Parameter prediction based on Improved Process neural network and ARMA error compensation in Evaporation Process

2017 3rd International Conference on Environmental Science and Material Application

生态环境科学;材料科学

Qian, Xiaoshan^1

Physical Science and Technology College, Yichun University, Yichun, Jiangxi

336000, China^1

关键词: Autoregressive moving average;    Error-correction procedures;    Evaporation process;    Forecasting methods;    Parameter prediction;    Prediction accuracy;    Sodium aluminate solution;    Traditional models;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/108/2/022078/pdf
DOI  :  10.1088/1755-1315/108/2/022078

来源: IOP

【 摘 要 】

The traditional model of evaporation process parameters have continuity and cumulative characteristics of the prediction error larger issues, based on the basis of the process proposed an adaptive particle swarm neural network forecasting method parameters established on the autoregressive moving average (ARMA) error correction procedure compensated prediction model to predict the results of the neural network to improve prediction accuracy. Taking a alumina plant evaporation process to analyze production data validation, and compared with the traditional model, the new model prediction accuracy greatly improved, can be used to predict the dynamic process of evaporation of sodium aluminate solution components.