Prediction method of volatile organic compounds mass concentration in spray dyeing workshop based on RF-SSA-LSTM
This paper made Volatile Organic Compounds(VOCs)in a spray dyeing workshop be the subject investigated,to study the prediction method of volatile organic compounds in a spray dyeing workshop.Firstly,the Random Forest(RF)algorithm was used to analyze the weight of the characteristic variable that affects the mass concentration of volatile organic compounds in the spray dyeing workshop.A prediction medal of volatile organic compounds mass concentration based on Long Short-Term Memory neural network(LSTM)was constructed and the Sparrow Search Algorithm(SSA)was introduced to optimize the parameters.Finally,with the data from July 29 to October 28 of an automobile spray dyeing workshop in Hangzhou,Zhejiang Province as the sample,temperature,humidity,indoor atmospheric pressure,and outdoor atmospheric pressure were selected as the model input variables and were compared with LSTM model,RF-LSTM model,RF-BP model,RF-SSA-LSTM model.The prediction effect of the SSA-LSTM model is the best,with IMAE,IRMSE,and R2 being 2.812 2,3.457 4,and 0.988,respectively.Besides,to validate the performance of the model,this paper also realizes the prediction of the mass concentration of volatile organic compounds in the spray dyeing workshop through different time steps.The average absolute errors(MAE)of volatile organic compounds in the spray dyeing workshop prediction in advance of 48 h,72 h,96 h,and 120 h were 5.984 2,8.624 3,12.708 4,and 19.333 8,respectively.The results show that the prediction error is small,within an acceptable range.The prediction model proposed in this paper improves the prediction accuracy of the mass concentration of volatile organic compounds,can more accurately guide the terminal management load and can provide a scientific basis for reducing the emission of volatile organic compounds,and the also has an ideal prediction effect on the frequency under the prediction of volatile organic compounds mass concentration of different time lengths,which confirms the feasibility of this research scheme.
NETL
NSTL
万方数据
维普
