A hybrid modeling strategy based on deep learning surrogate models for accurate process multi-objective optimization of iso-octanol oxidation

Abstract

Utilizing artificial intelligence to assist in the development of green processes for alcohol oxidation is a challenging and time-consuming task due to the lack of massive data and adequate optimization objectives. To solve these challenges, our work presents a hybrid surrogate model for iso-octanol oxidation to iso-octanal, integrating data-driven approaches with chemical equations grounded in mass transfer, heat transfer, momentum transfer, and reaction engineering, to enhance problem-solving efficiency. Specifically, a precise mechanistic model based on Aspen Plus generated database is developed to enhance the utility of experimental data, thereby overcoming the challenge of scarce oxidation experimental data caused by long operating cycles and hydrogen safety concerns. Based on this database, integrating machine learning techniques and intelligent optimization algorithms can quickly determine the optimal operating conditions for the iso-octanol oxidation reaction system. Compared to direct process simulation and multi-objective optimization methods, surrogate models exhibit higher efficiency, with computational speeds exceeding 400 times than those of traditional methods. The optimization results reveal significant reductions in both primary energy demand and greenhouse gas emissions, underscoring the effectiveness of the optimized solutions. Our work not only propels real-time optimization of alcohol oxidation production processes but also lays the groundwork for their widespread industrial application.

Graphical abstract

Keywords

deep learning / surrogate models / hybrid models / multi-objective optimization

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