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| Soft Sensor Modeling of Papermaking Wastewater Treatment Processes Based on ANN and LSSVR |
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| DOI：10.11981/j.issn.1000-6842.2017.01.50 |
| Key Words:artificial neural network; least squares support vector regression; papermaking wastewater treatment; soft sensor modeling; particle swarm optimization |
| Fund Project:制浆造纸工程国家重点实验室开放基金资助项目(201610)；南京林业大学高层次人才科研启动基金(163105996)；江苏省制浆造纸科学与技术重点实验室开放基金项目(201530)。 |
| Author Name | Affiliation | | 汪 瑶1 | 1.南京林业大学江苏省制浆造纸科学与技术重点实验室,江苏南京,210037 | | 徐 亮1 | 1.南京林业大学江苏省制浆造纸科学与技术重点实验室,江苏南京,210037 | | 殷文志1 | 1.南京林业大学江苏省制浆造纸科学与技术重点实验室,江苏南京,210037 | | 胡慕伊1 | 1.南京林业大学江苏省制浆造纸科学与技术重点实验室,江苏南京,210037 | | 黄明智3 | 3.中山大学水资源与环境系,广东广州,510275 | | 刘鸿斌1,2,* | 1.南京林业大学江苏省制浆造纸科学与技术重点实验室,江苏南京,210037；2.华南理工大学制浆造纸工程国家重点实验室,广东广州,510640 |
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| Abstract: |
| Concerning the time-varying, nonlinear, and complex characteristics of papermaking wastewater treatment systems, soft sensor modeling methods based on artificial neural network (ANN) and least squares support vector regression (LSSVR) were used to predict effluent chemical oxygen demand and suspended solids in a papermaking wastewater treatment process. ANN model was established by using error back propagation algorithm. The particle warm optimization was used to optimize model parameters in the LSSVR model. The results showed that the root mean square error of LSSVR model reduced by more than 50% compared with that of ANN model, and the correlation coefficient of LSSVR model increased by about 10% compared with that of ANN model. These results indicated that the LSSVR model had better prediction performance and higher accuracy compared to the ANN model in papermaking wastewater treatment process． |
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