Optimization of fed-batch fermentation processes with bio-inspired algorithms

The optimization of the feeding trajectories in fed-batch fermentation processes is a complex problem that has gained attention given its significant economical impact. A number of bio-inspired algorithms have approached this task with considerable success, but systematic and statistically significant comparisons of the different alternatives are still lacking. In this paper, the performance of different metaheuristics, such as Evolutionary Algorithms (EAs), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is compared, resorting to several case studies taken from literature and conducting a thorough statistical validation of the results. DE obtains the best overall performance, showing a consistent ability to find good solutions and presenting a good convergence speed, with the DE/rand variants being the ones with the best performance. A freely available computational application, OptFerm, is described that provides an interface allowing users to apply the proposed methods to their own models and data.The work is partially funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects Ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEst-OE/ES/UI0752/2011

Instrumentation and control of anaerobic digestion processes: a review and some research challenges

The final publication is available at Springer via http://dx.doi.org/10.1007/s11157-015-9382-6[EN] To enhance energy production from methane or resource recovery from digestate, anaerobic digestion processes require advanced instrumentation and control tools. Over the years, research on these topics has evolved and followed the main fields of application of anaerobic digestion processes: from municipal sewage sludge to liquid mainly industrial then municipal organic fraction of solid waste and agricultural residues. Time constants of the processes have also changed with respect to the treated waste from minutes or hours to weeks or months. Since fast closed loop control is needed for short time constant processes, human operator is now included in the loop when taking decisions to optimize anaerobic digestion plants dealing with complex solid waste over a long retention time. Control objectives have also moved from the regulation of key variables measured online to the prediction of overall process perfor- mance based on global off-line measurements to optimize the feeding of the processes. Additionally, the need for more accurate prediction of methane production and organic matter biodegradation has impacted the complexity of instrumentation and should include a more detailed characterization of the waste (e.g., biochemical fractions like proteins, lipids and carbohydrates)andtheirbioaccessibility andbiodegradability characteristics. However, even if in the literature several methodologies have been developed to determine biodegradability based on organic matter characterization, only a few papers deal with bioaccessibility assessment. In this review, we emphasize the high potential of some promising techniques, such as spectral analysis, and we discuss issues that could appear in the near future concerning control of AD processes.The authors acknowledge the financial support of INRA (the French National Institute for Agricultural Research), the French National Research Agency (ANR) for the "Phycover" project (project ANR-14-CE04-0011) and ADEME for Inter-laboratory assay financial support.Jimenez, J.; Latrille, E.; Harmand, J.; Robles Martínez, Á.; Ferrer Polo, J.; Gaida, D.; Wolf, C.... (2015). Instrumentation and control of anaerobic digestion processes: a review and some research challenges. Reviews in Environmental Science and Biotechnology. 14(4):615-648. doi:10.1007/s11157-015-9382-6S615648144Aceves-Lara CA, Latrille E, Steyer JP (2010) Optimal control of hydrogen production in a continuous anaerobic fermentation bioreactor. 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Process analytical technology in food biotechnology

