Process modeling based on nonlinear PLS models using a prior knowledge-driven time difference method

Multi-grained cascade forest for effluent quality prediction of papermaking wastewater treatment processes

The real time estimation of effluent indices of papermaking wastewater is vital to environmental conservation. Ensemble methods have significant advantages over conventional single models in terms of prediction accuracy. As an ensemble method, multi-grained cascade forest (gcForest) is implemented for the prediction of wastewater indices. Compared with the conventional modeling methods including partial least squares, support vector regression, and artificial neural networks, the gcForest model shows prediction superiority for effluent suspended solid (SS_eff) and effluent chemical oxygen demand (COD_eff). In terms of SS_eff, gcForest achieves the highest correlation coefficient with a value of 0.86 and the lowest root-mean-square error (RMSE) value of 0.41. In comparison with the conventional models, the RMSE value using gcForest is reduced by approximately 46.05% to 50.60%. In terms of COD_eff, gcForest achieves the highest correlation coefficient with a value of 0.83 and the lowest root-mean-square error value of 4.05. In comparison with the conventional models, the RMSE value using gcForest is reduced by approximately 10.60% to 18.51%.

Multivariate statistical monitoring of subway indoor air quality using dynamic concurrent partial least squares

To maintain the health level of indoor air quality (IAQ) in subway stations, the data-driven multivariate statistical method concurrent partial least squares (CPLS) has been successfully applied for output-relevant and input-relevant sensor faults detection. To cope with the dynamic problem of IAQ data, the augmented matrices are applied to CPLS (DCPLS) to achieve the better performance. DCPLS method simultaneously decomposes the input and output data spaces into five subspaces for comprehensive monitoring: a joint input-output subspace, an output principal subspace, an output-residual subspace, an input-principal subspace, and an input-residual subspace. Results of using the underground IAQ data in a subway station demonstrate that the monitoring capability of DCPLS is superior than those of PLS and CPLS. More specifically, the fault detection rates of the bias of PM₁₀ and PM_2.5 using DCPLS can be improved by approximately 13% and 15%, respectively, in comparison with those of CPLS.