Modeling And Optimization Of Lead-zinc Sintering Process For Product Index

The sintering agglomerate of the imperial updraft sintering process, which acts as an important flow process of Lead-Zinc pyrometallurgical smelter processes, has a direct influence on the subsequent states of the smelting furnace and the indexes of quantity and quality of Lead-Zinc. The synthetic permeability and the burn-through-point (BTP) are the two most important parameters of process states, and could directly reflect the performance of the sintering process. Considering that the information influencing the synthetic permeability and the burn-through-point (BTP) features uncertainties and incompletion, the thesis describes them from different aspects respectively, in order to build the corresponding integration prediction models for the synthetic permeability and the BTP.The prediction models, which are based on the study of the BTP and the synthetic permeability, for the quantity and the quality of the sintering agglomerate are proposed. To obtain good performance of sintering process with optimal states, the optimization of sintering process is divided into two parts. The one is the optimization of quantity and quality, and the other is the optimization of process states. First, to obtain the optimal set values of the process states, the optimization algorithm for the quantity and quality is devised. Then, to obtain the optimal set values of the operation parameters of the sintering process, the optimization algorithm for the parameters of process states is presented. The main achievements are as follows:(1) Integrated prediction models for parameters of the process statesFirst, the prediction model for the synthetic permeability based on the gray theory with the capability of modification on line is proposed, since the history data preserves the effective information that reflects the operation trend of future steady states. The corresponding formulas are proposed according to the monotonic changes of the datum sequence of the synthetic permeability and the data numbers after these changes. Then, the technological-parameter-based prediction model using neural networks for the synthetic permeability is established, which can timely reflect the influence brought by the technological parameters. Finally, the information entropy technology is applied to establish the integrated prediction model for the synthetic permeability, due to its abilities of decreasing the uncertainties and fusing the information.The soft measurement model for the BTP is developed, to solve the problem that the BTP can't be measured directly: Considering that the fluctuation of BTP is relatively big, the T-S prediction model for the BTP is presented to make full use of the history information. To decrease the effects produced by the uncertainties and lower the difficulty of modeling, the synthetic permeability, the strand velocity and the gas temperature of middle bellows are selected as the main factors impacting the BTP. Similarly, the prediction model based on neural networks is present. And the information entropy technology is applied to integrate the prediction models mentioned above.(2) Intelligent prediction models for the quantity and quality of the Lead-Zinc sintering agglomerateThe parameters of process states and some key parameters of raw material are chosen to build the prediction models for the quantity and quality of the sintering agglomerate using neural networks. The problem of the different time scales between the measurement period of quantity of the product and the sample period of parameters of process states is solved using the spatial integral method. To avoid the slow convergence and easily falling into the local optima of BP training algorithm based on gradient information, a hybrid particle swarm optimization (PSO) is employed to train the neural networks, which integrates the global search ability of the PSO and the powerful local search ability of the conjugate gradient algorithm.(3) Optimization of quantity and quality of the sintering agglomerate based on hybrid PSO algorithmAn optimization model with the constraint conditions of quality requirements is established to enhance quantity, and a hybrid PSO algorithm based on the improved line searching algorithm is presented to realize the optimization of quantity and quality. First, the optimization model is converted to a two-objective optimization problem, one of them is the origin objective function, and the other is the degree function of constraint violation. Then, to realize the parallel optimization, a constraint comparison method is applied to compare the searched results of the PSO. Finally, to handle the problem of premature convergence frequently appeared in the PSO, an improved line searching algorithm is introduced to maintain the particle activation when PSO stagnates. So the optimal set values of the synthetic permeability and the burn-through point could be achieved by using the hybrid PSO algorithm.(4) Optimization of parameters of the process states based on multi-objective particle swarm cooperative optimization algorithmWith the optimal set values of the synthetic permeability and the BTP as the optimization targets, the production frontier and quality requirements as the constraint conditions, and the parameters of process states as the optimization valuables, a multi-objective optimization model is developed. It is optimized by a multi-objective particle swarm cooperative optimization algorithm, which is improved using a new principle of selecting the particles' optima, and multiple swarms optimizing in a cooperative way, so that the optimal set values of process operation are obtained.Finally, the simulation experiments of the optimization of sintering process are implemented according to the characteristics of the practical production process. The results of experiments show the presented optimization method in the thesis improved the quantity and quality of the sintering agglomerate to some extent. So the presented optimization method provides technical means for the optimization of the whole sintering production process, and yields a practical and effective method for modeling and optimization of the complex industrial process.