煤调湿控制系统的智能建模与优化方法研究

煤调湿工艺是炼焦过程节省能源、减少污染并提高生产效率和焦炭质量的关键工艺环节，为实现煤调湿过程的精确控制，针对其具有强耦合、大时滞、非线性系统的特性，采用RBF（Radical Basis Function）神经网络建模， GA（Genetic Algorithm）、PSO（Particle Swarm Optimization）和BFO（Bacterial Foraging Optimization）优化方法进行比较，获得不同工艺需求下的煤调湿控制系统建模与优化方法，为煤调湿过程的精确控制提供依据，为实现炼焦过程节省减排增效和提高焦炭质量创造条件。

Research on the intelligent modeling and optimization method of Coal moisture control system

Abstract] Coal moisture control process is a critical process in energy saving, pollution reduction and improving production efficiency and the quality of coke. To achieve precise control of coal moisture control system, against their strong coupling, large, nonlinear systems with time-delay characteristics using the RBF artificial neural network approach for modeling. And use the intelligent methods such as: GA (Genetic Algorithm), PSO (Particle Swarm Optimization), BFO (Bacterial Foraging Optimization) according to the fitness to optimize the RBF Neural network parameters .Also compare the RBF Neural network performance optimized by these intelligent methods in order to achieve better results. This method provides a theoretical basis for accurate control of coal moisture process. To created the conditions for the reduction of energy and pollution with improving the quality of coke.