement of the use of toxic and hazardous chemical compounds by using green solvents; (ii) an increase in the recyclability of the chemicals used in the process and a reduction in downstream flows which must be treated; (iii) an improvement in the polymer quality ensuring the retention of mechanical properties.

Moreover, PHAs purification was required since MMCs are composed of gramnegative bacteria that can release endotoxins in the form of lipopolysaccharides, from the outer cell wall. Endotoxin can cause several diseases if introduced into the bloodstream of humans or other animals, and for this reason, in view of biomedical applications, the extracted polymer must be purified. Common methods of purification involve sodium hypochlorite or hydrogen peroxide treatment combined with the action of enzymes or chelating agents. An alternative procedure was taken into consideration for the treatment of the biopolymer contaminated by dangerous cellular residues, based on heterogeneous photocatalysis, involving photoinduced redox reactions at the surface of semiconductor ceramics. Furthermore, this approach can reduce the use of compounds that could affect the quality of the resulting biopolymer and the costs of the purification process thanks the application of a mature technology in the field of organic compounds detoxification processes.

#### **6.4 Developed Technologies, Methodologies and Tools**

On the basis of the sustainability principles, we ad hoc designed two differently sized plants. As starting point, we developed a working lab-scale system (1 g PHA/day) to produce completely biodegradable and biocompatible PHAs biopolymers from volatile fatty acids (VFA) obtainable from different waste sources.

Subsequently, the lab scale prototype was scaled up to 100 g PHA/day demonstration micro-plant. The system was operated for 150 days showing reliable operations with minimal maintenance. The extraction system showed to be effective in obtaining high purity PHA from bacterial slurries with acceptable yield. PHA can be recovered from MMCs by using dimethyl carbonate (DMC) or DMC plus a pre-treatment with sodium hypochlorite (NaClO). In the first case an overall polymer recovery of 66% can be achieved, with a very high polymer purity and molecular weight (98% and 1.2 MDa); in the second case, NaClO helps in increasing the recovery (82%) but decreases the molecular weight to 0.6–0.2 MDa.

**Fig. 6.2** Plant picture during operation, **a** pumps and controller and **b** the bioreactor

The scaled-up prototype consists of a module formed by two pieces of equipment that can be added downstream to a wastewater treatment plant or to a PHA producing bioreactor. The module is composed of (i) a biological reactor which allows the production of a PHA rich sludge (25–40 gPHA/kgslurry) and (ii) a complete dimethyl carbonate (DMC) extraction system able to provide high purity PHA (Fig. 6.2).

The micro-plant controller has been designed in order to assure autonomous operations, with local data logging, and to support remote supervisory interaction via a standard Internet browser. In order to deliver such functionalities with a limited plant cost, a solution based on open source micro-controllers has been developed. Given the required internet connectivity and control logic, the Arduino TIANTM micro-controller has been chosen. The control architecture is depicted in Fig. 6.3. Different types of sensor are foreseen for each plant tank: Dissolved Oxygen (DO) and pH sensors (connected via an I2C digital bus), temperature sensors (connected via One-wire digital bus). Digital buses allow the addition, for future developments, of additional sensors, identified by a unique address on the same bus line, without the need to upgrade the controller input/outputs. Plant valves are controlled via 18 digital outputs, whereas plant discrete states are read via 20 digital inputs. The Arduino TIAN has been selected because it joins a deterministic SAMD21 Atmel Cortex® M0+ 32-bit microcontroller (MCU) with 32 KB of SRAM program memory and a Qualcomm Atheros AR9342 processor running a Linux operative system (OpenWrt) that supports networking capabilities. A user interface based on a web page, that shows the plant state and allows the user to enter all required reference values and commands, has been implemented by exploiting Arduino libraries for web server creation. Given that web server functionalities are still not available on the Arduino TIAN two alternative solutions have been developed for the user interface. The first one via a web browser, using an additional Arduino YUN, connected to the TIAN, acting as web server, and the second one where the user interacts with the TIAN via a serial connection to a local micro-PC, located in the plant electric cabinet.

