**5. General conclusions**

Nowadays, an excessive use of fossil fuels has led to significant emissions of CO2 in the atmosphere which is responsible for causing extensive climate changes (Soccol et al., 2010). As a result of this with the increase in fossil fuel prices direct the efforts towards utilizing renewable energy sources. Considerable progress in searching for alternative energy sources has been made since the oil crisis of 1973. However, it must be noted that an only the renewable biomass used for energy production contributes to the reduction of negative environmental impacts, e.g. decreased GHG emissions.

Currently, commercial biofuels production, such as ethanol and biogas, relies mostly on the fermentation of cane sugar, molasses or glucose derived from corn, sugar beet, wheat or potatoes. It is not economically accepted because these biomass production for biofuels competes for the limited agricultural land needed for food and feed production. Much of the hydrogen produced in the world is obtained from natural gas, which is not environmental friendly. Therefore, a significant increase in biofuels production would be possible only if technologies are developed to convert the waste biomass.

Dairy industry, like most other food industries, generates strong wastewaters characterized by high COD concentrations representing their high organic content (Demirel et al., 2005). Whey is by-product of milk processing and is abundantly obtained during cheese production. According to Najafpour et al. (2008), worldwide cheese production generates more than 145 million tonnes of liquid whey per year. In the case of deproteination of whey for the production of a valuable human food additive, the residual whey permeate is still a waste with high COD and must be treated before disposal. Due to its lactose major component, whey permeate is a well defined and suitable substrate for anaerobic digestion (Kourkoutas et al., 2002; Najafpour et al., 2008; Venetsaneas et al., 2009; Zafar & Owais, 2006). UF whey permeate fermentation in UASB reactors to produce biofuels (bioethanol, biogas, biohydrogen) has been successfully tested in this study.


**3. Methodology**

The main steps in the proposed methodology are shown in the flowchart of Figure 1. The methodology is streamlined via the following tasks:

**Figure 1.** Flowchart summarizing the methodology.

First, the various configurational alternatives are embedded through a superstructure representation that represents the basis for formulating the optimization program. In order to explicitly include cost optimization within the optimization formulation, a correlation is developed for estimating capital cost of the proposed biorefineries. Three objectives are considered using economic, environmental, and safety metrics. The ε-constraintmethod is used to solve the multiobjective optimization problem. The economic metric (e.g., maximizing return on investment) is used as the objective function while the safety and environmental objectives are placed in the constraints with certain bounds that are repeatedly altered to generate the tradeoff results. First, the relaxed optimization problem is solved for maximizing profit with no bounds on the safety and environmental metrics. Next, the safety and environmental bounds are added and the optimization program is solved to generate alternate configurations with tradeoffs that constitute the basis for decision making.

The following sections summarize the key steps used in setting up the optimization formulation and the solution approach.

### *3.1. Development of Superstructure and Optimization Formulation*

The first step is to create a superstructure that embeds the options of interest. Figure 2 shows the superstructure with centralized and decentralized options. A source–interception–sink representation [41] is used. Sources represent the biomass available from each region. Sinks represent the market demands in the various regions. The centralized and decentralized biorefineries represent the interceptors. Biomass may be processed within the same city in a decentralized facility or hauled to a larger centralized plant that accepts feedstock from multiple regions. The products may also be used locally or transported for sales in other regions. The flowrates of biomass and products assigned within the superstructure (represented by dashed arrows) as well as the throughput through each biorefinery are unknown and to be determined via the solution of the optimization formulation. A zero value indicates the absence of an option.

**Figure 2.** Superstructure of centralized and decentralized biorefining options.

Using the aforementioned superstructure, the optimization formulation is given below:

Maximize Profitability (e.g., return on investment (ROI)) (1)

Subject to:

$$\text{Annual Sales} = \sum\_{i=1}^{N\_{\text{Cities}}+1} \sum\_{j=1}^{N\_{\text{Troch}}} \sum\_{p=1}^{N\_{\text{Products}}} \mathbb{C}\_{p} \* \mathbf{R}\_{i,j,p} \tag{2}$$

where *Ri,j,p* is the production capacity of product p in the plant in city *i* using technology *j* (which is an optimization variable).

