Comparative process optimization of pilot-scale total petroleum hydrocarbon (TPH) degradation by Paspalum scrobiculatum L. Hack using response surface methodology (RSM) and artificial neural networks (ANNs)

作者：Sanusi Salmi Nur Ain; Halmi Mohd Izuan Effendi^*; Abdullah Siti Rozaimah Sheikh^*; Abu Hassana Hassimi; Hamzah Firdaus Mohamad; Idris Mushrifah

来源：Ecological Engineering, 2016, 97: 524-534.

DOI：10.1016/j.ecoleng.2016.10.044

• Phytoremediation
• Total petroleum hydrocarbon (TPH)
• Paspalum scrobiculatum L. Hack
• Response surface methodology
• Artificial neural network

The aim of this study is to investigate an optimization process for the degradation of total petroleum hydrocarbon (TPH) by a tropical plant, Paspalum scrobiculatum L. Hack, using response surface methodology and artificial neural network. The optimum conditions predicted by RSM were found to be at a diesel concentration of 3%, 72 sampling days and an aeration rate of 1.77 L/min with a 76.8% maximum TPH removal. The coefficients of determination (R-2) and adjusted R-2 for the RSM model equations were 0.8530 and 0.7208. The optimum conditions predicted by the ANN were found to be at a diesel concentration of 3%, 72 sampling days and an aeration rate of 1.02 L/min with an 85.5% maximum TPH removal. Analysis using the ANN's prediction data, which showed a higher R-2 value of 0.957 and small values of Average Absolute Deviation (AAD) and Root Mean Square Error (RMSE), were 0.33% and 0.302, respectively. Validation analysis showed the predicted values by RSM and ANN were close to the validation values, whereas the ANN showed the lowest deviation, 2.57%, compared to the RSM. This finding suggests that the ANN showed a better prediction and fitting ability compared to the RSM for the non-linear regression analysis.

• 出版日期2016-12