**Fig. 6.3** Plant controller architecture: version with internet connection

The controller is implemented as a Discrete-State Controller. For each possible state of the plant, a control state is defined. When the state is activated, all requested control actions are performed (e.g. opening/closing valves, running a pump), then the associated activities (e.g. monitoring the solution DO or pH or counting a predefined time lap) are performed until the exiting conditions are satisfied and a new state become active. The defined states are: VFA dosing to R1 (SBR), Nutrients dosing to R2, Dilution with tap water, Mixing, VFA dosing to R2 (Accumulation Reactor), Mixing, Sedimentation, Collect the biomass, Remove excess water, Backflush for filter cleaning, and Standby. In a concurrent, parallel, control loop, the reactors are temperature controlled by reading their temperatures and switching on/off the corresponding heaters.

#### **6.5 Experiments**

The main experimental activities were carried out to:

• investigate the opportunity to develop model-based observers able to estimate, from the available feedback signals, crucial quantities (like bacteria mass and their activity, polymer concentration) that are not directly measurable.

#### *6.5.1 Optimization of PHA Production*

Activated sludge from a municipal wastewater treatment plant was used as inoculum for a lab-scale sequencing batch reactor (SBR, 5 L). The culture was fed with synthetic organic acids (acetic and propionic acids) and subjected to 12 h cycles each composed of an initial feed phase, an aerobic reaction phase, a sedimentation phase, and a withdrawal of depleted water. After that the microbial culture was sent to an accumulation reactor (working volume of 1.5 L) and fed with synthetic organic acids (acetic and propionic acids) for increasing the amount of PHA inside the cells [9]. After 3-months, this cultivation system provided a microbial biomass with 40±5 wt% of polymer content, with a 3HB and 3HV molar ratio of 76 and 24, respectively.

#### *6.5.2 Optimization of PHA Extraction from MMC*

Various pre-treatments (chemical, thermal, mechanical) were explored for increasing the extraction efficiency of DMC [18]. So far the extraction of PHAs from MMCs with organic solvents has been accomplished mainly with chlorinated compounds or a mixture of chlorinated and not chlorinated solvents. Patel et al. reported that the extraction efficiency of organic solvents reaches a maximum of 30%, against an almost complete polymer recovery from single strains [19].

The results achieved with various extraction systems are summarized in Table 6.1. DMC and CH2Cl2 provided the same recovery efficiency (49±2 and 52±1%, respectively, corresponding to PHA extraction yields of 20±1 and 22±1 wt%).

Among the pre-treatments, both the thermal (temperature > 150 °C) and the mechanical (glass beads and sonication) ones did not improve the DMC extraction performance. However, a pre-treatment with NaClO applied before the extraction with DMC increased PHA recovery up to 76±4% (NaClO for 5 min at 100 °C). Harsher oxidizing/bleaching conditions (NaClO treatment at 100 °C for 1 h) yielded the highest recovery equal to 82±3%.

Very good polymer purities were obtained after the treatment with DMC or CH2Cl2 (entries 1 and 3 in Table 6.1, 98 and 94%, respectively). The purity of PHA recovered through DMC after a pre-treatment with NaClO was generally high (88–98%). Promising results in PHA extraction from MMCs are reported in [20].

