Application Study Of Intelligent Control Method On Pellets Sintering Process

Grate-rotary kiln oxide pellets sintering process is a dynamic process with matter transmission, heat exchange and complicated chemical reaction. From the control view, the sintering process is a kind of typical complex object due to its nonlinearity, distributed parameters, time-varying and model uncertainty. A great deal of uncertainty information and multiplex magnanimity datum make it difficult to perform the control task of total sintering process effectively by using conventional control methods. The intelligent control theory provides an efficient approach to realize the control of this kind of complex systems.Based on the review of the related research references, the detailed investigation on the mechanism and technique and the comprehensive collection of production history datum, expert experience and manipulation regulations of pellets sintering process, an intelligent control strategy is proposed by comprehensive utilization of pellets sintering theory, computer control technology, modern control theory and artificial intelligent technique. The strategy includes the multivariable PID decoupling control model of ball milling pulverizing systems, the temperature controller of the rotary kiln process and the soft sensing model of chemical composition of the finished sinter mineral. This dissertation has carried on the following research.(1) A ball mill coal pulverizing system of pelletizing plant is a complex nonlinear multivariable process with strongly coupling and time-delay. A new multivariable PID decoupling controller is proposed based on the mathematics model of pulverizing system, which consists of diagonal matrix method-based decomposition compensatory unit, PID controller and fuzzy self-tuning components unit with scaling factor α . Particle swarm optimization (PSO) algorithm is adopted to optimize parameters of the PID controller. Simulation results show the validity of the obtained model and the proposed control method.(2) Based on the idea of the knowledge reduction of the rough sets (RS) theory and the nonlinearity mapping of Takagi-Sugeno fuzzy neural network (FNN), a kind of RS-FNN control method is presented and applied to the rotary kiln sintering process. The fuzzy c-means (FCM) clustering method based on a new cluster validity index function is used to obtain the optimal discrete values of the continuous attributes. RS theory is adopted to obtain the reductive rules using industrial history datum and corresponding T-S fuzzy model has better topology configuration reflecting system characteristics.