Development And Application Of Evaluation And Prediction Models Of Production Parameters And Quality-Energy Consumption During Fiber Refining

The fiber refining process is the important link in the fiberboard production,and the production parameters in the fiber refining greatly affect the quality of the fiber and energy consumption,which is directly related to the physical and mechanical properties of subsequent fiberboards,the production cost and profits of enterprises.Therefore,the deveopment and application of evaluation and prediction models of production parameters and quality-energy consumption during fiber refining needs to be studied,which has important theoretical value and practical significance to improve fiberboard quality and reduce energy consumption.Based on the factors influencing fiber quality and energy consumption,based on improved particle swarm optimization and support vector machine(IPSO-SVM)algorithm,the evaluation and prediction models of production parameters and quality-energy consumption during fiber refining were established,the effects of production parameters on fiber quality and energy consumption were explored,the intrinsic relationship between production parameters and quality of fiber and energy consumption were evaluated and predicted,which can provide theoretical and scientific basis for the scientific evaluation,accurate prediction,optimization of production parameters,quality improvement and energy reduction of fiber refining process.In this paper,the percentage of qualified fiber(PQF)and specific energy consumption(SEC)are respectively used to represent fiber quality and energy consumption during refining.The effects of various production parameters on the fiber quality and energy consumption were analyzed.The experimental data of fiber quality,energy consumption and their influence factors during refining were obtained through the fiberboard production site experiments,the main influencing factors(production parameters)are screened out by grey correlation analysis method,and finally,content of Chinese poplar(CP),accumulated chip height(CH),conveyer screw revolution speed(SR),opening ratio of the discharge valve(OV)were determined as production parameters of evaluating fiber refining quality and energy consumption,which laid a research foundation for the subsequent establishment of evaluation and prediction models of production parameters and quality-energy consumption during fiber refining.By dynamically adjusting inertia weight and learning factor,PSO algorithm was improved.The radial basis function was selected as the kernel function of SVM,and the IPSO-SVM algorithm was proposed to establish the evaluation model of production parameters and quality-energy consumption during fiber refining.Through case verification,accuracy of evaluation and classification of the two models is no less than 90%.Compared with probabilistic neural network(PNN),generalized regression neural network(GRNN),SVM and PSO-SVM,evaluation model of production paremeters and quality-energy consumption during fiber refining based on IPSO-SVM algorithm had better evaluation and classification accuracy.A bidirectional prediction model of the production parameters and quality-energy consumption during fiber refining was proposed based on IPSO-SVM.Forward prediction refers to taking production parameters as input and fiber quality and energy consumption as output,while backward prediction refers to taking fiber quality and energy consumption as input and production parameters as output.Bidirectional prediction model of production parameters and quality-energy consumption during fiber refining based on IPSO-SVM was compared and analyzed with back propagation neural network(BPNN),radial basis function neural network(RBFNN),SVM and PSO-SVM models.The results showed that the bidirectional prediction model of production parameters and quality-energy consumption during fiber refining based on IPSO-SVM algorithm had higher prediction accuracy and better generalization performance.Based on the feasibility of forward prediction model of fiber refining process and the actual production conditions,The variation trend of fiber quality and energy consumption with various production parameters during refining was predicted and from forward analysis.Based on the effectiveness of the backward prediction model and the expected PQF of 75%and SEC of 111 kW·h/t,the predicted production parameters CP of 25.9%,CH of 5 m,SR of 62 r/min and OV of 21.6%were obtained,and verified on the actual production line of the factory,which proved that the prediction accuracy of the model could meet the requirements of production.The fuzzy information granulation method was used to characterize the information of the original sample data,the SVM regression prediction method was used to train the granulated set samples,and a time series prediction model for fiber quality and energy consumption during refining based on fuzzy information granulation and SVM was proposed,and then the prediction interval reflecting the change trend of fiber quality and energy consumption is obtained.Evaluating results showed that the predicted variation range was consistent with the actual value,which was of guiding significance to further manage the development trend of fiber refining quality and energy consumption in the fiber-board production.