Introduction  Limestone-gypsum wet desulfurization is a flue gas desulfurization technology with the most extensive application and the most mature technology in coal-fired power plants. Limestone slurry preparation is one of the high-energy -consuming processes, which has complex production process and difficult to correspond to material consumption and energy consumption. There is no reasonable and effective energy consumption prediction model. In order to establish a reliable pulping system energy consumption model that can guide the optimization of production parameters.

  Method  Based on actual operating data of a 600 MW power plant, the controllable quantity in the production process was selected as input, and the time-delay relationship between each input variable was adjusted by mutual information theory. The improved BP neural network combined with genetic algorithm (GA) was used to establish the unit pulp energy consumption model of the pulping system.

  Result  The experimental results show that compared with the unadjusted timing GA-BP model and the standard BP algorithm model, the calculation results of the GA-BP model with time series adjustment can more accurately approach the actual production data of the pulping system.

  Conclusion  The established model can be applied to the energy optimization study of the slurry preparation process.