Process Systems Engineering Tools for Optimization of Trained Machine Learning Models: Comparative and Perspective,Industrial & Engineering Chemistry Research

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Process Systems Engineering Tools for Optimization of Trained Machine Learning Models: Comparative and Perspective
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2024-07-17 , DOI: 10.1021/acs.iecr.4c00632
Francisco Javier López-Flores ₁ , César Ramírez-Márquez ₁ , José María Ponce-Ortega ₁

Affiliation

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 optimizing them in PSE, thus highlighting their significance and applications. It offers a comprehensive comparison of several cutting-edge tools, including JANOS, MeLOn, ENTMOOT, reluMIP, OptiCL, Gurobi Machine Learning, OMLT, and PySCIPOpt-ML, highlighting their distinct abilities for performance and decision-making. Furthermore, challenges related to the explicit formulation of the main machine learning models are discussed. Guidance is provided to select the appropriate tool according to users’ requirements. Additionally, a comparative study of the tools is presented using a case study to analyze and compare the size and type of formulations, the optimal solution, and the computation times.

中文翻译：

用于优化经过训练的机器学习模型的过程系统工程工具：比较和透视

本文研究了创新过程系统工程 (PSE) 工具的相关性，这些工具可以重新制定受计算技术进步驱动的训练有素的机器学习模型，展示化学工程方法论的关键转变。本文还深入探讨了如何重新制定和优化训练有素的机器学习模型以完善工程决策，因为它提供了一种新颖的工具分析，通过重新制定模型并在 PSE 中优化它们来开发机器学习模型，从而强调了它们的重要性和应用。它对 JANOS、MeLOn、ENTMOOT、reluMIP、OptiCL、Gurobi Machine Learning、OMLT 和 PySCIPOpt-ML 等多种尖端工具进行了全面比较，突出了它们独特的性能和决策能力。此外，还讨论了与主要机器学习模型的显式表述相关的挑战。提供指导以根据用户的要求选择合适的工具。此外，还通过案例研究对这些工具进行了比较研究，以分析和比较配方的大小和类型、最佳解决方案和计算时间。

更新日期：2024-07-17

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