title: Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks.
abstract: Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied for modeling of Chemical Oxygen Demand (COD) and biodegradable organic matter (BOD) removal from palm oil mill secondary effluent (POMSE) by vetiver system. The independent variable, including POMSE concentration, vetiver slips density, and removal time, has been considered as input parameters to optimize the network, while the removal percentage of COD and BOD were selected as output. To determine the number of hidden layer nodes, the root mean squared error of testing set was minimized, and the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the quick propagation (QP) algorithm had minimum root mean squared error and absolute average deviation, and maximum coefficient of determination. The importance values of the variables was included vetiver slips density with 42.41%, time with 29.8%, and the POMSE concentration with 27.79%, which showed none of them, is negligible. Results show that the ANN has great potential ability in prediction of COD and BOD removal from POMSE with residual standard error (RSE) of less than 0.45%.
authors.affiliation: a Department of Chemical and Environmental Engineering , Faculty of Engineering, Universiti Putra Malaysia , Serdang , Selangor , Malaysia.
authors.forename: Negisa
authors.initials: N
authors.lastname: Darajeh
authors.affiliation: a Department of Chemical and Environmental Engineering , Faculty of Engineering, Universiti Putra Malaysia , Serdang , Selangor , Malaysia.
authors.forename: Azni
authors.initials: A
authors.lastname: Idris
authors.affiliation: b Department of Chemistry , Faculty of Science, Universiti Putra Malaysia , Serdang , Selangor , Malaysia.
authors.forename: Hamid Reza
authors.initials: HR
authors.lastname: Fard Masoumi
authors.affiliation: c Department of Mechanical and Manufacturing Engineering , Faculty of Engineering, Universiti Putra Malaysia , Serdang , Selangor , Malaysia.
authors.forename: Abolfazl
authors.initials: A
authors.lastname: Nourani
authors.affiliation: d TVNI Technical Director for Asia and Oceania , Brisbane , Australia.
authors.forename: Paul
authors.initials: P
authors.lastname: Truong
authors.affiliation: e Department of Environmental Engineering , Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM) , Johor , Malaysia.
authors.forename: Shahabaldin
authors.initials: S
authors.lastname: Rezania
mesh_terms: D001673:Biodegradation, Environmental; D057919:Biological Oxygen Demand Analysis; D049930:Chrysopogon / Q000378:metabolism*; D052918:Environmental Restoration and Remediation / Q000295:instrumentation / Q000379:methods*; D007220:Industrial Waste / Q000032:analysis*; D008296:Malaysia; D066192:Manufacturing Industry; D016571:Neural Networks, Computer; D000073878:Palm Oil; D010938:Plant Oils; D014865:Waste Disposal, Fluid / Q000295:instrumentation / Q000379:methods*; D014874:Water Pollutants, Chemical / Q000378:metabolism*
chemical_list: D007220:Industrial Waste; D010938:Plant Oils; D014874:Water Pollutants, Chemical; D000073878:Palm Oil
keywords: artificial neural network; biological oxygen demand; chemical oxygen demand; palm oil mill secondary effluent; vetiver