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Process Intensification and Miniaturization of Chemical and Biochemical Processes
Anton A. Kiss, Edwin Zondervan, Richard Lakerveld, Leyla Özkan (Eds.) Proceedings of the 29th European Symposium on Computer Aided Process Engineering June 16th to 19th , 2019, Eindhoven, The Netherlands. © 2019 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/B978-0-12-818634-3.50301-5
Process Intensification and Miniaturization of Chemical and Biochemical Processes Filip Strniša, Tomaž Urbiˇc, Polona Žnidaršiˇc-Plazl and Igor Plazl Faculty of Chemistry and Chemical Technology, University of Ljubljana, Veˇcna pot 113, SI-1001 Ljubljana, Slovenia [email protected]
Abstract In this work, the microscale (bio)process development based on scale-up/numbering-up concept in combination with model-based optimization is presented. The main features of microscale systems are reflected in fluid dynamics, therefore the understanding of fundamental mechanisms involved in fluid flow characteristics at the micro scale is essential since their behaviour affects the transport phenomena and microfluidic applications. Theoretical description of transport phenomena and the kinetics at the micro scale is discussed and illustrated on the cases of a lattice Boltzmann simulations for flow distribution in the packed bed microreactor “between two-plates” and the biocatalytic enzyme surface reaction. Keywords: process intensification, miniaturization, model-based design
1. Introduction Process intensification has been considered important for the future of (bio)chemical engineering for a while now. Stankiewicz and Moulijn (2000) defined process intensification as the development of novel and sustainable equipment that compared to the existing state-of-the-art, produces dramatic process improvements related to equipment sizes, waste production, and other factors. The application of microreactor technology in (bio)chemical processes meets these criteria, with the most obvious one being the equipment size reduction. However, besides spatial benefits, microreactors also provide enhanced heat and mass transfer, safety, environmental impact, and others (Pohar and Plazl, 2009). A key advantage that this technology brings is the precise process control that comes with it. This allows for repeatable conditions within the reactor system which leads to improved yields and product quality at a more consistent rate compared to the batch procedures. With the listed advantages there is no doubt that process intensification through the application of microreactor technology holds the potential to revolutionize (bio)chemical synthesis, but specific suggestions for possible replacement of established industrial processes are scarcely encountered. A number of highly innovative and systematic approaches, protocols, tools, and strategies are however currently being developed in both — industry and academia, all to minimize the gap between research and industry, and to enable a smooth transfer of lab-on-a-chip to the industrial scale. To deal with these challenges it is necessary to advance the field from (bio)catalyst discovery to (bio)catalytic microprocess design. Not only will this require a new level of understanding of the underlying reaction mechanisms and transport phenomena at the microscale, but also the
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development of relevant computational tools. The quest for high-performance manufacturing technology places the combination of biocatalysis and microscale technology as a key green engineering method for process development and production (Wohlgemuth et al., 2015). The aim of this work is to present the microscale (bio)process development based on scale-up/numbering-up concept in combination with model-based optimization. As microscale systems are flow-based it is essential to understand the fundamental mechanisms involved in fluid flow characteristics at the micro scale, because they affect the transport phenomena and microfluidic applications. Theoretical description of transport phenomena and the kinetics at the micro scale is discussed and illustrated on the cases of lattice Boltzmann simulations for flow distribution in packed bed microreactors “betweentwo-plates” and the biocatalytic enzyme surface reaction.
