Process Systems Engineering Tools for Optimization of Trained Machine Learning Models: Comparative and Perspective
Francisco Javier López-Flores,
César Ramírez-Márquez,
José María Ponce-Ortega
Abstract:This article studies the relevance of innovative Process Systems Engineering (PSE) tools that can reformulate trained machine learning models that are driven by advances in computational technologies, showcasing a pivotal transformation in chemical engineering methodologies. The article also delves into how trained machine learning models are reformulated and optimized to refine engineering decisions as it provides a novel analysis of tools to develop machine learning models by reformulating them, and optimizi… Show more
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