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Biogas is a well-established renewable energy source produced from anaerobic digestion (AD) of biomass/ feedstock. It is probably the most versatile and efficient biofuel in terms of utilisable feedstocks and energy applications. To monitor, optimise, and control anaerobic digestion (AD), numerous mathematical models describing AD have been developed and reported. Although literature on the use of these models and their reviews have been published, their differences have not been collectively analysed and no generalised classification criteria for these models has been proposed based on such an analysis. This review covers most reported AD models, from the simplest linear equation capturing biogas production rate to complex Anaerobic Digestion Model No. 1. In addition to model classification and biochemical stages in AD, other processes like feedstock hydrolysis or mass and heat transfer essential to AD were discussed, analysed, and available models on them reviewed. This collective and comprehensive review approach has been undertaken to enable the evaluation of the interdependence of all processes, process factors, and process estimations and their individual, and interactive effects on AD.

Biogas is a well-established renewable energy source produced from anaerobic digestion (AD) of biomass/ feedstock. It is probably the most versatile and efficient biofuel in terms of utilisable feedstocks and energy applications. To monitor, optimise, and control anaerobic digestion (AD), numerous mathematical models describing AD have been developed and reported. Although literature on the use of these models and their reviews have been published, their differences have not been collectively analysed and no generalised classification criteria for these models has been proposed based on such an analysis. This review covers most reported AD models, from the simplest linear equation capturing biogas production rate to complex Anaerobic Digestion Model No. 1. In addition to model classification and biochemical stages in AD, other processes like feedstock hydrolysis or mass and heat transfer essential to AD were discussed, analysed, and available models on them reviewed. This collective and comprehensive review approach has been undertaken to enable the evaluation of the interdependence of all processes, process factors, and process estimations and their individual, and interactive effects on AD.

Up to now, computational modeling of microbial electrosynthesis (MES) has been underexplored, but is necessary to achieve breakthrough understanding of the process-limiting steps. Here, a general framework for modeling microbial kinetics in a MES reactor is presented. A thermodynamic approach is used to link microbial metabolism to the electrochemical reduction of an intracellular mediator, allowing to predict cellular growth and current consumption. The model accounts for CO2 reduction to acetate, and further elongation to n-butyrate and n-caproate. Simulation results were compared with experimental data obtained from different sources and proved the model is able to successfully describe microbial kinetics (growth, chain elongation, and product inhibition) and reactor performance (current density, organics titer). The capacity of the model to simulate different system configurations is also shown. Model results suggest CO2 dissolved concentration might be limiting existing MES systems, and highlight the importance of the delivery method utilized to supply it. Simulation results also indicate that for biofilm-driven reactors, continuous mode significantly enhances microbial growth and might allow denser biofilms to be formed and higher current densities to be achieved., BT/Bioprocess Engineering

Up to now, computational modeling of microbial electrosynthesis (MES) has been underexplored, but is necessary to achieve breakthrough understanding of the process-limiting steps. Here, a general framework for modeling microbial kinetics in a MES reactor is presented. A thermodynamic approach is used to link microbial metabolism to the electrochemical reduction of an intracellular mediator, allowing to predict cellular growth and current consumption. The model accounts for CO2 reduction to acetate, and further elongation to n-butyrate and n-caproate. Simulation results were compared with experimental data obtained from different sources and proved the model is able to successfully describe microbial kinetics (growth, chain elongation, and product inhibition) and reactor performance (current density, organics titer). The capacity of the model to simulate different system configurations is also shown. Model results suggest CO2 dissolved concentration might be limiting existing MES systems, and highlight the importance of the delivery method utilized to supply it. Simulation results also indicate that for biofilm-driven reactors, continuous mode significantly enhances microbial growth and might allow denser biofilms to be formed and higher current densities to be achieved., BT/Bioprocess Engineering