2. Model-based design 2.1. Macrosopic models A theoretical description of transport phenomena and kinetics for different chemical and biochemical processes in the microfluidic devices have to be developed using the bases of continuum theory. The following set of partial differential equations regarding momentum and mass conservation (Eqs. 1, 2), heat conservation (Eq. 3), and species conservation (Eq. 4) have to be solved to describe the convection-diffusion dynamics in all three spatial directions and to depict the governing transport characteristics of processes in microfluidics:
∂v +v · ∇v + ∇pk − ν∇2v = 0 ∂t ∇ ·v = 0 ∂ (ρh) + ∇ · (ρhv) − ∇ · [λ ∇T ] − Sh = 0 ∂t ∂ ci +v · ∇ci − Di ∇2 ci = 0 ∂t
(1) (2) (3) (4)
where v is the velocity vector (m s-1 ), pk is the kinematic pressure (m2 s-1 ) and ν is the kinematic viscosity (m2 s-1 ) — Eqs. 1, 2; ρh is the thermal energy (W m-3 ), λ is the thermal conductivity (W m-2 K-1 ), T is the temperature (K) and Sh is the volumetric heat source (W m-3 ) — Eq. 3; ci is the concentration of the solute i, Di is the diffusion coefficient of the species i (W m-2 ) and v is the velocity vector obtained from Eqs. 1 and 2 — Eq. 4. 2.2. Mesoscopic models Lattice Boltzmann methods are computational methods, that were primarily developed to solve fluid-dynamic problems, but can also be used among other to simulate heat and mass transport (Succi, 2015). The core of the method is the lattice Boltzmann equation, which is a special form of the Boltzmann equation, discretized in both — space and time. This gives the methods a base in the statistical mechanics and therefore steers them away
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from the “traditional” continuum-based methods, which are usually applied in chemical engineering process simulation. The lattice Boltzmann equation reads as follows:
fa (x +ea Δt,t + Δt) − fa (x,t) = Ωa (u, ρ, τ)
(5)
f is a discrete distribution function, which represents a packet of particles with statistically the same momentum in direction a, which is represented by a set of basic lattice velocities e. x is the positional vector and t is time. Ω is the collision operator, and it can take on different forms, but they essentially all depend on the local flow velocity and fluid density u, and ρ respectively. τ is the relaxation time, which is directly related to species’ physical properties such as kinematic viscosity or molecular diffusivity. 2.3. Model verification and validation Microfluidic devices have justified the high expectations and successfully demonstrated their advantages for intensification of chemical and biochemical processes in many different research areas and fields. Schematic representation of some typical microfluidic devices, known as enzymatic microreactors is given in Figure 1 (a-g). On these schemes of different microfluidic systems, one can observe the most typical enzyme (or cell) immobilization techniques as well as the most typical flow patterns of two-phase flows: the parallel and droplet flow regime, which allow for efficient performance of various processes from chemical reaction, enzyme catalyzed reaction with immobilized enzyme or cells to separation, extraction, and purification. In order to transfer these systems, successfully developed and tested on the laboratory scale, to the industrial environment and to increase the productivity from microliter scale to bigger scales at the same process efficiency and minimized costs, the optimized scale-up/numbering-up concepts have to be implemented, where the model-based design plays a key role. For this purpose, only pre-verified and validated macroscopic and lower-lever mathematical models can be used. Only verified and especially validated models bring about the understanding of basic principles and mechanisms taking place in chemical and biochemical processes at the microscale. The model equations are usually solved numerically by writing them into computer code. Once the code is complete and error free, the model needs to be verified. In this process the model is tested whether it gives sensible results, which do not necessarily need to be correct (Plazl and Lakner, 2010). The correctness of the model’s predictions is put to the test during model validation. Models can be validated either by comparing them to other already valid models or by testing them against experimental observations. Validating via experimental methods can be done in different ways, such as e. g. online validation, and inline validation. Common to all is that theoretical predictions and simulations matched to experimental measurements without any fitting procedures. In our previous work, we showed that macroscopic mathematical models can reliably describe various complex processes in microfluidic devices. In the case of non-homogeneous system with parallel flow pattern the steroid extraction in a microchannel system was studied theoretically and experimentally. In order to analyze experimental data and to forecast microreactor performance, a three-dimensional mathematical model with convection and diffusion terms was developed considering the velocity profile for laminar flow of two parallel phases in
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Figure 1: Schematic representation of some typical homogeneous, non-homogeneous and heterogeneous microfluidics systems: a) immobilized biocatalyst on the inner walls of microreactor with integrated down-stream processes; b) immobilized biocatalyst on the inner surface and pillars to increase surface area and biocatalyst loading; c) free enzyme in a one-phase laminar flow; d) free enzyme in a two-phase parallel flow; f) free enzyme in a droplet flow; g) packed-bed microreactor with immobilized enzymes in a spherical particles (i) and gel film (ii) with scale-up in one dimension and numbering-up of microreactor systems “between two-plates”.
