Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
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We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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For the majority of the population, the term immediately transports us to the notion of an ancient age or ancient world (the Parthenon, Athens, and the Coliseum of Rome), which condenses in itself the Greco-Roman world. This reduces antiquity to antiquity that was structurally essential for the construction and emergence of the civilization called occidental.For others, because of their religious backgrounds, antiquity goes back in time and enlarges, in part, its space of action, allowing the emergence of Palestine as a primordial territory.But these two visions (old and supported by a scientific ignorance of the ancient geographies and chronologies) enclose the history in a limited time and space. As if there would never have been a world before that time. As if the civilization that we comfortably call ourselves as inheritors, the so-called "Occidental Civilization" was the first step in the history of man on earth.',isbn:"978-1-78984-561-7",printIsbn:"978-1-78984-560-0",pdfIsbn:"978-1-83968-408-1",doi:"10.5772/intechopen.82132",price:100,priceEur:109,priceUsd:129,slug:"antiquity-and-its-reception-modern-expressions-of-the-past",numberOfPages:86,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"6f206aec2c58f403b9f503989afc75ac",bookSignature:"Helena Trindade Lopes, Isabel Gomes de Almeida and Maria de Fátima Rosa",publishedDate:"January 8th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/8893.jpg",numberOfDownloads:5118,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:0,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 30th 2018",dateEndSecondStepPublish:"December 12th 2018",dateEndThirdStepPublish:"February 10th 2019",dateEndFourthStepPublish:"May 1st 2019",dateEndFifthStepPublish:"June 30th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"202246",title:"Prof.",name:"Helena",middleName:null,surname:"Trindade Lopes",slug:"helena-trindade-lopes",fullName:"Helena Trindade Lopes",profilePictureURL:"https://mts.intechopen.com/storage/users/202246/images/system/202246.png",biography:'Maria Helena Trindade Lopes is Full Professor of Egyptology at the Faculty of Social Sciences and Humanities, Universidade Nova de Lisboa, Portugal, and Executive Coordinator of the Department of History. She is also the coordinator of the \\"Antiquity to its Reception\\" Group at CHAM - Humanities Center.\nShe is the author of six scientific books, several chapters in collective works, two historical novels (The Woman who Loved Pharaoh and Ramses II: The Living God who Conquered Lands and Hearts), and a historical biography of Rome (Rome, Eternal City). She has also published more than 150 articles in journals and proceedings of scientific conferences. 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She is an integrated researcher at CHAM – Centre for the Humanities (CHAM/FCSH/NOVA | UAc) and Vice-Coordinator of CHAM’s research group "Antiquity and its Reception." Since 2018, she has been the editor of the second series of the Journal Res Antiquitatis – Journal of Ancient History. She has a Ph.D. in History with a specialization in Ancient History, from the Universidade NOVA de Lisboa (2015). Her thesis focused on the comprehension of the notions of time and order in the Syro-Mesopotamian kingdom of Mari, during the Amorite period. 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The most interesting fact for this study is that, upon analysis, there seems to be interesting similarities between construction techniques used to assemble both ancient furniture and the ones used nowadays for the same purposes. To test the hypothesis, this paper is focused in three particular objects, the Solid Ebony Chair of Tutankhamun JE 62033, from KV62, and the Chairs of Hetepheres MFA 38.957 and JE 53263, from G7000X, -a much older chair- and analyses its structures, the materials and techniques used to assemble them, the similarities and dissimilarities, if any, between these two examples and to see if the is any correlations with them and their analog structures built three and a half millennia later, in our current days.",signatures:"André Patrício",downloadPdfUrl:"/chapter/pdf-download/66442",previewPdfUrl:"/chapter/pdf-preview/66442",authors:[{id:"285154",title:"Dr.",name:"Andre",surname:"Patricio",slug:"andre-patricio",fullName:"Andre Patricio"}],corrections:null},{id:"67232",title:"Enlightenment and Neoclassicism in La Clemenza di Tito of Mozart: An Historical-Legal Perspective",doi:"10.5772/intechopen.85867",slug:"enlightenment-and-neoclassicism-in-em-la-clemenza-di-tito-of-mozart-em-an-historical-legal-perspecti",totalDownloads:577,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Opera seria had always chosen its settings and characters from classical antiquity drawing on Greek mythology, the histories of Livy and Suetonius, the Aeneid, Plutarch’s Lives, and Ovid’s Metamorphoses. 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1. Introduction
Multiphase systems comprising of more than two phases are a common occurrence in the fields of chemical and bioprocess engineering. Such multiphase systems may be comprised of a gas phase, a liquid phase and a solid phase as is the case in froth flotation processes in the minerals sector [1, 2]. Alternatively, such multiphase systems may be comprised of a gas phase and two immiscible liquid phases as is often found in biological gas stripping [3] or biocatalysis [4]. Irrespective of the application field, a common expectation among such multiphase systems is that they are characterised by more complex hydrodynamics than two phase systems which are reasonably well understood [5, 6]. Similarly, mixing and mass transfer effects are expected to be more complex in such systems.
Given the above considerations, the design and analysis of multiphase systems requires the use of comprehensive frameworks that are capable of taking into account the various mechanisms of action that are at play. Computational Fluid Dynamics (CFD) has been proposed as one such framework since it is able to describe the hydrodynamics of multiphase systems based on fundamental equations of flows [7]. Furthermore, coupling of CFD simulations to sub-models of mass transfer, mixing or flotation can enable the description of these effects at finer resolutions than can be obtained based on empirical modelling. Thus, significant effort has been recently directed towards the development and application of CFD techniques to simulate multiphase systems comprising of two phase reactors [8, 9, 10] as well as those with more than two phases [2, 11, 12, 13].
This chapter builds upon recent work by presenting a discussion on the use of CFD in the design and analysis of gas–liquid–liquid reactors within the context of mass transfer. Key considerations informing the modelling approach have been discussed with their implementation illustrated by the review of recently modelled case studies [12, 13]. Furthermore, the successes and challenges attending the CFD-based modelling of gas–liquid–liquid reactors have been highlighted and on the basis of these, recommendations have been given on areas requiring further investigation. The chapter thus addresses itself to graduate students, academics and industrial practitioners interested in a comprehensive modelling framework for the design and analysis of gas–liquid–liquid reactors.
2. Considerations in gas–liquid–liquid systems
Gas–liquid–liquid reactors are a common occurrence in the bioprocess field (see Table 1). Such reactors tend to be comprised of one continuous liquid phase (primary liquid phase) and two dispersed phases (gas phase and secondary liquid phase) as illustrated in Figure 1. The departure of such reactors from traditional gas–liquid reactors is through the introduction of a secondary liquid phase within the system. The introduction of the latter modifies the system in a manner that impacts mass transfer and this can be seen by changes in the gas hold up, the bubble diameters and the overall volumetric mass transfer coefficients [4]. Underpinning these observable changes, however, are modifications to the fluid properties, mass transfer properties and pathways as well as the reactor hydrodynamics. A consideration of these modifications is necessary for appropriate modelling and as such a brief review on the same is presented herein. In-depth reviews are available elsewhere [4, 19, 20].
Figure 1.
Illustration of gas–liquid–liquid stirred tank reactor.
2.1 Changes in fluid properties
The introduction of a secondary liquid phase into a traditional gas–liquid reactor can result in a change of fluid properties. For example, the surface tension between the primary liquid phase and the gas phase can change depending on the degree of solubility of the secondary liquid phase in the primary liquid phase. To characterise this, surface tension values based on mutually saturated liquids have been reported in literature [16, 18]. Such values have generally been obtained by mixing the primary liquid phase with the additional liquid phases for long periods followed by a separation of the phases and surface tension measurements using a tensiometer [18]. Table 2 illustrates selected results from literature.
Saturated surface tension for various liquid–liquid combinations.
The results in Table 2 generally point to a decrease in the saturated surface tension (σsat) with addition of the secondary liquid phase. Furthermore, this change in surface tension has been observed to be greater when the secondary liquid phase is more soluble in the primary liquid phase [18]. As a decrease in the surface tension results in smaller gas bubbles, an enhancement in mass transfer can be expected. However, this is not always the case. For example, Kundu et al. [18] observed a negative impact on mass transfer upon the addition of toluene, anisole, decyl alcohol and 2-ethyl-1-hexanol despite a decrease in σsat (refer to Table 2). This points to the presence of additional factors that need to be taken into account for a proper description of mass transfer in such systems.