To ensure a practical capacity of each plant between a lower bound (LB) and an upper bound (UB), the following constraint is used:

$$LB \* I\_{i,j,p} \le R\_{i,j,p} \le LB \* I\_{i,j,p} \tag{3}$$

where *Ii,j,p* is a binary integer variable that takes the value of 1 when product *p* is produced in region/city *i* using technology j. Otherwise, it is zero.

To limit the number of facilities per city to a maximum number, the following constraint is used:

$$\sum\_{j=1}^{N\_{\text{Tach}}} \sum\_{p=1}^{N\_{\text{Products}}} I\_{i,j,p} \le \text{NPlants}\_i^{\text{max}} \; \forall i \tag{4}$$

The fixed capital investment (FCI) is given by:

$$FCI = \sum\_{i=1}^{N\_{\text{Cities}}+1} \sum\_{j=1}^{N\_{\text{Tcell}}} \sum\_{p=1}^{N\_{\text{Products}}} FCI\_{i,j,p} \tag{5}$$

The total capital investment (TCI) is given by

$$\text{TCI} = \text{FCI} + \text{WCI} \tag{6}$$

where WCI is the working capital investment. In this work, WCI is taken to be 15/85 of the FCI (El-Halwagi, 2017a).

The annual net profit (ANP) is expressed as (El-Halwagi, 2017a):

ANP = (Annual Sales − Annual OPEX − Annualized FCI) \* (1 − Tax Rate) + Annualized FCI (7)

The ε-constraintmethod is used to account for the environmental and safety objectives:

$$EM \le EM^{\text{Max}} \tag{8}$$

$$RM \le RM^{\text{Max}} \tag{9}$$

where *EM* and *RM* are the environmental and risk metrics, respectively, and Max designates an upper bound on the metric. The metrics may be selected from a wide variety of options [34,35,41]. The case study in this paper shows an example of the calculation and use of such metrics.

The process yield is:

$$G\_{i,j,p} = \mathcal{Z}(F\_{i,j,p}) \tag{10}$$

where ∅ is the yield function that depends on the feed characteristics and technology.

The availability of feedstock is:

$$F\_{\vec{l}} \le F\_{\vec{l}}^{\max} \,\,\forall \vec{l} \tag{11}$$

The market limitations are:

$$G\_{i,p} \le G\_{i,p}^{\max} \,\,\forall i \tag{12}$$

The flowrate of the biomass received in city *i*, for technology *j*, to produce product *p* is obtained from shipments coming from all cities. Therefore,

$$F\_{i,j,p} = \sum\_{i'=1}^{N\_{\text{Cities}}+1} F\_{i,i',j,p} \tag{13}$$

where *Fi*,*i* ,*j*,*p* is the biomass flowrate assigned from city *i* to city *i*' and technology *j* to produce product *p*.

Therefore, the sum of all shipments of biomass from a city is given by the shipments distributed from sources to sinks:

$$F\_i = \sum\_{j=1}^{N\_{\text{Toch}}} \sum\_{p=1}^{N\_{\text{Products}}} F\_{i,j,p} \,\forall i \tag{14}$$

The net production of product *p* is:

$$\mathbf{G}\_{i,p} = \sum\_{j=1}^{N\_{\text{Cyl}}} \mathbf{G}\_{i,j,p} - \sum\_{i'=1}^{N\_{\text{Critic}+1}} \mathbf{G}\_{i,i',p}^{Tnsported} \,\,\forall i,p \tag{15}$$

The first term on the right-hand side represents that total amount of product *p* generated by all technologies in city *i*. Product *p* may also be shipped to or from city *i*. The second term represents the net amount of product *p* shipped out of city *i.* The term *GTrasported i*,*i* ,*p* has a positive value when it is shipped out of city *i* and a negative value when it is shipped to city *i*.