a microchannel at steady-state conditions. Very good agreement between model calculations and experimental data was achieved without any fitting procedure (Žnidaršiˇc Plazl and Plazl, 2007). Online oxygen measurements inside a microreactor with modelling of transport phenomena and enzyme catalyzed oxidation reaction was performed for online model validation of the homogeneous system with biochemical reaction. Continuum based mathematical models, with a full 3D description of transport phenomena, incorporating convection, diffusion and enzymatic reaction terms along with the parabolic velocity profile in a microchannel was developed to simulate the concentration of dissolved oxygen inside
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the microchannels, to assess the required model complexity for achieving precise results and to depict the governing transport characteristics at the microscale (Ungerböck et al., 2013). Droplet-based liquid-liquid extraction in a microchannel was studied, both theoretically and experimentally to online validate macroscopic models for complex two-phase droplet flow microfluidics. The finite elements method, as the most common macroscale simulation technique, was used to solve the set of partial differential equations regarding conservation of moment, mass and solute concentration in a two-domain system coupled by interfacial surface of droplet-based flow pattern (Eqs. 1, 2, 4). The model was numerically verified and validated online by following the concentrations of a solute in two phases within the microchannel by means of a thermal lens microscopic (TLM) technique coupled to a microfluidic system, which gave results of high spatial and temporal resolution. Very good agreement between model calculations and online experimental data was achieved without applying any fitting procedure to the model parameters (Lubej et al., 2015).
Figure 2: A snapshot of the pulse test simulation. Domain size = 2048 × 256 × 32 lattice units3 , Reynolds number = 0.95, Schmidt number = 1000. The flow is from west to east.
(a) Narrower, empty inlet.
(b) Narrower, filled inlet.
(c) Narrower, pillars.
(d) Wider, empty inlet.
(e) Wider, filled inlet.
(f) Wider, pillars.
Figure 3: Different scenarios for inlet flow distribution in one-dimensional scale-up. “Empty” channels do not utilize any obstacles to distribute the flow, “filled” channels use the same particles in the flow distribution zone as they do throughout the rest of the channel, and “pillars” channels have pillars in the flow distribution zone. The tracer is colored red, and flows from west to east. The lattice Boltzmann method was applied to obtain the velocity field in a packed bed
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microbioreactor, and to simulate mass transport in a pulse test through it. First a computer program generated a randomly packed bed, then the flow field was calculated, and finally the mass transport of an inert tracer was simulated (Figure 2). The results were recorded and compared to experimentally collected data. In the experiments the channel was packed with enzyme-containing particles, and an inert tracer was injected through it via an HPLC-type 6-port valve. At the outlet an inline biosensor was recording the tracer concentration. The simulations and the experiments show a good agreement (Strniša et al., 2018). A validated lattice Boltzmann model could then be further used to simulate mass transport in a “near-perfect” scaled-up microchannel, where only one dimension of the channel is increased — its width (Baji´c et al., 2017). Similar simulations as above were constructed, where 3 scenarios were tested: empty space at inlet, inlet filled with biocatalytic particles, and pillars in the inlet. Unlike previously the inert tracer was supplied into the system continuously. The results (Figure 3) show that filling the inlet with particles does not create uniform flow conditions downstream, where as leaving the space empty or introducing pillars in it improves the downstream hydrodynamics. Similar simulations should be used further to determine the optimal inlet geometry, which will assure the optimal flow conditions in the microchannel.
3. Conclusions Several studies have shown the advantages of microfluidic systems. Here it was shown how computer modelling and simulation can (or rather should) work within the research and development of such systems to optimize their performance. We now have the knowledge and technologies for building them, and the (bio)chemical industries need to recognize the potential of process intensification and miniaturization, with the consideration of new modelling approaches, such as multiscale modelling.