**Table 6.1** Physical characteristics of PHAs extracted from MMCs biomass

aevaluated by TGA mass loss intensity The data are expressed as mean±standard deviation of four independent replicates of each extraction condition (Tmax deg: maximum decomposition temperature, Mw: mean molecular weight; PDI: polydispersity index)

#### *6.5.3 Design and Prepare TiO2 and Ag0 Based Nanostructured Materials*

To prepare TiO2- and Ag-coated textiles, standard cotton textiles were used without any pre-treatment, while standard polyamide textiles were made more hydrophilic and the affinity for the nanosols was improved, by dipping samples in flasks containing ethanol (80% v/v) and demineralised water at 70 °C for 30 min. The liquor ratio was 80:1 ml/g (volume of solution to weight of fabric). The samples were dried at room temperature. The functionalization of textile was performed by dip-dappingcuring method. Standard cotton and pre-treated polyamide textile were dipped at room conditions (temperature-21°C and humidity of air-45%) in the titania (1.5 and 3 wt%) or silver nanosol (0.05%). Fabrics soaked for three minutes are then passed through a two-roller laboratory padder. The resulting coating is fixed on the surface after drying it in an oven at 100 °C and curing at 130 °C for 10 min. The excess of nanoparticles not adsorbed on the surface is removed by water washing coated fabrics in an ultrasound bath for 15 min and then drying at air. This process is called post-washing treatment.

TiO2- and Ag-coated textile samples were used to carry out antibacterial tests and preliminary tests in real experimental condition during pre-treatment step.

**Table 6.2** Bacterial reduction % on *Klebsiella pneumoniae* and Mixed microbial culture using TiO2- and Ag-coated fabrics

#### *6.5.4 Antibacterial Tests*

Antibacterial tests against pure microbial cultures of *Klebsiella pneumoniae* (ATCC 4352, Gram negative) and mixed microbial cultures (AATCC 100 Test Method) on different functionalized textile by silver and titanium dioxide were performed. Bacterial reduction is calculated using Eq. (6.1), where A is the number of colonies of the fabrics with titanium dioxide or silver, and B is the number of colonies of the untreated samples (references).

$$\% \text{ bacteria reduction } = \frac{B - A}{B} \cdot 100 \tag{6.1}$$

These antibacterial tests were carried out on both cotton and polyamide fabrics and the results are reported in Table 6.2.

The antibacterial tests against *Klebsiella pneumoniae* showed fairly good data with UV-activated TiO2-coated samples, while excellent results were obtained by Ag-coated fabrics. In general, the antibacterial activity shown by our coated textile samples seems insufficient to obtain a good bacterial reduction. However, bacteria membrane alteration could be detected as irregular profile as in the spot highlighted. The preliminary observation by SEM of membrane morphology after exposure to photocatalytic component was made, nevertheless further investigation needed to experimentally verify their effects on bacteria membranes degradation. The establishment of a physical correlation between exposure to photocatalytic agents and the direct effects on bacteria cellular membrane would support their integration within PHA bio-reactors, facilitating the performance evaluation of bacteria pre- and posttreatments.

#### *6.5.5 Pre- and Post-treatments with TiO2 Textiles*

The biomass slurry was too much *dense* and it prevented the UV-light to pass through. Unfortunately, the recovery of PHA after the pre-treatment was similar to the results achieved without any treatment (~50%). In post-treatment, PHA (standard or extracted from bacteria, 10 mg) was re-suspended in water and put in contact with a disk of TiO2-textile (20 mg) in order to remove any residual fraction of bacterial charge. The samples were irradiated for 1 or 2 h under UV.

In this case we experienced an unexpected and detrimental absorption of PHA on the textile that caused a loss of polymer after the treatment.

#### *6.5.6 Purification of Wastewater*

Despite the developed photocatalytic tools showed a high reactivity against pure bacteria and fair results against MMC, some difficulties in their integration within PHAs production process were found. Thus, as an alternative to the use of catalytic tools in pre-treatments and polymer purification steps, the developed TiO2 coated textiles were exploited in purification step of wastewaters, originated from PHAs production process. These wastewaters contain mainly organic pollutants, which could be easily degraded by UV activated nano-TiO2 based photocatalytic materials. Thus, with the aim to purify wastewater and controlling consuming and environmental sustainability, the integration of TiO2 based photocatalytic nanostructured surfaces was investigated as final purification step of PHA production process. The photodegradation of organic pollutants catalysed by nano-TiO2 occurs after the absorption of near-UV light by the surface that induces charge separation and the formation of electron (e−) and positive hole (h+) pair. This charge displacement at contact with water and air promotes the formation of reactive oxygen species (ROS) like hydroxyl and singlet oxygen radicals, responsible for the oxidative degradation of organic molecules [21]. As many papers demonstrate [22–25], photocatalytic oxidation is a promising approach in the purification of waters contaminated with organic pollutants.