#### *3.2. Capital Cost Estimation*

A convenient approach to including a parametric function for the estimation of CAPEX in the optimization formulation is to use a generic formulation that works for various biorefining technologies, feedstocks, and products. Because of the nature of this work intended to generate high-level directions for decision making, order-of-magnitude cost estimates are appropriate. Towards this end, we have carried out data analytics for the information extracted from 40 biorefineries. A functional form similar

to the correlation developed by Zhang and El-Halwagi [42] was used. The correlation relates the FCI to two main factors: process throughput (feed flowrate of biomass) and number of functional steps "*N*" (which represent primary operation such as separation, reaction, and waste treatment). The data sources are given in the Supplementary Materials.

The resulting correlation is expressed as:

$$\text{FCI (in USDM)} = 0.16 \ast N \ast \text{(Flowrate of biomass feed in 1000 tonnes/year)} \ast 0.84 \tag{16}$$

As indicated earlier, for the intended purposes of high-level decision making, an order-ofmagnitudes cost estimation has the proper level of accuracy [41,42].

#### *3.3. Life Cycle for RDF to Methanol Process*

The centralized vs. decentralized processing for the RDF to methanol process needs an account of emissions at different stages of the life cycle. A life cycle assessment framework [43] is used to compute these emissions. For the process, the system boundaries are considered to be at the processing plant gate, where the RDF is brought. For the transportation emissions, the latest U.S. Environmental Protection Agency (EPA) emission factors for transportation are considered from the Emission Factors for Greenhouse Gas Inventories [44].

#### *3.4. Safety Analysis*

Two types of risks are evaluated for the process and for transportation. A relative risk approach is used to compare and rank the risks of the various alternatives. For process risk, hazard identification is carried out using the Hazardous Process Stream Index (HPSI) [45]. The HPSI is proposed to define and compare the level of hazard of each stream. The estimation is based on five normalized indicators, which consider pressure, density, molar flowrate, heat of combustion, and flash point, as follows:

$$I\_P = \frac{\text{pressure value of individual stream}}{\text{average pressure for all streams}} \tag{17}$$

$$I\_{\rho} = \frac{\text{density value of individual stream}}{\text{average density for all streams}} \tag{18}$$

$$I\_{\rm MF} = \frac{\text{molar flow value of individual stream}}{\text{average molar flow for all streams}} \tag{19}$$

$$I\_{\Delta Hc} = \frac{\text{heat of combustion of individual stream}}{\text{average heat of combustion for all streams}} \tag{20}$$

$$I\_{Fp} = \frac{\text{flash point score of individual stream}}{\text{average flash point score for all streams}} \tag{21}$$

The result of *HPSI* is calculated through the following expression:

$$HPSI = \left(\frac{I\_P \cdot I\_{MF} \cdot I\_{\Delta Hc} \cdot I\_{FP}}{I\_P}\right) \cdot W \tag{22}$$

This index accounts for the relationship between the five dimensionless indicators of each process stream, and *W* is used as a scaling factor. As such, an increase in the capacity implies an increase in flowrate and inherently a more significant mass release in the case of loss of containment and its consequences. Therefore, *W* is defined as the ratio of the production capacity of the process with capacity *i* (*CPi*) to the reference process capacity (*CPbase*). It is worth noting that the proposed index takes into account the impact of process capacity. For the same process technology and flowsheet, the smallest-scale process risk is considered the reference if the production capacity is different.

$$\mathcal{W} = \frac{\text{CPi}}{\text{CP}\_{\text{base}}} \tag{23}$$

The process line with the highest value of HPSI poses the highest risk levels, reflecting the severity of the process stream in the loss of containment case, leading to a fire or explosion.

