A stochastic model for microbial fermentation process under Gaussian white noise environment

In this paper, we propose a stochastic model for the microbial fermentation process under the framework of white noise analysis, where Gaussian white noises are used to model the environmental noises and the specific growth rate is driven by Gaussian white noises. In order to keep the regularity of the terminal time, the adjustment factors are added in the volatility coefficients of the stochastic model. Then we prove some fundamental properties of the stochastic model: the regularity of the terminal time, the existence and uniqueness of a solution and the continuous dependence of the solution on the initial values

Modelling Mixed Microbial Culture Polyhydroxyalkanoate Accumulation Bioprocess towards Novel Methods for Polymer Production Using Dilute Volatile Fatty Acid Rich Feedstocks

Volatile fatty acid (VFA) rich streams from fermentation of organic residuals and wastewater are suitable feedstocks for mixed microbial culture (MMC) Polyhydroxyalkanoate (PHA) production. However, many such streams have low total VFA concentration (1–10 gCOD/L). PHA accumulation requires a flow-through bioprocess if the VFAs are not concentrated. A flow through bioprocess must balance goals of productivity (highest possible influent flow rates) with goals of substrate utilization efficiency (lowest possible effluent VFA concentration). Towards these goals, dynamics of upshift and downshift respiration kinetics for laboratory and pilot scale MMCs were evaluated. Monod kinetics described a hysteresis between the upshift and downshift responses. Substrate concentrations necessary to stimulate a given substrate uptake rate were significantly higher than the concentrations necessary to sustain the attained substrate uptake rate. A benefit of this hysteresis was explored in Monte Carlo based PHA accumulation bioprocess numerical simulations. Simulations illustrated for a potential to establish continuous flow-through PHA production bioprocesses even at a low (1 gCOD/L) influent total VFA concentration. Process biomass recirculation into an engineered higher substrate concentration mixing zone, due to the constant influent substrate flow, enabled to drive the process to maximal possible PHA production rates without sacrificing substrate utilization efficiency

Minimal-time bioremediation of natural water resources

We study minimal time strategies for the treatment of pollution of large volumes, such as lakes or natural reservoirs, with the help of an autonomous bioreactor. The control consists in feeding the bioreactor from the resource, the clean output returning to the resource with the same flow rate. We first characterize the optimal policies among constant and feedback controls, under the assumption of a uniform concentration in the resource. In a second part, we study the influence of an inhomogeneity in the resource, considering two measurements points. With the help of the Maximum Principle, we show that the optimal control law is non-monotonic and terminates with a constant phase, contrary to the homogeneous case for which the optimal flow rate is decreasing with time. This study allows the decision makers to identify situations for which the benefit of using non-constant flow rates is significant

Modeling and Predictive Control of Yeast Fermentation Process

This work mainly focus on development of advanced process control on the continuous fermentation process using Saccharomyces cerevisiae. Today, a lot of research has been done on renewable energy and it has been found that ethanol is one of the best alternatives fuels to substitute petroleum fuels. However, all of this can only be achieved if the production of ethanol is efficient and economical enough. A lot of industry nowadays uses fermentation in batch mode due to the problems occurred in 1970s such as low productivity, low yield, and high level of contamination. Recently, continuous fermentation processes are optimized based on kinetic models to achieve high productivities, high process flexibility and stability and less expensive production cost compared to batch processes. In addition, process control development for continuous fermentation is much better since a lot of research on advanced process is in the continuous mode and almost all kinetic models currently available for continuous fermentation with Saccharomyces cerevisiae are in steady state. One of the disadvantages of standard feedback controller is that the action can only be taken after the system has been affected by the disturbance. Thus, an advanced process control (APC) strategy will be developed based on this process. The objective of this work is to optimize the performance of the fermentation process in terms of yield and productivity by using model predictive control (MPC). In this process, the manipulated variable that has been considered is inlet temperature and inlet substrate concentration and the control variable is temperature in the reactor and ethanol concentration. The successful implementation of the controller is greatly affected by the accuracy of the process model

In silico Derivation of a Reduced Kinetic Model for Stationary or Oscillating Glycolysis in Escherichia coli Bacterium