In this work, the purification treatment of wastewaters with nano-TiO2 coated textiles was studied. The organic loading abatement of wastewater using photocatalytic activity was tested through TOC (total organic carbon) determination. In general, good photocatalytic results were found, in lab-scale systems (see Fig. 6.4). Specifically, with two hours of UV irradiation, an excellent TOC reduction was measured from the initial amount of organic C (about 0.45 g/L) to about 0.06 g/L (Table 6.3).

#### *6.5.7 Automation Tools for Process Energy and Efficiency Management*

The controller has been developed on the Arduino platform, in the different versions described in Sect. 6.4. Given the limitations of the available software libraries for the Arduino TIAN, the Serial User Interface was preferred: the full web interface will be supported by future versions of the Arduino development environment (IDE).<sup>1</sup> The adopted software architecture allows a simple addition of new control states and

<sup>1</sup>www.arduino.cc.

**Fig. 6.4** Nano TiO2 coated textile introduced as floating membrane, under UV irradiation, in **a** lab-scale system and **b** lab-scale (1 L capacity) plant

**Table 6.3** Photocatalytic test performed with bioreactor wastewater in lab-scale system

athe results show a photocatalytic efficiency up to 85% with a COD reduction from 450 a 60 mg/L in the condition of Test 8 (2 h of UV irradiation)

strategies: each state can access the whole data structure. The controller reads the inputs and proposes updated control actions by a timing defined by the user, down to 5 s. All plant data are transmitted via the Serial interface and saved by the connected micro-computer.

#### *6.5.8 Scale-up of Photocatalytic Advanced Oxidation Process*

The development and scale-up of photocatalytic plants designed for treating downstream PHA waste water (PHAWW) was carried out to decontaminate and recycle the bioreactor wastewater, thus adding value to the whole process. A pilot-scale UV/TiO2 photoreactor with capacity of 1 L/day of PHAWW was assembled (Fig. 6.4b), using lab scale system (Fig. 6.4a) as model and validated it experimentally, in order to obtain data for the evaluation of industrial scale-up. A further effort was made in the scale-up, achieving semi-pilot plant (Fig. 6.5a) and pilot plan (Fig. 6.5b) with 6 and 100 L capacity, respectively. Two main configuration systems: at fluidized (Fig. 6.4) and fixed bed (Fig. 6.5), were proposed in order to improve the versatility and provide the real environment where testing photocatalytic components in the advanced oxidation treatment of waste water.

Encouraging results, in terms of photocatalytic efficiency, were obtained using different TiO2-coated fabric as active membrane in semi-pilot plant (Table 6.4). Further study has been carried out to improve the photocatalytic efficiency in semipilot fixed bed system [26].

**Fig. 6.5** Nano TiO2 coated textile introduced as permeable fixed membranes irradiated by immersed UV lamps, in **a** semi-pilot plant, 6 L capacity and **b** pilot plant, 100 L capacity

**Table 6.4** Test performed with synthetic pollutant (Rhodamine B) in semi-pilot plant

akinetic constant

bthe pollutant degradation was for up to 90%

#### **6.6 Conclusions and Future Research**

The main goal addressed in this work about the PHA production, including the scaleup study, was to develop an original, cost-effective, fully-automated and industrial competitive downstream process for obtaining PHA from microbial cultures. This is in fact still an open challenge in the lack of automated PHA extraction processes and integrated advanced oxidation photo-catalytic systems.