**6. Conclusions**

The research presented in this book chapter summarizes the definition of a theoretical framework. This framework will help in the comparison and selection of the most appropriate authentication schemes or multifactor authentication methods for applications created by software developers. It has been created through the application of an action-research methodology that considered the utilization of various other research methodologies that helped to contribute in distinct ways to the research objective.

**References**

441-458

11737

[1] O'Gorman L. Comparing passwords,

*DOI: http://dx.doi.org/10.5772/intechopen.89876*

*Multifactor Authentication Methods: A Framework for Their Comparison and Selection*

[10] Colnago J, Devlin S, Oates M, Swoopes C, Bauer L, Cranor L, et al., editors. "It's not actually that horrible": Exploring adoption of two-factor authentication at a university. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing

[11] Huang X, Xiang Y, Chonka A, Zhou J, Deng RH. A generic framework

[12] Easttom II WC. Computer Security Fundamentals: Pearson IT Certification;

[13] Nissanke N, Khayat EJ, editors. Risk Based Security Analysis of Permissions

framework for comparative evaluation of web authentication schemes. In: 2012 IEEE Symposium on Security and

[15] Forget A, Chiasson S, Biddle R. User-centred authentication feature framework. Information and Computer

Security. 2015;**23**(5):497-515

[16] Genero M, Cruz-Lemus J,

Piattini M. Métodos de investigación en ingeniería del software. Madrid, Spain: Editorial RA-MA; 2014. pp. 171-199

[17] Kock N, Lau F. Information systems action research: Serving two demanding masters. Information Technology &

[18] Eden C, Ackermann F. Theory into practice, practice to theory: Action research in method development.

for three-factor authentication: Preserving security and privacy in distributed systems. IEEE Transactions on Parallel and Distributed Systems.

Systems; ACM. 2018

2011;**22**(8):1390-1397

in RBAC. WOSIS; 2004

Privacy; IEEE. 2012

People. 2001;**14**(1)

[14] Bonneau J, Herley C, Van Oorschot PC, Stajano F, editors. The quest to replace passwords: A

2019

authentication. Proceedings of the IEEE.

[2] Kumari S, Khan MK, Li X, Wu F. Design of a user anonymous password authentication scheme without smart

Communication Systems. 2016;**29**(3):

[3] Ranjan P, Om H. An efficient remote user password authentication scheme based on Rabin's cryptosystem. Wireless Personal Communications. 2016:1-28

[4] Yang TC, Lo NW, Liaw HT, Wu WC. A secure smart card authentication and authorization framework using in multimedia cloud. Multimedia Tools and Applications. 2017;**76**(9):11715-

[5] Mishra D, Das AK, Mukhopadhyay S. A secure and efficient ECC-based user anonymity-preserving session initiation authentication protocol using smart card. Peer-to-Peer Networking and Applications. 2016;**9**(1):171-192

Chellappa R. Facial attributes for active authentication on mobile devices. Image and Vision Computing. 2017;**58**:181-192

[6] Samangouei P, Patel VM,

[7] Antal M, Szabó LZ. Biometric authentication based on touchscreen swipe patterns. Procedia Technology.

[8] Usha K, Ezhilarasan M. Robust personal authentication using finger knuckle geometric and texture features. Ain Shams Engineering Journal. 2016;**9**

[9] Jacomme C, Kremer S, editors. An extensive formal analysis of multi-factor authentication protocols. In: 2018 IEEE 31st Computer Security Foundations Symposium (CSF); IEEE. 2018

2016;**22**:862-869

(4):549-565

**103**

tokens, and biometrics for user

card. International Journal of

2003;**91**(12):2021-2040

On the one hand, a systematic literature review, coupled with surveys and interviews, was performed to obtain the required knowledge base for generating the framework. The utilization of these two methodologies allowed to ascertain the perceptions on authentication from both the academy and the industry.

On the other hand, an expert panel and several case studies were realized to validate the adequacy of the framework. This permitted to obtain feedback from the end users of the framework so that it would provide adequate authentication scheme or method recommendations and have an appropriate usability.

Thus, this experience allowed to observe the usefulness of performing a research in collaboration with the industry, as it permits obtaining results that align more adequately with their needs while also providing more refined academic results.

Several future work lines can be followed based on this research. Namely, the framework could be adapted to work as a recommendation system so that its recommendations get refined through its usage. For the industry, it would be of interest that the framework not only recommends an authentication technique but that it also provides the required code for its implementation. Finally, the last cycle of the action-research, that is, the realization of case studies, could be replicated in other software development companies to further validate the adequacy of the framework.

#### **Acknowledgements**

This research is part of the following projects: DIUBB 144319 2/R and BuPERG (DIUBB 152419 G/EF).

#### **Author details**

Ignacio Velásquez, Angélica Caro\* and Alfonso Rodríguez Computer Science and Information Technologies Department, University of Bío-Bío, Chillán, Chile

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 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.

