Study On The Optimized Desulfuration Static Model Based On Intelligent Ant Algorithm

With the rapid development of economy, especially recent years, the domestic and overseas markets of steel-iron require the quality of steel, in particular the pure steel, higher and higher. However, the existence of sulfur brings about the large hidden trouble. So, it is a key strategy for many steel-iron-making enterprises which get victory relying on the quality to remove the sulfur efficiently, improve the quality of steel and design the new products in order to win more share in steel-iron market.However, presently the popular iron water pre-desulfuration method is mostly based on manual control which is too subjective to make a right decision for process and manipulation sometimes, which results in the instability of the process, harms the quality of steel and increases the production cost. On the background of panzhihua steel-iron corporation which is one of the big national steel-iron corporation, a static model of desulfuration is designed in this dissertation. The modeling process is based on Neural Network, which can automatically discover the rules and knowledge in desulfuration process, and then provide the decision support, improve the desulfuration effect and decrease the cost. The successful use of this static model will be a solid prerequisite for full automatic dusulfuration in the future.The Radial Based Function (RBF) neural network is adopted as the modeling algorithm. To overcome the difficulty in determining the RBF center numbers and spread, a kind of Intelligent Ant Algorithm is introduced, which follows the analysis of the basic rules of ant algorithm. The new hybrid algorithm determines the center numbers and spread adaptively to reach the optimal balance between the training accuracy and the generalization, so it increases the prediction accuracy of the model.Firstly the dissertation explains the ideas and characters of Ant Algorithm. Then on the basis of analyzing its development and limitation, Intelligent ant algorithm adding many advantages of some improved ant algorithms is adopted to optimize the center numbers and spread of RBF Neural Network. Aiming at the limitation of standard ant algorithm, this dissertation improve them by some modification: (1) The formula of transfer probability and the rule of pheromone updating is modified, i.e. Ant Colony Optimization Algorithm; (2)The intensity of trail ij is restricted between min and max , i,e- Max-Min Ant Algorithm; (3)Adding local optimization into ant algorithm to improve the most optimal path in every generation in order to shorten the optimal path and to quicken the speed of convergence; (4) analyzing the choice of some mainparameters. Finally the experiment results prove that Intelligent Ant Algorithm is obviously better than standard ant algorithm.After analyzing the desulfuration techniques, through the effective data preprocessing, this dissertation adopts Intelligent Ant Algorithm to optimize the desulfuration static model. Comparing the performance between the improved model and traditional RBF model by simulation, it proves that the former is better than the latter and has its own practicability and validity.