The relative process risk level is estimated from the HPSI value. A new normalization approach is proposed to enable the identification of relative risk using the following equation:

$$R\_i = \frac{HPSI\_i - HPSI\_{\min}}{HPSI\_{\max} - HPSI\_{\min}} \tag{24}$$

where *HPSIi* is the individual index value of the process streams, *HPSImin* is the smallest index value of the process streams, *HPSImax* is the maximum index value of the process streams, and *Ri* is the individual risk of the process streams.

Once the individual risk has been estimated, the total relative process risk (*RT*) is estimated by the arithmetic average of the individual risks as follows:

$$R\_T = \frac{\sum\_{i}^{n} R\_i}{PS} \tag{25}$$

where *PS* is the total number of process streams. The risk scale is defined according to Table 1.

**Table 1.** Risk scale based on the Hazardous Process Stream Index (HPSI).

If different process technologies are compared, HPSI must be assessed for each process using the same production capacity and then the assessment is carried out for the total relative process risk.

It is worth noting that alternate approaches for risk assessment may be used. For instance, an alternative approach to the aforementioned HPSI to determine the risk of the process at a different production scale is to consider the sum of the individual risks instead of the average used in this work. To classify risk based on summation, a risk scale may be defined to categorize/classify the different processes based on the sum.

For the methanol transportation risk analysis, the approach proposed by Zhang et al. (2018) is adopted. The following is a summary of the approach. Two transportation modes are considered: highway and railroad. Three variables were defined to assess the risk factor of methanol transportation using the two alternatives: (1) value loss per accident (*Ct*), (2) number of trips (*Nt*) per year, and (3) overall probability of accident as a function of distance (d) when methanol is moved among cities (*Pi*,*j*,*d*). The number of trips is calculated, dividing the maximum demand of each city by the load capacity of each transport. The overall probability of an accident during methanol transport is estimated by the product of general accident rate for transportation mode and the cumulative probability as a function of the natural logarithm of distance for methanol highway and railroad incidents. The product of these three variables provides the risk factor of each transport option


*Edited by Claudia Maria Simonescu*

Dendrimers are defined as nanoscale macromolecules having a particular architecture consisting of treelike arms or branches. They are characterized by special properties that make them promising candidates in medicine, biology, materials science, synthetic organic chemistry, biotechnology, environmental engineering, optics, electronics, catalysis, electrochemistry, photochemistry, and sensors and even for production of cosmetics and personal care products. The dendrimers research field is growing day by day, and scientists are exploring new synthesis and functionalization methods in order to improve and to determine new properties and thus new applications. The main purpose of this book is to highlight the issues regarding properties and applications of dendrimers in the field of biology, medicine, liquid crystal devices, electronics, quantum devices, and self-healing technology.

ISBN 978-1-78923-058-1

Dendrimers - Fundamentals and Applications

Published in London, UK © 2018 IntechOpen © weisschr / iStock


**Microbial Biocatalysis**

Editors

**Zhilong Wang Tao Pan**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Zhilong Wang State Key Laboratory of Microbial Metabolism Shanghai Jiao Tong University Shanghai China

Tao Pan Jiangxi Province Key Laboratory of Mining and Metallurgy Environmental Pollution Control Jiangxi University of Science and Technology Ganzhou China

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Catalysts* (ISSN 2073-4344) (available at: www.mdpi.com/journal/catalysts/special issues/ microbes biocatal).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-7191-1 (Hbk) ISBN 978-3-0365-7190-4 (PDF)**

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.


