Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge
arXiv:2606.08611v1 Announce Type: cross Abstract: We study data-driven real-time economic optimization of a multi-product chemical reactor when no reliable first-principles model is available beyond a steady-state energy balance. Instead of learning the economic objective directly as a black-box function, we use a composite formulation in which Gaussian process (GP) models predict physically meaningful outputs, including product concentrations and reactor temperature, while profit is computed analytically from these predictions together with raw-material, product, and utility prices. This pres
The paper demonstrates the application of advanced AI techniques, specifically Bayesian optimization with composite models, to a complex industrial process, aligning with current trends in AI for real-world applications.
This development allows for more efficient and optimized chemical production in scenarios with incomplete physics models, potentially leading to significant economic and environmental benefits in manufacturing.
The ability to economically optimize multi-product chemical reactors without full first-principles models shifts the paradigm for process control and efficiency in complex industrial settings.
- · Chemical manufacturing industry
- · AI/ML solution providers
- · Process control engineers
- · Industrial automation sector
- · Inefficient legacy chemical plants
Increased efficiency and reduced costs in chemical production through AI-driven optimization.
Broader adoption of data-driven composite models in other complex industrial processes where full first-principles models are elusive.
Accelerated development of adaptable, AI-powered 'lights-out' manufacturing facilities that continuously optimize diverse product lines.
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