Knowledge-based real-time scheduling for gas supply network using cooperative multi-agent reinforcement learning and predictive functional range control

Abstract

In steel enterprises, the real-time scheduling optimization of the gas supply network can provide strong support for stabilizing production and enhancing economic benefits. Due to the coupling of multiple gas/liquid products and numerous units, centralized scheduling methods require large training and coordination overhead. Consequently, the real-time scheduling problem of a multi-product gas supply network is modeled under the cooperative multi-agent reinforcement learning (MARL) architecture, which makes the scheduling strategy of each unit keep the same optimization goal. Decentralized execution mode reduces the computing cost and information exchange compared with centralized execution mode. Different from adding constraint penalty terms as soft constraints, a constraint monitor module is designed by utilizing the process knowledge, ensuring the various production constraints are satisfied. This strategy can reduce trail-and-error costs, making it more conducive to industrial safety. To deal with the unknown disturbance in production (typically gas leakage), a predictive functional range control (PFRC) algorithm is then developed to modify the future gas demand. Finally, case studies are carried out on a real-world gas supply network to verify the performance of the proposed method.