**Ring Opening Metathesis Polymerization**

DOI: 10.5772/intechopen.71085

Ring Opening Metathesis Polymerization

Alexey Lyapkov, Stanislav Kiselev, Galina Bozhenkova, Olga Kukurina, Mekhman Yusubov and Francis Verpoort Alexey Lyapkov, Stanislav Kiselev, Galina Bozhenkova, Olga Kukurina,

Additional information is available at the end of the chapter Mekhman Yusubov and Francis Verpoort

http://dx.doi.org/10.5772/intechopen.71085 Additional information is available at the end of the chapter

#### Abstract

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[42] X-J X, Chen F. Semi-continuous emulsion copolymerization of butyl methacrylate with

[43] Arz C. The use of nonionic polymerizable surfactants in latexes and paints. Macro-

[44] Eliseeva VI, Ivanchev S, Kuchanov S, Lebedev A. Emulsion polymerization and its appli-

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[50] Mahdavian A-R, Abdollahi M. Investigation into the effect of carboxylic acid monomer on particle nucleation and growth in emulsifier-free emulsion copolymerization of sty-

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[52] Shaffei K, Ayoub M, Ismail M, Badran A. Kinetics and polymerization characteristics for some polyvinyl acetate emulsions prepared by different redox pair initiation systems.

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In recent years, the olefins metathesis has established itself as a powerful tool for carboncarbon bonds forming and has found numerous applications in polymer chemistry. One of the important directions of metathesis is the polymerization with cycle opening. A study of new ruthenium catalysts, resistant to the many functional groups effects, has showed the possibility of synthesizing functionalized polymers with unique properties. In this chapter, reactivity and activation parameters of eight different norbornene dicarboxylic acid alkyl esters in the presence of a Hoveyda-Grubbs II catalyst for the ring opening metathesis polymerization were determined by 1 H NMR analysis in-situ. The molecules of esters differ in the aliphatic radical structure and the location of the substituent groups. Kinetic studies have shown that effective polymerization constants and activation parameters strongly depend on the monomer structure. It is shown that the elongation of the aliphatic radical does not significantly affect the reactivity, but significantly changes the activation parameters. The branching of the aliphatic radical significantly affects both the reactivity of the corresponding ester and the activation parameters of the polymerization. The position of the substituents in the norbornene ring of the ester also has a significant effect on the activation parameters of metathesis polymerization.

Keywords: ring opening metathesis polymerization, nuclear magnetic resonance, dicyclopentadiene, alkyl esters of norbornene dicarboxylic acid, Hoveyda-Grubbs catalysts, observed rate constant, activation energy


**2. Literature Review**

In recent years, the growth in tourism demand and increased economies opened the literature debate on tourism development models (Ashley et al. 2007). The COVID-19 pandemic, the shock in tourism demand, and the contained and planned tourism increased the discussion among the hosting communities, residents, stakeholders, and shareholders (Dangi and Jamal 2016). According to this new sensitivity from a tourist destination point of view, tourism planning has become more evident and relevant in supporting decision choices from local policymakers (Baggio 2008). Today, a permanent increase in tourist demand and a rapid increase in tourism supply seems to be an unconscious way to take advantage of tourism economies. Analysing some aspects of the literature, we can identify two directions based on two main drivers: the tourism industry-based model (IBTM) and the community-based model (CBTM). The following scientific literature review supports the two tourism development models.


**Abstract**

Adult articular cartilage presents poor intrinsic capacity for regeneration, and after injury, cellular or biomaterial-based therapeutic platforms are required to assist repair promotion. Cartilage tissue engineering (CTE) aims to produce cartilage-like tissues that recreate the complex mechanical, biophysical and biological properties found *in vivo*. In terms of biomaterials used for CTE, threedimensional (3D) self-assembling peptide scaffolds (SAPS) are very attractive for their unique properties, such as biocompatibility, optional possibility of rationally design cell-signaling capacity, biodegradability and modulation of its biomechanical properties. The most attractive cell types currently used for CTE are autologous chondrocytes and adult stem cells. The use of chondrocytes in cell-based therapies for cartilage lesions is limited by quantity and requires an *in vitro* 2D expansion, which leads to cell dedifferentiation. In the present chapter, we report the development of heparin-, chondroitin sulfate-, decorin-, and poly(ε-caprolactone)-based self-assembling peptide composite scaffolds to promote re-differentiation of expanded human articular chondrocytes and induction of adipose-derived stem cells to chondrogenic commitment.

