基于广义回归径向神经网络的工业超纯水智能远程运维系统
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摘要
为了满足通过远程运维系统控制电力、电子等制造业用水等质量,降低能耗的要求,文章针对电子工业超纯水制造,构建了超纯水智能化远程运维系统,利用径向基神经网络和广义回归神经网络对超纯水出水水质进行拟合预测。通过数据分析,采用上述算法实现了对超纯水系统的精确预测,以及智能自适应控制,提升了算法精度和收敛速度。结果表明,在所建模型仿真基础上,通过回水利用、变频调速,达到提升产水质量和节能降耗的目的。
In order to meet the requirements of controlling the quality of water used in power,electronics and other manufacturing industries through remote operation and maintenance systems,and reducing energy consumption, the ultra-pure water intelligent remote operation and maintenance system is constructed for the ultra-pure water manufacturing of the electronics industry in this paper.The radial basis neural network and the generalized regression neural network are used to carry out the fitting predict of ultra-pure water effluent quality. Through data analysis, the proposed algorithm can achieve accurate prediction of ultra pure water system, and intelligent adaptive control, which improves the accuracy and convergence speed of the algorithm. The results show that on the basis of the model simulation, the purpose of improving water quality and saving energy is achieved through recycling water utilization and frequency control.
In order to meet the requirements of controlling the quality of water used in power,electronics and other manufacturing industries through remote operation and maintenance systems,and reducing energy consumption, the ultra-pure water intelligent remote operation and maintenance system is constructed for the ultra-pure water manufacturing of the electronics industry in this paper.The radial basis neural network and the generalized regression neural network are used to carry out the fitting predict of ultra-pure water effluent quality. Through data analysis, the proposed algorithm can achieve accurate prediction of ultra pure water system, and intelligent adaptive control, which improves the accuracy and convergence speed of the algorithm. The results show that on the basis of the model simulation, the purpose of improving water quality and saving energy is achieved through recycling water utilization and frequency control.
引文
[1]张琳.半导体工业中超纯水制备工艺的特点和发展[J].洁净与空调技术,2001(4):32-37.
[2]何银平,唐建国,傅成华.超纯水处理PLC控制系统设计[J].电工技术,2008(3):32-34.
[3]薛张辉.半导体工业超纯水的技术指标及其制备概述[J].广东化工,2018,45(21):62-63.XUE Zhanghui.An overview on the technical qualifications and preparation of ultra-pure water in semiconductor industry[J].Guangdong Chemical Industry,2018,45(21):62-63.
[4]张凤西,郑萍,吴晨,等.PLC冗余控制在超纯水控制系统中的应用[J].自动化与仪表,2013,28(11):44-47.ZHANG Fengxi,ZHENG Ping,WU Chen,et al.Application of PLC redundancy control in ultrapure water control system[J].Automation and Instrumentation,2013,28(11):44-47.
[5]姚晓阳.超纯水处理系统设计及其信息化方案研究[D].杭州:浙江大学,2016.
[6]刘洁.某工厂超纯水处理技术能耗分析案例[J].节能与环保,2017(4):64-65.
[7]张秀云.半导体晶元厂厂务制程相关系统改进[D].上海:上海交通大学,2008.
[8]冯浩.浸没式光刻机超纯水处理系统的研制[D].杭州:浙江大学,2016.
[9]WANG Haifeng,ZHOU Bin,ZHANG Xie.Research on the remote maintenance system architecture for the rapid development of smart substation in China[J].IEEE Transactions on Power Delivery,2018,33(4):1845-1852.
[10]Sánchez C R,Fernández F J G,Simón Vena L C,et al.Industrial telemaintencance:remote management experience from subway toindustrial electronics[J].IEEE Transactions on Industrial Electronics,2011,58(3):1044-1051.
[11]Alessandro Emilio Pietro Rondelli,Galimberti M.Improving MVgrid control,remote operations and reliability through advanced TLC network and SCADA architecture[J].CIRED-Open Access Proceedings Journal,2017(1):1219-1222.
[12]贺倩.人工智能技术的发展与应用[J].电力信息与通信技术,2017,15(9):32-37.HE Qian.Development and application of artificial intelligence technology[J].Electric Power Information and Communication Technology,2017,15(9):32-37.
[13]樊邦奎,丁冠军,兰海滨,等.面向智能电网应用的云计算架构研究[J].电力信息与通信技术,2014,12(1):1-6.FAN Bangkui,DING Guanjun,LAN Haibin,et al.Research on cloud computing architecture for smart grid applications[J].Electric Power Information and Communication Technology,2014,12(1):1-6.
