Using Prior Knowledge to Improve Adaptive Real Time Exploration and Optimization

Real-time optimization strategies aim to improve the operating performance of a process using a model of its input-output behaviour. This is challenging when the true system characteristics are not fully known and there are safe operating limits that must be respected. In this work, we evaluate the performance of an adaptive, real-time, exploration and optimization algorithm on a simulated refrigeration plant, and show how incorporating prior knowledge based on engineering principles can improve its performance, especially during the early stages of learning when there are few observed data. The results indicate that exceedances of the safe operating limit are avoided and the improved models still learn the true system characteristic, albeit more slowly than the standard models fitted without prior knowledge.