Preface

The rapid growth of microelectronics and digital computing has stimulated a significant growth in the area of digital signal processing (DSP). The concepts of DSP proliferated in many areas such as telecommunications, digital television, biomedical engineering, digital audio, and power conversion. DSP now seems to be a core for many new emerging digital applications and for the information society. Today's information revolution paves the way for the engineers in the areas of electronics, computer and communication engineering to think about DSP concepts.

Just a decade ago, digital signal processing was more of theory than practice. The only sys‐ tems capable of doing signal processing were massive mainframes and supercomputers and, even then, much of the processing was not done in real time but off-line in batches. For ex‐ ample, seismic data were collected in the field, stored on magnetic tapes and then taken to a computing centre, where a mainframe might take hours or days to digest the information. The first practical real-time DSP systems emerged in the late 1970s and used bipolar 'bitslice' components. The economics began to change in the early 1980s with the advent of sin‐ gle-chip metal-–xide semiconductor (MOS) DSPs.

Digital signal processors were invented to handle digital signal processing tasks and were available in a variety of applications like audio signal processing, audio and video compres‐ sion, speech processing and recognition, digital image processing, digital communications, biomedicine, seismology and radar applications. Specific uses include speech transmission in mobile phones, seismic data processing, analysis of industrial processes, medical imaging such as computerized axial tomography (CAT) scans, MP3 compression and computer graphics.

#### **Scope of the Book**

Many books are available for understanding digital signal processing concepts. This book is an outcome of research done by various researchers and professors who have highly contributed to the field. This book would suit researchers in the field of digital signal processing.

#### **Structure of the Book**

The book contains six chapters divided into three sections. The reader is expected to know the fundamentals of digital signal processing, which are available in all the standard DSP books.

Section 1, consisting of three chapters, deals with applications of digital signal processing in communication engineering. Section 2 contains two chapters and describes the application of digital signal processing concepts in image processing. Section 3, consisting of a single chapter, focuses on the role of DSP in power conversion systems.

#### **Acknowledgements**

I thank the Almighty for showering His blessings and giving me the intelligence and energy to complete this work. My sincere thanks to the management of Dr. Mahalingam College of Engineering and Technology and Prof. C. Ramaswamy, Secretary, NIA Institutions, for their encouragement and patronage rendered to carry out this work. I am indebted to my wife Mrs. Vinitha Mohan. Her support, encouragement, quiet patience and unwavering love un‐ deniably led me to the successful completion of the work. I am at a loss for words to thank my son S.V. Hemesh for putting up with my preoccupation, for his understanding and for the love he gave me. I am grateful to InTech publisher, especially the Publishing Process Manager Ms. Iva Lipović who constantly helped me in bringing this book to completion.

#### **Dr. Sudhakar Radhakrishnan**

Department of Electronics and Communication Engineering, Dr. Mahalingam College of Engineering and Technology, India


2.8. Color, shape, number, and size of seeds as well as seed maturity

landraces result to higher variation within the population [10]. Landraces are also much

The higher germination percentage of seed depends on the availability of environmental factors, like adequate temperature, light, salinity, moisture, and water [30]. The germination stage is the most important stage in the crop survival, which is to determine the amount of water and nutrient resources that need to be applied [31]. In Mexico the germination percentage ranges from 58.27 to 73.51% among the P. vulgaris landraces [31]. P. vulgaris landraces from Uganda show uniformity in seed germination, where after 5 days of planting all genotypes

P. vulgaris differed in their growth habits which may be climbing or semiclimbing, erect or even bush type [1]. Their growth habit can either be determinate or indeterminate [17]. These growth habits are classified into four major classes, namely, Type 1 has determinate, upright, and bushy habit; Type 2 has indeterminate, upright, and bushy habit; Type 3 has indeterminate, prostrate with no climbing or semiclimbing, habit; and Type 4 has indeterminate and

The plant height of Brazilian P. vulgaris landraces ranges from 338 to 988 mm [27]. According to Stoilova et al. [32], plant height of landraces from Portugal and Bulgaria ranges from 195 to 1234 mm with the average of 447 mm. However, Sozen et al. [33] record plant height among Turkey landraces ranging from 200 and 3100 mm. The plant height shows wide variability among the landraces in Madeira where climbing landraces have a variation from 1086 to

P. vulgaris landraces from Portugal and Bulgaria with climbing growth form have the numerous branches than bushy type [10]. The number of shoots in the main stem shows variation, with a range either from 4 to 14 among the landraces in Uganda [11] or from 17 to 57 in Nigeria [1]. The number of leaves per plant varies with a range from 45 to 96 leaves among P. acutifolius

Days to flowering also vary among P. vulgaris landraces, which generally commences from 26 to 51 days after planting in Portugal and Bulgaria landraces [10], Honduras [35], and Uganda [11]. However, a variation in days to flowering from 35 to 75 days after sowing is evident among landraces from Mexico [32]. The color of the flowers among P. vulgaris landraces can be white, carmine, red, purple, pink, white with lilac edges, or white with red stripes [11, 32].

The color of immature pods in Uganda P. vulgaris landraces is pure green; green with purple, carmine, or red stripes; dark purple; carmine, red, or pink, whereas physiologically matured

appreciated for their taste, high nutritional value, and short cooking time [28].

2.5. Growth form, plant height, and number of branches and leaves

2.4. Germination percentage

64 Rediscovery of Landraces as a Resource for the Future

emerge from the soil [11].

strong climbing habit [17].

landraces in Botswana [34].

1441 mm and bushy from 138 to 382 mm [5].