Biotechnology is an area where precision and reproducibility are vital. This is due to the fact that products are often in form of food, pharmaceutical or cosmetic products and therefore very close to the human being. To avoid human error during the production or the evaluation of the quality of a product and to increase the optimal utilization of raw materials, a very high amount of automation is desired. Tools in the food and chemical industry that aim to reach this degree of higher automation are summarized in an initiative called Process Analytical Technology (PAT). Within the scope of the PAT, is to provide new measurement technologies for the purpose of closed loop control in biotechnological processes. These processes are the most demanding processes in regards of control issues due to their very often biological rate-determining component. Most important for an automation attempt is deep process knowledge, which can only be achieved via appropriate measurements. These measurements can either be carried out directly, measuring a crucial physical value, or if not accessible either due to the lack of technology or a complicated sample state, via a soft-sensor.Even after several years the ideal aim of the PAT initiative is not fully implemented in the industry and in many production processes. On the one hand a lot effort still needs to be put into the development of more general algorithms which are more easy to implement and especially more reliable. On the other hand, not all the available advances in this field are employed yet. The potential users seem to stick to approved methods and show certain reservations towards new technologies.Die Biotechnologie ist ein Wissenschaftsbereich, in dem hohe Genauigkeit und Wiederholbarkeit eine wichtige Rolle spielen. Dies ist der Tatsache geschuldet, dass die hergestellten Produkte sehr oft den Bereichen Nahrungsmitteln, Pharmazeutika oder Kosmetik angehöhren und daher besonders den Menschen beeinflussen. Um den menschlichen Fehler bei der Produktion zu vermeiden, die Qualität eines Produktes zu sichern und die optimale Verwertung der Rohmaterialen zu gewährleisten, wird ein besonders hohes Maß an Automation angestrebt. Die Werkzeuge, die in der Nahrungsmittel- und chemischen Industrie hierfür zum Einsatz kommen, werden in der Process Analytical Technology (PAT) Initiative zusammengefasst. Ziel der PAT ist die Entwicklung zuverlässiger neuer Methoden, um Prozesse zu beschreiben und eine automatische Regelungsstrategie zu realisieren. Biotechnologische Prozesse gehören hierbei zu den aufwändigsten Regelungsaufgaben, da in den meisten Fällen eine biologische Komponente der entscheidende Faktor ist. Entscheidend für eine erfolgreiche Regelungsstrategie ist ein hohes Maß an Prozessverständnis. Dieses kann entweder durch eine direkte Messung der entscheidenden physikalischen, chemischen oder biologischen Größen gewonnen werden oder durch einen SoftSensor. Zusammengefasst zeigt sich, dass das finale Ziel der PAT Initiative auch nach einigen Jahren des Propagierens weder komplett in der Industrie noch bei vielen Produktionsprozessen angekommen ist. Auf der einen Seite liegt dies mit Sicherheit an der Tatsache, dass noch viel Arbeit in die Generalisierung von Algorithmen gesteckt werden muss. Diese müsse einfacher zu implementieren und vor allem noch zuverlässiger in der Funktionsweise sein. Auf der anderen Seite wurden jedoch auch Algorithmen, Regelungsstrategien und eigne Ansätze für einen neuartigen Sensor sowie einen Soft-Sensors vorgestellt, die großes Potential zeigen. Nicht zuletzt müssen die möglichen Anwender neue Strategien einsetzen und Vorbehalte gegenüber unbekannten Technologien ablegen

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

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

Hybrid Modeling Approaches Integrating Physics-Based Models with Machine Learning for Predictive Control of Biological and Chemical Processes

Recently, there has been growing interest in data-based modeling as the amount of data available has increased tremendously. One such method is Dynamic Mode Decomposition with Control technique, which builds temporally local linear models using data. But its limited domain of applicability (DA) hinders its use for prediction purposes. To overcome this challenge, we proposed an algorithm that utilizes multiple "local" training datasets, and it was applied successfully to hydraulic fracturing. Although data-based modeling offers simplicity and ease of construction, it lacks robustness and parametric interpretability, unlike first-principles modeling. To balance the advantages and disadvantages of data-based models and first-principles models, hybrid modeling was proposed using artificial neural networks (ANNs). Since then, Machine Learning (ML) has advanced where deep neural networks (DNNs) with more than three layers can be trained to approximate any function accurately. In this work, we proposed a deep hybrid modeling (DHM) framework that integrates first-principles with DNNs and successfully applied it to two complex processes, i.e., hydraulic fracturing and full-scale fermentation reactor. Similarly, Universal Differential Equations (UDEs) was proposed in ML where DNNs are represented as ODEs and solved using ODE solvers. We utilized UDEs to successfully build a DHM using simulation and experimental data for batch production of ϐ-carotene. One limitation of DHM is that its DA is affected by the DNN within it, and its accuracy is high within its DA. Therefore, it is important to consider its DA when designing a model-based controller. To this end, we proposed a Control Lyapunov-Barrier Function (CLBF)-MPC to stabilize and ensure that the closed-loop system stays within DA of DHM. Theoretical guarantees were provided for the CLBF-MPC controller, and it was successfully implemented on a CSTR. The idea of integrating physics with ML can be extended to Reinforcement Learning (RL). In case when model-based controller design is not possible, we proposed a model-free Deep RL (DRL) controller that utilizes prior knowledge in its reward function to quicken the learning process. This DRL controller was successfully applied to hydraulic fracturing wherein Nolte’s law was included in the reward function for fast convergence

Enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains

Metabolic engineering is highly demanded currently for the production of various useful compounds such as succinate and lactate that are very useful in food, pharmaceutical, fossil fuels, and energy industries. Gene or reaction deletion known as knockout is one of the strategies used in in silico metabolic engineering to change the metabolism of the chosen microbial cells to obtain the desired phenotypes. However, the size and complexity of the metabolic network are a challenge in determining the near-optimal set of genes to be knocked out in the metabolism due to the presence of competing pathway that interrupts the high production of desired metabolite, leading to low production rate and growth rate of the required microorganisms. In addition, the inefficiency of existing algorithms in reconstructing high growth rate and production rate becomes one of the issues to be solved. Therefore, this research proposes Dynamic Flux Variability Analysis (DFVA) algorithm to identify the best knockout reaction combination to improve the production of desired metabolites in microorganisms. Based on the experimental results, DFVA shows an improvement of growth rate of succinate and lactate by 12.06% and 47.16% respectively in E. coli and by 4.62% and 47.98% respectively in S. Cerevisae. Suggested reactions to be knocked out to improve the production of succinate and lactate have been identified and validated through the biological database

Continuous Biochemical Processing: Investigating Novel Strategies to Produce Sustainable Fuels and Pharmaceuticals

Biochemical processing methods have been targeted as one of the potential renewable strategies for producing commodities currently dominated by the petrochemical industry. To design biochemical systems with the ability to compete with petrochemical facilities, inroads are needed to transition from traditional batch methods to continuous methods. Recent advancements in the areas of process systems and biochemical engineering have provided the tools necessary to study and design these continuous biochemical systems to maximize productivity and substrate utilization while reducing capital and operating costs. The first goal of this thesis is to propose a novel strategy for the continuous biochemical production of pharmaceuticals. The structural complexity of most pharmaceutical compounds makes chemical synthesis a difficult option, facilitating the need for their biological production. To this end, a continuous, multi-feed bioreactor system composed of multiple independently controlled feeds for substrate(s) and media is proposed to freely manipulate the bioreactor dilution rate and substrate concentrations. The optimal feed flow rates are determined through the solution to an optimal control problem where the kinetic models describing the time-variant system states are used as constraints. This new bioreactor paradigm is exemplified through the batch and continuous cultivation of β-carotene, a representative product of the mevalonate pathway, using Saccharomyces cerevisiae strain mutant SM14. The second goal of this thesis is to design continuous, biochemical processes capable of economically producing alternative liquid fuels. The large-scale, continuous production of ethanol via consolidated bioprocessing (CBP) is examined. Optimal process topologies for the CBP technology selected from a superstructure considering multiple biomass feeds, chosen from those available across the United States, and multiple prospective pretreatment technologies. Similarly, the production of butanol via acetone-butanol-ethanol (ABE) fermentation is explored using process intensification to improve process productivity and profitability. To overcome the inhibitory nature of the butanol product, the multi-feed bioreactor paradigm developed for pharmaceutical production is utilized with in situ gas stripping to simultaneously provide dilution effects and selectively remove the volatile ABE components. Optimal control and process synthesis techniques are utilized to determine the benefits of gas stripping and design a butanol production process guaranteed to be profitable

Model-based strategies for computer-aided operation of recombinant E. coli fermentation

