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 for the Design of Lithium-Ion Battery Packs and Battery Cells
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2013-12-05) Rodríguez Montoya, César Alejandro; Sosa Hernández, Víctor Adrián; emimmayorquin; School of Engineering and Sciences; Campus Estado de México
In the contemporary landscape, the prevailing shift towards the adoption of electric vehicles for personal transportation has propelled lithium batteries into the spotlight. Consequently, the demand for better-optimized batteries has surged, driven by the aspiration for enhanced performance without compromising cost-effectiveness or longevity. This research delves into the use of evolutionary algorithms in the pursuit of lithium battery optimization. To address this multifaceted challenge, we have formulated the battery design problem as a constrained many-objective optimization problem (CMaOP). Within this context, our set of objective functions encompasses critical battery attributes: the maximization of specific energy, and durability; and the minimization of heat generation, and price. The decision variables encapsulate various physical characteristics of the battery that can be fine-tuned during the manufacturing process. These variables include the choice of materials for the positive electrode, dimensions of individual layers, and geometric characteristics of the battery canister, among others. The investigation is conducted with a specific focus on three distinct applications: electric vehicles, drones, and cell phones. The requirements of these applications establish the constraints of the problem. To tackle this problem, we have extended and adapted the Island-based Multi-Indicator Algorithm (IMIA) framework yielding into the Island-based Multi-Indicator Constraint-handler Algorithm (IMICA). The algorithm relies on the cooperative work of various quality indicators to favor the generation of optimized solutions while meeting the concepts of coverage and distribution in the Pareto front approximation. The algorithm was able to solve the problem efficiently. When compared with the Non-dominated Sorting Genetic Algorithm-III (NSGA-III), the algorithm managed to find a greater number of non-dominated feasible solutions and a greater hypervolume. Furthermore, the solutions found by the algorithm also prove to be competitive against standardized batteries.

Ciencias Exactas y Ciencias de la Salud

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