There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill l...There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill load cell mass,SAG mill solid percentage,inlet and outlet water to the SAG mill and work index are studied.A total number of185full-scale SAG mill works are utilized to develop the artificial neural network(ANN)and the hybrid of ANN and genetic algorithm(GANN)models with relations of input and output data in the full-scale.The results show that the GANN model is more efficient than the ANN model in predicting SAG mill power.The sensitivity analysis was also performed to determine the most effective input parameters on SAG mill power.The sensitivity analysis of the GANN model shows that the work index,inlet water to the SAG mill,mill load cell weight,SAG mill solid percentage,mass flowrate and feed moisture have a direct relationship with mill power,while outlet water to the SAG mill has an inverse relationship with mill power.The results show that the GANN model could be useful to evaluate a good output to changes in input operation parameters.展开更多
燃煤锅炉普遍采用空气分级燃烧技术,此举虽可大幅降低NOx生成,但造成炉内火焰中心上移,导致屏式过热器(屏过)管壁超温严重。此外,调峰运行使锅炉负荷经常性不规则变化,进一步恶化了屏过传热,使爆管泄漏事故频发。为指导锅炉安全可靠运行...燃煤锅炉普遍采用空气分级燃烧技术,此举虽可大幅降低NOx生成,但造成炉内火焰中心上移,导致屏式过热器(屏过)管壁超温严重。此外,调峰运行使锅炉负荷经常性不规则变化,进一步恶化了屏过传热,使爆管泄漏事故频发。为指导锅炉安全可靠运行,提出一种基于遗传算法优化超参数的深度神经网络模型(deep neural network model with its hyperparameters optimized by genetic algorithm,GA-DNN),通过构建炉内风煤侧及汽水侧运行参数与屏过30片管屏出口温度之间的映射关联,对屏过超温进行分析和预测。该模型可实现对不同负荷工况下屏过温度分布的准确预测,在此基础上能够以97.5%以上的准确率识别出当前及未来5 min屏过超温(>550℃)的运行工况,同时可在89.2%的准确率下预测出未来5 min屏过超温最严重的管屏所在区域。展开更多
文摘There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill load cell mass,SAG mill solid percentage,inlet and outlet water to the SAG mill and work index are studied.A total number of185full-scale SAG mill works are utilized to develop the artificial neural network(ANN)and the hybrid of ANN and genetic algorithm(GANN)models with relations of input and output data in the full-scale.The results show that the GANN model is more efficient than the ANN model in predicting SAG mill power.The sensitivity analysis was also performed to determine the most effective input parameters on SAG mill power.The sensitivity analysis of the GANN model shows that the work index,inlet water to the SAG mill,mill load cell weight,SAG mill solid percentage,mass flowrate and feed moisture have a direct relationship with mill power,while outlet water to the SAG mill has an inverse relationship with mill power.The results show that the GANN model could be useful to evaluate a good output to changes in input operation parameters.
文摘燃煤锅炉普遍采用空气分级燃烧技术,此举虽可大幅降低NOx生成,但造成炉内火焰中心上移,导致屏式过热器(屏过)管壁超温严重。此外,调峰运行使锅炉负荷经常性不规则变化,进一步恶化了屏过传热,使爆管泄漏事故频发。为指导锅炉安全可靠运行,提出一种基于遗传算法优化超参数的深度神经网络模型(deep neural network model with its hyperparameters optimized by genetic algorithm,GA-DNN),通过构建炉内风煤侧及汽水侧运行参数与屏过30片管屏出口温度之间的映射关联,对屏过超温进行分析和预测。该模型可实现对不同负荷工况下屏过温度分布的准确预测,在此基础上能够以97.5%以上的准确率识别出当前及未来5 min屏过超温(>550℃)的运行工况,同时可在89.2%的准确率下预测出未来5 min屏过超温最严重的管屏所在区域。
