Ciencias Exactas y Ciencias de la Salud

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Computer aided molecular design coupled with molecular dynamics and deep learning techniques as a novel approach to design new compounds
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-05) Valencia Márquez, Darinel; Flores Tlacuahuac, Antonio; emimmayorquin; García Cuéllar, Alejandro Javier; Santibáñez Aguilar, José Ezequiel; Aguirre Soto, Héctor Alán; Gutiérrez Limón, Miguel Ángel; Escuela de Ingeniería y Ciencias; Campus Monterrey
In this work is proposed a novel approach to develop new compounds, through computer aided molecular design (CAMD) using group contribution methodologies for estimation of properties, follow of machine learning or molecular simulations techniques for estimation and validation of the target properties of the design; finally, Monte Carlo simulations is performed for validation of the thermodynamical stability of the molecule designed. This thesis is divided into three main sections, first a case study of the machine learning approach used in this work for properties predictions, follow of two case studies of molecular design coupled where the initial design is obtained by computer aided molecular design (CAMD) follows of the validation with molecular dynamics or machine learning techniques. In the first case a neural network analysis was performed to improve the predictions of 15 properties. Results of input analysis shown that reduced coulomb matrix saves CPU time and memory compared with full coulomb matrix with a similar accuracy. On the other hand, the neural network architecture exhibits the importance of the activation function, and the number of hidden layers in the neural network. Second case present the methodology to design a lubricant from scratch, using CAMD with group contribution methodologies to make a design of an Ionic Liquid-based lubricant with the purpose of use as automotive lubricant; then a molecular dynamics simulation was performed to validate the value of the design properties, finally a Monte Carlo simulation was performed to observe the thermodynamic feasibility of the molecule design. The result shows a feasible molecule previously designed where experimental values are similar with estimations of group contribution and molecular dynamics. In the third case, a classical CAMD methodology is used to make a list of molecule designs for photocatalytic applications, where the target property is the maximum absorption wave length (lmax); on the other hand, deep learning techniques is applied to predict the gap between HUMO and LUMO energies for the estimation of lmax showing a shorter list of candidates to experimental test, in the same way the NN used in this study can extend to other optical or photocatalytic applications, because HUMO and LUMO energies rules most of the photochemical properties. Finally, the mixture design is presented; however, the validations is left for future work.
Studies on optimal design, operation and control of process and energy systems
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-07-07) Andrés Martínez, Oswaldo; Flores Tlacuahuac, Antonio; tolmquevedo; Escobar Valderrama, Gerardo; Mayo Maldonado, Jonathan C.; Rico Ramírez, Vicente; Puebla Núñez, Héctor; School of Engineering and Sciences; Campus Monterrey
The quest for more sustainable process and energy systems requires optimal design, operation and control solutions at different temporal and spatial scales. Greenhouse gas emission and waste reduction, and renewable energy sources integration are some ongoing concerns in chemical and power generation plants. These goals should be achieved while maximizing profits and meeting technical and environmental restrictions. Process systems engineering provides reliable computational and mathematical tools to address many of these emerging issues. In this regard, control and optimization techniques have the potential to handle sustainability related challenges in chemical engineering and other fields. Optimization can be applied at molecular, process and enterprise levels, while control policies can be deployed along time intervals of milliseconds, minutes, hours and days. In this work, studies on optimal design, operation and control in the context of chemical process and energy systems are presented. Subjects such as recovery of waste energy, national power flow systems, reactor operating policies, and integration of renewable energy are discussed. Case studies of different nature and complexity are tackled by means of mathematical programming and optimal control methods. Particularly, optimal molecular design under uncertainty, singular optimal control of chemical processes, optimal power flow of the Mexican electricity system, and model predictive control of dc-dc boost converters. Results show the capability of the utilized methods to produce optimal solutions that can be implemented in real settings or in further theoretical and practical developments.

Ciencias Exactas y Ciencias de la Salud

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