References M. Baji´c, I. Plazl, R. Stloukal, P. Žnidaršiˇc Plazl, 2017. Development of a miniaturized packed bed reactor with ω-transaminase immobilized in lentikats® . Process Biochemistry 52, 63 – 72. M. Lubej, U. Novak, M. Liu, M. Martelanc, M. Franko, I. Plazl, 2015. Microfluidic droplet-based liquid-liquid extraction: online model validation. Lab on a Chip 15, 2233—2239. I. Plazl, M. Lakner, 2010. Modeling and finite difference numerical analysis of reaction-diffusion dynamics in a microreactor. Acta Chimica Slovenica 57 (1), 100—109. A. Pohar, I. Plazl, 2009. Process intensification through microreactor application. Chemical and Biochemical Engineering Quarterly 23 (4), 537–544. A. Stankiewicz, J. Moulijn, 2000. Process Intensification: Transforming Chemical Engineering. Chemical Engineering Progress 96, 22–34. F. Strniša, M. Baji´c, P. Panjan, I. Plazl, A. M. Sesay, P. Žnidaršiˇc Plazl, 2018. Characterization of an enzymatic packed-bed microreactor: Experiments and modeling. Chemical Engineering Journal 350, 541 – 550. S. Succi, 2015. Lattice boltzmann 2038. EPL (Europhysics Letters) 109 (5), 50001. B. Ungerböck, A. Pohar, T. Mayr, I. Plazl, 2013. Online oxygen measurements inside a microreactor with modeling of transport phenomena. Microfluidics and Nanofluidics 14, 565—574. P. Žnidaršiˇc Plazl, I. Plazl, 2007. Steroid extraction in a microchannel system: mathematical modelling and experiments. Lab on a Chip 7, 883—889. R. Wohlgemuth, I. Plazl, P. Žnidaršiˇc Plazl, K. V. Gernaey, J. M. Woodley, 2015. Microscale technology and biocatalytic processes: opportunities and challenges for synthesis. Trends in Biotechnology 33 (5), 302 – 314.
Process Intensification and Miniaturization of Chemical and Biochemical Processes Filip Strniša, Tomaž Urbiˇc, Polona Žnidaršiˇc-Plazl and Igor Plazl Faculty of Chemistry and Chemical Technology, University of Ljubljana, Veˇcna pot 113, SI-1001 Ljubljana, Slovenia [email protected]
Abstract In this work, the microscale (bio)process development based on scale-up/numbering-up concept in combination with model-based optimization is presented. The main features of microscale systems are reflected in fluid dynamics, therefore the understanding of fundamental mechanisms involved in fluid flow characteristics at the micro scale is essential since their behaviour affects the transport phenomena and microfluidic applications. Theoretical description of transport phenomena and the kinetics at the micro scale is discussed and illustrated on the cases of a lattice Boltzmann simulations for flow distribution in the packed bed microreactor “between two-plates” and the biocatalytic enzyme surface reaction. Keywords: process intensification, miniaturization, model-based design
1. Introduction Process intensification has been considered important for the future of (bio)chemical engineering for a while now. Stankiewicz and Moulijn (2000) defined process intensification as the development of novel and sustainable equipment that compared to the existing state-of-the-art, produces dramatic process improvements related to equipment sizes, waste production, and other factors. The application of microreactor technology in (bio)chemical processes meets these criteria, with the most obvious one being the equipment size reduction. However, besides spatial benefits, microreactors also provide enhanced heat and mass transfer, safety, environmental impact, and others (Pohar and Plazl, 2009). A key advantage that this technology brings is the precise process control that comes with it. This allows for repeatable conditions within the reactor system which leads to improved yields and product quality at a more consistent rate compared to the batch procedures. With the listed advantages there is no doubt that process intensification through the application of microreactor technology holds the potential to revolutionize (bio)chemical synthesis, but specific suggestions for possible replacement of established industrial processes are scarcely encountered. A number of highly innovative and systematic approaches, protocols, tools, and strategies are however currently being developed in both — industry and academia, all to minimize the gap between research and industry, and to enable a smooth transfer of lab-on-a-chip to the industrial scale. To deal with these challenges it is necessary to advance the field from (bio)catalyst discovery to (bio)catalytic microprocess design. Not only will this require a new level of understanding of the underlying reaction mechanisms and transport phenomena at the microscale, but also the
