Department of Chemistry, Gachsaran Branch, Islamic Azad University, P.O. Box 75818-63876, Gachsaran, Iran.
Department of Chemistry, Gachsaran Branch, Islamic Azad University, P.O. Box 75818-63876, Gachsaran, Iran.
Adv Colloid Interface Sci. 2017 Jul;245:20-39. doi: 10.1016/j.cis.2017.04.015. Epub 2017 Apr 26.
Artificial neural networks (ANNs) have been widely applied for the prediction of dye adsorption during the last decade. In this paper, the applications of ANN methods, namely multilayer feedforward neural networks (MLFNN), support vector machine (SVM), and adaptive neuro fuzzy inference system (ANFIS) for adsorption of dyes are reviewed. The reported researches on adsorption of dyes are classified into four major categories, such as (i) MLFNN, (ii) ANFIS, (iii) SVM and (iv) hybrid with genetic algorithm (GA) and particle swarm optimization (PSO). Most of these papers are discussed. The further research needs in this field are suggested. These ANNs models are obtaining popularity as approaches, which can be successfully employed for the adsorption of dyes with acceptable accuracy.
在过去的十年中，人工神经网络 (ANNs) 已被广泛应用于预测染料吸附。本文综述了 ANN 方法在染料吸附中的应用，包括多层前馈神经网络 (MLFNN)、支持向量机 (SVM) 和自适应神经模糊推理系统 (ANFIS)。报道的染料吸附研究分为四大类，如 (i) MLFNN、(ii) ANFIS、(iii) SVM 和 (iv) 与遗传算法 (GA) 和粒子群优化 (PSO) 的混合。本文对大多数论文进行了讨论。还提出了该领域进一步研究的需要。这些 ANN 模型作为一种方法正在获得普及，可以成功地用于染料的吸附，并且具有可接受的准确性。
