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首页> 外文期刊>Spectrochimica acta, Part A. Molecular and biomolecular spectroscopy >A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon
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A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon

机译：混合人工神经网络和粒子群优化算法，用于预测使用活性炭负载的硫化锌纳米粒子从水溶液中去除有害染料艳绿

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摘要

In the present study, zinc sulfide nanoparticle loaded on activated carbon (ZnS-NP-AC) simply was synthesized in the presence of ultrasound and characterized using different techniques such as SEM and BET analysis. Then, this material was used for brilliant green (BG) removal. To dependency of BC removal percentage toward various parameters including pH, adsorbent dosage, initial dye concentration and contact time were examined and optimized. The mechanism and rate of adsorption was ascertained by analyzing experimental data at various time to conventional kinetic models such as pseudo-first-order and second order, Elovich and intra-particle diffusion models. Comparison according to general criterion such as relative error in adsorption capacity and correlation coefficient confirm the usability of pseudo-second-order kinetic model for explanation of data. The Langmuir models is efficiently can explained the behavior of adsorption system to give full information about interaction of BG with ZnS-NP-AC. A multiple linear regression (MLR) and a hybrid of artificial neural network and partial swarm optimization (ANN PSO) model were used for prediction of brilliant green adsorption onto ZnS-NP-AC. Comparison of the results obtained using offered models confirm higher ability of ANN model compare to the MLR model for prediction of BG adsorption onto ZnS-NP-AC. Using the optimal ANN PSO model the coefficient of determination (R-2) were 0.9610 and 0.9506; mean squared error (MSE) values were 0.0020 and 0.0022 for the training and testing data set, respectively. (C) 2014 Elsevier B.V. All rights reserved.
机译：在本研究中，负载在活性炭上的硫化锌纳米颗粒（ZnS-NP-AC）可以在超声波的作用下简单地合成，并使用SEM和BET分析等不同技术进行表征。然后，将该材料用于亮绿色（BG）去除。依赖于BC去除率对包括pH值，吸附剂用量，初始染料浓度和接触时间在内的各种参数的检查和优化。通过分析常规动力学模型（如​​伪一阶和二阶，Elovich和粒子内扩散模型）在不同时间的实验数据来确定吸附的机理和速率。根据一般标准（如吸附容量的相对误差和相关系数）进行比较，证实了伪二级动力学模型可用于解释数据。 Langmuir模型可以有效地解释吸附系统的行为，从而提供有关BG与ZnS-NP-AC相互作用的完整信息。使用多元线性回归（MLR）以及人工神经网络和部分群优化（ANN PSO）模型的混合来预测ZnS-NP-AC上亮绿色的吸附。使用提供的模型获得的结果的比较证实，与MLR模型相比，ANN模型具有更高的预测BG吸附在ZnS-NP-AC上的能力。使用最佳的ANN PSO模型，确定系数（R-2）为0.9610和0.9506;训练和测试数据集的均方误差（MSE）值分别为0.0020和0.0022。 （C）2014 Elsevier B.V.保留所有权利。

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