Two additional points need to be highlighted with regard to surface tension. First, discrepancies in the reported values of σsat exist as seen in Table 2. For example, Ngo & Schumpe [16] measured an insignificant change in σsat (71.8 mN/m) upon the addition of n-Heptane whereas Kundu et al. [18] measured a significant change (σsat = 65 mN/m). Such discrepancies point to a need for additional experimental measurements of σsat.
A second point to note is that researchers have also attempted to report on dynamic values of the surface tension [21]. This was achieved by preparation of a “stable” liquid–liquid dispersion followed by measurement of the surface tension [21]. The values obtained were in the range of 17–26 mN/m for an n-C10-C13 alkane cut mixed with water [21]. These values were lower than those reported in Table 2 and tended towards the surface tension values of pure alkanes (23.9 mN/m for n-Decane, 25.41 mN/m for n-Dodecane [18]). Consequently, it may be suggested that a degree of separation of the liquid–liquid dispersion occurred during surface tension measurements despite the best efforts of the researchers. In this case, the settled-out and less dense alkane phase would form an intervening layer between the gas phase and the dispersion.
Besides the above changes to the surface tension, the presence of a secondary liquid phase can lead to changes in effective fluid properties such as the effective density, the effective viscosity as well as the effective solubility. The pre-qualifying term, “effective”, is used in this case since such properties are defined based on a view of the liquid–liquid dispersion as a single pseudo-homogenous liquid. This view permits for a simplification of the 3-phase gas–liquid–liquid reactor to an effective 2-phase reactor. Consequently, correlations derived for traditional 2-phase reactors, such as Eq. (1) with an empirical basis and Eq. (2) with a theoretical basis [5], can be used as a starting point for the design of 3-phase reactors. In these equations, KLa represents the overall volumetric mass transfer coefficients in a 2-phase reactor whereas xg and dg respectively represent the gas hold up and the bubble diameters (see Eq. (3) and (4)). Other variables are as defined in the Nomenclature.
Effective fluid properties can be specified on the basis of simple mixing rules such as Eq. (5) for the effective density (ρeff). Such equations consider the contributions of the individual liquid phases based on their volumetric proportions (xi) but neglect non-ideal effects that may arise from molecular interactions, commonly found in homogeneous mixtures, which may lead to excess volumes. This notwithstanding, experimental evidence suggests that such equations are sufficient for the effective density [22] and the effective solubility (see next section). This may be due to the poor mutual solubility of liquid phases typically tested for gas–liquid–liquid reactor applications: the properties of the individual liquid phases remain unchanged by the presence of a second, immiscible phase and the bulk properties can be estimated by simple volume averaging.
ρeff=∑i=1nxiρiE5
With regard to effective viscosity (μeff), models of a more complicated form than that given in Eq. (5) are required. This is due to the need to account for the perturbation of fluid flow in the presence of droplets of the secondary liquid phase. Models varying in complexity have been proposed. For example, the Taylor model (see Eq. (6)) has been proposed for dilute dispersions of spherical droplets [23]. Additionally, models such as those given in Eq. (7) and (8) have been proposed for non-dilute dispersions where the hydrodynamic interactions among droplets need to be accounted for [23, 24]. Besides these, however, models have also been proposed to account for non-zero shear rates in the fluid which introduce effects such as droplet deformation [23, 25].
μeffμc=1+2.5xd0.4+μr1+μr;xd→0E6
μeffμc2μeffμc+5μr2+5μr1.5=exp2.5xd1−xd/xmE7
μeffμc2μeffμc+5μr2+5μr1.5=1−xdxm−2.5xmE8
In the equations above, μr refers to the ratio of viscosity of the secondary liquid phase (μd) to that of the primary liquid phase (μc). Variables xd and xm, on the other hand, refer to the volume fraction of the secondary liquid phase and the maximum packing limit of its droplets respectively.
2.2 Changes in mass transfer properties and pathways
Properties of interest affecting mass transfer include the solubility and diffusivity of a species within the liquid–liquid dispersion. Taking the liquid–liquid dispersion as a pseudo-homogenous liquid, effective properties have been defined. For example, it has been observed that the effective solubility of a species (Ceff∗) can be specified according to the volumetric proportions of the liquids involved, once again recognising that the presence of immiscible phases has a negligible effect on the solubilities associated with individual phases [22, 26]. This is illustrated in Eq. (9) below, with Ci∗ representing the solubility in liquid phase i and n representing the total number of liquid phases.
Ceff∗=∑i=1nxiCi∗E9
Given the application fields of gas–liquid–liquid reactors (such as biological gas stripping), the effective solubility is usually higher than the solubility of a species in the primary liquid phase (Ceff∗>Cc∗). This implies that a longer duration is required to saturate the liquid–liquid dispersion with a given species in comparison to the time required to saturate the pure primary liquid phase [27, 28]. Consequently, for a fixed mass transfer rate, lower values of the overall volumetric mass transfer coefficients can be obtained upon the addition of a secondary liquid phase [28, 29].
With regard to the molecular diffusion of a species, the effective diffusivity Deff has been defined as illustrated in Eq. (10) for dilute liquid–liquid dispersions [30]. This equation, being of a general nature, has also been used to describe the effective diffusivity of solid–liquid suspensions [31]. The variable Dr in this equation represents the diffusivity ratio whereas Dd and Dc represent the diffusivity of the species in the secondary and primary liquid phases respectively.
Deff=DcDr1+2xd+21−xdDr1−xd+2+xd;Dr=DdDcE10
It should be noted that the effective properties defined by Eqs. (9) and (10) can be, in a general sense, regarded as bulk properties. Appropriate as it may be to define them, a full description of mass transfer in a liquid–liquid dispersion also requires an examination of changes introduced by the secondary liquid phase at the mass-transfer interface. Tied to this are the questions whether the secondary liquid phase will be present at the interface and whether it is involved in active uptake of dissolving species at the interface. Indeed, the concept of a pseudo-homogenous liquid implies that the secondary liquid phase will be present, not only in the bulk of the primary liquid, but also at the interface. Furthermore, homogeneity implies that a similar distribution of the secondary liquid phase is found at the interface as is found in the bulk of the primary liquid phase. However, researchers have considered different possible configurations of the interface as illustrated in Figure 2. In this way, the requirement for homogeneity has been relaxed at the interface while being maintained in the bulk.
Figure 2.
Mass transfer interface illustrating possible configurations – (A) parallel mass transfer, (B) series mass transfer without shuttling and (C) series mass transfer with shuttling.
Assuming active uptake by the secondary liquid phase, different possible pathways of mass transfer have arisen. These have included, for example, parallel mass transfer and series mass transfer with or without the shuttling of the droplets of the secondary liquid phase [14, 30, 32, 33]. These various pathways have been associated with an enhancement in mass transfer besides that occurring due to changes discussed earlier (refer to Eq. (9) and (10)). A review of these pathways as well as their associated enhancement factors can be found in the work by Dumont & Delmas [19].
2.3 Changes in reactor hydrodynamics
The hydrodynamics of a reactor are taken to refer to the mean velocity field and the turbulence field within a reactor. These affect mass transfer in various ways. For example, the transport of gas bubbles within a reactor (and hence overall gas hold up) is dependent on the magnitude and orientation of the mean velocity field. Additionally, mass transfer at the interface between the gas bubbles and the liquid phase will depend on the prevailing local turbulence. Consequently, a change in the reactor hydrodynamics will lead to a change in the mass transfer.
The addition of a secondary liquid phase has been observed to change both the mean velocity field and the turbulence levels in stirred tank reactors [34, 35, 36]. Direct measurements of the velocity through techniques such as Particle Image Velocimetry [34, 36] and Laser Doppler Anemometry [35] have revealed that a secondary liquid phase can dampen the mean velocities [34, 35] while either increasing or decreasing the turbulence levels [34, 35, 36]. Further evidence in literature for a change in the hydrodynamics has been largely indirect – inferred from examining the change in, for example, mixing time upon addition of a secondary liquid phase [37, 38].
