Ciencias Exactas y Ciencias de la Salud

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Optimization of kinetic and operating parameters in bioreactors using evolutionary algorithms
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Barrera Hernández, Gonzalo Irving; Sosa Hernández, Víctor Adrián; emipsanchez; Alfaro Ponce, Mariel; Aranda Barradas, Juan Silvestre; Corrales Muñoz, David Camilo; School of Engineering and Sciences; Campus Estado de México; Gómez Acata, Rigel Valentín
Bioreactors play a role in creating biological products such as medicines and biofuels by care fully controlling factors such as substrate levels and temperature within them to obtain optimal production results, bioreactor production process poses a challenge that poses a challenge to engineers due to the intricate setup involved. In the field of microbiology and biotechnology, conventional approaches such as the Monod model, logistic growth models, and fed-batch techniques have been employed to predict and improve the growth conditions of microor ganisms and the production of proteins of interest in fermenters. However, these approaches could face challenges when they encounter nonlinear systems and conflicting objectives. To address these challenges, our suggestion is to approach the configuration of factors in bioreactors as an optimization problem using an evolutionary algorithm that can improve the effectiveness and quality of the operating process. The objective of this study is to in vestigate and create a pipeline that integrates evolutionary algorithms to solve multi-objective and scalar optimization problems, aimed at identifying kinetic and critical parameters within a bioreactor system. The optimization process involves, in the first stage, a least squares ap proach that considers product, biomass, dissolved oxygen, and substrate concentrations as objectives, with the kinetic parameters (e.g., maximum specific growth rate and substrate affinity) serving as decision variables. The second stage focuses solely on maximizing the amount of produced product, specifically biomass, using critical operational variables, such as feed rate and aeration, as decision variables. The research employs Escherichia coli as a microorganism that has been genetically al tered to produce orange fluorescent protein (OFP) to test the validity of improvement frame works. Initially, in the simulation and process tuning phase, experimental information, from batch cultures, is used to accurately determine the factors. Later, in the fed-batch phase, the application of an algorithm is used to optimize biomass yield while considering operational constraints such as oxygen levels and maximum reactor volume. The findings show that this method accurately calculates factors during the fed-batch phase and efficiently increases biomass production in the continuous fed phase. The use of algorithms such as multiple NSGA-III and single-objective genetic algorithms provides valuable benefits when dealing with intricate bioreactor configurations that have conflicting objectives such as managing substrate consumption and improving production yield. This approach has promising prospects for improving the accuracy and efficiency of bioprocess optimization, while increasing its scalability, in the field of biotechnology in the future.
Introducing Sequence-based Hyper-heuristics with Multiple Points of Interpretation
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06) Garrafa Pacheco, Leonardo Francisco; Ortiz Bayliss, José Carlos; emimmayorquin; School of Engineering and Sciences; Campus Estado de México
Hyper-heuristics are a type of search methods used for solving optimization problems. This field is relatively new and has caught the attention of researchers because it employs existing heuristics to construct solutions for specific problems. In other words, instead of inventing new technics, they combine already available technics to tackle optimization problems. There are two kinds of hyper-heuristic models in the literature: "rule-based", which rely on a set of rules that guide the solver to decide what heuristic to perform next, and "sequence-based", which rely on a sequence of heuristics to apply. One remarkable characteristic of sequence-based models is that they do not need to identify features that map the problem state but represent the actions to make at each decision step. Furthermore, current works have shown that the sequence length does not need to equal the number of required decisions to find a good solution. Instead, the sequence can be small and repeated under a looping schema to fulfill the required number of decisions. Although employing looping schemas seems to provide suitable solutions, they may be somewhat restrictive due to their fixed nature and other limitations. For instance, the current looping schemas require repeating all the elements in the sequence of actions, which could be very disruptive during the learning stage of the hyper-heuristic because any change of the sequence is a change in each of their repetitions. In this sense, a relaxation of the looping schemas could improve the performance of the models. To that end, this work presents two models that learn their looping schemas by interpreting their sequence of actions from different positions and approaches: the Bidirectional Point Of Interpretation (BPOI) model and the Partial Bidirectional Point Of Interpretation (PPOI) model. We found that the PPOI not only can produce reasonable solutions to solve problems but also keeps the length of the sequence small. Furthermore, we introduced the notion of a length penalization to keep the sequence small, which from experiments, also seems to improve the models' performances.

Ciencias Exactas y Ciencias de la Salud

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