**Keywords:** 3D cell culture, cartilage, self-assembling peptide scaffold, biomimetic materials, tissue engineering


*3.11.1 Best management practices (BMPs)*

BMPs involve implementing strategies to reduce phosphorus inputs. This includes practices such as buffer zones, cover crops, and optimized fertilizer application in agriculture, as well as improved wastewater treatment methods. BMPs play a significant role in mitigating phosphorus pollution in aquatic environments. These practices are a set of guidelines, strategies, and techniques designed to minimize the release of phosphorus into water bodies. They address various pollution sources and promote sustainable land and water management.

Here are some key BMPs used to tackle phosphorus pollution in aquatic systems.


**4. Discussion**

The results presented in Section 3 have provided some insight into the developed electronic eye's capabilities to authenticate the three categories of tequila: Silver (S), Aged (A), and Extra-aged (EA). First, from the preliminary recognition model using PCA, it is important to highlight the close relationship between tequilas' aging time and their clustering from the RGB absorbance analysis. This same aging effect in tequilas has been observed using more complex analytical methods such as HPLC [8]. This method is responsible for identifying and quantifying low molecular weight phenolic compounds acquired by tequila during the oak barrels' maturing process. Once characterized, they are related to the mentioned age classifications using analysis of variance (ANOVA) combined with discriminant analysis.

Other works instead deal the authentication of tequila recurring to methods of analysis as GC-MS [34], and UV-Vis [35] coupling some chemometric methods commonly based on LDA, Partial Least Squares Discriminant Analysis (PLS-DA), Multilayer Perceptron Artificial Neuronal Networks (MLP-ANN), and Support Vector Machines (SVM) to name a few. However, although these contributions differ from our study in factors such as the nature of analytical data obtained and the number of tequila samples analyzed, they represent the most recent state-of-the-art in identifying certified tequilas' three main categories of interest. Added to this, they report performance parameters like sensitivity and specificity of the classifier models they used, which allows direct comparisons with our results. In this way, Table 4 summarizes these parameters' comparison, including the analytical methods, classification models, and kinds of tequila reported by each research group.

**Table 4.** Comparison of the current study with representative publications dealing with tequila identification (S = Silver, A = Aged and EA = Extra-aged tequilas).

In this way, it is clear that the model adopted in our study using PCA-LDA achieved superior performance in the individualized identification of classes (sensitivity for S = 1.00, A = 0.92, EA = 0.78 and specificity for S = 1.00, A = 0.92, EA = 0.95) than the LDA model (sensitivity for S = 0.66, A = 0.33, EA = 0.66 and specificity for S = 0.75, A = 0.92, EA = 0.73) reported by Ceballos-Magaña et al. [34], and the PLS-DA (sensitivity for S = 0.81, A = 0.71, EA = 1.00 and specificity for S = 0.89, A = 0.88, EA = 0.93) described by Pérez-Caballero et al. [35]. These results are remarkable because, in our study, a linear model was enough to identify tequilas from their RBG absorbances. In contrast, the authors mentioned above needed the use of models with non-linear strategies (e.g., MLP-ANN and SVM) that demand a high computational cost when performing their optimization process to tackle the classification problem properly.

On the other hand, if we compare the results obtained from the non-linear modeling of the PCA-LDA model described in the present work, the overall performance is competitive for the Silver and Aged tequila classes and limited for the Extra-aged class. Finally, the differentiation between non-aged tequilas and those with different maturity levels is closely related to the task of identifying mixed, fake, and adulterated tequilas. Taking into account that adulterations in tequila are also associated with practices such as dilution, the addition of alcohol or some prohibited substances, forbidden aging methods, or blending with lower quality tequila batches, these adulterations are closely related to changes in the UV-vis absorbance and, therefore, in samples' color [36,39]. Further work will attempt to include these kinds of samples applying the reported image processing procedure in order to find color variations (from RGB absorbances) to identify counterfeit tequilas.


