基于MI时序处理的GA-BP脱硫制浆系统能耗建模

Energy Consumption Modeling of GA-BP for Desulfurization Pulping System Based on MI Time Series Processing

[目的]石灰石—石膏湿法脱硫是目前燃煤电厂应用最广泛、技术最为成熟的一种烟气脱硫技术,石灰石浆液制备作为其中一道高耗能工序,具有生产过程复杂,物耗与能耗间存在非线性关系等特点,缺乏合理有效的能耗模型。为建立能够对生产参数优化提供指导的可靠的制浆系统能耗模型。[方法]基于某600 MW电厂实际运行数据,选择生产过程中的可控制量作为输入,并基于互信息(Mutual Information)理论调整各输入变量间的时滞关系,采用结合遗传算法(Genetic algorithm,GA)的改进BP神经网络建立了制浆系统的单位制浆能耗模型。[结果]试验结果表明:与未调整时序的GA-BP模型和标准BP算法的模型相比,经过时序调整的GA-BP模型的计算结果能够更为准确地接近制浆系统生产实际数据。[结论]所建立的模型可以应用到浆液制备过程的能耗优化研究中。

[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.