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DOI: https://doi.org/10.4491/eer.2019.085
Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes
Muhammad Yaqub1, Beytullah EREN2, and Volkan Eyupoglu3
1Kumoh National Institute of Technology Gumi, South Korea
2Department of Environmental Engineering, Sakarya University, Sakarya, Turkey
3Science Faculty, Chemistry Department, Cankiri Karatekin University, Cankiri, Turkey
Corresponding Author: Beytullah EREN ,Tel: +604-599-6412, Fax: +604-599-6908, Email: beren@sakarya.edu.tr
Received: March 4, 2019;  Accepted: June 13, 2019.
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In this study soft computing techniques including, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were investigated for the prediction of Cr(VI) transport efficiency by novel Polymer Inclusion Membranes (PIMs). Transport experiments carried out by varying parameters such as time, film thickness, carrier type, carier rate, plasticizer type, and plasticizer rate. The predictive performance of ANN and ANFIS model was evaluated by using statistical performance criteria such as Root Mean Standard Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). Moreover, Sensitivity Analysis (SA) was carried out to investigate the effect of each input on PIMs Cr(VI) removal efficiency. The proposed ANN model presented reliable and valid results, followed by ANFIS model results. RMSE and MAE values were 0.00556, 0.00163 for ANN and 0.00924, 0.00493 for ANFIS model in the prediction of Cr(VI) removal efficiency on testing data sets. The R2 values were 0.973 and 0.867 on testing data sets by ANN and ANFIS, respectively. Results show that the ANN-based prediction model performed better than ANFIS. SA demonstrated that time; film thickness; carrier type and plasticizer type are major operating parameters having 33.61%, 26.85%, 21.07% and 8.917% contribution, respectively.
Keywords: Adaptive neuro-fuzzy inference system | Artificial neural networks | Chromium | Removal efficiency | Sensitivity analysis
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