2.6. Days to first flower formation and flower color

2.7. Color, shape, number, and size of pods

Genetic variability in P. vulgaris landraces is sometimes indicated by seed color, size, and shape (Figure 1) [28, 38]. Shininess of seeds can either be shiny, intermediate, or opaque [39]. There is a wide variation in both seed coat main and secondary colors. Seed coat main color can be brown, cream, red, white, or yellow, while the secondary color can be black, red, or violet on the entire grain [28, 40]. White grain seed is commonly used by commercial farmers [41]. Seed shape can be round (circular), oval, kidney, hook, truncate, as well as cuboid (rectangular) shape [12, 40].

The number of seeds per pod among the P. vulgaris landraces has comparable ranges from 4.96 to 5.01 in Greece [37], from 2.8 to 6.6 in Italy [13], and from 3.60 to 5.53 in Zimbabwe [42]. Landraces from Italy that are categorized into traditional and nontraditional agro-food products vary from 3.2 to 6.3 and from 3.0 to 4.9 seeds per pod, respectively [43].

Seed size varies widely among P. vulgaris landraces. In Kosovo, seed length has a range of 12.8–18.3 mm, width 7.4–10.1 mm, and thickness 4.6–6.9 mm [44]. In Turkey, seed length has a variation of 11.8–23.1 mm, width 5.8–15.4 mm, and thickness 0.7–10.0 mm [38]. Seed length also ranges from 10.0 to 16.7 mm, from width 6.1 cm to 9.8 mm, and thickness from 4.2 to 8.2 mm, in Iran [14]. Consumers normally favor medium-sized to large-sized seeds probably because of their mass, taste, and easiness in hydration when cooked [45]. Seeds have certain properties such as early or late maturity, as some physiological maturity ranges from 65 to 120 days [10, 46].

#### 2.9. Plant resistance to diseases and pests

In Tanzania, the screening of different P. vulgaris landraces and released varieties against Phaeoisariopsis griseola (Sacc) [Ferr], which causes angular leaf spot disease, shows that landraces were resistant, while varieties were either intermediate resistant or susceptible to this disease [20]. This suggests the presence of resistant genes on these landraces toward the P. griseola. The response of P. vulgaris parental lines to infestation by bean fly (Ophiomyia phaseoli) ranges from susceptible to resistant in Kenya [47].

3. Conclusion

Author details

Nontuthuko R. Ntuli

References

Natal; 2013

Evolution. 2011;58:861-874

seed quality. Diversity. 2010;2:837-862

sition and Analysis. 2012;26:72-80

Address all correspondence to<EMAIL_ADDRESS>
University of Zululand, South Africa

A wide variation in growth and yield of P. vulgaris landraces discussed in this review will enable a possible breeding selection for leaf consumption based on bigger and soft-textured leaves. A selection for green beans can be on pod size, texture, and yield. Further, selection for dry beans can be based on seed yield, size, taste, and cooking time, to name a few. Breeding for resistance toward pests and diseases can be enhanced on landraces with resistant genes.

Morpho-Agronomic Variation among *Phaseolus vulgaris* Landraces: A Review

http://dx.doi.org/10.5772/intechopen.71359

67

[1] Nwadike C, Terkimbi V. Effects of planting date on performance of common bean (Phaseolus vulgaris L) landraces of the Jos plateau: A preliminary studies. International

[2] Adesoye AI, Ojobo OA. Phaseolus spp.: Valuable but underutilized genetic resource in

[3] Kouam EB, Ndomou M, Gouado I, Pasquet RS. Assessment of the genetic diversity of cultivated common beans (Phaseolus vulgaris L.) from Cameroon and Kenya using allozymes

[4] Njoki NB. Breeding for durable resistance to angular leaf spot (Pseudocercospora griseola) in common bean (Phaseolus vulgaris) in Kenya [thesis]. South Africa: University of KwaZulu-

[5] Freitas G, Gananca JFT, Nobrega H, Nunes E, Costa G, Slaski JJ. Morphological evaluation of common bean diversity on the island of Madeira. Genetic Resources and Crop

[6] Piergiovanni AR, Lioi L. Italian common bean landraces: History, genetic diversity, and

[7] Doria E, Campion B, Sparvoli F, Tava A, Nielsen E. Anti-nutrient components and metabolites with health implications in seeds of 10 common bean (Phaseolus vulgaris L. and Phaseolus lunatus L.) landraces cultivated in southern Italy. Journal of Food Compo-

[8] Camara CRS, Carlos A, Urrea CA, Schlegel V. Pinto beans (Phaseolus vulgaris L.) as a

functional food: Implications on human health. Agriculture. 2013;3:90-111

markers. Journal of Experimental Biology and Agricultural Sciences. 2017;5:088-097

Nigeria. International Journal of Plant Breeding and Genetics. 2015;9(1):1-9

Journal of Current Research and Academic Review. 2015;3:309-324

Figure 1. Variation in shape, size, and color of some Italian P. vulgaris landraces [28].


**1. Introduction**

Waste management in emerging countries has become urgent during recent years, because economic growth and rise of consumption have caused a rise in waste production. The escalation of generation of residues has resulted in a shortage of disposal sites and higher waste management costs. In Accra, Ghana, the expenditure for municipal solid waste (MSW), for example, increased from 2013 to 2015 by 8% [1]. Other important waste management issues which developing countries must face include lack of proper governance instruments, inefficient resource use, overdependence on imported equipment, improper financing methods and application of technology, inequality in service stipulation, and deficient technical expertise [2]. In many cases, especially in lower-income countries, governance issues play a more important role than technical aspects [3]. Therefore, it is vital to find implementable, knowledge-based solutions for the governments of countries with poor municipal waste management performance. For example, in Mexico City, which produces 12,920 Mg per day of MSW and experiences the problem of shortage of landfill capacity, the waste infrastructure requires an alternative scenario. However, with the variety of technologies for treatment, recovery, and energy generation from waste, it is hard for decision makers, not experts themselves, to make a correct choice. Diverse technologies have distinctive climate change effects, and different investment and operation costs. An overall assessment of a waste management system is needed in such cases. This assessment should combine aspects such as environmental performance, economic viability, and social acceptability. Also, local conditions should always be a consideration before a system is implemented.

