Self-optimization of energy consumption in complex bulk good processes using reinforcement learning

This paper presents a novel approach to the optimization of energy consumption in large scale industrial bulk good processes. The approach is based on a model-free self-learning algorithm solely based on available process data using ideas from the well known reinforcement learning framework. To this end energy consumers of the plant are integrated in the optimization framework such that each consumer learns its own optimal energy profile for a given production task. The approach is implemented on a laboratory size testbed where the task is the supply of bulk good to a subsequent dosing section. The capability of the approach is underlined by the results obtained at the testbed.