Based On Multi-rate Sampling Of Coke Oven Flue Temperature Soft Measurement Of The Integrated Model

Coke is the main raw material in metallurgy industry, and is widely used in various industries. The combustion process is an important part in coking process and the flue temperature has direct impacts on the coke quality and resource consumption. However, it is difficult to measure the flue temperature through real-time method with low cost. And even the manual measurement is used, it also has the shortage of long circle and time-delay. The flue temperature has the characteristics of nonlinearity and time-varying, which is difficult to be measured. An integral model design of soft sensor based on multi-rate sampling is put forward to acquire the real-time flue temperature in this thesis.Based on the careful analysis of the background and significance of this subject, the thesis firstly decides to use the integral model, basing on the soft sensor, to analyze the coking combustion process. In the view of technical mechanism from coking process, the regenerator top temperature is selected, which to be used as the process parameter of the coke oven flue temperature's soft sensor. Aiming at analyzing the complex relationship between regenerator top temperature and flue temperature, linear regress models (LR) are employed to reflect the linear relationship between them. Then in order to reflect the nonlinear relationship between them and predict multi-step ahead, the model combines the temporal difference method and Elman neural network (TD-ENN) is created. The TD-ENN is built on the basis of linear regress models' errors and then, the LR and TD-ENN are integrated as the combined predictable model (CPM) with some important rules. However, when the CPM is created on the long period data, it lack of good real-time ability. Then the multi-rate method is used to solve the CPM's deficiency. The curve fitting predictive model and the polynomial predictive model are built on the multi-rate method, consequently, when the CPM is valid or invalid, the shortage of the CPM is offset by one of the models respectively and the coking system is transforming from multi-rate to single rate successfully. At last, in order to fit the variable working condition well, the self-learning strategy is integrated in the combined model and the ability of the model is improved obviously. The effective drawings of the model simulation are built on the field data, the accurate precision of the prediction proves the high performance of the integrated coke oven flue temperature soft sensor model basing on the multi-rate.