Extensive research has been done to determine sustainable decision-making models to assess waste management scenarios. Among the existing models, the most popular ones are life-cycle assessment (LCA), cost–benefit analysis, and multicriteria analysis. LCA calculates the environmental impact of all processes of the waste treatment from "cradle to grave"; cost–benefit analysis considers the monetary dimension, while multicriteria decision analysis (MCDA) compares social, economic, and environmental criteria [4]. There is literature available which compares different waste management systems in emerging and developing countries. Brunner and Fellner [5] conducted a study to determine appropriate waste management systems in less-developed economies. Zubruegg [6] developed an assessment tool based on a questionnaire. Elsaid and Aghezzaf [7] presented a progress-indicator-based assessment guide for integrated municipal solid waste management systems.

MCDA is often used in waste management. One of the MCDA methods widely applied in waste management is ELECTRE (Elimination and Choice Expressing the Reality). ELECTRE can incorporate many evaluation criteria for selecting an optimal alternative, coupled with the possibility of involving several decision-makers. It has been applied to the real choice process of a solid waste management system in Bosnia and Herzegovina by Vuˇcijak et al. [8]. Another, PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations), was developed by Brans [9] and further extended by Brans and Vincke [10]. Queiruga et al. [11] used PROMETHEE, combined with a survey of experts, to rank Spanish municipalities for the installation of Waste Electrical and Electronic Equipment (WEEE) recycling plants. Demesouka et al. [12] used a combination of geographical information systems (GIS) and MCDA methods in the analysis of municipal solid waste landfill suitability.

In this study, the alternative scenarios of MSW management in Mexico City are compared based on MCDA. The purpose of the analysis is to determine the most sustainable scenario for the city. The method used was chosen because it allows the involvement of environmental, economic, and social criteria, by comparing diverse quantitative and qualitative dimensions to produce a ranking. Moreover, it allows the participation of different stakeholders with various interests. Therefore, it has been chosen as a tool to assess the sustainability of waste management in this work.

The goal of this study is to find the most sustainable waste management scenario for Mexico City based on waste composition, experts' opinions, and overall assessment of state-of-the-art technologies, by using MCDA. No research combining economic, environmental, and social dimensions has been previously conducted to determine a sustainable residues management model for Mexico City. This study is the first to visualize and compare the economic benefits, the impact of waste treatments on the environment, and social benefits of specific waste technologies for Mexico City. The outcome should help decision makers in the introduction of a successful, sustainable waste management system. The results are assumed to be transferable to other megacities in emerging countries and help decision makers in the selection of waste management scenarios with energy and resource recovery.


**4. Selection of tools**

There are different tools which can be used in QRM, both unspecific and specific for this task "in [2, 6]". In reality, and very unpleasantly for some people, tools just organize in a more or less sophisticated way the information that we have. They will neither provide us with the knowledge that we don't have nor liberate us from the task of thinking about the question. Brainstorming will always be inevitable.

According to their function QRM tools can be classified in three main groups: risk analysis, risk comparison and statistical support.

Risk analysis tools are either inductive or deductive. The first, starting with the normal operation, try to detect possible problems. The second, starting with the problem, try to find the chain of events that led to it. It is also very common to talk about formal and informal tools. In fact the former have really been devised for this purpose, whereas the latter are just data given in a certain way and they can be only considered "tools" in a loose sense.

It might seem surprising to explain that there are QRM tools that do not consider risk, but hazard. Although this is discussed in more detail below, here we need only point out that "risk analysis or management" is a general concept which can consider just hazards or their associated risk too.

#### **Table 4.** Basic classification of tools used in QRM

392 Risk Management – Current Issues and Challenges

An interesting question that might arise as a consequence of the application of QRM is about precedence. What is more important a GMP statement or the result of a QRA? This is however a completely false question because if GMP can be considered the logic baseline in

Problem / question / doubt

3rd Establish its effect/s

5th Look for possible solutions

6th What about GMP?

4th Gather information on it

1st Define it

In fact QRM is complementary to GMP because it provides the frame for taking a decision, whereas GMP proposes us some practical and well known solutions. It is important to understand that "good manufacturing practice" has been, and still is, accompanied by some amount of "bad practice". And this is the result of seeing in GMP as a kind of oracle which

**Adopted solution / approach**

No solution for a problem should be looked for, before having defined it perfectly. This assertion might seem surprising but experience shows that analysis is often left aside

Once the problem is well understood and its causes and likely effects have been determined is when the search for a solution can be started. In any case this requires, as said before, possessing knowledge. It is evident that GMP has to be taken into account, but only when the problem has been analyzed and understood in depth. Then, an answer to the problem can be found and it is evident that it will be science and knowledge-based and GMP

There are different tools which can be used in QRM, both unspecific and specific for this task "in [2, 6]". In reality, and very unpleasantly for some people, tools just organize in a more or less sophisticated way the information that we have. They will neither provide us with the knowledge that we don't have nor liberate us from the task of thinking about the

because all attention is eagerly focused on the quick search of a solution.

pharmaceutical production, then how can QRA be anything different?

**3. GMP and QRM**

2nd Look for its cause/s

**Figure 3.** Problem management

compliant.

**4. Selection of tools**

question. Brainstorming will always be inevitable.

will provide us with magical solutions.

