Data-driven Modeling and Self-organizing Control of Municipal Solid Waste Incineration Process

Municipal solid waste incineration (MSWI) is one of the main means to dispose of municipal solid waste (MSW). MSW in China has a wide range of sources, complex components, and large fluctuations in calorific value. Its incineration process usually relies on manual intervention. This will lead to a low degree of intelligence in the MSWI process and it is difficult to meet the increasing control requirements. MSWI has many uncertain characteristics such as multivariable coupling and working condition drift, so it is difficult to build the model of controlled object and design the on-line controller. To solve the above problems, this paper proposes a data-driven modeling and self-organizing control method for MSWI process. Firstly, the model of controlled object based on multi-input multi-output Takagi Sugeno fuzzy neural network (MIMO-TSFNN) is constructed. Secondly, a multi-task learning self-organizing fuzzy neural network controller (MTL-SOFNNC) is designed to synchronously control the furnace temperature and flue gas oxygen content, which can self-organize the structural parameters of the controller by calculating the similarity of neurons and the ability of multi-task learning (MTL). Meanwhile, the stability of MTL-SOFNNC is proved by Lyapunov theorem. Finally, the effectiveness of the model and controller is verified by the process data of an MSWI plant in Beijing. © 2023 Science Press. All rights reserved.