中国造纸学报2025，Vol.40Issue(2)：173-182,10.DOI:10.11981/j.issn.1000-6842.2025.02.173
基于集成深度学习的造纸废水出水指标预测模型研究
Study on Intelligent Prediction Model of Papermaking Wastewater Effluent Quality Index Based on Ensemble Deep Learning
摘要
Abstract
To address the limitations of single model and improve overall robustness,this study focused on a wastewater treatment dataset from a single processing stage of a small-scale papermaking plant,took kernel principal component analysis(KPCA)for dimensionality re-duction to effectively extract key features from the data,firstly.Then,integrated multiple long-short term memory(LSTM)learners using the Bagging ensemble strategy,and constructed a KPCA-Bagging-LSTM prediction model,where LSTM was capable of modeling the temporal characteristics of wastewater data.The results showed that the KPCA-Bagging-LSTM model achieved a coefficient of determination(R2)of 0.76,significantly outperforming other methods.The Root Mean Square Error(RMSE)and Mean Absolute Percentage Error(MAPE)were 3.55 mg/L and 4.01%,respectively,indicating lower prediction error and higher accuracy.By combining feature dimensionality reduction with ensemble learning,the proposed KPCA-Bagging-LSTM model enhanced prediction performance and provided an effective solution for forecasting COD and other effluent indicators in papermaking wastewater treatment.关键词
造纸废水过程处理/数据降维/长短期记忆网络/集成学习/软测量模型Key words
papermaking wastewater treatment process/data dimensionality reduction/long short-term memory network/ensemble learning/soft sensor mode分类
轻工纺织引用本文复制引用
王金咏,王新元,魏文光,张凤山,黄鹏,周景蓬,万兵,牛国强,刘鸿斌..基于集成深度学习的造纸废水出水指标预测模型研究[J].中国造纸学报,2025,40(2):173-182,10.基金项目
山东省自然科学基金(ZR2021MF135) （ZR2021MF135）
江苏省高等学校自然科学研究重大项目(22KJA530003). （22KJA530003）
