CAJ | 학술논문

为了缩短印制线路板产业( PCB)废水处理的调试周期,控制化学试剂用量,节约能源,采用反向传播( BP)神经网络训练并建立了线路板废水处理的神经网络模型。以混凝沉淀水处理工艺的5个主要影响因素作为输入层参数,以出水水质指标作为输出层参数,设置单隐含层。将10组调试数据作为训练样本,网络运行得到的系统误差为0.00099996,将3组调试数据作为预测样本,网络预测值与实际数据值吻合较好。说明该网络具有较好的泛化能力,能很好地对在不同水质参数下线路板废水的处理效果进行预测,在达到所要求的水处理效果的基础上,降低进水水量及水质变化系数较大等不利因素的影响,合理投加化学试剂,使水处理系统在最优的状态下安全、稳定、低成本及高效率运行。
위료축단인제선로판산업( PCB)폐수처리적조시주기,공제화학시제용량,절약능원,채용반향전파( BP)신경망락훈련병건립료선로판폐수처리적신경망락모형。이혼응침정수처리공예적5개주요영향인소작위수입층삼수,이출수수질지표작위수출층삼수,설치단은함층。장10조조시수거작위훈련양본,망락운행득도적계통오차위0.00099996,장3조조시수거작위예측양본,망락예측치여실제수거치문합교호。설명해망락구유교호적범화능력,능흔호지대재불동수질삼수하선로판폐수적처리효과진행예측,재체도소요구적수처리효과적기출상,강저진수수량급수질변화계수교대등불리인소적영향,합리투가화학시제,사수처리계통재최우적상태하안전、은정、저성본급고효솔운행。
In order to shorten the debugging cycle of wastewater treatment in Printed Circuit Board ( PCB) production,control the chemi-cal reagent consumption and save energy sources,Back Propagation ( BP) neural network is trained and built for PCB wastewater treat-ment. The five key influential factors on coagulation water treatment technology are regarded as characteristic input vectors,and the efflu-ent quality index as output vectors. The debug data are divided into train group and prediction group. Running the BP neural network,the system error is 0. 000 999 96 and the network prediction is in good agreement with the actual data values,showing the precision and the generalization of network is good. Based on the wastewater treatment efficiency,the BP neural network provides a window to reduce the effect of unfavorable factors such as the influent water quantity and quality,adding the chemical reagent reasonably,ensuring the smooth operation of the system,minimizing the operation costs and improving the treatment efficiency.