Optimization in preparation process of V₂O₅ using symbolic regression α-β. - González Campos, Guillermo (2019)
In this work a symbolic regression algorithm was used for modeling the preparation process of the compound V2O5. This algorithm was used with the proposal method on this work, were 8 steps are proposed to have better and faster results when symbolic regression is applied for industrial processes. The preparation of V2O5 consists in modifying the solvent of the medium and calcination temperature to obtain a compound with different physical properties. A mathematical model of four input variables and one output response was generated. This compound is widely used in catalysis and photocatalysis to solve environmental problems, such as water and air purification. The aim of creating a model was to obtain the best combination of preparation variables that result in a compound with the ideal properties, which promote a better performance in catalysis and photocatalysis. A design of experiments was made to obtain a datadriven model. Initial population, selection and iterations were considered to enhance the results when symbolic regression is applied. Genetic programming, artificial neural networks and linear regression were compared with symbolic regression in order to know which technique has better efficiency to generate models. Some metrics related with quality and deliveries of the model were proposed to compare the results. The model generated with symbolic regression showed better results in comparison with other techniques used, due to the metrics used describes less error and better performance to predict new data. Responses surfaces were made using the generated SR model and were compared showing different perspective in order to optimize the quantity of materials used for the preparation process of V2O5.