Journal
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
Volume 26, Issue 13, Pages 12828-12841Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11356-019-04671-8
Keywords
Anaerobic wastewater treatment; Soft sensor; Least-square support-vector machine; COD; Steady-state; Shock loads
Categories
Funding
- National Natural Science Foundation of China [31570568, 31670585]
- State Key Laboratory of Pulp and Paper Engineering [201535]
- Science and Technology Planning Project of Guangzhou City, China [201607010079, 201607020007]
- Science and Technology Planning Project of Guangdong Province, China [2016A020221005, 2017A040405022]
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Since anaerobic wastewater treatment is a nonlinear and complex biochemical process, reasonable monitoring and control are needed to keep it operating stably and efficiently. In this paper, a least-square support-vector machine (LS-SVM) was employed to construct models for the prediction of effluent chemical oxygen demand (COD) in an anaerobic wastewater treatment system. The result revealed that the performance of the steady-state model based on LS-SVM for predicting effluent COD was acceptable, with the maximum relative error (RE) of 11.45%, the mean average percentage error (MAPE) of 0.79% and the root mean square error (RMSE) of 3.08 when training, and the performance fell slightly when testing. Even though, the correlation coefficient value (R) between the predicted value and the actual value of 0.9752 could be achieved, which means this model can predict the variation of effluent COD in general. The dynamic-state models under three kinds of shock loads, which were concentration, hydraulic, and bicarbonate buffer absent, showed good forecasting performance, the correlation coefficient values (R) all excelled 0.99. Among these three shocks, the dynamic LS-SVM model under bicarbonate buffer absent shock achieved the optimal performance and followed by the dynamic-state model under hydraulic shock. This paper provides a meaningful reference to improve the monitoring level of the anaerobic wastewater treatment process.
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