Energy Fuels 2010, 24, 4721–4727 Published on Web 03/30/2010
: DOI:10.1021/ef9012483
Efficient Biogas Production through Process Simulation† Andree Blesgen* and Volker C. Hass Institute for Environmental and Bio-Technology, University of Applied Sciences Bremen, Neustadtswall 27b, 28199 Bremen, Germany Received October 30, 2009. Revised Manuscript Received March 16, 2010
An interactive simulator of the process of anaerobic digestion was developed. It is based on four interacting submodels describing the biological, physicochemical, reactor, and plant subsystems of the overall process. The simulator was adapted to simulate a 10 L lab-scale reactor processing three different well-defined substrates as well as varying mixtures of these compounds. Because of its structured configuration, the simulator can be adapted to other types of biogas plants with respect to plant setup, reactor volume, and substrate composition. The biological and physicochemical submodels were implemented in the programming language FORTRAN; the reactor and plant submodels were implemented in an industrial process control and simulation program. The combined submodels result in an interactive process simulator that was equipped with various control loops, automations, data acquisition systems, and graphical user interfaces. The simulator can be used for the design, test, and optimization of automation strategies, industrial and academic education, and process optimization. Its application in these three fields can lead to increased biogas and energy yields as well as a stable and safe operation of biogas plants. composition and yield of a given substrate. Supposing complete substrate use, the maximum biogas yield can be calculated given that the elementary composition of the digested substrate is known.1 The advantage of such a stationary “model” is its simplicity. However, dynamic effects and varying process conditions (e.g., temperature, pH, etc.) cannot be considered. Thus, dynamic models of the process of anaerobic digestion have been created since the late 1960s.2 Whereas some models3-7 are of comparably basic nature with respect to the number of modeled state variables and process steps, other models simulate the biogas process almost entirely, including a wide variety of intermediate products.8-10 Another distinction can be drawn with respect to modeling techniques. A large group of models is mainly based on mass and energy balances as well as different growth and product
Introduction Every operator of a biogas plant aims at converting organic substrates into as much energy as possible. To achieve this, optimal process conditions are necessary. Therefore, an efficient operation of biogas plants hugely depends upon the quality of applied process control strategies. During anaerobic digestion, complex biological, chemical, and physical processes take place in a technical reactor system that is influenced by suitable process control strategies. These can be carried out either manually by plant personnel or automatically through a process control software. To develop optimal process control strategies, two factors have to be considered: (1) plant personnel have to be educated appropriately to perform the necessary control inputs, and (2) control and automation strategies must be adjusted, tested, and optimized during a wide variety of process states. Performing these tasks using a real biogas plant during normal operation is difficult because certain process states cannot be attained safely. In many branches of industry, interactive simulators are successfully used for these tasks. However, in the field of biogas production, process modeling is often limited to describing biological and chemical processes. Reactor and plant systems, as important subsystems for a comprehensive training simulator, are often omitted. In this paper, an interactive training simulator is described that models the biological, physicochemical, reactor, and plant subsystems. Its application can benefit in enhancing process efficiency with regard to process stability and energy yield.
(1) Buswell, A.; Mueller, H. Ind. Eng. Chem. 1952, 44, 550–552. (2) Andrews, J. J. Sanit. Eng. Div., Am. Soc. Civ. Eng. 1969, 95, 95– 116. (3) Andrews, J.; Graef, S. Dynamic modeling and simulation of the anaerobic digestion process. In Anaerobic Biological Treatment Processes; American Chemical Society: Washington, D.C., 1971; Advances in Chemistry Series, Vol. 105, Chapter 8, pp 126-162. (4) Buhr, H.; Andrews, J. Water Res. 1977, 11, 129–143. (5) Bolte, J.; Hill, D. Biol. Wastes 1990, 31, 275–289. (6) Jeyaseelan, S. Water Sci. Technol. 1997, 35, 185–191. (7) Vavilin, V.; Shchelkanov, M.; Rytov, S. Water Res. 2001, 36, 2405–2409. (8) Batstone, D.; Keller, J.; Angelidaki, I.; Kalyuzhnyi, S.; Pavlosthathis, S.; Rozzi, A.; Sanders, W.; Siegrist, H.; Vavilin, V. The IWA Anaerobic Digestion Model No. 1 (ADM1). Scientific and Technical Report No. 13, IWA Publishing, London, U.K., 2002. (9) Vavilin, V.; Vasiliev, V.; Ponomarev, A.; Rytow, S. Bioresour. Technol. 1994, 48, 1–8. (10) Vavilin, V.; Lokshina, L.; Rytov, S. Khimiya 2000, 41, 22–26. (11) Bernard, O.; Zakaria, H.-S.; Dochain, D.; Genovesi, A.; Styer, J.-P. Biotechnol. Bioeng. 2001, 75, 424–438. (12) Angelidaki, I.; Ellegaard, L.; Ahring, B. Biotechnol. Bioeng. 1999, 63, 363–372. (13) St€ otemann, S.; Ristow, N.; Wentzel, M.; Ekama, G. Characterization of sewage sludge with a mass balance based steady state model for anaerobic digestion. Proceedings of the 1st International Workshop on the IWA Anaerobic Digestion Model No. 1 (ADM1), Lyngby, Denmark, 2005; pp 177-184.