Two schools of thought have been postulated to explain the interaction of the secondary liquid phase with the hydrodynamics of a reactor. The first has been focused on the change in effective fluid properties. In this line of thinking, it has been suggested that a decrease in the effective density should lead to an increase in the velocities [34]. On the other hand, it has been suggested that an increase in the effective viscosity should have a dampening effect on both the mean velocities and the turbulence levels [34, 35].
The second school of thought, on the other hand, has been focused on the augmentation or dampening of turbulence by the droplets of the secondary liquid phase [34, 35]. Various mechanisms have been suggested in this regard although these are still the subject of active research [39, 40, 41, 42]. For example, whether a particle (solid, liquid or gas) augments or dampens turbulence has been traditionally associated with the size of the particle (dp) in relation to the integral (or large) scales of turbulence (l) [43]. Large particles dp/l>0.1 with characteristically large particle’s Reynolds numbers Rep>400 have generally been associated with turbulence augmentation through mechanisms such as vortex shedding in the wakes behind the particles [43, 44]. On the other hand, small particles dp/l<0.1 have generally been associated with a dampening of turbulent kinetic energy (TKE) that occurs as the particles are accelerated/dragged by the flow [39, 43].
It should be noted, however, that the above observations do not represent a fixed rule; exceptions have been observed. For example, it has been observed that particles of a size dp/l<0.1 can both augment and dampen turbulence depending on the flow’s Reynolds number [45]. Furthermore, it has been observed that turbulence can be augmented by particles of a size in the order of the Kolmogorov (smallest) scales of turbulence [46, 47, 48, 49, 50]. In the latter cases, however, the non-uniform modification of the spectrum of TKE by particles was considered with the augmentation of TKE observed to occur at the small scales of turbulence [46, 47, 48, 49, 50]. Mechanisms that were proposed for the turbulence augmentation included flow forcing due to the inertia [50] or buoyancy [49] of small particles that were well correlated with the fluctuating fluid flow.
3. Computational fluid dynamics for gas–liquid–liquid reactors
As illustrated in the previous sections, the introduction of a secondary liquid phase into a traditional gas–liquid reactor can result in a variety of changes. Consequently, the modelling of mass transfer in gas–liquid–liquid reactors requires a comprehensive modelling framework that can account for the different changes. An empirical approach, such as illustrated in Eq. (11) [51], may not suffice as the numerous effects introduced by the secondary liquid phase are reduced into a single term with an adjustable exponent requiring optimisation (compare to Eq. (1)). This is not an easily generalizable approach.
KLa′=Γ·PgVxνsy1−xdzE11
Computational Fluid Dynamics (CFD), on the other hand, offers a fundamental framework that can be built upon to incorporate as much level of detail (or physics) as necessary/desired. This is the case since CFD offers an approximate/numerical solution to the fundamental equations of flow governing a system/reactor [7, 52]. Consequently, the hydrodynamics of a reactor are resolved in space and time and such hydrodynamic data can be coupled to fundamental models of mass transfer so as to predict parameters of interest such as the overall volumetric mass transfer coefficient, KLa′.
As CFD-based approaches are inherently computationally intensive, a major constraint to such approaches is the computational resources available [7, 52]. Thus, a compromise has to be made between the level of physics to be captured and computational resources available [7]. For example, prior consideration must be given as to the level of resolution to be employed for the flow field. Similarly, an appropriate continuum description of the phases involved must be chosen a priori. Such compromises notwithstanding, a higher level of detail, accuracy and generality is still maintained with a CFD-based approach in comparison to empirical approaches. This will be illustrated in Section 4.
3.1 Modelling frameworks for hydrodynamics
As noted in the introduction to CFD above, several prior considerations have to be made with regard to the modelling approach. These considerations tend to give rise to different modelling frameworks or techniques. For example, as relates to the resolution of flow fields, it is possible to simulate the usually turbulent flow in a reactor at all length- and time-scales using Direct Numerical Simulations but the associated computational expense is prohibitive [53]. Therefore, filtering or averaging of the flow field is usually done [53]. Filtering techniques such as those used in Large Eddy Simulations offer an enhanced resolution of the flow field as compared to averaging techniques [53]. However, they are still considered computationally expensive and their use has been largely limited to single-phase reactors [53]. Averaging techniques such as those used to generate the Reynolds-Averaged Navier–Stokes (RANS) equations, on the other hand, lead to tractable simulations [53] and are a practical choice for the modelling of multiphase reactors.
With regard to the description of the phases, an Eulerian framework is typically used for the continuous phase (primary liquid phase) and it involves describing the flow based on a fixed observer position [54]. For the dispersed phases (gas and secondary liquid phase), on the other hand, either an Eulerian framework or a Lagrangian framework can be employed [54]. The Lagrangian framework involves the tracking of individual particles of the dispersed phase within the flow field of the continuous phase [54]. The particles either follow the flow field without interaction (one-way coupling) or interact with it thus modifying it (two-way coupling) [55].
The Lagrangian framework provides a greater degree of detail and as can be expected, it is costly to implement [54]. Consequently, an Eulerian framework for both the continuous and dispersed phases tends to provide a practical choice for modelling. An Eulerian description of both phases assumes that the phases involved can be treated as a continuum [56]. In this case, a phase indicator function is introduced into the governing equations of flow to account for the possible realisation of a phase i at a given position and a given time [56]. Averaging of the governing equations after decomposing the instantaneous flow field into its mean and fluctuating components results in Eq. (12) and (13) [57].
DDtαiρi=0E12
DDtαiρiVi¯=−αi∇p+∇·τ¯¯i+αiρig¯+∑j=1nR¯jiE13
In Eqs. (12) and (13), subscripts i and j represent individual phases whereas α represents the respective phase volume fraction. This volume fraction is based on the total volume of all phases as opposed to the total liquid volume (see variable x in earlier equations). The respective phases can be either the gas phase, the primary liquid phase or the secondary liquid phase. Alternatively, if the liquid–liquid dispersion is treated as a pseudo-homogenous liquid, then the subscripts would refer to either the gas phase or the pseudo-homogenous liquid.
Other variables in Eqs. (12) and (13) such as ρ and V¯ represent the density and the mean velocity of the respective phases whereas p represents the shared pressure field. Additionally, g¯ represents the gravitational acceleration while R¯ represents the interphase momentum exchange terms. These terms include the lift force, the drag force, the turbulent dispersion force and the added mass force among others [56].
There are two key points to note regarding the interphase momentum exchange terms. First, their significance varies with the set up being considered. For example, the drag force has been observed to be the most significant interphase exchange term in the bulk of a stirred tank reactor [58, 59]. On the other hand, terms such as the lift force have been observed to significantly affect the flow in a bubble column reactor [60]. Consequently, modelling can be simplified by only accounting for significant terms.
The second point to note touches on the various models that have been proposed to specify the interphase momentum exchange terms. Such models chiefly consider 2-phase interactions, that is, gas–liquid or liquid–liquid interactions [61, 62, 63]. Though such models have been improved upon to consider effects such as particle-particle interaction at high volume fractions of a single dispersed phase [61, 62, 63], there is still a need for appropriate models that specify the interphase exchange terms when more than two phases are present. To this end, work such as that by Baltussen et al. [64, 65] investigating the effective gas–liquid drag in the presence of an additional solid phase may provide direction.
Finally, τ¯¯ in Eq. (13) represents the stress tensor accounting for both viscous and turbulent (Reynolds’) stresses. The specification of the stress tensor is non-trivial due to the Reynolds’ stresses that need to be solved directly or modelled. A direct solution of the Reynolds’ stresses provides a greater amount of detail and is able to resolve complex features of the turbulence field such as anisotropy [53, 54]. However, the computational costs associated with this approach as well as reported solution difficulties favour the specification of the stress tensor using alternative simplified approaches [53, 54]. Modelling of the stress tensor using the Boussinesq hypothesis is one such alternative approach [53, 54]. In the latter, the Reynolds’ stresses are related to the gradients of the mean velocity with the eddy/turbulent viscosity (μt) arising as a proportionality constant (see Eq. (14)) [57].