Even if it is true that specific tools have been developed with an intended objective, and this somewhat restricts their scope of use, there is often overseen that they can have a wider utility. In fact tools are at our service and we should use them to organize information in order to get the most of it. Except in those cases when there is a need for coordination, e. g., different sites of the same group, or a requirement by the Authorities we should feel free in the way we use QRM tools.

In practice, it is possible to distinguish six basic cases in QRM:

1. As it has been indicated before the presentation of data is a basic need in QRM, hence informal tools, such as flow charts are necessary.

Quality Risk Analysis: Value for Money in the Pharmaceutical Industry 395

Airlock doors are interlocked. Air is filtered.

verified

Double verification. Materials are controlled and registered while entering and while being used.

Isolator is provided with a pressure gauge. Isolator has an alarm system for pressure.

 Contamination (external): Any contamination of a material or of a product not related to other materials or products manufactured in the factory (e. g., pollen, sand, hair,

Cross-contamination: Contamination of a material or of a product with another material

 Environmental contamination: Contamination of the production rooms, the operators or the surroundings of the pharmaceutical unit because of the voluntary or accidental

Mix up / error: Operation inadequately performed (error) or where one thing is taken

Degradation: Loss of quality of the product because of inadequate conditions

material Inadequate product

(outer) Inflow of dirty air Separation of production

Inadequate CIP

Isolator is not kept at depression

Inadequate

Inadequate humidity

**Hazard Cause Preventive measures Comments**

areas and air control

Room and people contaminated

Air conditioning with

Note: The contents of this table are just given as an example; they don't intend to represent any real situation.

In order to analyze quality risks in the operations, when there is a significant amount of operator participation, the choice tool is FMECA (failure mode, effects and criticality analysis) "in [2, 6, 7]". It is performed by using a table possessing these main headings:

 Failure mode: the way an element can have a potential failure (in relation to specifications) or do not develop its functions. They are detected as an answer to the question "what might go wrong? It has to be pointed out that a simple function may

cleaning Vessel is dirty Cleaning is validated. Dust liberation Dust extraction system Effectiveness of extraction is

temperature Air conditioning Temperature is controlled

control of humidity Relative humidity is kept ≤ 30%

scales, dandruff, fibers, microorganisms, etc.)

liberation of materials or products

by another one (mix up)

The following table uses this approach:

Mix up / Error Transfer of wrong

or product.

Contamination

Crosscontamination

Environmental contamination

Degradation

**Table 6.** PHA table

**4.3. Risk assessment**

have several failure modes.

These six cases will cover practically all the needs regarding QRM. Understanding and using them can thus be considered a must.

**Table 5.** QRM most common tools and their practical utilization

## **4.1. Basic data presentation**

Any system may be used to gather and present data and further on we provide some examples. Ishikawa and Pareto diagrams not only show data but the first organizes them at a certain level and the second treats them statistically. This is why they are also mentioned for the root cause identification.

#### **4.2. Basic hazard assessment**

In every situation (product, process, etc.) there is a period of time, in the beginning, when knowledge is very limited and unsure. Suppositions count more than facts. Then, there is a very simple tool, perfectly adapted to this situation: PHA (Primary hazard analysis) "in [2, 6]".

It is developed by using a table, which might vary slightly according to specific needs or requirements, but which considers basically these items: hazards, causes, effects and preventive measures. Note that often it is not necessary to consider the "effects", as they are either evident or are already somewhat included in the "hazard".

The hazards which put the quality of the products at risk during their manufacturing may belong to five categories:

The following table uses this approach:

394 Risk Management – Current Issues and Challenges

parameters.

6. RRF allows for comparisons.

Check lists, reports, graphs, etc.

**4.1. Basic data presentation**

for the root cause identification.

**4.2. Basic hazard assessment**

belong to five categories:

using them can thus be considered a must.

Diagrams (flow, Pareto, Ishikawa), histograms, etc.

informal tools, such as flow charts are necessary.

3. If risk assessment is desired, then FMECA is what we need.

1. As it has been indicated before the presentation of data is a basic need in QRM, hence

2. A first task in any QRM is hazard assessment and for doing this PHA is the right tool.

4. HACCP is an appropriate tool for the monitoring of processes by means of their

These six cases will cover practically all the needs regarding QRM. Understanding and

Any system may be used to gather and present data and further on we provide some examples. Ishikawa and Pareto diagrams not only show data but the first organizes them at a certain level and the second treats them statistically. This is why they are also mentioned

In every situation (product, process, etc.) there is a period of time, in the beginning, when knowledge is very limited and unsure. Suppositions count more than facts. Then, there is a very simple tool, perfectly adapted to this situation: PHA (Primary hazard analysis) "in [2, 6]".

It is developed by using a table, which might vary slightly according to specific needs or requirements, but which considers basically these items: hazards, causes, effects and preventive measures. Note that often it is not necessary to consider the "effects", as they are

The hazards which put the quality of the products at risk during their manufacturing may

QRM tool Practical utilization

Basic data presentation

5. When the search for the root cause of an event is required then FTA will do.

**PHA (Primary hazard analysis)** Basic hazard assessment

**FMECA (Failure Mode, Effects & Criticality Analysis)** Risk assessment **HACCP (Hazard Analysis and Critical Control Points)** Process monitoring **FTA (Fault Tree Analysis)** Root cause identification

**RRF (Risk ranking and filtering)** Comparison

**Table 5.** QRM most common tools and their practical utilization

either evident or are already somewhat included in the "hazard".

Note: The contents of this table are just given as an example; they don't intend to represent any real situation.

**Table 6.** PHA table

#### **4.3. Risk assessment**

In order to analyze quality risks in the operations, when there is a significant amount of operator participation, the choice tool is FMECA (failure mode, effects and criticality analysis) "in [2, 6, 7]". It is performed by using a table possessing these main headings:

 Failure mode: the way an element can have a potential failure (in relation to specifications) or do not develop its functions. They are detected as an answer to the question "what might go wrong? It has to be pointed out that a simple function may have several failure modes.

