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The recycle fluidization roasting in alumina production was studied and a temperature forecast model was established based on wavelet neural network that had a momentum item and an adjustable learning rate. By analyzing the roasting process, coal gas flux, aluminium hydroxide feeding and oxygen content were ascertained as the main parameters for the forecast model. The order and delay time of each parameter in the model were deduced by F test method. With 400 groups of sample data (sampled with the period of 1.5 min) for its training, a wavelet neural network model was acquired that had a structure of {7^-21^-1}, i.e., seven nodes in the input layer, twenty-one nodes in the hidden layer and one node in the output layer. Testing on the prediction accuracy of the model shows that as the absolute error ±5.0 ℃ is adopted, the single-step prediction accuracy can achieve 90% and within 6 steps the multi-step forecast result of model for temperature is receivable. 相似文献
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采用OPC(OLE for Process Control)技术架构了三层模式的过程优化控制系统,实现了优化控制系统与现有PLC系统的数据通讯、信息共享.优化控制系统由FLUENT仿真分析、过程智能建模、流程理论分析和优化控制四大模块组成.主要模块采用神经网络架构,实现复杂炉况的系统辩识以及工艺参数的优化计算.生产应用表明:该系统具有很好的指导性,能稳定生产、优化操作,为节能和降废的整体优化提供了新手段和途径. 相似文献
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