The eddy viscosity can be modelled in various ways such as that illustrated in Eq. (15) [53, 57]. In this case, it is related to the turbulent kinetic energy (k) and its dissipation rate (ϵ), both of which need to be solved for. Various models have been proposed for these latter parameters (k and ϵ) such as the dispersed k–ϵ turbulence model, the mixture k–ϵ turbulence model and the per-phase k–ϵ turbulence model [57]. These models represent an extension of the single phase k–ϵ turbulence model to multiphase situations and their applicability depends on the expected/prevailing type of flow [57]. For example, the mixture k–ϵ model is recommended for stratified flow whereas the dispersed k–ϵ model is recommended where there is one clear continuous phase and the other phases are dispersed within it [57].
It should be noted that irrespective of the choice of turbulence model, one key point to consider is the interaction of the dispersed phases with the prevailing turbulence. As noted in Section 2.3, the dispersed phase can modify the prevailing turbulence and this needs to be captured. To this end, various models have been proposed in literature and these vary in complexity depending on the level of detail captured. Various authors have recently examined the sufficiency of these models and their work is recommended [66, 67, 68, 69, 70, 71].
3.2 Modelling frameworks for mass transfer
Similar to the modelling of hydrodynamics, the modelling of mass transfer involves the selection of appropriate frameworks prior to the actual simulation. The choice of a particular framework involves a compromise between the level of detail captured and computational cost involved. Furthermore, the choice of a modelling framework for mass transfer tends to be influenced by choices made during the modelling of the hydrodynamics. For example, the use of an Eulerian description of the phases together with averaged equations of flow offers a practical choice for the modelling of a reactor hydrodynamics. However, this approach involves a loss of specifics on the mass transfer interface thus necessitating the prescription of models to approximate the expected mass transfer behaviour. On the other hand, a Lagrangian tracking of particles coupled with interface tracking algorithms better resolves the mass transfer interface but is unfeasible for reactors with a large number of dispersed particles (droplets or bubbles). Consequently, the discussion below focuses on the modelling of mass transfer within the context of an Eulerian description of the phases and the modelling of hydrodynamics using averaged equations of flow.
Mass transfer in a gas–liquid–liquid reactor can be solved for by tracking the concentration of a species in each phase within the reactor. This results in three equations as given in Eqs. (16)–(18). In these equations, C represents the concentration of the species in the respective phase (subscripts g,c,d) whereas J¯ and S represent the diffusive flux and the interphase mass transfer source terms respectively. Closure models are required for the interphase mass transfer source terms depending on the expected mass transfer behaviour.
DDtαgCg=−∇·αgJ¯g−Sgc−SgdE16
DDtαcCc=−∇·αcJ¯c+Sgc−ScdE17
DDtαdCd=−∇·αdJ¯d+Sgd+ScdE18
In the most general case, mass transfer can be assumed to occur between the gas phase and both the primary and the secondary liquid phases (Sgc≠0,Sgd≠0). This would correspond to the case of parallel mass transfer as illustrated in Figure 2(A). Evidence for this mass transfer pathway has been provided based on observations of the formation by oil films on gas bubbles during “static” experiments [72]. The question arises, however, as to whether sufficient time for film formation occurs in a dynamic/agitated reactor [72, 73].
Series mass transfer is often taken to be the more probable mass transfer pathway in an agitated reactor (cases (B) or (C) in Figure 2 with Sgd=0) [34]. In addition, the formation of small droplets of the secondary liquid phase with a large interfacial area implies that mass transfer between the respective liquid phases is usually faster than that occurring between the gas phase and the primary liquid phase [72]. Thus, based on the timescale of mass transfer between the gas phase and the primary liquid phase, it may be assumed that equilibrium conditions exist between the respective liquid phases. Furthermore, one need only track the concentration of the species in two phases – the gas phase (Eq. (16)) and the primary liquid phase (Eq. (17) with Scd=0). With these simplifying assumptions, the only unknown left is the interphase source term between the gas phase and the primary liquid phase (Sgc). Gakingo et al. [13, 28] proposed definitions for Sgc that account for possible mass transfer pathways at the gas–liquid interface plus an apparent decrease in the overall volumetric mass transfer coefficients that occurs due to a larger total solubility of the species in the liquid–liquid dispersion (refer to Section 2.2). This illustrated in Eqs. (19)–(21) below [13, 28].
Sgc=KLa′Cc∗−CcE19
KLa′≈E′·Λ·Dcρcϵcμc0.25·6αgdgE20
E′=11−αd+αdmφE21
In the equations above, KLa′ represents the overall volumetric mass transfer coefficient in the presence of the secondary liquid phase. The latter has been expanded in Eq. (20) based on an eddy cell model [74] with Λ and E′ representing a constant and the enhancement factor respectively. The enhancement factor is given in Eq. (21) where m represents the solubility ratio (m=Cd∗/Cc∗) and the exponent φ varies between 0.5 and 1. A value of φ=1 represents the series mass transfer pathway without shuttling whereas a value of φ=0.5 represents the series mass transfer pathway with shuttling [13, 28]. Other variables are as previously defined.
4. Case studies
Few studies have reported on the CFD-based modelling of gas–liquid–liquid reactors. Two such studies are reviewed in this section with the one having focussed on the modelling of mixing time [12] while the other focussed on the modelling of mass transfer [13].
In the case studies of interest, stirred tank reactors were considered as illustrated in Table 3. In addition, the modelling in both cases was done based on an Eulerian description of the phases involved (refer to Eqs. (12) and (13)). There were certain similarities in the modelling and these included, for example, a consideration of drag force as the only interphase momentum exchange term. The latter was specified according to Eq. (22) [13] with different models for the drag coefficient (CD) employed as illustrated in Table 4. Constant sizes were also assumed for both the gas phase bubbles and the droplets of the secondary liquid phase and these were predicted by Eqs. (3) and (23) respectively [12, 13]. Finally, turbulence was modelled based on the dispersed k–ϵ model (see Eqs. (24) and (25)) though the use of the Reynolds stress model was additionally considered in one study [12].
The above similarities, notwithstanding, there were some notable differences between the two studies. For example, Gakingo et al. [13] tested two approaches in the modelling of the liquid–liquid dispersion. The first approach involved the treatment of the dispersion as a pseudo-homogenous liquid (herein referred to as the P-HOM approach) and the mixture properties were obtained from Eq. (5) for effective density or through experimental measurements for effective viscosity. The second approach, also used by Cheng et al. [12], involved a consideration of the heterogeneous nature of the dispersion and a modelling of each individual liquid phase (herein referred to as the HET approach). In this second approach, there was a need to specify models for turbulence modulation by the dispersed phases (Πkc in Eq. (24)) and different models were employed as illustrated in Table 4. Last but not least, mass transfer was considered in only one study [13] where the authors employed the previously reviewed frameworks, that is, Eqs. (16) and (17) with Sgd=0, Scd=0 and Eqs. (19)–(21). Further specifics on the implementation of the modelling approaches (meshing, boundary conditions and solver settings) can be found in the respective studies [12, 13].
Several key observations can be made from the reported modelling works [12, 13]. First, it was observed that the hydrodynamics of a gas–liquid–liquid stirred tank reactor were better captured based on the HET modelling approach as opposed to the P-HOM modelling approach [13]. The failure by the latter approach was attributed to the fact that the hydrodynamics in a turbulent stirred tank reactor are dominated by turbulence as opposed to the effective (mixture) viscosity. To this end, it was reported that the ratio of turbulence viscosity to mixture viscosity was in the order of O(10−100) based on the P-HOM modelling approach [13]. Consequently, a minimal change in the hydrodynamics was observed despite an almost 2-fold increase in the values of mixture viscosity [13]. This is illustrated through the minimal change in gas hold up trends shown in Figure 3.
Figure 3.
Gas hold up versus volume fraction of secondary liquid phase at 600 rpm. P-HOM refers to the pseudo-homogenous modelling approach whereas HET refers to the individual treatment of the liquid phases. Data from Gakingo et al. [13].