Modeling Anaerobic Digestion First attempts of modeling anaerobic digestion resulted in stationary models that tried to predict theoretical biogas † This paper has been designated for the Bioenergy and Green Engineering special section. *To whom correspondence should be addressed. E-mail: andree.blesgen@ hs-bremen.de.
r 2010 American Chemical Society
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: DOI:10.1021/ef9012483
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formation/degradation kinetics. A smaller number of models uses various mathematical methods and control engineering theories, such as generic algorithms, fuzzy logic, neural networks, or stochastic methods.15-19 A subgroup of models can be characterized by its specialization on particular substrates,20-22 reactor types,23-25and specific modeling purposes,26-29 respectively. Generally, one has to weigh high modeling complexity against the necessary effort for model parameterization. The more state variables are being modeled, the more parameters have to be identified (in order) to calibrate a model and adapt it to a real process. Model Development Model Requirements. During the process of biogas production, complex substrates are converted to two main products, methane and carbon dioxide, by a consortium of microorganisms in a number of process steps. Main intermediate products are volatile fatty acids (VFAs) that have an inhibiting effect on acidogenic bacteria. At the same time, the process can be limited by VFAs because they are further used by acetogenic and methanogenic bacteria. The three groups of bacteria that are involved in anaerobic digestion have different optima with respect to a variety of state variables. Bacterial growth, substrate conversion, and product formation rates mainly depend upon current concentrations of organic substrate and VFAs, pH, and temperature. Additionally, biological availability, degradability, and chemical composition of digested substrates play an integral role. The dynamics of biogas generation depend upon microbial processes, gas transport between the liquid and gaseous phase, as well as pH-dependent variations of carbon dioxide solubility. The biogas process is technically realized in various reactor configurations, including heating and mixing equipment, as well as sensors, pumps, valves, and other mountings that all have specific response characteristics. Furthermore, mixing and dilution effects influence process dynamics. Against this background, the model described in this paper should be able to simulate anaerobic digestion processes of various
Figure 1. Overall structure of the developed model.
substrates in batch, fed batch, and continuous mode. Modeled state variables (i.e., output variables that are available to the model user) should include methane and carbon dioxide concentrations, biogas production rate, pH, temperature in the reactor/medium, and pressure in the headspace of the reactor. Apart from all relevant biological, biochemical, and physicochemical processes, dynamics of sensors and actuators should be modeled. The model has to be able to simulate the mentioned state variables with adequate accuracy (i.e., deviation between simulation and experimental data less than 10%) while maintaining the mass balance. Computation of the model must be possible on conventional PC systems maintaining numerical stability and high computing speed. Model Structure. The overall model is structured into four submodels describing different aspects of the process (Figure 1). This structured configuration makes it possible to quickly and flexibly adapt and change parts of each submodel without necessitating the change of the whole model (e.g., implementation of new kinetic expressions or adaptation to changed plant setups). A comprehensive mathematical description