*Multifactor Authentication Methods: A Framework for Their Comparison and Selection DOI: http://dx.doi.org/10.5772/intechopen.89876*


*3.7. Fabrication Method*

Photocatalysts can be prepared by numerous methods, including the coprecipitation, cation exchange, chemical bath deposition, hydrothermal, and solvothermal methods. The co-precipitation method may lead to differences between the obtained final element composition in solid solution and the stoichiometric ratio [118]. The hydrothermal method requires a great amount of time to prepare a well-crystallized solid solution [119]. In the thermolysis method, the products are treated under high temperature to achieve fast fabrication, high crystallinity, and photocatalytic performance of the solid solution [120].

Li et al. found that Zn1−xCdxS photocatalyst fabricated using the simple Zn-Cd-Tu complex thermolysis method showed better performance than did those synthesized by the coprecipitation and hydrothermal methods [118,121]. Such a thermolysis method is preferred for the following reasons: (i) the precursors can be well mixed and reacted to prepare Zn-Cd-Tu complex by the ultrasonication process; (ii) the loss of precursors can be prevented during the fabrication process; and (iii) Zn1−xCdxS with a tiny crystallite size can be prepared because thiourea releases S2− ions slowly and offers some N and C atoms as the pinning points in the Zn-Cd-Tu complex [122].

Zhang et al. reported that a particular fabrication method will enable a photocatalyst to perform better in photocatalytic activity. The photocatalysts Cd1−xZnxS are prepared by three different methods: thermal sulfuration, co-precipitation without thermal treatment, and co-precipitation with thermal treatment. The photocatalyst which is synthesized by the thermal sulfuration method has better performance because the fabrication method allows uneven distribution of S2− ions, thus leading to a charge gradient [123]. A similar trend is also evidenced in the work done by Park et al. on a ternary CdS/TiO2/Pt hybrid. CdS/(Pt-sgTiO2) has the highest hydrogen production because of the electron transfer from CdS to Pt through TiO2 [124].

#### *3.8. Crystal Size*

Hydrogen production activity is also influenced by the crystal size of the photocatalyst. Li et al. [125] studied the photocatalytic hydrogen production performances of size-selected CdS nanoparticles decorated with co-catalyst Pt nanoparticles. When the size of CdS nanoparticles decreases from 4.6 to 2.8 nm, the H2 generation quantum yield can increase from 11% to 17%. Such a dependence was observed because the driving force of photoinduced carrier transfer from CdS to vacant states of Pt nanoparticles is size-dependent. Baldovi et al. [126] prepared MoS2 quantum dots by laser ablation of MoS2 particles in suspension and investigated their photocatalytic hydrogen production performance. Two types of MoS2 nanoparticles exhibited higher activity than that of bulk MoS2. When the size of MoS2 nanoparticles was decreased from 15–25 nm to 5 nm, the photocatalytic hydrogen generation

performance was almost doubled. Holmes et al. [127] and Grigioni et al. [128] reported the dependence of the photocatalytic water splitting activity on the size of CdSe nanoparticles. The photocatalytic activity increases as the size of CdSe nanoparticles decreases. Figure 10 presents the UV–vis absorption spectra and quantum yields of CdSe quantum dots photocatalysts with various size. They also reported that the light harvesting capability and the conduction band energy should compromise to achieve maximal photocatalytic H2 generation activity. There is an optimal size of 2.8 nm to achieve maximal photocatalytic H2 generation activity considering the compromise among light harvesting, band structure, and charge separation.

**Figure 10.** UV–vis absorption spectra and quantum yields of CdSe quantum dot photocatalysts with various size**.** Figure adapted from [128].


**1. Introduction**

At the industrial level, optimization of chemical processes, including those based on structured catalytic reactors, is an inherent issue of the design procedure. Process optimization considers the prices of raw materials, energy, products and installations (apparatus); the prices may change rapidly and unpredictably due to market fluctuations, even at the negotiation stage. Therefore, process optimization is usually regarded as being within the engineering domain, it is in fact more connected with business and economic issues. These issues usually exceed the knowledge of an engineer or a scientist and require input from other individuals.

Structured reactors are very important in chemistry and catalysis [1–3]. The process design, i.e., the apparatus and the process conditions, has to secure some economic profitability in spite of potential changes of costs. Regardless of possible economic fluctuations (excluding any collapses), the process has to be profitable during the following years.

A review of the literature provides hints about recommended flow velocities, process temperatures and catalyst carriers. The data originate from the long-standing technical and economic experience of engineers and entrepreneurs. Recently, a new generation of structured catalytic reactors has been introduced into industry, and there is a paucity of knowledge and experience about their optimization. Moreover, the inner-structure design of the reactors is complicated because many geometrical parameters need to be optimized.