Kinetic models are nowadays a basic tool to ensure food safety. Most models used in predictive microbiology have model parameters, whose precision is crucial to provide meaningful predictions. Kinetic parameters are usually estimated based on experimental data, where the experimental design can have a great impact on the precision of the estimates. In this sense, Optimal Experiment Design (OED) applies tools from optimization and information theory to identify the most informative experiment under a set of constrains (e.g. athematical model, number of samples, etc). In this work, we develop a methodology for the design of optimal isothermal inactivation experiments. We consider the two dimensions of the design space (time and temperature), as well as a temperature-dependent maximum duration of the experiment. Functions for its application have been included in the bioOED R package.We identify design patterns that remain optimum regardless of the number of sampling points for three inactivation models (Bigelow, Mafart and Peleg) and three model microorganisms (Escherichia coli, Salmonella Senftenberg and Bacillus coagulans). Samples at extreme temperatures and close to the maximum duration of the experiment are the most informative. Moreover, the Mafart and Peleg models require some samples at intermediate time points due to the non-linearity of the survivor curve. The impact of the reference temperature on the precision of the parameter estimates is also analysed. Based on numerical simulations we recommend fixing it to the mean of the maximum and minimum temperatures used for the experiments. The article ends with a discussion presenting guidelines for the design of isothermal inactivation experiments. They combine these optimum results based on information theory with several practical limitations related to isothermal inactivation experiments. The application of these guidelines would reduce the experimental burden required to characterize thermal inactivation.

Kinetic models are nowadays a basic tool to ensure food safety. Most models used in predictive microbiology have model parameters, whose precision is crucial to provide meaningful predictions. Kinetic parameters are usually estimated based on experimental data, where the experimental design can have a great impact on the precision of the estimates. In this sense, Optimal Experiment Design (OED) applies tools from optimization and information theory to identify the most informative experiment under a set of constrains (e.g. mathematical model, number of samples, etc). In this work, we develop a methodology for the design of optimal isothermal inactivation experiments. We consider the two dimensions of the design space (time and temperature), as well as a temperature-dependent maximum duration of the experiment. Functions for its application have been included in the bioOED R package. We identify design patterns that remain optimum regardless of the number of sampling points for three inactivation models (Bigelow, Mafart and Peleg) and three model microorganisms (Escherichia coli, Salmonella Senftenberg and Bacillus coagulans). Samples at extreme temperatures and close to the maximum duration of the experiment are the most informative. Moreover, the Mafart and Peleg models require some samples at intermediate time points due to the non-linearity of the survivor curve. The impact of the reference temperature on the precision of the parameter estimates is also analysed. Based on numerical simulations we recommend fixing it to the mean of the maximum and minimum temperatures used for the experiments. The article ends with a discussion presenting guidelines for the design of isothermal inactivation experiments. They combine these optimum results based on information theory with several practical limitations related to isothermal inactivation experiments. The application of these guidelines would reduce the experimental burden required to characterize thermal inactivation.

Transport limitation is regarded as one of the major phenomena leading to process yield reduction in large-scale fermentations. Knowledge of both the fluid dynamics and the microbial kinetics is needed for understanding and describing situations in large-scale production bioreactors. Microbial kinetics of Escherichia coli including flow metabolism was determined in lab-scale batch and fed-batch experiments. The effect of high substrate fluctuations on metabolism was quantified in scale-down experiments. This knowledge was incorporated into a flow model based on the compartment model approach (CMA). The flow model was verified by mixing time experiments on aerated reactors mixed with multiple impellers at different regimes with liquid volumes 8-22 m(3). The integral model, predicting local glucose, acetate and biomass concentrations in different parts of the reactor, was compared to three large-scale fermentations performed in two different reactors. If lab-scale kinetics was used, the biomass prediction overestimated the biomass concentration. Lab-scale kinetics modified by the results of scale-down experiments incorporating the effect of substrate fluctuations gave a rather satisfying description of biomass concentration. Glucose gradients in different parts of the reactor and acetate produced as a result of overflow metabolism were predicted on a qualitative level. The simulations show that at present the decisive factor for a successful integration of fluid dynamics and microbial kinetics is the kinetics., QC 20100525