including all model equations can be found in ref 30. Biological Submodel. The biological submodel is based on a model described by Bernard et al.11 It was structurally changed and adapted to process three single substrates with different and varying properties. The basic structure is shown in Figure 2. A complex substrate (i.e., a mixture of carbohydrates, proteins, and lipids) is degraded by acidogenic bacteria (X1) to produce VFAs. Byproducts of this first reaction are carbon dioxide (CO2/TIC) and new biomass. The intermediate product VFA is further degraded by methanogenic bacteria (X2) into methane (CH4), CO2, and new biomass. Both reactions consume water and produce some heat of reaction. Biological subprocesses were mathematically modeled with the aid of 13 single differential equations modeling biomass, substrate, and product concentrations. Furthermore, reaction kinetics (Monod type) and substrate uptake rates are calculated. Lastly, functions describing the inhibition of the biological process through sub-optimal temperature, pH, and VFA concentration were implemented. Physicochemical Submodel. The physicochemical submodel describes the pH in the reaction medium, the fractionation of inorganic carbon (TIC) into hydrogen carbonate (HCO3-), carbonic
(14) Siegrist, H.; Renggli, D.; Gujer, W. Water Sci. Technol. 1993, 27, 25–36. (15) Polit, M.; Estaben, M.; Labat, P. Eng. Appl. Artif. Intell. 2002, 15, 385–390. (16) Tay, J.-H.; Zhang, X. Water Res. 2001, 34, 2849–2860. (17) Tenno, R.; Uronen, P. Control Eng. Pract. 1995, 3, 793–804. (18) Ozkaya, B.; Demir, A.; Bilgili, M. Environ. Modell. Software 2007, 22, 815–822. (19) Chen, L.; Kiong Nguang, S.; Dong Chen, X.; Mei Li, X. Biochem. Eng. J. 2005, 22, 51–61. (20) Converti, A.; Del Borghi, A.; Arni, S.; Molinari, F. Chem. Eng. Technol. 1999, 22, 429–437. (21) Jian, T.; Zhang, X. Resour., Conserv. Recycl. 1999, 27, 145–149. (22) Spagni, A.; Giordano, A.; Cellamare, C.; Molinaro, S.; Farina, R. Modelling anaerobic treatment of distillery wastewaters using the Anaerobic Digestion Model No. 1. Proceedings of the 1st International Workshop on the IWA Anaerobic Digestion Model No. 1 (ADM1), Lyngby, Denmark, 2005; pp 203-204. (23) Klinghofer, K.; Schnitzhofer, W.; Rattay, F.; Bergmair, J. Modelling anaerobic dry fermentation of solid wastes in a continuous percolation reactor. Proceedings of the 1st International Workshop on the IWA Anaerobic Digestion Model No. 1 (ADM1), Lyngby, Denmark, 2005; pp 121-128. (24) Rahemann, H. Energy 2002, 27, 25–34. (25) Skiadas, I.; Gavala, H.; Lyberatos, G. Water Res. 2000, 34, 3725– 3736. (26) Bozinis, N.; Alexiou, I.; Pistikopoulos, E. Water Sci. Technol. 1996, 34, 383–392. (27) Beteaua, J.; Otton, V.; Hihn, J.; Delpech, F.; Cheruya, A. Biochem. Eng. J. 2005, 24, 255–267. (28) Kalyuzhnyi, S.; Fedorovich, V.; Lens, P.; Hulshoff Pol, L.; Lettinga, G. Biodegradation 1998, 9, 187–199. (29) Straub, A.; Conklin, A.; Ferguson, J.; Stensel, H. Use of the ADM1 to investigate the effects on mesophilic digester stability of acetoclastic methanogens population dynamics. Proceedings of the 1st International Workshop on the IWA Anaerobic Digestion Model No. 1 (ADM1), Lyngby, Denmark, 2005; pp 51-58.
(30) Blesgen, A. Ph.D. Thesis, Faculty of Chemistry and Biology, University of Bremen, Bremen, Germany, 2009.
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Figure 2. Structure of the biological submodel (CSi, concentrations of substrates; CXaci, concentration of acidogenic bacteria; CXmeth, concentration of methanogenic bacteria; CVFA, concentration of volatile fatty acids; Q_ Raci and Q_ Rmeth, heat of reaction produced by acidogenic and methanogenic bacteria, respectively; CTIC, concentration of inorganic carbon (CO2(aq) þ HCO3- þ CO32-); CCH4liq, concentration of methane in the liquid phase).