As pertains to the HET modelling approach, the better performance at capturing the hydrodynamics and hence gas hold up trends (see Figure 3) was attributed to the capture of turbulence modulation by droplets of the secondary liquid phase [13]. In particular, it was reported that there was an increase in the turbulence viscosity which served to dampen the mean velocity field (see Figure 4) thus reducing the effective drag and dispersion experienced by the gas bubbles [13]. Cheng et al. [12], on the other hand, did not make an explicit mention of changes in the turbulence viscosity. Rather, they hypothesised that their mixing time was reduced at low volume fractions of the secondary liquid phase due to an increase in turbulence caused by the droplets of the latter [12]. Furthermore, they hypothesised that an increase in mixing time at high volume fractions of the secondary liquid phase was due to a dampening of the turbulence arising from an increase in the effective viscosity [12]. This reference to notable changes due to the effective viscosity stands in contrast with the findings on the P-HOM modelling approach [13].
Figure 4.
Velocity contours based on the HET modelling approach on a mid-baffle plane at 600 rpm. Left – 0% alkane volume fraction (2-phase). Right −10% alkane volume fraction.
A recent study on a gas–liquid–solid reactor has reported similar gas hold up trends as those reported for the HET approach [75]. Furthermore, the modelling of such a reactor based on non-Newtonian models for the liquid–solid slurry has been observed to result in the formation of regions of localised fluid motion near the impeller (caverns) with stagnant fluid elsewhere in the tank [75, 76]. This corresponds to the reports of a dampened mean velocity field according to the HET approach as seen in Figure 4. Observations such as these suggest that the dampening of the mean velocity field may be a common feature in three-phase reactors. However, questions arise as to the appropriate manner of describing such effects. For example, the question may be posed as to whether liquid–liquid dispersions in stirred tanks should be treated based on non-Newtonian models with a yield stress and shear-thinning behaviour rather than equations of the type given in Section 2.1 (Eqs. (6)–(8)). It is to be noted that Eqs. (6)–(8) predict the effective viscosity of the dispersion assuming no non-Newtonian behaviour. Furthermore, the experimental effective viscosity values that were used for the P-HOM modelling approach were in close agreement with those predicted by these equations [13].
Further observations made in the case studies of interest touched on the trends of pumping capacity and power drawn by the impellers. It was observed that the pumping capacity of the impellers decreased at high volume fractions of the secondary liquid phase [12, 13]. This was attributed to a decrease in the mean velocity field as dissipated by an increasing turbulence viscosity [13]. As for the power drawn, both an increase and a decrease in the power was reported. The decrease in power, as noted by Cheng et al. [12], was attributed to a decrease in the effective density upon addition of the secondary liquid phase. On the other hand, Gakingo et al. [13] attributed a noted increase in power drawn to increased energy dissipation by the droplets of the secondary liquid phase. This latter effect was not reported by Cheng et al. [12] and this could have been due to the use of different models to capture turbulence modulation by the dispersed phases (see Table 4). Cheng et al. [12] used a model by Katoaoka et al. [77] which considers only the mean velocity differences Vi¯−Vc¯ in computing the work done by interfacial drag. Gakingo et al. [13], on the other hand, employed the model by Simonin et al. [57, 78, 79, 80] which considers not only the effects arising from mean velocity differences but also the effects arising from the fluctuating velocities of the dispersed phase particles. These latter effects were expected to become more significant than the former as the size of the dispersed phase particles decreased (Vi¯−Vc¯→0asdp→0) [68]. Consequently, it may be suggested that the use of the model by Simonin et al. [57, 78, 79, 80] was more appropriate as it is more comprehensive.
With regard to mass transfer, it was illustrated that potentially accurate predictions can be obtained by using the HET modelling approach for hydrodynamics and Eqs. (16), (17) and (19)–(21) for mass transfer with Sgd=Scd=0 (see Figure 5) [13]. These equations correspond to a case where the concentrations of a species in the two liquid phases are at equilibrium and no direct uptake by the secondary liquid phase occurs at the gas–liquid interface. Though this scenario implied the occurrence of either series mass transfer or series mass transfer with shuttling, minimal differences were reported based on a consideration of these two alternatives [13]. Thus, it may be suggested that mass transfer in gas–liquid–liquid reactors is less sensitive to the assumed configuration at the gas–liquid interface and more sensitive to other varying parameters such as the energy dissipation rate and the gas hold up. More evidence for or against this suggestion should, however, be provided based on a re-examination of mass transfer using a different set of modelling assumptions.
Figure 5.
Comparison of predicted versus experimental overall volumetric mass transfer coefficients at 600 rpm and varying alkane concentrations. Experimental measurements have been obtained by the pressure step method. Data from Gakingo et al. [13].
In conclusion, the usefulness of a CFD-based mass transfer model may be glimpsed from the amount of detail that it can generate. For example, spatial resolutions of variables of interest can be quickly generated as illustrated in Figure 6 to support the visualisation of changes made during in situ reactor design. This is indeed the chief advantage of a CFD-based approach over an empirical approach. However, before the full potential of the CFD-based approach can be realised, a number of areas will need improvement or further investigation. More on this is presented in the subsequent section.
Figure 6.
Contours of local KLa′ values predicted based on the HET modelling approach on a mid-baffle plane at 600 rpm. Left – 0% alkane volume fraction (2-phase). Right −10% alkane volume fraction.
5. Areas requiring further investigation
The results reviewed in the previous section illustrate that the addition of a secondary liquid phase can have a great impact on both the hydrodynamics and the mass transfer in a reactor. In particular, the results suggest that turbulence modulation by the secondary liquid phase is the key mechanism of action through which changes in a reactor occur. A CFD-based framework that is able to capture this mechanism of action has also been proposed and its potential has been illustrated. This notwithstanding, care ought to be taken in generalising the results obtained and the implications arising from them. This is because there still are a number of issues that need further investigation. Two such pertinent issues are highlighted below.
The first pertinent issue concerns the applicability of the obtained results to different reactors. It is the view of the authors that the results reported above should be taken as being particular to stirred tank reactors operating in the turbulent regime. This would be in line with existing experimental evidence for turbulence modulation by secondary liquid phases in turbulent stirred tank reactors [34, 35, 36, 37]. Different reactors, on the other hand, may have alternative mechanisms of action. For example, recent studies in a gas–liquid–liquid–solid bubble column reactor have illustrated that the addition of a secondary liquid phase did not significantly impact the hydrodynamics of the reactor [81, 82]. Rather, a greater impact on the hydrodynamics was observed for the solid phase and this depended on the type of the solid phase employed [81, 82]. Consequently, it may be suggested that a pseudo-homogenous treatment of the liquid–liquid dispersion would suffice for such a bubble column reactor contrary to what has been established in the case study above.
The second point worth investigation is the sensitivity of CFD-based results to the sub-models and simplifying assumptions employed. As noted in sections 3.1 and 3.2, quite a number of decisions have to be made prior to the actual simulations. Though these decisions are necessitated by a need to keep the simulations tractable, the quality of results obtained may be impacted. It is the view of the authors that particular attention should be given to the sub-models used to capture turbulence modulation. This is because of the seemingly large effects that turbulence modulation had on the results presented in the case study. To this end, it is recommended that comprehensive sets of experiments should be conducted on gas–liquid–liquid reactors and these should involve a concurrent measurement of the mass transfer and the hydrodynamics. Current literature is fragmented with authors who have investigated mass transfer not having measured the potential changes in the hydrodynamics and vice versa.
Acknowledgments
The authors would like to acknowledge Ayman A. Abufalgha for performing the surface tension measurements.
The first author would also like to acknowledge funding received from the DST-NRF Centre of Excellence in Catalysis (c*change) and the DAAD-AIMS In Region Scholarship Programme.
ratio of concentration of a species dissolved in secondary liquid phase to that dissolved in primary liquid phase at equilibrium.
n
Total number of phases.
νs
Superficial gas velocity (m/s).
xg
Volume fraction of gas phase based on total volume in a gas–liquid reactor.
xd
Volume fraction of secondary liquid phase based on total volume of un-gassed reactor.
xm
Maximum packing limit.
C
Dissolved concentration of a species (mol/m3).
C∗
Dissolved saturation concentration of a species (mol/m3).
CD
Drag coefficient.
CD,∞
Drag coefficient in the absence of turbulence.
D
Diffusivity of a species in a liquid phase (m2/s).
Dr
Diffusivity ratio
E′
Enhancement factor.
Eo
Eotvos number.
I¯¯
Identity tensor.
