Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge

2026-06-07

AI Summary

This work proposes a real-time optimization method for multi-product chemical reactors lacking reliable mechanistic models, balancing economic performance and safety. The approach employs Gaussian processes to model product concentrations and temperature, incorporating steady-state energy balance as a physics-informed residual constraint. Within a Bayesian optimization framework, an analytical profit expression preserves the structure of the economic objective, while an upper confidence bound strategy conservatively enforces temperature constraints. Feasible operating points are further refined using the physical residual to eliminate unsafe candidates. In simulation studies, the method demonstrates significantly improved economic performance over conventional safe Bayesian optimization and effectively mitigates the risk of temperature violations inherent in purely data-driven approaches.

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 preserves the structure of the economic objective, makes it parametric in changing prices without needing retraining, and allows candidate operating points to be checked against the available energy balance through a physics residual. The GPs also provide predictive uncertainty, which is exploited in a Bayesian optimization (BO) framework both for data-efficient exploration and for conservative enforcement of the reactor temperature constraint through an upper confidence bound. The acquisition function additionally penalizes large energy-balance mismatch obtained by substituting the GP-predicted outputs and candidate inputs into the available steady-state energy balance. The approach is demonstrated on a benchmark simulation of a non-isothermal multi-product reactor. Relative to a trust-region safe BO implementation, the proposed method achieves better simulated economic performance within the available iteration budget. Relative to a purely data-driven BO approach that does not use the available physics information, it avoids reactor temperature constraint violations.

Problem

• Bayesian optimization
• multi-product chemical reactor
• economic optimization
• partial physics knowledge
• temperature constraint

Innovation

• Bayesian Optimization
• Composite Modeling
• Physics-Informed Machine Learning
• Gaussian Processes
• Real-Time Economic Optimization