In the literature, different criteria can be found, which help identify optimal operating conditions of chemical reactors. "The technical" or "engineering" optimization, with which this work deals, focuses on reactor optimization in terms of fluid velocities, process (reaction) temperature, structured catalyst carrier shape and dimensions. This kind of optimization has begun in energetics due to the introduction of compact heat exchangers that usually exploit a combination of fins, turbulence mixers and other features. In the current literature, even more sophisticated criteria are proposed for multiparameter optimization of different equipment such as heat exchangers. So far, similar criteria for catalytic reactors have been derived. The comprehensive performance evaluation criteria (PEC) use three components: transport coefficients, reaction kinetics and pressure drop [4,5]. Another approach is the comparison of reactor length (or catalyst mass) with the resulting flow resistance as shown in [4,6]. For heat exchanger optimization, there are also evaluation criteria based on entropy production during the process, as presented, e.g., by London [7] and Bejan [8], who also predicted the extension of entropic criteria to chemical reactors. Entropy in economic analysis is treated as trade-off factor and can be a substitute of currency [9]. The application of entropic criterion can also be found in [10–12].

The aim of the study is to propose a highly simplified approach, based on irreversible thermodynamics, suitable for engineering optimization of chemical reactors. The entropic criterion is proposed to optimize structured catalytic reactors. The assumed model process is the catalytic combustion of methane.

#### **2. Theoretical Background**

To derive the equations governing entropy production, the reactor model must be specified. For the purposes of this paper, the one-dimensional plug-flow model (neglecting axial dispersion) in the steady-state was assumed. Due to the very thin catalyst layer deposited on the structured carrier, the internal diffusional resistance can be neglected.

Mass balance of reactant A, in the flowing fluid, per unit surface area of the reactor cross-section, is as follows:

$$k w\_0 \frac{d\mathbb{C}\_A}{d\mathbf{x}} + k\_C \mathbb{S}\_v(\mathbb{C}\_A - \mathbb{C}\_{AS}) = \begin{array}{c} 0 \end{array} \tag{1}$$

The initial conditions are: (i) *x* = 0; *CA* = *CA*0 and (ii) the reactant A, mass transferred from the gas bulk to the catalyst surface is balanced by the first-order catalytic reaction:

$$k\_{\mathbb{C}}(\mathbb{C}\_A - \mathbb{C}\_{AS}) = k\_r \mathbb{C}\_{AS}.\tag{2}$$

Deriving concentration of A, at the catalyst surface from Equation (2), Equation (1) becomes:

$$-w\_0 \frac{d\mathbb{C}\_A}{d\mathbf{x}} = S\_v \frac{k\_\mathbb{C} k\_r}{k\_\mathbb{C} + k\_r} \mathbb{C}\_{A\prime} \tag{3}$$

and, after integration, local concentration *CAx* and the reactor length *L*, required for the outlet concentration *CAL* are:

$$\mathcal{C}\_{Ax} = \,^\circ \mathcal{C}\_{A0} \exp\left(-\frac{x}{w\_0} \frac{S\_v k\_\mathcal{C} k\_r}{k\_\mathcal{C} + k\_r}\right) \,^\circ \tag{4}$$

$$L = \frac{w\_0}{S\_v} \frac{k\_\mathbb{C} + k\_r}{k\_\mathbb{C} k\_r} \ln \left(\frac{\mathbb{C}\_{A0}}{\mathbb{C}\_{AL}}\right). \tag{5}$$

The energy balance may be presented (assuming no heat losses to the environment) as:

$$a v\_0 \varrho c\_p \frac{dT}{d\mathbf{x}} + a S\_v (T - T\_S) = \ 0,\tag{6}$$

the initial conditions: at *x* = 0, *T* = *T*0.

The mass and heat transfer in a heterogeneous catalytic reactor are strictly bound up (released reaction heat depends on the reactants mass transferred to the catalyst), thus

$$q = \; \alpha (T\_{\mathbb{S}} - T) = \; -\Delta H\_R I\_A = \; -\Delta H\_R k\_{\mathbb{C}} (\mathbb{C}\_A - \mathbb{C}\_{\text{AS}}) . \tag{7}$$

The above equations assume an isothermal process. In reality, the process is adiabatic. However, the concentration of organic air pollutants is usually low. For the volatile organic compounds (VOCs), a concentration of very few ppm is typical; for methane, it depends on the kind of source and may be within 1–1000 ppm. The level of concentrations of 100 ppm and higher can be treated by homogeneous combustion in, e.g., reverse-flow reactors due to important reaction heat. Thus, we assumed the concentration of methane at 200 ppm as rational for our analysis. In such a case, the adiabatic temperature rise is about 6 K, so the temperature increase along the reactor can be securely neglected.