J¯
Total diffusive flux of a species.
Gk,c
Production of turbulent kinetic energy from mean velocity gradients.
KLa
Overall volumetric mass transfer coefficient in the absence of a secondary liquid phase (s−1).
KLa′
Overall volumetric mass transfer coefficient in the presence of a secondary liquid phase (s−1).
Kji
Interphase momentum exchange coefficient between phases i and j.
N
Impeller speed (rps).
Pg
Gassed power (W).
Re
Reynolds number.
St
Stokes number.
T
Impeller diameter (m).
V
Volume (m3).
V¯
Velocity vector (m/s).
V¯dr
Drift velocity (m/s).
α
Volume fraction based on total volume in a gassed reactor.
ϵ
Turbulent kinetic energy dissipation rate (m2/s3).
ρ
Density (kg/m3).
σ
Surface/interfacial tension (N/m).
τ¯¯
Stress tensor (Pa).
μ
Dynamic viscosity (Pa s).
μt
Turbulent viscosity (Pa s).
μr
Viscosity ratio.
λ
Kolmogorov length scale.
ζic
Covariance of fluctuating velocities between phase i and the continuous liquid phase (subscript c).
Πkc
Production (destruction) of turbulent kinetic energy by motion of dispersed particles.
Πϵc
Production (destruction) of turbulent kinetic energy dissipation rate by motion of dispersed particles.
x,y,z,φ
Exponents.
Γ,Λ,γ,Ω,Θ,Cμ
Constants.
σk,σϵ,C1ϵ,C2ϵ
C3ϵ,CAM
c
Continuous/primary liquid phase.
d
Secondary liquid phase.
g
Gas phase.
p
Dispersed particle (gas, liquid or solid).
i,j
Phases i,j.
cap
Spherical cap.
ell
Elliptical shape.
eff
Effective.
sph
Spherical shape.
sat
Saturated.
\n',keywords:"Gas–liquid–liquid reactors, stirred tanks, hydrodynamics, mass transfer, Computational Fluid Dynamics, predictive modelling",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/77792.pdf",chapterXML:"https://mts.intechopen.com/source/xml/77792.xml",downloadPdfUrl:"/chapter/pdf-download/77792",previewPdfUrl:"/chapter/pdf-preview/77792",totalDownloads:100,totalViews:0,totalCrossrefCites:0,dateSubmitted:"May 28th 2021",dateReviewed:"June 30th 2021",datePrePublished:"August 2nd 2021",datePublished:null,dateFinished:"August 2nd 2021",readingETA:"0",abstract:"Gas–liquid–liquid reactors are typically found in bioprocess setups such as those used in alkane biocatalysis and biological gas stripping. The departure of such reactors from traditional gas–liquid setups is by the introduction of a secondary (dispersed) liquid phase. The introduction of the latter results in complicated hydrodynamics as observed through measurements of velocity fields, turbulence levels and mixing times. Similarly, changes in mass transfer occur as observed through measurements of gas hold up, bubble diameters and the volumetric mass transfer coefficients. The design and analysis of such reactors thus requires the adoption of an approach that can comprehensively account for the various observed changes. This chapter proposes Computational Fluid Dynamics as an approach fit for this purpose. Key considerations, successes and challenges of this approach are highlighted and discussed based on a review of previously published case studies.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/77792",risUrl:"/chapter/ris/77792",signatures:"Godfrey Kabungo Gakingo and Tobias Muller Louw",book:{id:"10687",type:"book",title:"Energy Efficiency",subtitle:null,fullTitle:"Energy Efficiency",slug:null,publishedDate:null,bookSignature:"Dr. Muhammad Wakil Shahzad, Prof. Muhammad Sultan, Dr. Laurent Dala, Prof. Ben Bin Xu and Dr. Yinzhu Jiang",coverURL:"https://cdn.intechopen.com/books/images_new/10687.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83969-828-6",printIsbn:"978-1-83969-827-9",pdfIsbn:"978-1-83969-829-3",isAvailableForWebshopOrdering:!0,editors:[{id:"174208",title:"Dr.",name:"Muhammad Wakil",middleName:null,surname:"Shahzad",slug:"muhammad-wakil-shahzad",fullName:"Muhammad Wakil Shahzad"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Considerations in gas–liquid–liquid systems",level:"1"},{id:"sec_2_2",title:"2.1 Changes in fluid properties",level:"2"},{id:"sec_3_2",title:"2.2 Changes in mass transfer properties and pathways",level:"2"},{id:"sec_4_2",title:"2.3 Changes in reactor hydrodynamics",level:"2"},{id:"sec_6",title:"3. Computational fluid dynamics for gas–liquid–liquid reactors",level:"1"},{id:"sec_6_2",title:"3.1 Modelling frameworks for hydrodynamics",level:"2"},{id:"sec_7_2",title:"3.2 Modelling frameworks for mass transfer",level:"2"},{id:"sec_9",title:"4. Case studies",level:"1"},{id:"sec_10",title:"5. Areas requiring further investigation",level:"1"},{id:"sec_11",title:"Acknowledgments",level:"1"},{id:"sec_13",title:"Nomenclature",level:"1"}],chapterReferences:[{id:"B1",body:'Kasat GR, Pandit AB. Review on mixing characteristics in solid-liquid and solid-liquid-gas reactor vessels. Canadian Journal of Chemical Engineering. 2005;83:618–43. 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DOI: 10.1002/jctb.6625.'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Godfrey Kabungo Gakingo",address:null,affiliation:'
Department of Process Engineering, Stellenbosch University, Stellenbosch, South Africa
Department of Process Engineering, Stellenbosch University, Stellenbosch, South Africa
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Our journals are currently in their launching issue. They will be applied to all relevant indexes as soon as they are eligible. These include (but are not limited to): Web of Science, Scopus, PubMed, MEDLINE, Database of Open Access Journals (DOAJ), Google Scholar and Inspec.
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IntechOpen books are indexed by the following abstracting and indexing services:
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BKCI is a part of Web of Science Core Collection (WoSCC) and the world’s leading citation index with multidisciplinary content from the top tier international and regional journals, conference proceedings, and books. The Book Citation Index includes over 104,500 editorially selected books, with 10,000 new books added each year. Containing more than 53.2 million cited references, coverage dates back from 2005 to present. The Book Citation Index is multidisciplinary, covering disciplines across the sciences, social sciences, and arts & humanities.
Produced by the Web Of Science group, BIOSIS Previews research database provides researchers with the most current sources of life sciences information, including journals, conferences, patents, books, review articles, and more. Researchers can also access multidisciplinary coverage via specialized indexing such as MeSH disease terms, CAS registry numbers, Sequence Databank Numbers and Major Concepts.
Produced by the Web Of Science group, Zoological Record is the world’s oldest continuing database of animal biology. It is considered the world’s leading taxonomic reference, and with coverage back to 1864, has long acted as the world’s unofficial register of animal names. The broad scope of coverage ranges from biodiversity and the environment to taxonomy and veterinary sciences.
Provides a simple way to search broadly for scholarly literature. Includes peer-reviewed papers, theses, books, abstracts and articles, from academic publishers, professsional societies, preprint repositories, universities and other scholarly organizations. Google Scholar sorts articles by weighing the full text of each article, the author, the publication in which the article appears, and how often the article has been cited in other scholarly literature, so that the most relevant results are returned on the first page.
Microsoft Academic is a project exploring how to assist human conducting scientific research by leveraging machine’s cognitive power in memory, computation, sensing, attention, and endurance. Re-launched in 2016, the tool features an entirely new data structure and search engine using semantic search technologies. The Academic Knowledge API offers information retrieval from the underlying database using REST endpoints for advanced research purposes.
The national library of the United Kingdom includes 150 million manuscripts, maps, newspapers, magazines, prints and drawings, music scores, and patents. Online catalogues, information and exhibitions can be found on its website. The library operates the world's largest document delivery service, providing millions of items a year to national and international customers.
The digital NSK portal is the central gathering place for the digital collections of the National and University Library (NSK) in Croatia. It was established in 2016 to provide access to the Library’s digital and digitized material collections regardless of storage location. The digital NSK portal enables a unified search of digitized material from the NSK Special Collections - books, visual material, maps and music material. From the end of 2019, all thematic portals are available independently: Digital Books, Digitized Manuscripts, Digitized Visual Materials, Digital Music Materials and Digitized Cartographic Materials (established in 2017). Currently available only in Croatian.
