Intelligent Hybrid Prediction Method of Reducing Zone Temperature in Shaft Furnace

摘要：

The temperature of the reducing zone in the shaft furnace roasting process is a key controlled variable, but its hard to be measured continuously. In this instance, an intelligent hybrid prediction model is developed to predict the temperature punctually based on the neural networks and case-based reasoning. This model consists of five modules: a data collection and data processing module, a decision-making module, a prediction module, an online modified module and an effect estimating module. The whole models framework, the main function of each module and the implementation of the algorithms are discussed. Applications to a shaft furnace roasting process show that the model is effective in both normal and abnormal operating conditions. The obvious benefits of it are low maintenance expense, good real time character, high reliability and perfectly precision.

作者: YAN Ai-jun CHAI Tianyou WANG Pu

作者单位: Beijing University of Technology, China Northeastern University, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）