Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework

Many industrial applications rely on real-time optimization to improve key performance indicators. In the case of unknown process characteristics, real-time optimization becomes challenging, particularly for the satisfaction of safety constraints. In this paper, we demonstrate the application of an adaptive and explorative real-time optimization framework to an industrial refrigeration process, where we learn the process characteristics through changes in process control targets and through exploration to satisfy safety constraints. We quantify the uncertainty in unknown compressor characteristics of the refrigeration plant by using Gaussian processes and incorporate this uncertainty into the objective function of the real-time optimization problem as a weighted cost term. We adaptively control the weight of this term to drive exploration. The results of our simulation experiments indicate the proposed approach can help to increase the energy efficiency of the considered refrigeration process, closely approximating the performance of a solution that has complete information about the compressor performance characteristics.

Adaptive Real Time Exploration and Optimization for Safety-Critical Systems

We consider the problem of decision-making under uncertainty in an environment with safety constraints. Many business and industrial applications rely on real-time optimization with changing inputs to improve key performance indicators. In the case of unknown environmental characteristics, real-time optimization becomes challenging, particularly for the satisfaction of safety constraints. We propose the ARTEO algorithm, where we cast multi-armed bandits as a mathematical programming problem subject to safety constraints and learn the environmental characteristics through changes in optimization inputs and through exploration. We quantify the uncertainty in unknown characteristics by using Gaussian processes and incorporate it into the utility function as a contribution which drives exploration. We adaptively control the size of this contribution using a heuristic in accordance with the requirements of the environment. We guarantee the safety of our algorithm with a high probability through confidence bounds constructed under the regularity assumptions of Gaussian processes. Compared to existing safe-learning approaches, our algorithm does not require an exclusive exploration phase and follows the optimization goals even in the explored points, which makes it suitable for safety-critical systems. We demonstrate the safety and efficiency of our approach with two experiments: an industrial process and an online bid optimization benchmark problem.