Paper Drying Process Energy Consumption of Non-best Reasons Traceable Model Based on T-PLS-GRA
In this study,an energy consumption non-optimal cause identification model combining T-PLS total latent structural projection and GRA grey correlation analysis in paper drying process was established.The model firstly removed the non-core characteristic variables of pro-duction data in paper drying process based on the mechanism knowledge and variance characteristics,and eliminated the outliers through the 3σ principle and box plots;then the model used the data of pre-sizing quantitatively and winding speed,and realized the classification of dif-ferent production states by combining with the K-Means clustering algorithm;finally,in view of the different production states,the model compared the economic indexes calculation models established by T-PLS and PLS,and choosed energy efficiency non-optimal reason tracing model based on the T-PLS-GRA in paper drying process.The model was verified with the real-time production data of a paper mill in China,and the results showed that the model judged the industrial production state process based on the economic indexes,and the prediction accu-racy rate of the non-optimal process was 77.7％,which can better track the change process of the running state of the equipment in the paper-making process.The model can trace the reasons for the non-optimal state and the frequency of occurrence of the largest contributing variable in the non-optimal production state during the whole working process,which provides a reference basis for the enterprise to improve the process and optimize the energy saving.
paper drying processenergy efficiencynon-optimal cause identificationmachine learning
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