Quality Risk Analysis: Value for Money in the Pharmaceutical Industry 397

**Seriousness of harm** Insignificant Minor Severe Critical Catastrophic

Always High risk

**significant? Why? Preventive**

filter Yes Product not sterile Process validation

Is there in this stage a hazard with enough probability and seriousness to make it necessary its control?

Yes No

Yes No Yes No

Modify as necessary

Yes No

**measures**

and monitoring Yes

It is not a CCP

It is not a CCP

allowed Monitoring Yes

Is it necessary to control this stage in order to ensure the quality of the product

**Is it a CCP?**

HACCP (hazard analysis and critical control points) is a method that detects the hazards for the quality of the products (or for the safety) and then their "critical control points" (CCP)

The flow chart is studied to identify potential hazards, which might affect the quality of the

"in [2, 6, 8]". The rational of HACCP is exposed in the annexed figure.

**Probability of harm**

**Operation /**

Test for endotoxin in water

Frequently (probable)

Rare (improbable)

**Table 9.** Example of risk evaluation

Filter sterilization Viable microbes in the

**Table 10.** HACCP – I: CCP establishment

**Figure 5.** Rationale for the establishment of a CCP

Is it necessary to control this stage in order to reduce / eliminate the hazard?

It is a CCP It is not a CCP

Non observable Low risk

This leads to the determination of the CCPs.

**Process stage Potential hazard Is risk**

Presence of endotoxin

product. Then these hazards have to be assessed. Do they have to be controlled?

Sometimes Medium risk

in water Yes Endotoxin is not

Note: The contents of this table are just given as an example; they don't intend to represent any real situation.

Is there a control measure for this hazard?

The rationale for the establishment of CCP can be summarized as follows.

Measure of pH pH outside range Yes Precipitation Verify pH Yes

Note: The contents of this table are just given as an example; they don't intend to represent any real situation.

#### **Table 7.** FMECA table

This table may include risk reduction and be used as an instrument of risk management.

**Table 8.** FMECA table for risk reduction

#### **4.4. Process monitoring**

It is evident that if a process is well understood it is possible to identify its CPPs and if we can keep them under control by a process monitoring system then the quality of the products will be ensured.

**Figure 4.** HACCP rationale

HACCP (hazard analysis and critical control points) is a method that detects the hazards for the quality of the products (or for the safety) and then their "critical control points" (CCP) "in [2, 6, 8]". The rational of HACCP is exposed in the annexed figure.

The flow chart is studied to identify potential hazards, which might affect the quality of the product. Then these hazards have to be assessed. Do they have to be controlled?

**Table 9.** Example of risk evaluation

396 Risk Management – Current Issues and Challenges

**Failure mode S**

5 Dissolution Degradation 3 Temperature >

dosage 3

**mode S Cause PControl**

**Acceptable?**

rpm

**#**

**#**

**Operation / Process stage**

3 Agitation Speed < 1.400

9 Filling Inadequate

**Table 7.** FMECA table

**Operation / Process stage**

**Failure**

**4.4. Process monitoring**

products will be ensured.

**Figure 4.** HACCP rationale

**CCP management and periodical review**

**CCP monitoring**

**Define CCPs and establish control limits for them**

**Table 8.** FMECA table for risk reduction

Cause: the grounds which provoke a failure.

Effect: the results which appear when the failure mode comes out.

(Severity) **Cause P**

2 Failure 1

Failure / wrong adjustment

80ºC 1

(Probability)

1

Note: The contents of this table are just given as an example; they don't intend to represent any real situation.

This table may include risk reduction and be used as an instrument of risk management.

**in place D RP Risk**

It is evident that if a process is well understood it is possible to identify its CPPs and if we can keep them under control by a process monitoring system then the quality of the

**Definition of the product / process / system**

**Hazard identification**

**Application of control measures**

**Risk analysis and evaluation** **Inacceptable?**

**Establish corrective measures to be implemented when a CCP is out of range**

**Existing controls**

Monitoring by computer

Monitoring by computer

Every tube is weighed after filling

**accepted? Actions**

Risk evaluation Risk reduction

**D**  (Difficulty of detection)

1 2

1 3

1 3

Risk priorisation **(PR = S x P x D)**

**Risk accepted? (comments)**

Yes (a tachymeter is in place; speed can vary without sensible effect)

Yes (T is controlled and materials are not affected)

Yes (equipment is qualified and scales are calibrated)

**Reevaluation Risk S P D PR accepted?**

> **Implement measures for a better process control and for reducing risk**

This leads to the determination of the CCPs.

Note: The contents of this table are just given as an example; they don't intend to represent any real situation.

**Table 10.** HACCP – I: CCP establishment

The rationale for the establishment of CCP can be summarized as follows.

**Figure 5.** Rationale for the establishment of a CCP

Then, for each CCP are defined alert and acceptance limits. As a consequence, monitoring procedures are established and they are accompanied by the set up of corrective actions in case of deviation. Finally as in any monitoring system it is necessary to define how it will be managed and periodically reviewed to verify that it performs as expected.

Quality Risk Analysis: Value for Money in the Pharmaceutical Industry 399

Two simple tools can be used for a primary analysis of causes.

the elements are classified in relation to their cumulative frequency.

effect.

"in [2, 6, 9]".

they belong.