F. Strniša et al.
1802
development of relevant computational tools. The quest for high-performance manufacturing technology places the combination of biocatalysis and microscale technology as a key green engineering method for process development and production (Wohlgemuth et al., 2015). The aim of this work is to present the microscale (bio)process development based on scale-up/numbering-up concept in combination with model-based optimization. As microscale systems are flow-based it is essential to understand the fundamental mechanisms involved in fluid flow characteristics at the micro scale, because they affect the transport phenomena and microfluidic applications. Theoretical description of transport phenomena and the kinetics at the micro scale is discussed and illustrated on the cases of lattice Boltzmann simulations for flow distribution in packed bed microreactors “betweentwo-plates” and the biocatalytic enzyme surface reaction.
2. Model-based design 2.1. Macrosopic models A theoretical description of transport phenomena and kinetics for different chemical and biochemical processes in the microfluidic devices have to be developed using the bases of continuum theory. The following set of partial differential equations regarding momentum and mass conservation (Eqs. 1, 2), heat conservation (Eq. 3), and species conservation (Eq. 4) have to be solved to describe the convection-diffusion dynamics in all three spatial directions and to depict the governing transport characteristics of processes in microfluidics:
∂v +v · ∇v + ∇pk − ν∇2v = 0 ∂t ∇ ·v = 0 ∂ (ρh) + ∇ · (ρhv) − ∇ · [λ ∇T ] − Sh = 0 ∂t ∂ ci +v · ∇ci − Di ∇2 ci = 0 ∂t
(1) (2) (3) (4)
where v is the velocity vector (m s-1 ), pk is the kinematic pressure (m2 s-1 ) and ν is the kinematic viscosity (m2 s-1 ) — Eqs. 1, 2; ρh is the thermal energy (W m-3 ), λ is the thermal conductivity (W m-2 K-1 ), T is the temperature (K) and Sh is the volumetric heat source (W m-3 ) — Eq. 3; ci is the concentration of the solute i, Di is the diffusion coefficient of the species i (W m-2 ) and v is the velocity vector obtained from Eqs. 1 and 2 — Eq. 4. 2.2. Mesoscopic models Lattice Boltzmann methods are computational methods, that were primarily developed to solve fluid-dynamic problems, but can also be used among other to simulate heat and mass transport (Succi, 2015). The core of the method is the lattice Boltzmann equation, which is a special form of the Boltzmann equation, discretized in both — space and time. This gives the methods a base in the statistical mechanics and therefore steers them away
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from the “traditional” continuum-based methods, which are usually applied in chemical engineering process simulation. The lattice Boltzmann equation reads as follows:
fa (x +ea Δt,t + Δt) − fa (x,t) = Ωa (u, ρ, τ)
(5)
f is a discrete distribution function, which represents a packet of particles with statistically the same momentum in direction a, which is represented by a set of basic lattice velocities e. x is the positional vector and t is time. Ω is the collision operator, and it can take on different forms, but they essentially all depend on the local flow velocity and fluid density u, and ρ respectively. τ is the relaxation time, which is directly related to species’ physical properties such as kinematic viscosity or molecular diffusivity. 2.3. Model verification and validation Microfluidic devices have justified the high expectations and successfully demonstrated their advantages for intensification of chemical and biochemical processes in many different research areas and fields. Schematic representation of some typical microfluidic devices, known as enzymatic microreactors is given in Figure 1 (a-g). On these schemes of different microfluidic systems, one can observe the most typical enzyme (or cell) immobilization techniques as well as the most typical flow patterns of two-phase flows: the parallel and droplet flow regime, which allow for efficient performance of various processes from chemical reaction, enzyme catalyzed reaction with immobilized enzyme or cells to separation, extraction, and purification. In order to transfer these systems, successfully developed and tested on the laboratory scale, to the industrial environment and to increase the productivity from microliter scale to bigger scales at the same process efficiency and minimized costs, the optimized scale-up/numbering-up concepts have to be implemented, where the model-based