Journal
ECOLOGICAL ENGINEERING
Volume 97, Issue -, Pages 524-534Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ecoleng.2016.10.044
Keywords
Phytoremediation; Total petroleum hydrocarbon (TPH); Paspalum scrobiculatum L. Hack; Response surface methodology; Artificial neural network
Funding
- Universiti Kebangsaan Malaysia
- Ministry of Higher Education (MOHE) [GUP-2015-022]
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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. (C) 2016 Elsevier B.V. All rights reserved.
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