Reinforcement learning-based control system for biogas plants in laboratory scale

Reinforcement learning techniques can be used to learn effective policies for complex tasks, but they are rarely applied for control of biogas plants.While control of the anaerobic process is necessary for optimal plant operation, process complexity and instability prevent the usage of advanced control mechanisms in industrial settings.In this study, a proximal-policy optimization algorithm has been applied on the feeding schedule of a lab-scale biogas reactor for biomethane conversion to electrical energy depending on dynamic energy prices.The algorithm effectively optimizes feeding and selling strategies, outperforming traditional methods.