Abstract:
:Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research compares the efficacies of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models and evaluates their capability in estimating the adsorption efficiency of biochar for the removal of Cu (II) ions based on 480 experimental sets obtained in a laboratory batch study. The effects of operational parameters such as contact time, operating temperature, biochar dosage, and initial Cu (II) ion concentration on removing Cu (II) ions were investigated. Eleven different training algorithms in ANN and 8 different membership functions in ANFIS were compared statistically and evaluated in terms of estimation errors, which are root mean squared error (RMSE), mean absolute error (MAE), and accuracy. The effects of number of hidden neuron in ANN model and fuzzy set combination in ANFIS were studied. In this study, ANFIS model with Gaussian membership function and fuzzy set combination of [4 5 2 3] was found to be the best method, with accuracy of 90.24% and 87.06% for training and testing dataset, respectively. Contribution of this study is that ANN, ANFIS, and MLR modeling techniques were used for the first time to study the adsorption of Cu (II) ions from aqueous solutions using rambutan peel biochar.
journal_name
Environ Monit Assessjournal_title
Environmental monitoring and assessmentauthors
Wong YJ,Arumugasamy SK,Chung CH,Selvarajoo A,Sethu Vdoi
10.1007/s10661-020-08268-4subject
Has Abstractpub_date
2020-06-17 00:00:00pages
439issue
7eissn
0167-6369issn
1573-2959pii
10.1007/s10661-020-08268-4journal_volume
192pub_type
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