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.
The use of evolutionary algorithms to tackle the energy economy parameter-dependent multi-objective optimization problem in fuel-cell hybrid electric vehicles
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-11) Sánchez Spínola, Luis Fernando; Sosa Hernández, Víctor Adrián; dnbsrp; Falcón Cardona, Jesús Guillermo; Pescador Rojas, Miriam; Escuela de Ingeniería y Ciencias; Campus Estado de México
Many real-world applications seek the optimal parameter settings to enhance the performance of specific targets. For example, to travel from point A to point B in the least amount of time. In some cases, one function’s improvement causes the worsening of another, leading to searching for the combination of these design parameters that provides a balanced trade-off between these objective functions. For instance, to have the best fuel consumption efficiency while reducing the traveling time from point A to point B. These kinds of problems are named multi-objective optimization problems (MOPs). Furthermore, an extension of these problems where external parameters influence the behavior of the objective functions are named parameter-dependent MOPs (PMOPs). The essence of these parameters is that they cannot be controlled by the decision-maker and are independent of the optimization process. In the latter example, the environmental temperature or the traffic density are denoted as independent parameters. Despite the state-of-the-art proposals to tackle PMOPs, the lack of further research regarding these problems is addressed. Therefore, the scope of this work is based on expanding the application fields of evolutionary algorithms for tackling a real-world PMOP application. The energy economy problem in fuel-cell hybrid electric vehicles (FCHEVs) can be stated as a PMOP, where some design parameters involved in the performance optimization of the fuel consumption and total mechanical power produced by the electric motor are subject to the total vehicle’s mass. By implementing an evolutionary algorithm embedded with a generated surrogate model for emulating the responses of the objective functions, broader solutions in terms of speed and quality can be produced. A FCHEV is modeled using the MATLAB Simulink software toolbox. Then, we generate several surrogate models for reducing computational time by using the Latin hypercube sampling to build the training and testing data. Subsequently, the Bayesian correlated t-test is performed to select the best surrogate model out of four available choices. Afterward, the Parameter-dependent island-based multi-indicator algorithm (P-IMIA) is proposed to take advantage of the information on the family of fronts simultaneously instead of tackling one front at a time. In addition, P-IMIA is compared to the state-of-the-art adaptations by utilizing the Wilcoxon rank sum test and the Bayesian correlated t-test. The obtained results showed that the proposed algorithm surpasses the speed of convergence of the state-of-the-art adaptations and maintains a competitive quality concerning the encountered optimal solutions sets. Besides, introducing a surrogate model approach for emulating the objective function responses for the evolutionary optimization provides quality solutions when comparing them to the MATLAB simulation model responses.

Ciencias Exactas y Ciencias de la Salud

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