Entropy production is an increase of system entropy due only to the irreversible phenomena [13]. This means that there is no entropy production at equilibrium or during a quasi-static process that runs infinitely close to the equilibrium. Any industrial process runs far from the equilibrium, and it produces entropy at irreversible conditions. In irreversible thermodynamics, entropy production is derived as the product of flux *Ji* and the driving force Δ π (causing the stream) divided by absolute temperature *T* [13,14]:

$$S\_{\bar{i}} = \frac{f\_{\bar{i}} \Delta \pi}{T}.\tag{8}$$

Assuming that the stream *Ji* is proportional to the driving force:

$$J\_i = \; k\_i \Delta \pi,\tag{9}$$

entropy production is proportional to the square of the driving force, thus it increases rapidly with the distance from the equilibrium:

$$S\_i = \frac{k\_i \left(\Delta \pi\right)^2}{T}.\tag{10}$$

In this paper, entropy production is considered due to the following irreversible phenomena:

Total entropy production (per 1 mole of reactant A consumed in the reactor) is the sum of all the components:

$$S\_P = \ S\_H + S\_D + S\_R + S\_F. \tag{11}$$

The above-mentioned components of entropy production are gathered in Table 1.

In the first column, basic equations of local entropy production are presented. In the second and third columns, the equations for the stream and the driving force are presented, respectively, derived using the reactor model. The last column presents reactor-integrated entropy production per 1 mole of substrate A consumed (e.g., burned) in the reactor. Detailed derivations, simple in fact, are not presented for reason of conciseness. The last position in Table 1, *flow friction* needs further comment. The entropy source considered is the volume fluid flow. The stream (flux) is the flow velocity and the driving force is the pressure gradient. The entropy produced is tantamount to viscous dissipation of pumping energy. This approach seems more friendly for engineers than viscous momentum flux often presented by irreversible thermodynamics; the flux is the pressure tensor and the driving force is the velocity gradient [11].

The impact of the reaction rate constant, *kr*, and the heat and mass transfer coe fficients, α and *kC*, respectively, on the entropy produced by the heat (*SH*) and mass (*SD*) transfer is illustrated in Figure 1

for the combustion process and exemplary *kr* and *kC* values. The heat and mass transfer coefficients are bound by the Chilton–Colburn analogy [15], Equation (12), which allows the influence of mass transport on *SH* to be determined.

$$j^\* = \frac{Nu}{\mathrm{Re}Pr^{1/3}} = \frac{Sh}{\mathrm{ReSc^{1/3}}}.\tag{12}$$

$$S\_D = R \ln\left(1 + \frac{k\_r}{k\_C}\right) \tag{13}$$

$$S\_H = \frac{k\_r}{k\_C + k\_r} \left[ \frac{(-\Delta H\_R)^2 (\mathbf{C}\_{A0} - \mathbf{C}\_{AL}) D\_A \mathbf{S} \mathbf{c}^{1/3}}{2\lambda T^2 \mathbf{Pr}^{1/3}} \right]. \tag{14}$$

**Table 1.** Local and reactor-averaged components of entropy produced.

**Figure 1.** Impact of the mass transfer coefficient and reaction rate on entropy production due to: (**a**) mass transfer and (**b**) heat transfer.

In Figure 1, a distinct increase of entropy produced with the reaction rate constant, *kr*, is observed. Conversely, entropy decreases with the mass transfer coefficient, *kC* (due to heat, *SH*, and mass, *SD*, transfer). A rapid chemical reaction (i.e., high *kr*) generates intense mass transport of substrates to the catalyst surface and adequate heat transfer in the opposite direction. The faster the reaction, the further the process runs from the equilibrium. When the transfer coefficients are small compared to the reaction rate, the concentration and temperature gradients are large, and even the substrates concentration on the catalyst goes to zero. Entropy production is large, being proportional to the square of the driving force (concentration or temperature gradient, cf. Equation (10)).

The impact of the mass transfer coefficient is opposite. The higher the transfer coefficient for a given reaction rate, the lower the temperature and concentration gradients are and the closer to the equilibrium the process runs. Smaller driving forces lead to lower entropy according to Equation (10). However, when analysing the plots in Figure 1, the impact of mass transfer intensification is distinct only if *kC* is close to the *kr* value. If *kC* is much smaller than *kr*, slight transfer enhancement will give nothing as the concentration and temperature gradients are still large (zero concentration at the catalyst surface). The gradients start to decrease as the reaction and transfer become comparable.

Obviously, the values of *kr* and especially of *kC* in Figure 1, may not be found in reality as the plots presented are theoretical, to illustrate the common impact of transfer and reaction rates on entropy production.

#### **3. Catalyst Supports Considered**

The aim of this study is to show the optimal adjustment of the catalyst carrier geometry, as well as its transfer and friction characteristics to the catalytic reaction kinetics. The catalyst performance (reaction kinetics) is treated as a model parameter only. Therefore, analysed catalyst supports were selected on the basis of similar value of specific surface area. This means that, in all considered cases, approximately, the same area was available for active layer catalyst deposition. For comparison, monolith and packed bed are also examined.

Correlations for the heat transfer and Fanning friction factor were derived experimentally and presented in detail in our earlier papers [4,16]. A photo of catalyst supports considered in the study is presented in Figure 2, and a summary of equations for Fanning friction factor, Nusselt number and Sherwood number of investigated supports are presented in Table 2 and compared in Figure 3.