The official DOI (digital object identifier) link registration agency for scholarly and professional publications. Crossref operates a cross-publisher citation linking system that allows a researcher to click on a reference citation on one publisher’s platform and link directly to the cited content on another publisher’s platform, subject to the target publisher’s access control practices. This citation-linking network covers millions of articles and other content items from several hundred scholarly and professional publishers.
Dimensions is a next-generation linked research information system that makes it easier to find and access the most relevant information, analyze the academic and broader outcomes of research, and gather insights to inform future strategy. Dimensions delivers an array of search and discovery, analytical, and research management tools, all in a single platform. Developed in collaboration with over 100 leading research organizations around the world, it brings together over 128 million publications, grants, policy, data and metrics for the first time, enabling users to explore over 4 billion connections between them.
The primary aim of DOAB (Directory of Open Access Books) is to increase discoverability of Open Access books. Metadata will be harvestable in order to maximize dissemination, visibility and impact. Aggregators can integrate the records in their commercial services and libraries can integrate the directory into their online catalogues, helping scholars and students to discover the books.
OAPEN is dedicated to open access, peer-reviewed books. OAPEN operates two platforms, the OAPEN Library (www.oapen.org), a central repository for hosting and disseminating OA books, and the Directory of Open Access Books (DOAB, www.doabooks.org), a discovery service for OA books.
OpenAIRE aims at promoting and implementing the directives of the European Commission (EC) and the European Research Council on the promotion and funding of science and research. OpenAIRE supports the Open Access Mandate and the Open Research Data Pilot developed as part of the Horizon 2020 projects.
An integrated information service combining reference databases, subscription management, online journals, books and linking services. Widely used by libraries, schools, government institutions, medical institutions, corporations and others.
SFX® link resolver gives patrons and librarians a wealth of features that optimize management of and access to resources. It provides patrons with a direct route to electronic full-text records through OpenURL linking, delivers alternative links for further resource discovery, access to journals, and more. Released in 2001 as the first OpenURL resolver, SFX is continuously enhanced to support the newest industry developments and meet the evolving needs of customers. The records include a mix of scholarly material – primarily articles and e-books – but also conference proceedings, newspaper articles, and more.
A non-profit, membership, computer library service and research organization dedicated to the public purposes of furthering access to the world's information and reducing information costs. More than 41,555 libraries in 112 countries and territories around the world use OCLC services to locate, acquire, catalogue, lend and preserve library materials.
The world’s largest collection of open access research papers. CORE's mission is to aggregate all open access research outputs from repositories and journals worldwide and make them available to the public. In this way CORE facilitates free unrestricted access to research for all.
Since 2002, Research4Life has provided researchers at more than 10,500 institutions in over 125 lower and middle-income countries with free or low-cost online access to up 151,000 leading journals and books in the fields of health, agriculture, environment, applied sciences and legal information. There are five programs through which users can access content: Research for Health (Hinari), Research in Agriculture (AGORA), Research in the Environment (OARE), Research for Development and Innovation (ARDI) and Research for Global Justice (GOALI).
Perlego is a digital online library focusing on the delivery of academic, professional and non-fiction eBooks. It is a subscription-based service that offers users unlimited access to these texts for the duration of their subscription, however IntechOpen content integrated on the platform will always be available for free. They have been billed as “the Spotify for Textbooks” by the Evening Standard. Perlego is based in London but is available to users worldwide.
MyScienceWork provides a suite of data-driven solutions for research institutions, scientific publishers and private-sector R&D companies. MyScienceWork's comprehensive database includes more than 90 million scientific publications and 12 million patents.
CNKI (China National Knowledge Infrastructure) is a key national information construction project under the lead of Tsinghua University, and supported by PRC Ministry of Education, PRC Ministry of Science, Propaganda Department of the Communist Party of China and PRC General Administration of Press and Publication. CNKI has built a comprehensive China Integrated Knowledge Resources System, including journals, doctoral dissertations, masters' theses, proceedings, newspapers, yearbooks, statistical yearbooks, ebooks, patents, standards and so on. CNKI keeps integrating new contents and developing new products in 2 aspects: full-text academic resources, software on digitization and knowledge management. Began with academic journals, CNKI has become the largest and mostly-used academic online library in China.
As one of the largest digital content platform in China,independently developed by CNPIEC, CNPeReading positions herself as “One Platform,Vast Content, Global Services”. Through their new cooperation model and service philosophy, CNPeReading provides integrated promotion and marketing solutionsfor upstream publishers, one-stop, triune, recommendation, online reading and management servicesfor downstream institutions & libraries.
ERIC (Education Resources Information Center), sponsored by the Institute of Education Sciences (IES) of the U.S. Department of Education, provides access to education literature to support the use of educational research and information to improve practice in learning, teaching, educational decision-making, and research. The ERIC website is available to the public for searching more than one million citations going back to 1966.
The ACM Digital Library is a research, discovery and networking platform containing: The Full-Text Collection of all ACM publications, including journals, conference proceedings, technical magazines, newsletters and books. A collection of curated and hosted full-text publications from select publishers.
BASE (Bielefeld Academic Search Engine) is one of the world's most voluminous search sengines especially for academic web resources, e.g. journal articles, preprints, digital collections, images / videos or research data. BASE facilitates effective and targeted searches and retrieves high quality, academically relevant results. Other than search engines like Google or Bing BASE searches the deep web as well. The sources which are included in BASE are intellectually selected (by people from the BASE team) and reviewed. That's why data garbage and spam do not occur.
Zentralblatt MATH (zbMATH) is the world’s most comprehensive and longest-running abstracting and reviewing service in pure and applied mathematics. It is edited by the European Mathematical Society (EMS), the Heidelberg Academy of Sciences and Humanities and FIZ Karlsruhe. zbMATH provides easy access to bibliographic data, reviews and abstracts from all areas of pure mathematics as well as applications, in particular to natural sciences, computer science, economics and engineering. It also covers history and philosophy of mathematics and university education. All entries are classified according to the Mathematics Subject Classification Scheme (MSC 2020) and are equipped with keywords in order to characterize their particular content.
IDEAS is the largest bibliographic database dedicated to Economics and available freely on the Internet. Based on RePEc, it indexes over 3,100,000 items of research, including over 2,900,000 that can be downloaded in full text. RePEc (Research Papers in Economics) is a large volunteer effort to enhance the free dissemination of research in Economics which includes bibliographic metadata from over 2,000 participating archives, including all the major publishers and research outlets. IDEAS is just one of several services that use RePEc data.
As the authoritative source for chemical names, structures and CAS Registry Numbers®, the CAS substance collection, CAS REGISTRY®, serves as a universal standard for chemists worldwide. Covering advances in chemistry and related sciences over the last 150 years, the CAS content collection empowers researchers, business leaders, and information professionals around the world with immediate access to the reliable information they need to fuel innovation.
BKCI is a part of Web of Science Core Collection (WoSCC) and the world’s leading citation index with multidisciplinary content from the top tier international and regional journals, conference proceedings, and books. The Book Citation Index includes over 104,500 editorially selected books, with 10,000 new books added each year. Containing more than 53.2 million cited references, coverage dates back from 2005 to present. The Book Citation Index is multidisciplinary, covering disciplines across the sciences, social sciences, and arts & humanities.
Produced by the Web Of Science group, BIOSIS Previews research database provides researchers with the most current sources of life sciences information, including journals, conferences, patents, books, review articles, and more. Researchers can also access multidisciplinary coverage via specialized indexing such as MeSH disease terms, CAS registry numbers, Sequence Databank Numbers and Major Concepts.
Produced by the Web Of Science group, Zoological Record is the world’s oldest continuing database of animal biology. It is considered the world’s leading taxonomic reference, and with coverage back to 1864, has long acted as the world’s unofficial register of animal names. The broad scope of coverage ranges from biodiversity and the environment to taxonomy and veterinary sciences.
