Darajeh Negisa, Idris Azni, Fard Masoumi Hamid Reza, Nourani Abolfazl, Truong Paul, Rezania Shahabaldin
a Department of Chemical and Environmental Engineering , Faculty of Engineering, Universiti Putra Malaysia , Serdang , Selangor , Malaysia.
b Department of Chemistry , Faculty of Science, Universiti Putra Malaysia , Serdang , Selangor , Malaysia.
Int J Phytoremediation. 2017 May 4;19(5):413-424. doi: 10.1080/15226514.2016.1244159.
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%.
人工神经网络（ANNs）因其在捕捉复杂系统中变量间非线性关系方面具有可靠、稳健且显著的特性，已被广泛用于解决各种问题。在本研究中，人工神经网络被应用于对香根草系统去除棕榈油厂二级出水（POMSE）中化学需氧量（COD）和可生物降解有机物（BOD）进行建模。自变量，包括POMSE浓度、香根草幼苗密度和去除时间，被视为优化网络的输入参数，而COD和BOD的去除率则被选作输出。为确定隐藏层节点的数量，测试集的均方根误差被最小化，并通过决定系数和绝对平均偏差对算法的拓扑结构进行比较。比较结果表明，快速传播（QP）算法具有最小的均方根误差和绝对平均偏差，以及最大的决定系数。变量的重要性值包括香根草幼苗密度为42.41%，时间为29.8%，POMSE浓度为27.79%，这表明它们中没有一个是可以忽略不计的。结果表明，人工神经网络在预测从POMSE中去除COD和BOD方面具有很大的潜力，残余标准误差（RSE）小于0.45%。
