Modeling And Intelligent Optimization Of Cobalt Removal Process In Zinc Hydrometallurgy And Its Application

Above 80% of total yield of zinc is prodecued by hydrometallurgy in the world. The cobalt removal in purification process is the critical step in zinc hydrometallurgy, where the cobalt in the zinc sulfate solutions is removed by cementation reaction using zinc powder. The cobalt removal mechanism is complex and there are many factors that influence the removal effects. Especially, the ion concentration of the zinc sulphate solution cannot be measured online. These factors lead to the difficulty in operation parameters optimization and more consumption of Zinc powder in cobalt removal processs.Based on the analysis of influencing factors and the characteristics of the process data in the cobalt removal process, the process modeling and optimization control strategy is studied with industrial data and the whole structure of optimization control technology is proposed. Firstly, a preprocess method of process data including the outlier identification and imputation of missing data is proposed. Then, an on-line estimation method of the influent ionic concentration is researched based on time series analysis. Finally, the process index prediction model based on SVM is estabilished and an optimization control strategy for cobalt removal process combining the preset model based on case-base reasonning and SVM-based compensating model is presented. In addition, the optimization control system is developed based on the proposed methods mentioned above. The major innovation research achievements include:(1) The data preprocess technique of process data was proposed. On the basis of analysis of the main source of abnormal data, the detection method of abnormal data is studied by using the local average distance and estimation of technological situation parameter. Aimed at the problem of the missing data caused by lossing data and abnormal data, the imputation method was proposed based on case-based reasoning. The verified results of on-site industrial production data showed that the proposed data preprocess method can effectively improve the process data quality and provide the condiction for modeling and optimization control of cobalt removal process based on data-driven.(2) An on-line estimation method of the influent iconic concentration was proposed for the cobalt removal process based on the time series analysis. Using wavelet decomposition, the time series of ionic concentration were decomposed into multiple subsequences and these subsequences were reconstructed into each subspace in phase space. Then, the support vector machine (SVM) model was built in each subspace. Finally, the outputs of each subsequence model were synthesized as the result of on-line estimation model for the influent iconic concentration in the cobalt removal process. The parameters in SVM models were optimized by using a chaotic particle swarm (PSO) algorithm. The verified results of the industrial production data showed that the samples with relative error less than 10% in the proposed model is up to 97.5%. It also indicated that the on-line estimation precision met the technological requirement of practical industrial production. The proposed model provides reliable information for the optimization control of cobalt removal process.(3) A prediction model of technology index for cobalt removal process is built based on fuzzy c-means (FCM) clustering and fuzzy SVM. According to characteristics of the non-linearity and large-scale production data of cobalt removal process, the weight FCM clustering algorithm was used to separate the whole training data set into several clusters. Then, each cluster subset was trained by fuzzy SVM respectively and obtains its corresponding sub-model. These fuzzy sub-models use a hybrid fuzzy membership, in accordance with the different effect of the sample in different periods and in different sample space. Finally, a weighted integrated model was formed with these outputs of sub-models. A hierarchical PSO method is used in feature attributes selection and weight vector optimization. In addition, a correction method is used to improve the model precision. The experimental results of industrial data demonstrated the effectiveness of the proposed prediction method.(4) An optimization control strategy of zinc powder addition amount is studied, which integrates the optimal preset model based on case-based reasonning with the compensation model based on support vector machine. Using the ionic concentration information provided by on-line estimation model and the influent solution flow rate as input variables, the optimal preset model based on case-based reasonning determines the preset value of zinc powder addition amount, which effectively overcomes the influences of the fluctuations of ionic concentration and the influent solution flow rate. Then, according to the deviation between the expected set-point value and the feedback prediction value of the effluent ionic concentration, the compensation model of zinc powder addition amount is proposed based on SVM. The experimental validation results showed that the proposed optimization control strategy can effectively reduce the zinc powder addition amount by 6.48% in average.(5) An optimization control system for the cobalt removal process has been developed. The data communication between the developed system and the distributed control system (DCS) is realized with OPC technology. It realized the functions including the on-line estimation of the influent iconic concentration, process technological index prediction, operation optimization of the zinc powder adding, as well as the functions of process monitoring, data inquiry and process report analysis and so on. The practical running results showed the effectiveness and feasibility of the developed system.