Provides a simple way to search broadly for scholarly literature. Includes peer-reviewed papers, theses, books, abstracts and articles, from academic publishers, professsional societies, preprint repositories, universities and other scholarly organizations. Google Scholar sorts articles by weighing the full text of each article, the author, the publication in which the article appears, and how often the article has been cited in other scholarly literature, so that the most relevant results are returned on the first page.
Microsoft Academic is a project exploring how to assist human conducting scientific research by leveraging machine’s cognitive power in memory, computation, sensing, attention, and endurance. Re-launched in 2016, the tool features an entirely new data structure and search engine using semantic search technologies. The Academic Knowledge API offers information retrieval from the underlying database using REST endpoints for advanced research purposes.
The national library of the United Kingdom includes 150 million manuscripts, maps, newspapers, magazines, prints and drawings, music scores, and patents. Online catalogues, information and exhibitions can be found on its website. The library operates the world's largest document delivery service, providing millions of items a year to national and international customers.
The digital NSK portal is the central gathering place for the digital collections of the National and University Library (NSK) in Croatia. It was established in 2016 to provide access to the Library’s digital and digitized material collections regardless of storage location. The digital NSK portal enables a unified search of digitized material from the NSK Special Collections - books, visual material, maps and music material. From the end of 2019, all thematic portals are available independently: Digital Books, Digitized Manuscripts, Digitized Visual Materials, Digital Music Materials and Digitized Cartographic Materials (established in 2017). Currently available only in Croatian.
The official DOI (digital object identifier) link registration agency for scholarly and professional publications. Crossref operates a cross-publisher citation linking system that allows a researcher to click on a reference citation on one publisher’s platform and link directly to the cited content on another publisher’s platform, subject to the target publisher’s access control practices. This citation-linking network covers millions of articles and other content items from several hundred scholarly and professional publishers.
Dimensions is a next-generation linked research information system that makes it easier to find and access the most relevant information, analyze the academic and broader outcomes of research, and gather insights to inform future strategy. Dimensions delivers an array of search and discovery, analytical, and research management tools, all in a single platform. Developed in collaboration with over 100 leading research organizations around the world, it brings together over 128 million publications, grants, policy, data and metrics for the first time, enabling users to explore over 4 billion connections between them.
The primary aim of DOAB (Directory of Open Access Books) is to increase discoverability of Open Access books. Metadata will be harvestable in order to maximize dissemination, visibility and impact. Aggregators can integrate the records in their commercial services and libraries can integrate the directory into their online catalogues, helping scholars and students to discover the books.
OAPEN is dedicated to open access, peer-reviewed books. OAPEN operates two platforms, the OAPEN Library (www.oapen.org), a central repository for hosting and disseminating OA books, and the Directory of Open Access Books (DOAB, www.doabooks.org), a discovery service for OA books.
OpenAIRE aims at promoting and implementing the directives of the European Commission (EC) and the European Research Council on the promotion and funding of science and research. OpenAIRE supports the Open Access Mandate and the Open Research Data Pilot developed as part of the Horizon 2020 projects.
An integrated information service combining reference databases, subscription management, online journals, books and linking services. Widely used by libraries, schools, government institutions, medical institutions, corporations and others.
SFX® link resolver gives patrons and librarians a wealth of features that optimize management of and access to resources. It provides patrons with a direct route to electronic full-text records through OpenURL linking, delivers alternative links for further resource discovery, access to journals, and more. Released in 2001 as the first OpenURL resolver, SFX is continuously enhanced to support the newest industry developments and meet the evolving needs of customers. The records include a mix of scholarly material – primarily articles and e-books – but also conference proceedings, newspaper articles, and more.
A non-profit, membership, computer library service and research organization dedicated to the public purposes of furthering access to the world's information and reducing information costs. More than 41,555 libraries in 112 countries and territories around the world use OCLC services to locate, acquire, catalogue, lend and preserve library materials.
The world’s largest collection of open access research papers. CORE's mission is to aggregate all open access research outputs from repositories and journals worldwide and make them available to the public. In this way CORE facilitates free unrestricted access to research for all.
Since 2002, Research4Life has provided researchers at more than 10,500 institutions in over 125 lower and middle-income countries with free or low-cost online access to up 151,000 leading journals and books in the fields of health, agriculture, environment, applied sciences and legal information. There are five programs through which users can access content: Research for Health (Hinari), Research in Agriculture (AGORA), Research in the Environment (OARE), Research for Development and Innovation (ARDI) and Research for Global Justice (GOALI).
Perlego is a digital online library focusing on the delivery of academic, professional and non-fiction eBooks. It is a subscription-based service that offers users unlimited access to these texts for the duration of their subscription, however IntechOpen content integrated on the platform will always be available for free. They have been billed as “the Spotify for Textbooks” by the Evening Standard. Perlego is based in London but is available to users worldwide.
MyScienceWork provides a suite of data-driven solutions for research institutions, scientific publishers and private-sector R&D companies. MyScienceWork's comprehensive database includes more than 90 million scientific publications and 12 million patents.
CNKI (China National Knowledge Infrastructure) is a key national information construction project under the lead of Tsinghua University, and supported by PRC Ministry of Education, PRC Ministry of Science, Propaganda Department of the Communist Party of China and PRC General Administration of Press and Publication. CNKI has built a comprehensive China Integrated Knowledge Resources System, including journals, doctoral dissertations, masters' theses, proceedings, newspapers, yearbooks, statistical yearbooks, ebooks, patents, standards and so on. CNKI keeps integrating new contents and developing new products in 2 aspects: full-text academic resources, software on digitization and knowledge management. Began with academic journals, CNKI has become the largest and mostly-used academic online library in China.
As one of the largest digital content platform in China,independently developed by CNPIEC, CNPeReading positions herself as “One Platform,Vast Content, Global Services”. Through their new cooperation model and service philosophy, CNPeReading provides integrated promotion and marketing solutionsfor upstream publishers, one-stop, triune, recommendation, online reading and management servicesfor downstream institutions & libraries.
ERIC (Education Resources Information Center), sponsored by the Institute of Education Sciences (IES) of the U.S. Department of Education, provides access to education literature to support the use of educational research and information to improve practice in learning, teaching, educational decision-making, and research. The ERIC website is available to the public for searching more than one million citations going back to 1966.
The ACM Digital Library is a research, discovery and networking platform containing: The Full-Text Collection of all ACM publications, including journals, conference proceedings, technical magazines, newsletters and books. A collection of curated and hosted full-text publications from select publishers.
BASE (Bielefeld Academic Search Engine) is one of the world's most voluminous search sengines especially for academic web resources, e.g. journal articles, preprints, digital collections, images / videos or research data. BASE facilitates effective and targeted searches and retrieves high quality, academically relevant results. Other than search engines like Google or Bing BASE searches the deep web as well. The sources which are included in BASE are intellectually selected (by people from the BASE team) and reviewed. That's why data garbage and spam do not occur.
Zentralblatt MATH (zbMATH) is the world’s most comprehensive and longest-running abstracting and reviewing service in pure and applied mathematics. It is edited by the European Mathematical Society (EMS), the Heidelberg Academy of Sciences and Humanities and FIZ Karlsruhe. zbMATH provides easy access to bibliographic data, reviews and abstracts from all areas of pure mathematics as well as applications, in particular to natural sciences, computer science, economics and engineering. It also covers history and philosophy of mathematics and university education. All entries are classified according to the Mathematics Subject Classification Scheme (MSC 2020) and are equipped with keywords in order to characterize their particular content.
IDEAS is the largest bibliographic database dedicated to Economics and available freely on the Internet. Based on RePEc, it indexes over 3,100,000 items of research, including over 2,900,000 that can be downloaded in full text. RePEc (Research Papers in Economics) is a large volunteer effort to enhance the free dissemination of research in Economics which includes bibliographic metadata from over 2,000 participating archives, including all the major publishers and research outlets. IDEAS is just one of several services that use RePEc data.
As the authoritative source for chemical names, structures and CAS Registry Numbers®, the CAS substance collection, CAS REGISTRY®, serves as a universal standard for chemists worldwide. Covering advances in chemistry and related sciences over the last 150 years, the CAS content collection empowers researchers, business leaders, and information professionals around the world with immediate access to the reliable information they need to fuel innovation.
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\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
\r\n
\r\n\t
\r\n
\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"15",type:"subseries",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. 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