Multi-condition operational optimization with adaptive knowledge transfer for municipal solid waste incineration process

Abstract

To satisfy incineration efficiency and nitrogen oxides ultra-low emission requirements, it is of great importance to improve the operational performance of the municipal solid waste incineration (MSWI) process. However, due to the complex and fluctuating composition of municipal solid waste, it is challenging to guarantee optimal operation under changing conditions. To address this issue, a multi-condition operational optimization method based on adaptive knowledge transfer (MCOO-TR) is proposed for the MSWI process. First, an integrated optimization scheme, where different operation conditions are identified based on cluster analysis and expert knowledge, is developed. Second, a surrogate-assisted multi-objective particle swarm optimization algorithm is designed to solve the data-driven optimization problems for optimal operation, where the surrogate model selection mechanism is proposed to cope with multi-condition characteristics. Third, an adaptive knowledge transfer strategy, corresponding to condition identification, is designed aiming to improve optimization efficiency. Finally, the proposed MCOO-TR method is evaluated based on actual industrial data. The experimental results demonstrate that the MCOO-TR method can achieve satisfactory operational performance under changing operation conditions.