Adaptive Real-Time Exploration and Optimization of Safety-Critical Industrial Systems with Ensemble Learning

Real-time optimization plays a key role in improving energy efficiency and the operational effectiveness of industrial systems. To deal with unknown process characteristics and safety constraints, a novel safe adaptive real-time exploration and optimization (ARTEO) algorithm is proposed recently for safety-critical industrial systems. ARTEO utilizes the Gaussian process (GP) regression to model unknown plant characteristics and enforces safety constraints using confidence intervals provided by the GP models. Due to changing process characteristics, the GP models need to be updated online by incorporating new observations and the computational complexity of model adaptation increases with a growing dataset. This work proposes an alternative ARTEO implementation by using ensemble learning, namely Ensemble-ARTEO. The Ensemble-ARTEO learns unknown plant characteristics through an ensemble of parametric regression models and calculates uncertainty by the variance of ensemble predictions. The predictive uncertainty is integrated into the optimization objective to further drive exploration. The ensemble members are updated efficiently online to capture the changing process characteristics. We demonstrate the effectiveness of our proposed Ensemble-ARTEO approach in an industrial refrigeration process. Experimental results show that our method enables tracking the desired cooling demand while satisfying the safety constraints.