**Figure 2.** Catalyst supports: (**a**) triangular short-channel structure, (**b**) wire gauze, and (**c**) nickel chromium foam.

(**a**) (**b**) (**c**)

The kinetic tests were performed experimentally. Two di fferent catalyst deposition methods were applied: (1) for Pd/ZrO2, the incipient wetness (IW) method [20] and (2) for Pd/Al2O3, sonochemical (SC) method [4]. The kinetic studies were conducted in the temperature range of 373–823 K [20]. Kinetic data are presented in Table 3. As was found in [21], the sonochemical method allows higher catalyst activity to be obtained in comparison to the incipient wetness method.

**Figure 3.** (**a**) Average Nusselt number and (**b**) Fanning friction factor for considered catalyst carriers.

**Table 2.** Correlations used to calculate flow resistance, heat and mass transfer for analysed catalyst supports.

#### **4. Results and Discussion**

Plots referring to analysis of entropy production were constructed assuming reactor length required for 90% conversion and show the entropy produced per 1 kmole of methane combusted in the reactor under given process conditions. Entropy production is presented as a function of process temperature and the Reynolds number. Entropy is produced due to the four components denoted as *R*—reaction, *H*—heat transfer, *D*—diffusional mass transfer and *F*—flow friction. The subscript *HDFR* means total entropy produced due to the *H*, *D*, *F* and *R* components.

The components of entropy production (according to Table 1) for the knitted wire gauze are compared for the methane catalytic combustion process vs. process temperature (Figure 4) and the Reynolds number (Figure 5) for the fast (Pd/Al2O3) and slow (Pd/ZrO2) kinetics assuming initial methane concentration of 200 ppm in both cases.

**Figure 4.** Comparison of entropy production components vs. process temperature for knitted wire gauze, Re = 1000, CH4 inlet concentration: 200 ppm: (**a**) fast kinetics, Pd/Al2O3 and (**b**) slow kinetics, Pd/ZrO2.

**Figure 5.** Comparison of entropy production components vs. Reynolds number for knitted wire gauze, T = 773 K, CH4 inlet concentration: 200 ppm: (**a**) fast kinetics, Pd/Al2O3 and (**b**) slow kinetics, Pd/ZrO2.

When analysing the Pd/Al2O3 catalyst (Figure 4a) within the lower temperature range, entropy due to flow friction, *SF*, is the major component, and it is close to the total entropy production *SHDFR*. The heat and mass transport components, *SH* and *SD*, play less important roles. However, for higher temperatures, the kinetics become much faster, causing significant shortening of reactor length necessary to attain 90% conversion. The share of flow friction entropy decreases; simultaneously, the entropy components due to heat and mass transport play more important roles. For the highest temperature range analysed, total entropy *SHDFR* is close to the reaction component *SR,* while the remaining components are comparable. Increased entropy production due to heat and mass transport at higher temperatures is a result of faster reaction rate. This leads to lower methane concentration on the catalyst surface, and thus to higher temperature and concentration gradients, in consequence of more intense entropy production (cf. Table 1, Equation (10) and Figure 1).

For the Pd/ZrO2 catalyst (Figure 4b), total entropy production is close to the flow friction component in the whole temperature range analysed. The transport component *SD*, *SH* are minor due to low gradients (a result of slow kinetics), and even the reaction component *SR* is much lower than the flow friction one, *SF*.

Figure 5 illustrates entropy production as a function of the Reynolds number for knitted wire gauze assuming a rather high temperature of 773 K. The transport components *SD* and *SH* are almost constant within the whole Re range analysed. The flow friction component *SF* increases with Re, reaching an even higher value than *SR*, especially in the case of the Pd/ZrO2 catalyst. Moreover, in Figure 5b, the total entropy produced is close to the flow friction component, with a minor role played by the remaining components.

Large entropy production is due to the irreversible reaction of methane catalytic combustion. Moreover, this entropy component is almost the same per mole of reactant, regardless of process conditions ( *T*, Re and catalyst); analysis of the equation for *SR* (Table 1) should render this as no surprise. Chemical a ffinity is close to the standard Gibbs energy of reaction (at the process temperature) Δ *GRo,T,* because the sum of the concentration logarithms is minor. For optimization purposes, the place of the minimum total entropy production reflects the process optimum, making the precise value less important. Analysis of Figures 4 and 5 shows that the *SR* component is nearly constant within the ranges studied. Note that reaction component, *SR*, is the lowest possible entropy that can be produced in the chemical reactor. For engineering purposes, such as process optimization, the remaining components are more interesting because they make entropy production higher than that due to chemical reaction (*SR*) and they are dependent on the physical properties of carriers. For slow reaction, there is no di fference between the analysed approaches, because, in this case, flow resistance plays a major role (cf. Figures 4b and 5b) and the minimum is not observed within the considered temperature range. In summarising the catalytic structures displaying close specific surface area *Sv* (i.e., similar catalyst amount), *SR* will be neglected during next analysis.

