The least-square support vector regression model for the dyes and heavy metal ions removal prediction

Abstract

Wastewater treatment plants are typically complex because they involve physical, chemical, and biological processes. Meanwhile, the efficiency of the removal of pollutants such as auramine (AO), methylene blue (MB), and ion Cadmium (Cd (II)) from the wastewater are difficult to be measured directly in real-time, as this measurement requires laboratory instruments that are time-consuming. The soft sensors could be the solution to perform the real-time prediction of the AO, MB, and Cd (II) removals’ capability. Hence, this study investigates the performances of a soft sensor, namely least-square support vector regression (LSSVR) to estimate the AO, MB, and Cd (II) removals’ ability. In this study, two wastewater-related case studies involving AO, MB, and Cd (II) removals were used to evaluate the predictive performance of the LSSVR. Additionally, its results were compared and analyzed with other soft sensors. For both case studies, notice that LSSVR gives the best results for AO, MB, and Cd (II) removals as compared to other soft sensor models where its root means square errors, mean absolute errors, and the approximate error, are lowered by 83% to 1,756%. Moreover, its coefficients of determination, denoted R 2 are the highest which are all more than or close to 0.9 for all the AO, MB, and Cd (II) removals even for the testing data for the case studies that were not used to develop the LSSVR model. In conclusion, LSSVR is more suitable for evaluating the effectiveness of the AO, MB, and Cd (II) removals at present.