**Figure 7.** FTA diagram

Contaminated object

**and**

Dirty gloves Glove contact

**Figure 8.** FTA: Examples of "and" and "or" gates

**Intermediate event**: It connects the top event to the basic events.

**Gate**: It connects the events defining their causality reports

The first one is the diagram of Ishikawa, also known as cause-effect or fishbone diagram, which shows in a graphical way cause relations and their interaction to provoke an

The second one is the diagram of Pareto, which orders the data in relation to their importance and this allows for the distinction among frequent and infrequent causes of failures. It is prepared by listing all the elements and determining their frequencies. Then,

FTA (fault tree analysis) is a deductive tool which uses a pictogram to represent in an organized way the factors (causes) which produce or contribute to the production of an undesirable event

The tree is started by placing the top event. Then the events which contributed to it are

Gates can be very varied, but the most common ones are the "and" and "or" gates. Although there are specific gate symbols defined by an international standard, in practice for most cases it would suffice to represent them by a circle and write inside to which type

Faulty sterilization Inadequate manipulation

**or**

Contaminated vial

Contaminated environment

**Basic event**: It initiates a failure and does not need further development.

**Top event**: The failure or deviation, which starts the AAP

analyzed. Events are united by gates, which show the relation amongst them.

**Event**

Note: The contents of this table are just given as an example; they don't intend to represent any real situation

**Table 11.** HACCP – II: CCP monitoring

#### **4.5. Root cause identification**

When a deviation is detected it is necessary to implement corrective and preventive actions (CAPA system), But this is only possible if the root cause has been identified. To do this we can use several tools, both unspecific (such as Ishikawa or Pareto diagrams) and specific (FTA).

**Figure 6.** Diagrams of Ishikawa and Pareto

Two simple tools can be used for a primary analysis of causes.

398 Risk Management – Current Issues and Challenges

**CCP Acceptable**

Test for endotoxin in

(FTA).

**Ishikawa**

**Pareto**

**range**

Measure of pH pH = 6-7 Production

Filter sterilization Sterile Production

**Table 11.** HACCP – II: CCP monitoring

**4.5. Root cause identification**

**Figure 6.** Diagrams of Ishikawa and Pareto

Secondary cause

Primary cause

water < 0,25 U. QC technician LAL test

Then, for each CCP are defined alert and acceptance limits. As a consequence, monitoring procedures are established and they are accompanied by the set up of corrective actions in case of deviation. Finally as in any monitoring system it is necessary to define how it will be

**Monitoring**

Control process parameters (P, T and t)

**A B C D E F G H I J Most important causes Causes of deviation**

**Corrective actions Who? How? When**

supervisor pH-meter In process Call the supervisor. Add

**Factor 1 Factors which contribute to the effect**

**Factor 2**

Before starting the production

After sterilization Stop production and call the supervisor

more sodium hydroxide

Stop production and call the supervisor

**100%**

**Causes** (primary and secondary)/

**Causes** (primary and secondary)/

**Factors which contribute to the effect**

**EFFECT**

**50%**

**0%**

managed and periodically reviewed to verify that it performs as expected.

technician

Note: The contents of this table are just given as an example; they don't intend to represent any real situation

**Factor 3 Factor 4**

When a deviation is detected it is necessary to implement corrective and preventive actions (CAPA system), But this is only possible if the root cause has been identified. To do this we can use several tools, both unspecific (such as Ishikawa or Pareto diagrams) and specific The first one is the diagram of Ishikawa, also known as cause-effect or fishbone diagram, which shows in a graphical way cause relations and their interaction to provoke an effect.

The second one is the diagram of Pareto, which orders the data in relation to their importance and this allows for the distinction among frequent and infrequent causes of failures. It is prepared by listing all the elements and determining their frequencies. Then, the elements are classified in relation to their cumulative frequency.

FTA (fault tree analysis) is a deductive tool which uses a pictogram to represent in an organized way the factors (causes) which produce or contribute to the production of an undesirable event "in [2, 6, 9]".

The tree is started by placing the top event. Then the events which contributed to it are analyzed. Events are united by gates, which show the relation amongst them.

Gates can be very varied, but the most common ones are the "and" and "or" gates. Although there are specific gate symbols defined by an international standard, in practice for most cases it would suffice to represent them by a circle and write inside to which type they belong.

Faulty sterilization Inadequate manipulation

Contaminated environment

**Figure 8.** FTA: Examples of "and" and "or" gates

Dirty gloves Glove contact

Quality Risk Analysis: Value for Money in the Pharmaceutical Industry 401

Risk

**Risk filtration Difficulty of detection**

Class 3 **3rd priority**

Class 2 **2nd priority**

Low Middle High

Class 1 **1st priority**

Risk classification

Filtering based on risk

Filtering based on resources

It is also possible to rank and filter risks by using tables combining the three well-known

**Risk classification**

Elements / Components

**A**

**E**

**C**

**B**

**D**

**F**

This is not the kind of stupid question that, at first sight, might seem. And in fact this is not a question, but two. The first one might be related to the fact that in everyday's life we somehow tend to mix-up hazard and risk. As the latter is the consequence of the former, we tend to consider both practically as synonymous. The second one comes out because of the fact that identification of hazards is a prerequisite for the determination of their risk, and risk allows for an assessment of hazard. Thus, why to limit ourselves to something of "low level" like hazard when we can get something "better" like risk? Unfortunately this is not correct. By definition, to determine risk we have to start by knowing the probability of occurrence of the hazard. And this is often very difficult and in the end it turns to be just an inference. Then, we should know the seriousness of the harm and, although this is usually clearer, it is neither an easy task and often requires some degree of imagination. Consequently, in many cases, risk is not more than an estimation, that has to be improved along the time as more experience is collected. The case might be that hazard, usually a concrete thing, is substituted by risk, an estimated value and this can hardly be something

In practice, our first aim should be to determine hazard and then, only if there is an objective possibility of estimating risk, do it. Speaking in general terms the evolution should be hazard detection > qualitative risk estimation > quantitative risk estimation. In projects or new processes we would move towards the left (hazard), whereas as we gain process

**Figure 11.** Hazard – Risk - Harm

**Problem / Hazard**

**A**

**Problem / Hazard**

**B**

**Problem / Hazard**

**C**

**Problem / Hazard**

**D**

**Problem / Hazard**

**E**

**F**

**Problem / Hazard**

**Risk classification Severity**

**Probability**

better.

factors (probability, severity and difficulty of detection).

