Mind Evolutionary Algorithm And Support Vector Machines As Well As Theirs Applications In Optimizating Blend Coal Ratio For Coking

The technology of two or more different coals in certain proportion blending coking is called Coal Blending Cokemaking. Because of the complexity of the composition, structure and nature of coal. man's knowledge of coal and it's coking have many limitations.The cur-rently available prediction models of blend coal quality and coke quality are suitable for a certain area coal or a certain coking enterprises, which are not common. For the researched coking plant of the paper, using these existing model doesn't fit. So according to the factory's production data and coking experience accumulated for many years, establishing blend caking index model and the coke mechanical strength model of it's own, and applying the models to optimizating coal blending proportion are studied in the paper. The main content of the paper is listed as follows:The caking index G of blend coal can be incorrectly predicted with the additive value of the every single coal G and its proportion, additivity of blend G is not satisfied. Despite the indicator of caking index G. the paper also introduces the Volatile Vdaf indicator. Nonlinear transform is made for the coking-coal Vdaf and the lean-coal Vdaf by Gaussian function, which together with the blend G additive value, formed three components of predicting the blend G. The paper uses the MEA algorithms to find the optimal solution of the 7 parameters of the previous nonlinear model. After establishing the regression predicting model, the predicted effects of the blend G model is improved obviously and the relative error is not more than±6%.When the accumulative contribution rate is more 90%, KPCA extracts the 2 principal components from 5 blend quality indicators and 3 coking technology indicators. These two principal components employed as the inputs of SVM, we respectively set up the coke mechanical strength prediction models of M25and M10. Be compare with the effect of feature extraction and modeling of PCA, the coke strength prediction models based on KPCA-SVR have drastic dimension reduction. good generalization performance, less forecasting error and can meet the requirement of actual production.During the establishment of the coke strength model by SVR, A samples selection method based on G-SVR (granularity support vector regression) is proposed for filtering training samples. We pretreat the sample sets in different granulose of the kernel space. Abnormal sample data (noise data) are excluded in the coarse-granularity level and partial dense repeated samples are removed in fine-granularity level. In the reasonable range of granularity, the distribution of the original samples substituted by a small sample subset is not changed, while the learning experience is reduced, the coke prediction model is good and the efficiency of SVR algorithm is improved.In the process of building the coke mechanical strength SVR model, the optimizing method based on MEA is applied to determine the parameters ofε-SVR and Kernel function. Be compare with the mesh scanning algorithm, the optimization time of MEA decreases greatly but the optimization results are similar.The coal blending proportion can not be set without the coking test. The paper designs a 20 kg iron drum experiment that put iron drum into the industrial coke oven for coking, the coal of iron drum and the coal of industry oven are burnt in the same environment. This iron drum test method has small investment, easy operation, less intensity of labour and flexible trial adjustment. Upon examination, when the space between the iron drum and the coal pie within the iron drum is 23mm, the bulk density is 1.2t/m3 and the burned time is in 24～48 hours, There is a strong correlation between the 20 kg iron drum coke mechanical strength and the industrial oven coke mechanical strength. The relationship of the two mechanical strengths satisfies the one variable linear regression equation. The 20 kg iron drum experiment can predict the industrial oven coke mechanical strength perfectlyConsidering the cost price of blend coal and the income from coking products, the paper chooses the objective function of the coal blending ratio optimization model, which fully embodies the biggest benefits of the coking plant. Setting coke quality indicators and blend quality indicators, knowing the major existing single coals, its quality parameters, prices, and the conditions of pretreating coal and coking, a MEA algorithm is adopted to optimize coal blending ratio. The prediction models of blend G and coke mechanical strength established in the paper are applied in the process of optimizing blend ratio. The MEA method is higher retrieval efficiency than GA method and the results of the 20 kg iron drum experiment show that the blend ratio optimized by MEA method is scientific and reasonable.