Figure 3. Concept of model implementation. Dependent upon specific submodel characteristics, implementation can be performed either externally in a DLL or directly in the process control software WinErs. GUIs, data acquisition systems, as well as control cycles and automations can be designed and programmed in WinErs to form a complete interactive simulator.
acid (CO32-), and carbon dioxide (CO2), partial pressure of carbon dioxide and methane in the liquid phase, and mass flows of these gases into the headspace of the reactor. pH is influenced by some of the initial substrate components (i.e., proteins), VFAs, and influent acid and alkali flows (pH control). pH calculation is based on a model described by Kuhnen31 and Meiwes.32 Reactor Submodel. In the reactor submodel, total influent and effluent flow rates of substrates, correctional agents, and other fluids are calculated. Liquid and gaseous volumes, concentrations of methane, carbon dioxide, air, and water vapor are computed, and medium, headspace, and heating bath temperatures are calculated. Plant Submodel. The plant submodel comprises all up- and downstream processes and plant components. The plant model can be adapted to simulate different plant configurations. Modeled aggregates include valves, pumps, tanks, conduits, and sensors. The behavior of these units accounts for the main part of this submodel. Measuring noise was added to the calculated state variables. Actuators were accurately modeled to be able to realistically design control loops and automations.
opportunities of a modern PCS. Using this PCS as the basis for the “virtual anaerobic digester”, it is possible to simulate the process in real time (or in accelerated mode) and realize different controllers and automations. Model Parameterization and Verification. The developed submodels were parameterized and verified using well-defined substrates. To simulate the three fractions of carbohydrates, proteins, and lipids, aqueous solutions of sucrose (C12H22O11), gelatin (C3.87H8.08O2.36N1.18S0.01), and rapeseed oil (C17.89H33.23O1.99; with lecithin as the emulsifier) were used (elementary composition was calculated from Hollmach34 and Kaufmann35). While model parameterization was performed using these solutions individually, model verification was carried out using experimental data acquired by digesting a mixture of the aforementioned substrates. As Figure 4 shows, a good correlation between simulated and measured data could be achieved. A wide range of industrial and agricultural substrates can be modeled by changing the fractions of the initial substrates together with parameters influencing degradability and bioavailability.
Model Implementation, Parameterization, and Verification Model Implementation. The biological and physicochemical submodels were implemented in the programming language FORTRAN. The reactor and plant submodels were implemented in an industrial process control and simulation program called WinErs (see below). The FORTRAN source code was compiled into a Dynamic Link Library (DLL) that was then implemented into WinErs (Figure 3). Further equipment of the simulator includes graphical user interfaces (GUIs), data acquisition systems, control loops, and automations that were developed in the process control and simulation software WinErs. WinErs is a modular process control system (PCS) with an integrated soft programmable logic controller (PLC) that can be used for process visualization, control, simulation, protocol generation, and archiving of measurement data or as a complete PCS on the basis of a standard personal computer running with Windows 95/98/NT/2000/XP/VISTA. This flexible, cheap, and quick to learn PCS is used in industry as well as education.33 WinErs contains a fourthorder Runge-Kutta algorithm for the numerical solution of systems of differential equations. In this manner, functionality of simulated virtual processes can be combined with the
Simulator Development and Application The combined submodels together with additionally developed control loops, automations, data acquisition systems, and GUIs form an interactive simulator of the process of anaerobic digestion that can be used for training purposes and the design of innovative control strategies. Control Loops and Automations. The simulator was equipped with closed loop controls for pH, level, pressure, and temperature using different controller configurations (Figure 5). pH is controlled by a three-point controller and two dosing pumps for acid and alkali, respectively. The level in the reactor is controlled by a two-point controller that regulates an effluent pump. The pressure in the headspace of the reactor is controlled by a PID controller and a regulation valve. The (34) Hollmach, S. Amino acid composition of gelatine, www.unibayreuth.de/departments/-ddchemie/umat/gelatine (accessed on Nov 12, 2005). (35) Kaufmann, H. Analyse der Fette und Fettprodukte einschliesslich der Wachse, Harze und verwandter Stoffe; Springer-Verlag: Berlin, Germany, 1958.
(31) Kuhnen, F. pH model description, unpublished. (32) Meiwes, C. M.Sc. Thesis, Department of Civil Engineering, University of Applied Sciences Bremen, Bremen, Germany, 2005. (33) Schoop, K.-M. Technical description of the process control system WinErs, www.schoop.de (accessed on Dec 2, 2008).