High Middle Low

High **Class 1**  Middle **Class 2** Low **Class 3**

**5. Is it really necessary to determine risk in QRM?**

knowledge we might move towards the right (risk).

**Figure 12.** RRF: Combination of the risk factors

**Figure 9.** RRF: Example of hazard or problem decomposition (factors intervening in cleaning)

#### **4.6. Comparison**

RRF (Risk ranking and filtering) is a tool specifically devised for the comparison of different sets (units, processes, companies, etc.) possessing varied levels of risks "in [2, 6, 10]". Once they are reduced to a common denominator they can be compared and this allows for the establishment of priorities.

As in any method, it is necessary to individualize first the hazards or problems and then the different attributes, components or elements, which contribute to them.

Then each attribute, component or element is evaluated in terms of risk.

**Figure 10.** RRF: Risk estimation

It is possible to get a comprehensive evaluation taking into account all the intervening elements. This allows for the ranking of the problems or hazards, which can be filtered.

Quality Risk Analysis: Value for Money in the Pharmaceutical Industry 401

**Figure 11.** Hazard – Risk - Harm

400 Risk Management – Current Issues and Challenges

**Product manufactured afterwards**

**Maximum daily dose Batch size**

**…**

**4.6. Comparison**

**…**

**manufactured previously**

**Solubility in water Therapeutic single dose Adsorption to surfaces Particle size Moisture Stability Batch size**

establishment of priorities.

**Figure 10.** RRF: Risk estimation

**Problem / Hazard Classification Evaluation Low Middle High**

**Figure 9.** RRF: Example of hazard or problem decomposition (factors intervening in cleaning)

RRF (Risk ranking and filtering) is a tool specifically devised for the comparison of different sets (units, processes, companies, etc.) possessing varied levels of risks "in [2, 6, 10]". Once they are reduced to a common denominator they can be compared and this allows for the

**CLEANING**

**… Product**

**Surface extension Type of surface Material of construction Design Mode of use Type of automatisation**

**…**

**Type of automatisation Temperature Time Products used**

**Time remaining dirty**

**…**

**Characteristics of the cleaning process**

**Characteristics of the equipment used**

**Characteristics of the manufacturing process**

**Moisture Dispersion Time befor cleaning**

**…**

As in any method, it is necessary to individualize first the hazards or problems and then the

**Supplier compliance** (example) **Element Classification Evaluation Low (1) Middle (2) High (3)**  GMP Deficient Acceptable Certified 2 Q system Deficient Acceptable Certified 1 Audits None By other By us 3 Documents Deficient Good Excellent 2 History >5 problems <5 problems No problems 2 10

It is possible to get a comprehensive evaluation taking into account all the intervening elements. This allows for the ranking of the problems or hazards, which can be filtered.

different attributes, components or elements, which contribute to them.

Then each attribute, component or element is evaluated in terms of risk.

It is also possible to rank and filter risks by using tables combining the three well-known factors (probability, severity and difficulty of detection).


**Alejandro Tristán 1,2,\*, Flurina Heuberger 1 and Alexander Sauer 1,2**

Received: 13 September 2020; Accepted: 7 November 2020; Published: 11 November 2020

**Abstract:** Industrial energy flexibility enables companies to optimize their energy-associated production costs and support the energy transition towards renewable energy sources. The first step towards achieving energy flexible operation in a production facility is to identify and characterize the energy flexibility measures available in the industrial systems that comprise it. These industrial systems are both the manufacturing systems that directly execute the production tasks and the systems performing supporting tasks or tasks necessary for the operation of these manufacturing systems. Energy flexibility measures are conscious and quantifiable actions to carry out a defined change of operative state in an industrial system. This work proposes a methodology to identify and characterize the available energy flexibility measures in industrial systems regardless of the task they perform in the facility. This methodology is the basis of energy flexibility-oriented industrial energy audits, in juxtaposition with the current industrial energy audits that focus on energy efficiency. This audit will provide industrial enterprises with a qualitative and quantitative understanding of the capabilities of their industrial systems, and hence their production facilities, for energy flexible operation. The audit results facilitate a company's decision making towards the implementation, evaluation and management of these capabilities.

**Keywords:** energy flexibility; industrial energy management; demand response; demand side management


**Author details**

Avijit Ghosh CSIR—Central Glass and Ceramic Research Institute, Kolkata, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**5. Conclusions**

With the implementation of the Paris Agreement signed by the HICs and NICs, the results show different effects on each country. Some countries have efficiently utilized their renewable energy resources, but some are not doing so, while others have maintained their levels and it seems they are not moved by the Agreement terms. The use of the undesirable model has successfully calculated each country's position in terms of the utilization of their resources while considering greatly a very bad factor, which is the carbon emission.