[14]陈亮,王震,王刚.深度学习框架下LSTM网络在短期电力负荷预测中的应用[J].电力信息与通信技术,2017,15(5):8-11.CHEN Liang,WANG Zhen,WANG Gang.Application of LSTM networks in short-term power load forecasting under the deep learning framework[J].Electric Power Information and Communication Technology,2017,15(5):8-11.
[15]王晨辉,张晓亮,梁晓传.云计算架构下基于BP神经网络负载预测策略的研究[J].电力信息与通信技术,2016,14(11):46-50.WANG Chenhui,ZHANG Xiaoliang,LIANG Xiaochuan.Research on load forecasting strategy based on BP neural network under cloud computing architectures[J].Electric Power Information and Communication Technology,2016,14(11):50-54.
[16]辛焕平.MATLAB R2017a模式识别与智能计算[M].北京:电子工业出版社,2018:131-154.
[17]郭金伟,周渝慧.基于RBF改进模型的电力系统短期负荷预测[J].电力信息化,2007,5(7):77-79.
[2]何银平,唐建国,傅成华.超纯水处理PLC控制系统设计[J].电工技术,2008(3):32-34.
[3]薛张辉.半导体工业超纯水的技术指标及其制备概述[J].广东化工,2018,45(21):62-63.XUE Zhanghui.An overview on the technical qualifications and preparation of ultra-pure water in semiconductor industry[J].Guangdong Chemical Industry,2018,45(21):62-63.
[4]张凤西,郑萍,吴晨,等.PLC冗余控制在超纯水控制系统中的应用[J].自动化与仪表,2013,28(11):44-47.ZHANG Fengxi,ZHENG Ping,WU Chen,et al.Application of PLC redundancy control in ultrapure water control system[J].Automation and Instrumentation,2013,28(11):44-47.
[5]姚晓阳.超纯水处理系统设计及其信息化方案研究[D].杭州:浙江大学,2016.
[6]刘洁.某工厂超纯水处理技术能耗分析案例[J].节能与环保,2017(4):64-65.
[7]张秀云.半导体晶元厂厂务制程相关系统改进[D].上海:上海交通大学,2008.
[8]冯浩.浸没式光刻机超纯水处理系统的研制[D].杭州:浙江大学,2016.
[9]WANG Haifeng,ZHOU Bin,ZHANG Xie.Research on the remote maintenance system architecture for the rapid development of smart substation in China[J].IEEE Transactions on Power Delivery,2018,33(4):1845-1852.
[10]Sánchez C R,Fernández F J G,Simón Vena L C,et al.Industrial telemaintencance:remote management experience from subway toindustrial electronics[J].IEEE Transactions on Industrial Electronics,2011,58(3):1044-1051.
[11]Alessandro Emilio Pietro Rondelli,Galimberti M.Improving MVgrid control,remote operations and reliability through advanced TLC network and SCADA architecture[J].CIRED-Open Access Proceedings Journal,2017(1):1219-1222.
[12]贺倩.人工智能技术的发展与应用[J].电力信息与通信技术,2017,15(9):32-37.HE Qian.Development and application of artificial intelligence technology[J].Electric Power Information and Communication Technology,2017,15(9):32-37.
[13]樊邦奎,丁冠军,兰海滨,等.面向智能电网应用的云计算架构研究[J].电力信息与通信技术,2014,12(1):1-6.FAN Bangkui,DING Guanjun,LAN Haibin,et al.Research on cloud computing architecture for smart grid applications[J].Electric Power Information and Communication Technology,2014,12(1):1-6.
[14]陈亮,王震,王刚.深度学习框架下LSTM网络在短期电力负荷预测中的应用[J].电力信息与通信技术,2017,15(5):8-11.CHEN Liang,WANG Zhen,WANG Gang.Application of LSTM networks in short-term power load forecasting under the deep learning framework[J].Electric Power Information and Communication Technology,2017,15(5):8-11.
[15]王晨辉,张晓亮,梁晓传.云计算架构下基于BP神经网络负载预测策略的研究[J].电力信息与通信技术,2016,14(11):46-50.WANG Chenhui,ZHANG Xiaoliang,LIANG Xiaochuan.Research on load forecasting strategy based on BP neural network under cloud computing architectures[J].Electric Power Information and Communication Technology,2016,14(11):50-54.
[16]辛焕平.MATLAB R2017a模式识别与智能计算[M].北京:电子工业出版社,2018:131-154.
[17]郭金伟,周渝慧.基于RBF改进模型的电力系统短期负荷预测[J].电力信息化,2007,5(7):77-79.