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Figure 4. Comparison between simulated and measured data from a digestion of 42.8 g of sucrose, 1.1 g of gelatin, and 24.1 g of rapeseed oil. From top to bottom: methane and carbon dioxide concentrations, biogas production rate (q_ BG), and accumulated biogas yield (VBG). The decrease of methane and carbon dioxide concentrations from t = 145 h is due to dilution and mixing effects in the gas system of the laboratory plant.
Graphical User Interfaces and Data Acquisition. Interactive GUIs were designed using the “virtual anaerobic digester” and then transferred to the lab-scale plant to operate both the real and virtual plants. The user has access to the whole functionality of the plant through a main GUI (top of Figure 6). Users can view relevant process states and alarm notifications, both numerically and graphically. Advanced functions of the PCS are displayed in subwindows, where set points, substrate dosing intervals, alarm settings, and other parameters can be altered. Relevant process data is continuously stored for later evaluation and numerical and graphical display (bottom of Figure 6). Functions such as statistical assessment, etc. are available here. Application of the “Virtual Anaerobic Digester”. The “virtual anaerobic digester” can be used in a variety of industrial and academic fields. Its main application lies in the design, test, parameterization, and optimization of control and automation strategies. The opportunities that process simulation presents can be employed to achieve an effective and safe controller design. Control loops and automations for pH, temperature,
Figure 5. General structure of a single closed control loop with implemented controllers for the shown state variables (w, set point; e, control deviation; y, actuating variable; z, disturbance variable; x, controlled variable; 3P, three-point controller, 2P, two-point controller).
temperature control is performed by either a two-point or PI controller. The respective controllers activate a heating rod that heats a heating bath in which the reactor is placed. 4724
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Figure 6. Main graphical user interface of the “virtual anaerobic digester” (top) and screenshot of the data acquisition system (bottom).
level, pressure, and substrate dosing were designed, tested, parameterized, and optimized using the “virtual anaerobic digester”. Consecutively, they were transferred to the PCS of the aforementioned lab-scale biogas plant. Thus, the behavior and interaction of these automations were able to be evaluated during a wide range of process states, resulting in an efficient controller design and safe plant operation. Another application lies in the education of plant operators. Plant personnel have to be trained to be able to run a given process efficiently and safely. Especially during critical process states, it is crucial that plant operators make the right decision. However, these critical process states cannot be safely attained in a real plant. Using interactive process simulation, this problem can be overcome. Additionally, plant operators have to be qualified for an optimal (i.e., most efficient) mode of operation. Dependent upon the decisions of plant operators, biogas and energy yield, biogas composition, and space-time yield are affected. Using the “virtual anaerobic digester”, plant staff have the opportunity to simulate different operation modes and evaluate the effects of their own process control strategy. Generally, interactive training simulators can assist lecturers and trainers to convey the following competences:36
(i) operational competence, decision-making, and responsibility (normal operation, exceptional situations, emergencies, and accidents), (ii) analytical competence (process state and process dynamics), and (iii) planning competence (optimization of process structures and process control strategies). Furthermore, using such simulators, interdependencies between fundamental processes, such as heat and mass transfer, process kinetics, and other phenomena, can be illustrated. Thus, they are able to contribute to an improved teaching of fundamentals. Lastly, the “virtual anaerobic digester” can be employed in the education of (engineering) students. Experiences with other unit operations, such as the “virtual bioreactor” (fermentation/cultivation of yeast, animal cell cultures, and Escherichia coli), “virtual heat exchanger”, or “virtual distillation”, in the international B.Sc. and M.Sc. courses on Environmental Engineering at the University of Applied Sciences Bremen have been very positive.37-39 First experiences with (37) Blesgen, A.; Hass, V. Chem. Ing. Tech. 2006, 78, 1321–1322. (38) Blesgen, A.; Kuhnen, F.; K€ uhn, K.; Hass, V. Towards a virtual (bio-)chemical engineering laboratory. Presentation at the 17th International Congress on Chemical and Process Engineering, Prague, Czech Republic, 2006.
(36) Hass, V. Chem. Ing. Tech. 2005, 77, 161–167.
