| 戴景波,陈晓彬,方子言,郑启富,张垚,张敏,董云渊,廖建明.基于T-PLS-GRA的造纸干燥过程能耗非优原因追溯模型[J].中国造纸学报,2024,39(1):91-99 |
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| 基于T-PLS-GRA的造纸干燥过程能耗非优原因追溯模型 |
| Paper Drying Process Energy Consumption of Non-best Reasons Traceable Model Based on T-PLS-GRA |
| 投稿时间：2023-05-19 修订日期：2023-06-06 |
| DOI：10.11981/j.issn.1000-6842.2024.01.91 |
| 中文关键词: 造纸干燥过程 能效 非优原因追溯 机器学习 |
| Key Words:paper drying process energy efficiency non-optimal cause identification machine learning |
| 基金项目:国家自然科学基金（62303265）；浙江省重点研发计划（2024C03120）；浙江省基础公益研究计划（LTGN24B060001，LGN21C030001）；衢州市科技计划项目（2023K230）。 |
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| 中文摘要: |
| 本研究建立了一种结合T-PLS（全潜结构投影）和GRA（灰色关联度分析）的造纸干燥过程能耗非优原因追溯模型。该模型首先基于机理知识和方差特性，去除造纸干燥过程生产数据中的非核心特征变量，并通过3σ原则和箱形图剔除异常值；然后使用施胶前定量与卷取车速数据，结合K-Means聚类算法，实现不同生产状态的分类；最后针对不同的生产状态，对比T-PLS和PLS建立的经济指标计算模型，选用基于T-PLS-GRA算法，构建造纸干燥过程能效非优原因追溯模型。结合国内某造纸厂实时生产数据对该模型进行了验证。结果表明，该模型基于经济指标判断工业生产状态过程，对非优过程预测精准率为77.7%，可较好地跟踪造纸过程设备运行状态的变化过程；并且能追溯非优状态原因及整个工况下，非优生产状态中最大贡献变量出现频次，为企业改进工艺流程及节能优化提供了参考依据。 |
| Abstract: |
| 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 production 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 different 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 accuracy 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 papermaking 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. |
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