Application of multiple linear regression, central composite design, and ANFIS models in dye concentration measurement and prediction using plastic optical fiber sensor. (October 2015)

• Record Type: Journal Article
• Authors: Chong, Su Sin; Abdul Aziz, A.R.; Harun, Sulaiman W.; Arof, Hamzah; Shamshirband, Shahaboddin
• Is Part Of: Measurement. Volume 74(2015:Oct.)
• Page Start: 78
• Page End: 86
• Publication Date: 2015-10
• Subjects: Remazol black B -- Central composite design -- ANFIS -- Prediction -- Multiple linear regression -- Environmental sensing; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2015.06.019
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Performances of POF sensors used to detect concentration of RBB are evaluated. Predictive models are useful at the preliminary stage of designing a structure. MLR, CCD and ANFIS models are developed for prediction purpose. These models register high accuracy with ANFIS showing the best performance. Abstract: The measurement and prediction of dye concentration is important in the design, planning and management of wastewater treatment. Soft computing techniques can be used as a support tool for analyzing data and making prediction. In this study, Central Composite Design (CCD) and adaptive neuro-fuzzy inference system (ANFIS) are employed to identify and predict the output intensity ratio of light that passes through a plastic optical fiber (POF) sensor in Remazol Black B (RBB) dye solution of different concentrations. The predictive performances of these models are compared to that of the traditional Multiple Linear Regression (MLR). The accuracies of MLR, CCD and ANFIS models are evaluated in terms of square correlation coefficient ( R 2 ), root mean square error (RMSE), value accounted for (VAF), and mean absolute percentage error (MAPE) against the empirical data. It is found that the ANFIS model exhibits higher prediction accuracy than the MLR and CCD models.