Analysis of entropy production due to the heat and mass transfer and flow friction (denoted as *SHDF*) is presented in Figures 6 and 7 presents *SHDF* as a function of the Reynolds number and process temperature for the five catalyst supports considered. In the following figures, minimal entropy production for each support is shown; these points give optimal process conditions for particular catalyst supports.

**Figure 6.** Entropy production vs. Reynolds number for di fferent catalyst supports for the fast kinetics, Pd/Al2O3 at temperature: (**a**) 573 K and (**b**) 773 K.

In Figure 6, entropy is presented for two selected temperatures, moderate (573 K) and high (773 K). For the moderate temperature of 573 K (Figure 6a), packed bed seems the best for Re < 20. For Re < 500, knitted wire gauze is optimal (minimum value at Re = 84) in that this results in the lowest entropy production and the most profitable behaviour within this analysis. For a higher Reynolds number, monolith displays the lowest entropy production, undoubtedly due to its lowest flow resistance. For higher temperatures of 773 K (Figure 6b), the impact of transfer properties is more pronounced as a result of faster reaction rate, and knitted wire gauze appears to be the best with classic and short-channel monoliths. Packed bed produces the largest entropy in almost the entire Reynolds range, due to the highest flow resistance. For the higher temperature (773 K), the minima are generally slightly shifted to higher Reynolds numbers and entropy production is several times higher.

**Figure 7.** Entropy production vs. temperature for the fast kinetics, Pd/Al2O3 for different catalyst supports at Reynolds number: (**a**) Re = 100 and (**b**) Re = 500.

When considering temperature influence on entropy production (Figure 7), the same conclusions may be derived. Low process temperature is favourable for the classic monolith, while for higher temperatures, wire gauze and monolith seem to be the best choice. For Re = 100 (Figure 7a), above 650 K, all the internals display close entropy production. Interestingly, all the structures except packed bed show minima within the narrow range of 500–540 K. For Re = 500 (Figure 7b), entropy produced is higher, especially for packed bed. The minima are shifted towards higher temperatures by 60–100 K. Above 600 K, knitted gauze and monolith are the best.

Analogous plots for slow kinetics (Figures 8 and 9) show quite different behaviour. Here, the reactor is long due to the slow reaction rate. Moreover, slow reaction does not require intense heat and mass transfer. Concentration and temperature differences between the flowing fluid and catalyst surface are very small; entropy production due to transfer is small compared to that due to flow friction. Consequently, entropy produced for the slow kinetics is ordered identically to the friction factors (Figure 3b) vs. the Reynolds number and process temperature. Flow friction is the main entropy source (when neglecting chemical reaction). For slow kinetics, entropy production characteristic considered for all the internals is similar. The shift observed (towards higher or lower entropy produced) results mainly from the flow resistance. All the curves are nearly parallel, and only slight convergence is observed for low Re as a result of different transport properties. The internals displaying the lowest flow resistance (monolith and short-channel structure, cf. Figure 3) offer the lowest entropy production, while those of high flow resistance (packed bed, cf. Figure 3) produce larger entropy, so are less profitable.

**Figure 8.** Entropy production vs. Reynolds number for different catalyst supports for the slow kinetics, Pd/ZrO2 at temperature 673 K.

**Figure 9.** Entropy production vs. temperature for the slow kinetics, Pd/ZrO2 for different catalyst supports at Reynolds number 1500.

## **5. Conclusions**

The results obtained by entropy analysis indicate that wire gauze is the best choice for the Pd/Al2O3 catalyst and the packed bed is the worst one. In the case of the Pd/ZrO2 catalyst, the best carriers are monolith and short-channel structures, while the worst solution is a packed bed. However, meeting the efficiency criteria cannot be regarded as the ultimate verdict. Any process has its own characteristics and limitations. It is rare for a process to occur separately, as it is usually part of a larger installation. For example, process temperature is limited by catalyst thermal deactivation, and the flow resistance may be limited by the gas pressure available. Therefore, each process needs to be considered individually, and any overall limiting parameters must also be taken into consideration during optimization.

The entropy-based optimization methodology is able to optimize reactor structure (indicating the best geometry, specific surface, etc.), as well as the process temperature and fluid velocity for considered reaction kinetics. The criterion, ensuring the minimum entropy production*,* ignores the reactor cost and is able to indicate the best structure from among the considered ones, as well as the optimal working conditions of a reactor (e.g., temperature and flow velocity).

Irreversible chemical reaction produces almost the same entropy, per mole of reactant, regardless of the process conditions. Therefore, it can be safely neglected during entropic optimization. The hypothesis is confirmed by analysis presented in Figure 4. For proper results, entropy produced by heat transfer, mass transfer and flow friction should be accounted for.