**5. Conclusions**

This study performed unsteady-state RANS analyses to investigate the e ffects of anti-cavity fins in the DT of a Francis turbine model on unsteady internal flow and pressure characteristics under low flow rate conditions. Reductions of around 0.5%–1.0% in hydraulic performance within the range of observed GV angles were observed, and the head losses of each component in the Francis turbine model were compared via the application of anti-cavity fins. Furthermore, the magnitudes and locations of the vortex ropes in the DT were verified via iso-surface distributions both with and without anti-cavity fins according to varying flow rate conditions. The existence of low-pressure regions due to flow resistance at the prominent air injection outlet on the anti-cavity fins was also confirmed.

The causes of the complicated flow phenomena in the DT respective to varying flow rates were confirmed via an analysis of the velocity triangles at the runner outlet. Furthermore, quantitative and qualitative investigations were conducted using the flow angle distributions at the runner outlet and the streamline distributions in the DT. A comparison of velocity distributions on the observed line confirmed the e ffects of the anti-cavity fins on the axial and circumferential velocity components in the DT. Furthermore, FFT analyses confirmed that the largest magnitudes of unsteady pressure were observed under low flow rate conditions with developed vortex ropes. There was a tendency for these magnitudes to gradually increase along the flow direction in the DT. Additionally, an approximate 41% reduction in the maximum unsteady pressure was confirmed with the application of anti-cavity fins.

Therefore, the use of anti-cavity fins was confirmed to a ffect the degradation of hydraulic performance under conditions of low flow rate, including BEP; however, the e ffects of reducing the unsteady pressure in the low-frequency regions were confirmed in the DT, which induced operational instability in the Francis turbine model. Thus, the anti-cavity fins can be presented as one alternative to suppress unsteady pressure fluctuations with vortex rope in the DT. Furthermore, by minimizing the loss induced by the anti-cavity fins through the optimization of the shape and length of the anti-cavity fins, it can be expected to improve the hydraulic performance with suppressing pressure fluctuation effectively. In the addition, based on the results of this research, the unsteady flow and pressure phenomena in the DT will be investigated in a future study by injecting air into the DT.

**Author Contributions:** Conceptualization, validation, investigation, data curation, S.-J.K.; Y.-S.C.; Y.C.; J.-W.C.; J.-J.H.; W.-G.J. and J.-H.K.; resources, Y.-S.C.; Y.C.; J.-W.C.; J.-J.H. and J.-H.K.; writing—original draft preparation, S.-J.K.; Y.-S.C. and J.-H.K.; writing—review and editing, supervision, project administration, J.-H.K.; funding acquisition, Y.C.; J.-W.C. and J.-J.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure, and Transport [grant number 20IFIP-B128593-04].

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


*3.1. Antimicrobial Susceptibility Testing*

Both isolates revealed only resistance to tetracycline and tigecycline but stayed susceptible to all other tested antibiotics. Isolate MurTR-KL001 revealed a minimal inhibition concentration to tigecycline of 4 μg/mL and MurTR-KL002 of 8 μg/mL (Table 1).

#### *3.2. Genetic Analyses*

The two isolates belonged to two different unrelated MLST types: ST2392 (rpoB:1, gapA:2, mdh:172, pgi:1, phoE:9, infB:1, tonB:116) and ST2394 (rpoB:4, gapA:126, mdh:1, pgi:1, phoE:4, infB:3, tonB:351). Both MLST profiles had not been described prior to our study. Notably, *tonB* of MurTR-KL002 revealed a new allele (*tonB* 351) (Table 1).

**Table 1.** Characterization of tigecycline-resistant *Klebsiella pneumoniae* isolates.


**5. Simulation Results**

In this section, two simulated examples are used to demonstrate the efficiency of the designed interval state estimator compared with the traditional interval observer design methods [27,28]. Example 1 is a general numerical type, whereas Example 2 is based on the 2014 West African Ebola virus outbreak [47].