Research on Modeling Prediction Methods of Process Optimization for Thermoelectric Production In order to process massive historical data generated by heating steam boiler in thermoelectric production and assist in optimizing production process. To solve this problem, we propose a modeling method for coal boiler production process optimization. This method has designed optimized modeling algorithm flow. The fundamental steps of this method are: data processing, discovery of correlation chain, modeling and prediction by using the flexible neural tree algorithm. Finally, the trend function of data, namely the fitting function, is obtained. Through fitting function to predict and simulate the links in the boiler production process, we can obtain the implicit regularity knowledge in the data. We can be able to adjust the main steam pressure, oxygen content, rotational speed of blower and other production parameters. Through the design algorithm and experimental analysis, the better experimental results are obtained. By applying this method to thermoelectric production, the production efficiency is improved, energy saving and emission reduction are achieved, and the safety of production is guaranteed.

A Novel IsomapSVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction Soft sensing technology has been proved to be an effective tool for the online estimation of unmeasured or variables that are difficult to directly measure. The performance of a soft sensor depends heavily on its convergence speed and generalization ability to a great extent. Based on this idea, we propose a new soft sensor model, IsomapSVR. First, the sample data set is divided into training set and testing set by using selforganizing map (SOM) neural network to ensure the fairness and symmetry of data segmentation. Isometric feature mapping (Isomap) method is used for dimensionality reduction of the model input data, which could not only reduce the structure complexity of the proposed model but speed up learning speed, and then the Support Vector Machine Regression (SVR) is applied to the regression model. A novel bat algorithm based on Cauchy mutation and Levy flight strategy is used to optimize parameters of Isomap and SVR to improve the accuracy of the proposed model. Finally, the model is applied to the prediction of the temperature of rotary kiln calcination zone, which is difficult to measure directly. The simulation results show that the proposed soft sensor modeling method has higher learning speed and better generalization ability. Compared with other algorithms, this algorithm has obvious advantages and is an effective modeling method.