Knowing the economic capabilities of the highly industrialized nations, the results show that Russia is the least efficient among them. A mean efficiency score of 0.1801 verifies the country's lack of attention to its renewable energy program. In fact, in 2015, renewable energy only comprised 20% of the total installed power capacity, which mostly came from bioenergy and hydropower. The country must focus on other sources such as geothermal, wind, and solar photovoltaic (PV) [42]. Canada, with a mean efficiency score of 0.4221 manages to place second to the last among the HICs. A report from 2015 reveals that Vancouver alone sourced its 69% of energy from fossil fuels and 31% from renewable sources [43]. The situation in Japan is a little different, as due to the nuclear disaster from a major earthquake years ago, the country aims their energy to be completely 100% supplied by renewable energy, especially in their local regions, by 2020 [44]. Additionally, a report states that there is a decrease in the use of fossil fuels in 2018, 78% from 81% share. However, a dependency of the country on nuclear energy still exists and is evident from the increase of its generation from 2.8% to 4.7% of the same year [45]. This can be one of the reasons for Japan's efficiency score drop from 2016 to 2018 and its acquisition of an average of 0.6671 throughout the studied period. The same goal is seen in Italy's energy program. With the introduction of the 20-20-20 EU goals, the Italian government aims to decrease the emission of greenhouse gases by 20%, improve energy savings to up to 20%, and attain a 20% generation of renewable energy by 2020 [46]. The result of this study shows that Italy has quite improved its renewable energy utilization with increased and maintained efficiency scores from 2015 to 2018. While reaching a perfect efficiency of 1.0 in 2018, the average score becomes 0.7108 due to lower scores acquired from previous years. These scores imply that the German government has effectively improved its renewable energy utilization throughout the study period. Gaining an efficiency score of 1.0 throughout the study period, France, the United Kingdom, and United States, have been consistently utilized their renewable energies even before or after the Paris agreement. Their energy and sustainability programs can be a benchmark for other HICs that aim to improve their use of own renewable resources.

In contrast with the efficiency scores of the HICs, none from the list of NICs is able to get a consistent perfect efficiency. In spite of this, Indonesia manages to acquire perfect efficiency during the period of 2015 to 2018 rising from low-e fficiency scores. Getting the highest mean e fficiency score of 0.7645 among the NICs, this can be due to the fact that the country is considered to have the biggest resources of geothermal energy in the world [47]. South Africa demonstrates an interesting e fficiency score as it can be seen that during the period of 2013 to 2014, the country was able to gain a perfect efficiency. This performance is attributable to their noticeable increase in renewable energy production in previous years [48]. However, this performance by South Africa was not maintained since the country's e fficiency dropped tremendously in succeeding years resulting in an average e fficiency score of only 0.5185. Mexico, Turkey, Brazil, Malaysia, and Thailand are performing consistently at a low-level efficiency, with a minimum of 0.2278 and a maximum of 0.2734 mean scores. These results can be caused by the following factors: Mexico's dependency on fossil fuels wherein 85% of their total power was produced in 2012 [49]; Turkey's power sector industry as the biggest contributor to CO2 emissions and a high reliance to coal-powered electricity generation accounting to 37.2% of their total electricity [50]; the old structure of Brazil's energy sector which limits their capacities in handling renewable energy demands, and other political factors that hinder the country's development to successfully execute their national energy programs [51]; and, Malaysia and Thailand's high proportional use of crude oil, coal, and natural gas, giving less importance to renewable energies [52,53].

The HIC group is expected to keep increasing the e fficiency level by 7.76% from the past period to the projected period. This can be due to the expected development to the future renewable energy developments by Japan and Italy. However, the NIC group is expected a bit decline in the average efficiency with negative 1.23% due to the e fficiency drop performance by Turkey and Mexico along with the low e fficiency performances by other countries.

Future implications from the result of combined data gathered using grey prediction and the undesirable DEA model shows that the HICs and NICs will continuously follow the trend of the efficiency scores for all these countries with Germany joining the group of perfectly e fficient ones together with Indonesia, France, United Kingdom, and the United States. All other HICs and NICs will perform otherwise, especially if they will not concede significant importance to renewable energy. Most of the developments in the use of the renewable energy begin with the improvement in the political and legal structure surrounding its use and regulation, providing incentives or tax exemptions to private power producers to shift in using the renewable energy. Countries can also build strong partnerships with non-government and international organizations that are focusing on providing assistance to countries that want to achieve sustainable and renewable energy production. Strengthening the support to the research and development sector to determine the suitable sites to deploy renewable energy sources such as solar, wind and wave energy. Long-term strategic plans for renewable energy development that will also consider the economic, social, and environmental impacts to the country.

This study contributes as a method to mathematically evaluate the energy utilization e fficiency of HICs and NICs based on available public data. The DEA undesirable model treatment of the CO2 emission factor as a less important factor made this study di fferent from the others. Hence, this model is the most appropriate method to evaluate the energy sector that uses data with undesired factors. The results of this research may guide each country for improvement of its production and consumption towards sustainable renewable energy development. This can also help policymakers, government agencies and the energy sectors to address the problem in the existence of bad outputs such as CO2 emissions. For future studies involving quantitative measurements, the authors recommend combining DEA with a qualitative evaluation approach such as the one described in Wang et al. [54], which uses the fuzzy analytical hierarchy process for analysis to improve studies of this kind.

**Author Contributions:** Conceptualization, C.-N.W. and H.T.; data curation, H.T. and D.H.D.; formal analysis, H.T. and D.H.D.; funding acquisition, C.-N.W.; investigation, H.T. and D.H.D.; methodology, C.-N.W., H.T., and D.H.D.; project administration, C.-N.W.; writing—original draft, H.T. and D.H.D.; writing—review and editing, C.-N.W. and H.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was partly supported by National Kaohsiung University of Science and Technology, and project number 108-2622-E-992-017-CC3 from the Ministry of Sciences and Technology in Taiwan.

**Acknowledgments:** The authors appreciate the support from Taiwan National Kaohsiung University of Science and Technology, Philippines Technological University of the Philippines Taguig, and Taiwan Ministry of Sciences and Technology.

**Conflicts of Interest:** The authors declare no conflict of interest.