design plays a key role. For this purpose, only pre-verified and validated macroscopic and lower-lever mathematical models can be used. Only verified and especially validated models bring about the understanding of basic principles and mechanisms taking place in chemical and biochemical processes at the microscale. The model equations are usually solved numerically by writing them into computer code. Once the code is complete and error free, the model needs to be verified. In this process the model is tested whether it gives sensible results, which do not necessarily need to be correct (Plazl and Lakner, 2010). The correctness of the model’s predictions is put to the test during model validation. Models can be validated either by comparing them to other already valid models or by testing them against experimental observations. Validating via experimental methods can be done in different ways, such as e. g. online validation, and inline validation. Common to all is that theoretical predictions and simulations matched to experimental measurements without any fitting procedures. In our previous work, we showed that macroscopic mathematical models can reliably describe various complex processes in microfluidic devices. In the case of non-homogeneous system with parallel flow pattern the steroid extraction in a microchannel system was studied theoretically and experimentally. In order to analyze experimental data and to forecast microreactor performance, a three-dimensional mathematical model with convection and diffusion terms was developed considering the velocity profile for laminar flow of two parallel phases in
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Figure 1: Schematic representation of some typical homogeneous, non-homogeneous and heterogeneous microfluidics systems: a) immobilized biocatalyst on the inner walls of microreactor with integrated down-stream processes; b) immobilized biocatalyst on the inner surface and pillars to increase surface area and biocatalyst loading; c) free enzyme in a one-phase laminar flow; d) free enzyme in a two-phase parallel flow; f) free enzyme in a droplet flow; g) packed-bed microreactor with immobilized enzymes in a spherical particles (i) and gel film (ii) with scale-up in one dimension and numbering-up of microreactor systems “between two-plates”.
a microchannel at steady-state conditions. Very good agreement between model calculations and experimental data was achieved without any fitting procedure (Žnidaršiˇc Plazl and Plazl, 2007). Online oxygen measurements inside a microreactor with modelling of transport phenomena and enzyme catalyzed oxidation reaction was performed for online model validation of the homogeneous system with biochemical reaction. Continuum based mathematical models, with a full 3D description of transport phenomena, incorporating convection, diffusion and enzymatic reaction terms along with the parabolic velocity profile in a microchannel was developed to simulate the concentration of dissolved oxygen inside
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the microchannels, to assess the required model complexity for achieving precise results and to depict the governing transport characteristics at the microscale (Ungerböck et al., 2013). Droplet-based liquid-liquid extraction in a microchannel was studied, both theoretically and experimentally to online validate macroscopic models for complex two-phase droplet flow microfluidics. The finite elements method, as the most common macroscale simulation technique, was used to solve the set of partial differential equations regarding conservation of moment, mass and solute concentration in a two-domain system coupled by interfacial surface of droplet-based flow pattern (Eqs. 1, 2, 4). The model was numerically verified and validated online by following the concentrations of a solute in two phases within the microchannel by means of a thermal lens microscopic (TLM) technique coupled to a microfluidic system, which gave results of high spatial and temporal resolution. Very good agreement between model calculations and online experimental data was achieved without applying any fitting procedure to the model parameters (Lubej et al., 2015).
Figure 2: A snapshot of the pulse test simulation. Domain size = 2048 × 256 × 32 lattice units3 , Reynolds number = 0.95, Schmidt number = 1000. The flow is from west to east.
(a) Narrower, empty inlet.
(b) Narrower, filled inlet.
(c) Narrower, pillars.
(d) Wider, empty inlet.