Modelling bacteria glycolysis is a classical subject but still of high interest. Glycolysis, together with the phosphotransferase (PTS)-system for glucose transport into the cell, the pentose-phosphate pathway (PPP), and tricarboxylic acid cycle (TCA) characterize the central carbon metabolism. Such a model usually serves as the foundation for developing modular simulation platforms used for consistent analysis of the control / regulation of target metabolite synthesis. The present study is focused on analyzing the advantage and limitations of using a simplified but versatile ‘core’ model of mTRM) of glycolysis when incomplete experimental information is available. Exemplification is made for a reduced glycolysis model from literature for Escherichia coli cells, by performing a few modifications (17 identifiable parameters) to increase its agreement with simulated data generated by using an extended model (127 parameters) over a large operating domain of an experimental bioreactor. With the expense of ca. 8–13 % increase in the relative model error vs. extended simulation models, derivation of reduced kinetic structures to describe some parts of the core metabolism is worth the associated identification effort, due to the considerable reduction in model parameterization (e.g. 17 parameters in mTRM vs. 127 in the extendedChassM model of Chassagnole et al.), while preserving a fair adequacy over a wide experimental domain generated in-silico by using the valuable extended ChassM. The reduced model flexibility is tested by reproducing stationary or oscillatory glycolysis conditions. The reduced mTRM model includes enough information to reproduce not only the cell energy-related potential through the total A(MDT)P level, but also the role played by ATP/ADP ratio as a glycolysis driving force. The model can also reproduce the oscillatory behaviour occurrence conditions, as well as situations when homeostatic conditions are not fulfilled

Facing bioprocess modeling: a review of the methodologies of modeling

This article presents a detailed review of different approaches for process modeling, indicating their deficiencies and limitations when applied to bioprocesses modeling. As a result of the analysis it is concluded that these methodologies fail in bioprocesses modeling because they do not explicitly take into account the interaction between environment and cellular material, at least descriptively. It is noted that so far the way to bring these two worlds has been through purely predictive functions. Finally, the trends in bioprocess model are described; concluding that the approach is oriented to phenomenological based mathematical models with descriptive or explanatory features, to represent the relationship between the cell and its environment.En este artículo se presenta una revisión detallada de las diferentes metodologías para el modelado de procesos, señalando sus deficiencias y limitaciones al aplicarlas al modelado de bioprocesos. Como resultado del análisis se encuentra que, al aplicar esas metodologías a los bioprocesos, todas fallan porque no consideran explícitamente la interacción existente entre el medio ambiente y el material celular, al menos de forma descriptiva. Se resalta que hasta ahora la forma de unir estos dos mundos ha sido a través de funciones puramente predictivas. Finalmente, se describen las tendencias en el modelado de bioprocesos, concluyéndose que el enfoque está orientado al planteamiento de modelos matemáticos de base fenomenológica, con rasgos descriptivos o explicativos, para representar la relación existente entre la célula y su medio ambiente.Neste artigo apresenta-se uma revisão detalhada das diferentes metodologias para a modelagem de processos, assinalando suas deficiências e limitações ao aplicar à modelagem de bioprocessos. Como resultado da análise se encontra que ao aplicar essas metodologias de bioprocessos, todos falham porque não consideram explicitamente a interação existente entre o meio ambiente e o material celular, ao menos de forma descritiva. Ressalta-se que até agora a forma de unir estes dois mundos tem sido  através de funções puramente preditivas. Finalmente, descrevem-se as tendências na modelagem de bioprocessos, concluindo-se que o enfoque está orientado à proposta de modelos matemáticos de base fenomenológica, com características descritivas ou explicativas, para representar a relação entre a célula e seu meio ambiente. &nbsp

Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201

Model-based scale-up methodology for aerobic fed-batch bioprocesses: application to polyhydroxybutyrate (PHB) production

This work presents a general model-based methodology to scale-up fed-batch bioprocesses. The idea behind this approach is to establish a dynamics hierarchy, based on a model of the process, that allows the designer to determine the proper scale factors as well as at which point of the fed-batch the process should be scaled up. Here, concepts and tools of linear control theory, such as the singular value decomposition of the Hankel matrix, are exploited in the context of process design. The proposed scale-up methodology is first described in a bioprocesses general framework highlighting its main features, key variables and parameters. Then, it is applied to a polyhydroxybutyrate (PHB) fed-batch bioreactor and compared with three empirical criteria, that are traditionally employed to determine the scale factors of these processes, showing the usefulness and distinctive features of this proposal. Moreover, this methodology provides theoretical support to a frequently used empirical rule: scale-up aerobic bioreactors at constant volumetric oxygen transfer coefficient. Finally, similar process dynamic behavior and PHB production set at the laboratory scale are predicted at the new operating scale, while it is also determined that is rarely possible to reproduce similar dynamic behavior of the bioreactor using empirical scale-up criteria.Facultad de IngenieríaInstituto de Investigaciones en Electrónica, Control y Procesamiento de Señale