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Application of the Simultaneous Perturbation Stochastic Approximation Algorithm for Process Optimization: Case Study
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Author(s): Juan Carlos Castillo Garcia (Universidad Autónoma de Baja California, Mexico), Jesús Everardo Olguín Tiznado (Universidad Autónoma de Baja California, Mexico), Claudia Camargo Wilson (Universidad Autónoma de Baja California, Mexico), Juan Andrés López Barreras (Universidad Autónoma de Baja Calirfonia, Mexico)and Rafael García Martínez (Instituto Tecnológico de Guaymas, Mexico)
Copyright: 2020
Pages: 26
Source title:
Design of Experiments for Chemical, Pharmaceutical, Food, and Industrial Applications
Source Author(s)/Editor(s): Eugenia Gabriela Carrillo-Cedillo (Universidad Autónoma de Baja California, Mexico), José Antonio Rodríguez-Avila (Universidad Autónoma del Estado de Hidalgo, Mexico), Karina Cecilia Arredondo-Soto (Universidad Autónoma de Baja California, Mexico)and José Manuel Cornejo-Bravo (Universidad Autónoma de Baja California, Mexico)
DOI: 10.4018/978-1-7998-1518-1.ch014
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Abstract
There are different techniques for the optimization of industrial processes that are widely used in industry, such as experimental design or surface response methodology to name a few. There are also alternative techniques for optimization, like the Simultaneous Perturbation Stochastic Approaches (SPSA) algorithm. This chapter compares the results that can be obtained with classical techniques against the results that alternative linear search techniques such as the Simultaneous Perturbation Stochastic Approaches (SPSA) algorithm can achieve. Authors start from the work reported by Gedi et al. 2015 to implement the SPSA algorithm. The experiments allow authors to affirm that for this case study, the SPSA is capable of equalizing, even improving the results reported by the authors.
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