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Figure 7. Two examples of simulated anaerobic digestion processes using the “virtual anaerobic digester”. Both simulations are compared to data from the literature. (Top) Optimal digestion of sunflower oil [biogas production rate (q_ BG) was calculated from Voss40; accumulated biogas yield of 0.3 F]. (Bottom) Overloaded digestion process, fed with protein-rich flotation sludge (measured data from Blesgen41; normalized biogas production rate, calculated from an 80 L reactor). Simulation was performed with a 10 L reactor at 38 °C.
the application of the “virtual anaerobic digester” on a Master’s level are very promising. To show the educational capabilities of the “virtual anaerobic digester”, simulation studies were performed aiming at modeling biogas production using industrial substrates. As an example of an optimal process, the digestion of sunflower oil was simulated (top of Figure 7; data from Voss40). In this example, a stable biogas production with comparably high yields (approximately 1.4 L h-1, after the startup phase) was achieved by choosing an optimal feeding strategy. In a second simulation (bottom of Figure 7; data from Blesgen41), the digestion of protein-rich flotation sludge from a meat processing plant was modeled. This simulation experiment represents an overloaded process during which a sub-optimal feeding strategy was chosen; i.e., the digester was fed with too much substrate per time unit. Intermittently, biogas production decreases substantially. The process could only be recovered by considerably reducing the feed rate. This comparison between simulated and measured results shows that the “virtual anaerobic digester” is able to simulate very diverse, partially highly dynamic, processes. Trainees can be educated close to reality because effects of choosing various substrates and/or different feed rates become obvious immediately.
control loops, automations, and a data acquisition system, an interactive simulator, the “virtual anaerobic digester”, was completed. Both the different submodels and the whole simulator were parameterized and verified using a 10 L lab-scale reactor. A comparison between simulated and measured data showed a very good correlation. The modular design of the simulator together with the possibility of flexibly changing different parameters, such as substrate composition, degradability, and bioavailability, as well as plant setup and reactor specifications, make it possible to quickly and easily adapt the simulator to other plant configurations and substrate compositions. Possibilities for the application of such a simulator can be found in the design, test, and optimization of process control strategies, general process optimization, as well as industrial and academic education. The “virtual anaerobic digester” and its fields of application are good examples for general advantages that can be achieved by employing interactive training simulators: (i) realistic education of students, engineers, and plant operators with the aim of an optimized plant operation, (ii) quick, efficient, and reliable design, test, and optimization of various types of controllers and control strategies that can easily be transferred to the real plant or process, (iii) reduction of commissioning periods, especially if the simulator is already available in the planning stage of a given process, and (iv) process optimization, e.g., increase in product or energy yield, through safely testing a wide range of operational modes. As could be shown, these advantages can particularly be achieved if training simulators are custom-made and specifically adapted to a given plant or process. In this way, the overall behavior of all subprocesses, including sensors and actuators, can be realistically modeled. A future aim is the combination of a variety of single virtual unit operations to develop a “virtual laboratory” (Figure 8) that can be applied for a wide range of purposes. Apart from the above-mentioned applications in education and controller design, the dynamic behavior of multiple interacting unit operations could be simulated and optimized.
Summary and Outlook By combining four different submodels of the process of anaerobic digestion and through the designing of GUIs, (39) Hass, V.; Schauenburg, A.; Schoop, K.-M.; Ringel, D.; van Schooten, A. Simulation based optimization of the control of a waste heat boiler during an in-house control seminar. Book of Abstracts of the European Congress of Chemical Engineering (ECCE-6); ECCE-6: Copenhagen, Denmark, 2007; pp 709-710. (40) Voss, S. Ph.D. Thesis, Department of Civil Engineering, BauhausUniversity Weimar, Weimar, Germany, 2006. (41) Blesgen, A. Anaerobic digestion of flotate sludge. Experimental results, unpublished.
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Figure 8. Possible combination of “virtual unit operations” to build a “virtual laboratory” for educational and industrial applications (from left to right: “virtual bioreactor”, yeast fermentation; “virtual heat exchanger”, preheating of fermentation broth; “virtual distillation”, ethanol separation; and “virtual anaerobic digester”, biogas production from distillation residue).
In conclusion, the use of process modeling and simulation in general and the use of the “virtual anaerobic digester” in particular can lead to a more stable process with higher biogas yields and optimized process control strategies. It could be shown that these aims can be achieved by both education of
plant personnel and simulator-based design of control and automation strategies. Industrial-scale plants can benefit from these advantages by implementing an adapted and properly calibrated simulator into their planning and operating procedures.
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