Deep reinforcement learning optimization framework for a power generation plant considering performance and environmental issues

In the electric power generation sector, striking a balance between maximum power production and acceptable emission limits is a challenging task that requires sophisticated techniques. With traditional methods, this is an extremely complex issue due to the large number of process variables that are involved. In this paper, a deep reinforcement learning optimization framework (DRLOF) is proposed to determine the optimal operating conditions for a commercial circulating fluidized bed (CFB) power plant that strikes a good balance between performance and environmental issues. The DRLOF included the CFB as an environment created from over 1.5 years of plant data with a 1 min sampling time which interacted with an advantage actor-critic (A2C) agent of two architectures named 'separate-A2CN' and 'sharedA2CN'. The framework was optimized by maximizing electric power generation within the constraints of the plant's capacity and environmental emission standards, taking into consideration the cost of operations. After training, the framework of the separate-A2CN architecture achieved a 1.97% increase in electricity generation and 1.59% emission reduction for NOx at 14.3 times lower computational cost. Furthermore, we demonstrated the framework's flexibility, adaptability and lower computational burden by carrying out different test scenarios which demonstrated the effectiveness of the DRLOF. The findings of this study are not limited to the CFB power plant but can be extended to other chemical processes and industries. This approach minimizes the need for costly experiments, online optimization challenges and associated customizations. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The paper introduces a deep reinforcement learning optimization framework (DRLOF) to determine optimal operating conditions for a commercial circulating fluidized bed (CFB) power plant, striking a balance between power production and emission limits. By training on plant data for 1.5 years and maximizing electricity generation within plant constraints, the framework achieved a 1.97% increase in power generation and 1.59% reduction in NOx emissions at a lower computational cost.