The gauze structures are assessed as being very effective due to their satisfactory transfer and friction properties. The monolith and short triangular channel display good efficiency for slow kinetics (Pd/ZrO2 catalyst) due to their low flow resistance. The packed bed usually appears as an unsatisfactory solution.

For fast kinetics (Pd/Al2O3 catalyst), the transfer properties of the catalyst support are the most important for low entropy production. The intense transfer properties of, e.g., knitted wire gauze, make the support excellent for such processes. The impact of flow resistance is minor as, for a fast reaction not hampered by insufficient transfer rate, the reactor is very short.

For slow kinetics (Pd/ZrO2 catalyst), the reactor is long. The impact of flow resistance becomes important. In contrast, heat and mass transfer contributions to entropy production are minor. Heat and mass transfer resistance is low, so temperatures (concentrations) gradients between fluid and catalyst surface are low, and the process runs near to the equilibrium.

The optimization methodology presented in this study obviously requires further development, including thorough experimental industrial and economic application. In spite of this, the entropic criterion seems able to indicate technically rational solutions of the reactor process considering the heat and mass transfer, flow resistance and reaction kinetics.

**Author Contributions:** Conceptualization, A.K. and M.K.; methodology, A.K.; formal analysis, A.K. and M.K.; investigation, A.G., M.I., and K.S.; writing—original draft preparation, A.K. and M.K.; writing—review and editing, M.K., M.I., and A.G.; supervision, A.K., A.G., and P.J.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Polish National Science Centre (Project No. DEC-2011/03/B/ST8/05455, DEC-2016/21/B/ST8/00496). The financial support is gratefully acknowledged.

**Acknowledgments:** The authors are sincerely grateful for the constructive suggestions and advices provided by Andrzej Burghardt. We regre<sup>t</sup> to inform that A. Burghardt passed away on 1 March 2020.

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


**2. Does CSR depend on innovation?**

Since the second half of the 20th century, the corporate responsibility towards ecological and social matters has attracted a lot of interest, especially with the metaenvironments in which firms operate nowadays [22, 23].

First, corporate sustainability has been presented as an exception [24]. In order to survive, firms have to provide continuously several resources and energies mobilized in a strategic plan, consistent with the framework rules and norms; otherwise, it will eventually fade. In other words, companies need to allocate their resources to create value and competitive advantage through a greater network development as well as an innovation encouragement [25]. According to the slack resources theory, due to the resources scarcity firms should arbitrate to select sustainable investments. Nevertheless, combining the divergent goals of stakeholders to find the optimal resources allocation function is the hardest mission for every company.

With the increase of multiple pressures and law evolvement, firms' ethical and social practices no longer present a simple voluntary decision, which explains the CSR development over time. Visser [13] considered that CSR versions missed the promotion of our community and ecosystem health, quite the contrary, they made it worse. Specifically, they failed to introduce innovative tools dealing with the existing environmental issues. He argued that the CSR understanding has been

evolving according to overlapping ages1 : The age of Greed, the age of philanthropy, the Marketing age and the Management age. While the three first ages have introduced a responsive CSR stream, the management age has established CSR in the core of business. It generates a strategic CSR.

When the firm is involved in CSR activities that meant to exclusively respond to stakeholders' basic needs and reporting standards, its CSR policy is responsive. However, when more pioneering initiatives are undertaken and going beyond standards and regulations, CSR activities are strategic [9, 27, 28]. We should also mention that another more civil version of CSR is taking place, namely the transformative CSR [13]. Nonetheless, we consider that this version is still in an embryonic stage for the profit-oriented enterprises.

The coexistence of these ages depends on the space–time setting. Hence, to explain the CSR and Innovation nexus we have to understand the evolvement of the CSR concept and its continuous interaction with corporate innovation.


**Abstract**

Milk composition and production varies from species to species, reflecting its diversified benefits on health. Lipids from caprine and ovine milk are anti-obesity and anti-atherogenic while prebiotic in the case of caprine. Higher contents of selenium from caprine and iron from camel milk play a role in immune system and oxygen transport system, respectively, whereas enriched vitamins like riboflavin, folic acid, B6, vitamin A of bovine, and foliate of cattle are effective in the synthesis of hemoglobin, and high niacin content of caprine is anti-cancerous. Camel milk is found to have characteristics of anti-carcinogenic, antidiabetic, and autoimmune therapeutic. Various processing techniques like pasteurization, skim milk powder processing, and ultra-high temperature processing are necessary for safe provision of milk to meet consumers' demand. Change in flavor, loss of micronutrients, biofilm production, and spore-forming bacteria are prominent challenges during processing. Antimicrobial resistance and disease conditions are exaggerating factors of milk deterioration with respect to quality and quantity. Preclinical trials like somatic cell count, California mastitis test, proteomic analysis, Raman spectroscopy-based analysis, and X-ray fluorescence analysis are helpful in avoiding the spread of disease and controlling of economic losses. This chapter focuses differential functions of bioactive of milk, issues arising during processing techniques, and preclinical studies of milk for safer production and consumption of milk.

