Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework
This work addresses the problem of safe and efficient optimization in industrial refrigeration for engineers and operators, representing an incremental improvement by applying known methods to a specific domain.
The paper tackled the challenge of real-time optimization for an industrial refrigeration process with unknown compressor characteristics, using an adaptive framework that incorporates Gaussian process uncertainty and adaptive exploration to satisfy safety constraints, resulting in energy efficiency improvements that closely approximate the performance of a solution with complete information.
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.