Tese de Doutoramento em Engenharia Química e BiológicaThe main objectives of this thesis were the development of model-based strategies for improving the performance of a high-cell density recombinant Escherichia coli fed-batch fermentation. The construction of a mathematical model framework as well as the derivation of optimal and adaptive control laws were used to accomplish these tasks. An on-line data acquisition system was also developed for an accurate characterization of the process and for the implementation of the control algorithms. The mathematical model of the process is composed of mass balance equations to the most relevant state variables of the process. Kinetic equations are based on the three possible metabolic pathways of the microorganism: glucose oxidation, fermentation of glucose and acetate oxidation. A genetic algorithm was used to derive the kinetic structure and to estimate both yield and kinetic coefficients of the model, minimizing the normalized quadratic differences between simulated and real values of the state variables. After parameter estimation, a sensitivity function analysis was applied to evaluate the influence of the various parameters on model behavior. Sensitivity functions revealed the sensitivity of the state variables to variations in each model parameter. Thus, essential parameters were selected and the model could be re-written in a simplified version that could also describe accurately experimental data. A system for the on-line monitoring of the major state variables was also developed. Glucose and acetate concentrations were measured with a developed Flow Injection Analysis system, while the carbon dioxide and oxygen transfer rates were calculated from data obtained with exhaust gas analysis. The fermentation culture weight was also continuously assessed with a balance, allowing the use of more precise mass-based concentrations, while environmental variables like pH, dissolved oxygen and temperatures were controlled and assessed via a Digital Control Unit. The graphical programming environment LabVIEW was used to acquire and integrate these variables in a supervisory computer, allowing the performance of integrated monitoring and control of the process. A model-based adaptive linearizing control law was derived for the regulation of acetate concentration during fermentations. The non-linear model was subjected to transformations in order to obtain a linear behavior for the control loop when a non-linear control is applied. The implementation of the control law was performed through a C script embedded in the supervisory LabVIEW program. Finally, two optimization techniques for the maximization of biomass concentration were compared: a first order gradient method and a stochastic method based on the biological principle of natural evolution, using a genetic algorithm. The former method revealed less efficient concerning to the computed maximum, and dependence on good initial values.A presente tese teve como principais objectivos o desenvolvimento de estratégias baseadas em modelos para melhorar o desempenho da fermentação em modo semi-continuo em altas densidades celulares de Escherichia coil recombinada. Para o efeito, foi construído um modelo matemático representativo do processo e a partir deste foram desenvolvidos algoritmos de controlo óptimo e adaptativo. De forma a possibilitar a implementação de leis de controlo em linha e a caracterização do processo fermentativo, foi desenvolvido um sistema informático de aquisição e envio de dados. O modelo matemático representativo do processo em estudo foi elaborado tendo por base as equações dinâmicas de balanço mássico para as variáveis de estado mais relevantes, contemplando as três possíveis vias metabólicas do microrganismo. A estrutura cinética, bem como os parâmetros do modelo foram determinados por recurso a uma abordagem sistemática tendo por base a minimização das diferenças quadráticas entra dados reais e dados simulados, com recurso a uma ferramenta de optimização estocástica denominada de Algoritmos Genéticos. Após a etapa de identificação do modelo matemático, foram calculadas as sensibilidades relativas ao longo do tempo das variáveis de estado do modelo relativamente aos vários parâmetros determinados. Os resultados desta análise de sensibilidade possibilitaram avaliar a relevância de cada um dos parâmetros em causa, permitindo propor uma estrutura de modelo menos complexa, por exclusão dos parâmetros menos importantes. O sistema elaborado para a aquisição e envio em linha de dados da fermentação inclui um sistema de FIA (Flow Injection Analysis) desenvolvido para a medição das concentrações de acetato e glucose, uma unidade de controlo digital que controla as variáveis físicas mais relevantes para o processo, e um equipamento de Espectrometria de Massas para analisar as correntes gasosas de entrada e saída do fermentador. O sistema dispõe ainda de duas balanças, uma das quais para a aferição em linha do peso do caldo de fermentação, permitindo o use de concentrações mássicas que proporcionam resultados mais exactos. A aquisição e integração destas variáveis medidas são, efectuadas através de um software de supervisão elaborado no ambiente de programação gráfico LabVIEW. Adicionalmente, foi elaborada uma lei de controlo adaptativo linearizante para a regulação da concentração de acetato no meio de fermentação. A síntese da lei de controlo não linear foi efectuada por técnicas de geometria diferencial com linearização do sistema por retroacção de estado. A adaptação foi feita tendo por base a estimação de parâmetros variáveis no tempo, nos quais se concentram as incertezas do modelo. A implementação ao processo real da referida lei de controlo foi efectuada por recurso a um programa elaborado em C incluindo no programa supervisor elaborado em LabVIEW. Finalmente, para a optimização da quantidade de biomassa formada no final da fermentação por manipulação do caudal de alimentação, foram estudadas duas ferramentas de optimização: um método de gradiente e uma ferramenta baseada em Algoritmos Genéticos. Esta última revelou-se mais eficaz tanto na convergência para o valor óptimo, como na estimativa inicial fornecida.Fundação para a Ciência e a Tecnologia (FCT) – PRAXIS XXI/16961/98.União Europeia - Fundo Social Europeu (FSE) – III Quadro Comunitário de Apoio (QCA III).Fundação Calouste Gulbenkian (FCQ) - Educação e Bolsas.Agência de Inovação (ADI) - PROTEXPRESS