(e) Wider, filled inlet.
(f) Wider, pillars.
Figure 3: Different scenarios for inlet flow distribution in one-dimensional scale-up. “Empty” channels do not utilize any obstacles to distribute the flow, “filled” channels use the same particles in the flow distribution zone as they do throughout the rest of the channel, and “pillars” channels have pillars in the flow distribution zone. The tracer is colored red, and flows from west to east. The lattice Boltzmann method was applied to obtain the velocity field in a packed bed
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microbioreactor, and to simulate mass transport in a pulse test through it. First a computer program generated a randomly packed bed, then the flow field was calculated, and finally the mass transport of an inert tracer was simulated (Figure 2). The results were recorded and compared to experimentally collected data. In the experiments the channel was packed with enzyme-containing particles, and an inert tracer was injected through it via an HPLC-type 6-port valve. At the outlet an inline biosensor was recording the tracer concentration. The simulations and the experiments show a good agreement (Strniša et al., 2018). A validated lattice Boltzmann model could then be further used to simulate mass transport in a “near-perfect” scaled-up microchannel, where only one dimension of the channel is increased — its width (Baji´c et al., 2017). Similar simulations as above were constructed, where 3 scenarios were tested: empty space at inlet, inlet filled with biocatalytic particles, and pillars in the inlet. Unlike previously the inert tracer was supplied into the system continuously. The results (Figure 3) show that filling the inlet with particles does not create uniform flow conditions downstream, where as leaving the space empty or introducing pillars in it improves the downstream hydrodynamics. Similar simulations should be used further to determine the optimal inlet geometry, which will assure the optimal flow conditions in the microchannel.
3. Conclusions Several studies have shown the advantages of microfluidic systems. Here it was shown how computer modelling and simulation can (or rather should) work within the research and development of such systems to optimize their performance. We now have the knowledge and technologies for building them, and the (bio)chemical industries need to recognize the potential of process intensification and miniaturization, with the consideration of new modelling approaches, such as multiscale modelling.
References M. Baji´c, I. Plazl, R. Stloukal, P. Žnidaršiˇc Plazl, 2017. Development of a miniaturized packed bed reactor with ω-transaminase immobilized in lentikats® . Process Biochemistry 52, 63 – 72. M. Lubej, U. Novak, M. Liu, M. Martelanc, M. Franko, I. Plazl, 2015. Microfluidic droplet-based liquid-liquid extraction: online model validation. Lab on a Chip 15, 2233—2239. I. Plazl, M. Lakner, 2010. Modeling and finite difference numerical analysis of reaction-diffusion dynamics in a microreactor. Acta Chimica Slovenica 57 (1), 100—109. A. Pohar, I. Plazl, 2009. Process intensification through microreactor application. Chemical and Biochemical Engineering Quarterly 23 (4), 537–544. A. Stankiewicz, J. Moulijn, 2000. Process Intensification: Transforming Chemical Engineering. Chemical Engineering Progress 96, 22–34. F. Strniša, M. Baji´c, P. Panjan, I. Plazl, A. M. Sesay, P. Žnidaršiˇc Plazl, 2018. Characterization of an enzymatic packed-bed microreactor: Experiments and modeling. Chemical Engineering Journal 350, 541 – 550. S. Succi, 2015. Lattice boltzmann 2038. EPL (Europhysics Letters) 109 (5), 50001. B. Ungerböck, A. Pohar, T. Mayr, I. Plazl, 2013. Online oxygen measurements inside a microreactor with modeling of transport phenomena. Microfluidics and Nanofluidics 14, 565—574. P. Žnidaršiˇc Plazl, I. Plazl, 2007. Steroid extraction in a microchannel system: mathematical modelling and experiments. Lab on a Chip 7, 883—889. R. Wohlgemuth, I. Plazl, P. Žnidaršiˇc Plazl, K. V. Gernaey, J. M. Woodley, 2015. Microscale technology and biocatalytic processes: opportunities and challenges for synthesis. Trends in Biotechnology 33 (5), 302 – 314.