**Keywords:** milk composition, bovine, camel, caprine, ovine, mare, differential functions, processing techniques, preclinical milk tests

### **1. Introduction**

 Milk, according to USDA, is a sterile lacteal secretion from mammary glands by full milking of one or more animals and considered free of colostrum. Basically, milk is composed of significant components that may be categorized into macro and micro milk components. The former category is comprised of protein, lipids, and oligosaccharides mainly lactose, whereas the latter contains minerals and vitamins [1]. Utilization of milk from various species of animals depends upon likelihood of people and access to the dairy animals. In such situations, some of dairy animals are overlooked due to their limited population in specific regions of the world. Camel and caprine milk is specifically medicinal in nature that is limitedly utilized as

staple use. The production systems are fewer than needed which is a grave situation. It is a dire need to explore bioactive components of milk from various animals and to investigate alternative resources to feed the hungry and to be benefited by pharmacological aspects.

The likelihood of food consumption stresses it to be natural, free from chemical preservatives, and microbiologically safe with extended shelf life [2]. The rapid development of our society in the past few decades and the careless use of large amount of agricultural services are appearing to be a burden over human health. The hunger of the increasing population cannot be satisfied with fresh milk due to unequal production and utilization system. Previous 15 years have noticed dairy industry emerging as technology revolution in product processing [3]. But there are several harms to the soundness of milk associated with processing techniques in terms of quality and quantity losses. The current chapter encompasses bioactive components of milk from different milk-producing animals and their chances of being deteriorated by processing techniques.


**9. Tamarind juice concentrate**

Tamarind concentrate is an acid pulp concentrate made from tamarind pulp that is free of skin, seed, fibers, and other impurities. The pulp from the tamarind pods is collected, and the juice is extracted from the pulp. The shells are broken by hand and agitated in water during the pulping process. The suggested tamarind pulp content in syrup is 20–24% to make a beverage with a distinct flavor and acidity [13]. By adding gelatine to clarified tamarind juice, the structural and colloidal phases are fully eliminated. The juice is translucent and retains its color and flavor. Under vacuum, insoluble particles are removed and soluble extracts are concentrated (**Figure 12**).

Deepika [14] studied Spray Drying of Tamarind Juice Concentrate and Powder Characteristics, the results are shown in **Table 5**.


*Edited by Pankaj Abrol*

The book Hemophilia - Recent Advances covers various rapid advances being made in this field. The authors have produced state-of-the art chapters. Over some decades, management of hemophilia has progressed from episode based to prophylaxis. It has moved from plasma and cryoprecipitate to new generations of recombinant coagulation factors. Efforts have been made to cover recent advances in the field. The intricacies of genotype and phenotype of hemophilia are explained. Management with recombinant factors has added to problems like inhibitors, which require more skillful handling. Perioperative management of hemophilia is also explained. Every chapter of this book is peer reviewed and evidence based. The information provided in this book makes the readers well informed and more inquisitive, thereby raising new issues, innovation, and research.

ISBN 978-1-78985-817-4

Hemophilia - Recent Advances

Published in London, UK © 2019 IntechOpen © StMax89 / iStock

## Hemophilia Recent Advances

*Edited by Pankaj Abrol*


**4. Greater than following generations**

The recent survey of N-glycosylation in a number of mosses surfaced several species that harbored N-glycans even larger than the fully developed Lewis A containing pride of the plant kingdom. The masses of the novel peaks indicated elongation of Lewis A determinants by 160 Da structures (**Figure 3**). Tentatively, we assume that a pentose plus methylation accounts for this mass increase. More precise information is not available so far.

### **Figure 2.**

*Detection and structures of methylated N-glycans in mosses. Panels A shows the matrix assisted-laser desorption/ time-of-flight mass spectrometry (MALDI-TOF MS) spectrum of the N-glycans of P. patens in full width and in detail for the major affected N-glycan species. Panel B depicts the structures of the two examples that were structurally analyzed in detail derived from Funaria hygrometrica Hedw. and Plagiomnium undulatum (Hedw.) Kop.*


4.4. Processing

In the processing stage, the time step was set at 0.2 s, the number of steps was 1800, and the maximum number of iterations for one step was 10.

The simulation was performed on a Pentium IV, DualCore 6400, 2.4 GHz system with 4 GB RAM; for 6 min of simulation and a total number of 18,000 iterations, the computing time was approximately 12 h.

In order to prevent the equation coefficients from changing too quickly, the change in dependent variables from one iteration to another was slowed by "relaxing" them. Thus, the linear relaxation method was used for the control of the CFD simulation in order to maintain its stability; Table 3 presents the values of the relaxation factors for the different physical parameters.

Table 3. Linear relaxation factors.

In this CFD simulation, the convergence criterion was selected so that the residues remained lower than 10�6 .