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

Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm

Fed-batch fermentation has gained attention in recent years due to its beneficial impact in the economy and productivity of bioprocesses. However, the complexity of these processes requires an expert system that involves swarm intelligence-based metaheuristics such as Artificial Algae Algorithm (AAA), Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Differential Evolution (DE) for simulation and optimization of the feeding trajectories. DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation. In this work, an improved version of DE namely Backtracking Search Algorithm (BSA) has edged DE and other recent metaheuristics to emerge as superior optimization method. This is shown by the results obtained by comparing the performance of BSA, DE, CMAES, AAA and ABC in solving six fed batch fermentation case studies. BSA gave the best overall performance by showing improved solutions and more robust convergence in comparison with various metaheuristics used in this work. Also, there is a gap in the study of fed-batch application of wastewater and sewage sludge treatment. Thus, the fed batch fermentation problems in winery wastewater treatment and biogas generation from sewage sludge are investigated and reformulated for optimization

Modelling of Biotechnological Processes - An approach based on Artificial Neural Networks

In this chapter we describe a software tool for modelling fermentation processes, the FerMoANN, which allows researchers in biology and biotechnology areas to access the potential of Artificial Neural Networks (ANNs) for this task. The FerMoANN is tested and validated using two fermentation processes, an Escherichia coli recombinant protein production and the production of a secreted protein with Saccharomyces cerevisiae in fed-batch reactors. The application to these two case studies, tested for different configurations of feedforward ANNs, illustrate the usefulness of these structures, when trained according to a supervised learning paradigm

Evolutionary algorithms for optimal control in fed-batch fermentation processes

In this work, Evolutionary Algorithms (EAs) are used to achieve optimal feedforward control in a recombinant bacterial fed-batch fermentation process, that aims at producing a bio-pharmaceutical product. Three diferent aspects are the target of the optimization procedure: the feeding trajectory (the amount of substrate introduced in a bioreactor per time unit), the duration of the fermentation and the initial conditions of the process. A novel EA with variable size chromosomes and using real-valued representations is proposed that is capable of simultaneously optimizing the aforementioned aspects. Outstanding productivity levels were achieved and the results are validated by practice

A Genetic Algorithm for Feeding Trajectory Optimisation of Fed-batch Fermentation Processes

In this work a genetic algorithm is proposed with the purpose of the feeding trajectory optimization during a fed-batch fermentation of E. coli. The feed rate profiles are evaluated based on a number of objective functions. Optimization results obtained for different feeding trajectories demonstrate that the genetic algorithm works well and shows good computational performance. Developed optimal feed profiles meet the defined criteria. The ration of the substrate concentration and the difference between actual cell concentration and theoretical maximum cell concentration is defined as the most appropriate objective function. In this case the final cell concentration of 43 g·l-1 and final product concentration of 125 g·l-1 are achieved and there is not significant excess of substrate

Benchmarking real-time monitoring strategies for ethanol production from lignocellulosic biomass

The goal of this paper is to review and critically assess different methods to monitor key process variables for ethanol production from lignocellulosic biomass. Because cellulose-based biofuels cannot yet compete with noncellulosic biofuels, process control and optimization are of importance to lower the production costs. This study reviews different monitoring schemes, to indicate what the added value of real-time monitoring is for process control. Furthermore, a comparison is made on different monitoring techniques to measure the off-gas, the concentrations of dissolved components in the inlet to the process, the concentrations of dissolved components in the reactor, and the biomass concentration. Finally, soft sensor techniques and available models are discussed, to give an overview of modeling techniques that analyze data, with the aim of coupling the soft sensor predictions to the control and optimization of cellulose to ethanol fermentation. The paper ends with a discussion of future needs and developmentsThis work was partially financed by the European Regional Development Fund (ERDF) and Region Zealand (Denmark) through the BIOPRO-SMV project. Furthermore, the work received funding from Innovation Fund Denmark (BIOPRO2 strategic research center, project number 4105-00020B). This project has also been supported partially by the EUDP project ‘Demonstration of 2G ethanol in full scale, MEC’ (Jr. no. 64015–0642). Finally, we wish to acknowledge the support obtained from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement number 713683 (COFUNDfellowsDTU) and from the Danish Council for Independent Research in the frame of the DFF FTP research project GREENLOGIC (grant agreement number 7017-00175A). Miguel Mauricio-Iglesias belongs to the Galician Competitive Research Group GRC2013-032 and the CRETUS strategic partnership (AGRUP2015/02), co-funded by FEDER (EU)S

Development of Biomimetic-Based Controller Design Methods for Advanced Energy Systems

