Global flowsheet optimization for reductive dimethoxymethane production using data-driven thermodynamic models

Burre, Jannik; Kabatnik, Christoph; Al-Khatib, Mohamed; Bongartz, Dominik; Jupke, Andreas; Mitsos, Alexander

doi:10.1016/j.compchemeng.2022.107806

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Cited by 3 publications

References 61 publications

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Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points

Esders,

Ramirez,

Gastegger

et al. 2024

Computers & Chemical Engineering

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Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points

Esders,

Ramirez,

Gastegger

et al. 2024

Computers & Chemical Engineering

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Predictive data-driven models of thermophysical properties using Gaussian process regression

Zhou,

Ni,

Wang

2024

AIAA SCITECH 2024 Forum

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An Integrated Method of Bayesian Optimization and D-Optimal Design for Chemical Experiment Optimization

Huang

Zhang

et al. 2022

Processes

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The smart chemical laboratory has recently emerged as a promising trend for future chemical research, where experiment optimization is of vital importance. The traditional Bayesian optimization (BO) algorithm focuses on exploring the dependent variable space while overlooking the independent variable space. Consequently, the BO algorithm suffers from becoming stuck at local optima, which severely deteriorates the optimization performance, especially with bad-quality initial points. Herein, we propose a novel stochastic framework of Bayesian optimization with D-optimal design (BODO) by integrating BO with D-optimal design. BODO can balance the exploitation in the dependent variable space and the exploration in the independent variable space. We highlight the excellent performance of BODO even with poor initial points on the benchmark alpine2 function. Meanwhile, BODO demonstrates a better average objective function value than BO on the benchmark Summit SnAr chemical process, showing its advantage in chemical experiment optimization and potential application in future chemical experiments.

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Global flowsheet optimization for reductive dimethoxymethane production using data-driven thermodynamic models

Cited by 3 publications

References 61 publications

Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points

Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points

Predictive data-driven models of thermophysical properties using Gaussian process regression

An Integrated Method of Bayesian Optimization and D-Optimal Design for Chemical Experiment Optimization

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