Model prediction and optimal control of gas oxygen content for a municipal solid waste incineration process

Abstract

In the municipal solid waste incineration process, it is difficult to effectively control the gas oxygen content by setting the air flow according to artificial experience. To address this problem, this paper proposes an optimization control method of gas oxygen content based on model predictive control. First, a stochastic configuration network is utilized to establish a prediction model of gas oxygen content. Second, an improved differential evolution algorithm that is based on parameter adaptive and t-distribution strategy is employed to address the set value of air flow. Then, model predictive control is combined with the event triggering strategy to reduce the amount of computation and the controller's frequent actions. Finally, the sampled data of a solid waste incineration plant in Beijing are obtained for testing and validation. The experimental results show that the optimization control method proposed in this paper obtains a smaller degree of fluctuation in the air flow set value, which can ensure the tracking control performance of the gas oxygen content while reducing the amount of calculation.