A biologically inspired optimal control strategy, denoted as BIO-CS, is proposed for advanced energy systems applications. This strategy combines the ant\u27s rule of pursuit idea with multi-agent and optimal control concepts. The BIO-CS algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower agents\u27 local interactions. The developed BIO-CS is integrated with an Artificial Neural Network (ANN)-based adaptive component for further improvement of the overall framework. In particular, the ANN component captures the mismatch between the controller and the plant models by using a single-hidden-layer technique with online learning capabilities to augment the baseline BIO-CS control laws. The resulting approach is a unique combination of biomimetic control and data-driven methods that provides optimal solutions for dynamic systems.;The applicability of the proposed framework is illustrated via an Integrated Gasification Combined Cycle (IGCC) process with carbon capture as an advanced energy system example. Specifically, a multivariable control structure associated with a subsystem of the IGCC plant simulation in DYNSIMRTM software platform is addressed. The proposed control laws are derived in MATLAB RTM environment, while the plant models are built in DYNSIM RTM, and a previously developed MATLABRTM-DYNSIM RTM link is employed for implementation purposes. The proposed integrated approach improves the overall performance of the process up to 85% in terms of reducing the output tracking error when compared to stand-alone BIO-CS and Proportional-Integral (PI) controller implementations, resulting in faster setpoint tracking.;Other applications of BIO-CS addressed include: i) a nonlinear fermentation process to produce ethanol; and ii) a transfer function model derived from the cyber-physical fuel cell-gas turbine hybrid power system that is part of the Hybrid Performance (HYPER) project at the National Energy Technology Laboratory (NETL). Other theoretical developments in this work correspond to the integration of the BIO-CS approach with Multi-Agent Optimization (MAO) techniques and casting BIO-CS as a Model Predictive Controller (MPC). These developments are demonstrated by revisiting the fermentation process example. The proposed biologically-inspired approaches provide a promising alternative for advanced control of energy systems of the future

The Artificial Intelligence Workbench: a retrospective review

Last decade, biomedical and bioinformatics researchers have been demanding advanced and user-friendly applications for real use in practice. In this context, the Artificial Intelligence Workbench, an open-source Java desktop application framework for scientific software development, emerged with the goal of provid-ing support to both fundamental and applied research in the domain of transla-tional biomedicine and bioinformatics. AIBench automatically provides function-alities that are common to scientific applications, such as user parameter defini-tion, logging facilities, multi-threading execution, experiment repeatability, work-flow management, and fast user interface development, among others. Moreover, AIBench promotes a reusable component based architecture, which also allows assembling new applications by the reuse of libraries from existing projects or third-party software. Ten years have passed since the first release of AIBench, so it is time to look back and check if it has fulfilled the purposes for which it was conceived to and how it evolved over time

Hybrid simulation-optimization based approach for the optimal design of single-product biotechnological processes

In this work, we present a systematic method for the optimal development of bioprocesses that relies on the combined use of simulation packages and optimization tools. One of the main advantages of our method is that it allows for the simultaneous optimization of all the individual components of a bioprocess, including the main upstream and downstream units. The design task is mathematically formulated as a mixed-integer dynamic optimization (MIDO) problem, which is solved by a decomposition method that iterates between primal and master sub-problems. The primal dynamic optimization problem optimizes the operating conditions, bioreactor kinetics and equipment sizes, whereas the master levels entails the solution of a tailored mixed-integer linear programming (MILP) model that decides on the values of the integer variables (i.e., number of equipments in parallel and topological decisions). The dynamic optimization primal sub-problems are solved via a sequential approach that integrates the process simulator SuperPro Designer® with an external NLP solver implemented in Matlab®. The capabilities of the proposed methodology are illustrated through its application to a typical fermentation process and to the production of the amino acid L-lysine.Support from the Spanish Ministry of Education and Science (projects DPI2008-04099 and CTQ2009-14420-C02) and the Spanish Ministry of External Affairs (projects A/023551/09, A/031707/10 and HS2007-0006)

Model-Based Evolutionary Operation Design for Batch and Fed- Batch Antibiotic Production Bioprocesses

The control policy determination for batch and fed-batch antibiotic production bioprocesses is an important practical issue due to the high added value of these bioproducts. Since it is highly desirable to optimize the antibiotic production, several methods have been proposed aimed at this objective. Once having a mathematical model for the bioprocess, the optimization problem can be formulated within the framework of Pontryagin\u27s maximum principle and of the optimal control theory to determinate the best control trajectory for certain key manipulated variables, such as temperature, pH, and substrate feed rate. In this chapter, applications of these model-based techniques to optimize and control antibiotics production bioprocesses are reviewed and new aspects are emphasized. The cases analyzed included the optimization of the substrate feed rate in a fed-batch reactor and of the temperature in a batch reactor during penicillin fermentations. The main contributions of this study were: (i) the proposition of a different procedure for calculating the second switching time of substrate feed rate, (ii) the application of simpler numerical methods to solve the two-point boundary-value problem associated with the temperature profile optimization, and (iii) the demonstration that the non-isothermal operation is more productive in antibiotic than the operation under constant temperature