不同人工智能方法对2,4-二氯苯酚光催化去除效果的评价

IF 1 Q4 ENGINEERING, CHEMICAL Chemical Product and Process Modeling Pub Date : 2022-04-13 DOI:10.1515/cppm-2021-0065
Narjes Esmaeili, Fatemeh Esmaeili Khalil Saraei, Azadeh Ebrahimian Pirbazari, Fatemeh-Sadat Tabatabai-Yazdi, Ziba Khodaee, Ali Amirinezhad, Amin Esmaeili, Ali Ebrahimian Pirbazari
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引用次数: 5

摘要

摘要光催化降解是去除生活和工业废水中各种污染物的有效方法之一。几个操作参数可以影响光催化降解的效率。执行实验方法以获得不同操作条件下污染物的降解百分比(%降解)既昂贵又耗时。因此,计算模型的使用对于呈现各种操作条件下的%退化非常有用。在我们之前的工作中,合成了含有不同量银纳米颗粒的Fe3O4/TiO2纳米复合材料(Fe3O4/TiO2/Ag),并通过各种分析技术进行了表征,并将其应用于2,4-二氯苯酚(2,4-DCP)的降解。在这项工作中,开发了一系列模型,包括随机梯度提升(SGB)、人工神经网络(ANN)、自适应神经模糊推理系统(ANFIS)、用遗传算法改进ANFIS(GA-ANFIS)和粒子群优化(PSO-ANFIS)来估计2,4-DCP的去除率。模型输入包括催化剂剂量、辐射时间、2,4-DCP的初始浓度和不同体积的AgNO3。对所开发的模型的评估表明,所有模型都可以预测发生的现象,具有良好的兼容性,但PSO-ANFIS和SGB模型给出了很高的精度,决定系数(R2)为0.99。此外,还评估了输入参数的相对贡献和相关因素。催化剂用量和辐射时间对2,4-DCP去除率的预测相对贡献最高(32.6%),最低(16%)。
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Estimation of 2,4-dichlorophenol photocatalytic removal using different artificial intelligence approaches
Abstract Photocatalytic degradation is one of the effective methods to remove various pollutants from domestic and industrial effluents. Several operational parameters can affect the efficiency of photocatalytic degradation. Performing experimental methods to obtain the percentage degradation (%degradation) of pollutants in different operating conditions is costly and time-consuming. For this reason, the use of computational models is very useful to present the %degradation in various operating conditions. In our previous work, Fe3O4/TiO2 nanocomposite containing different amounts of silver nanoparticles (Fe3O4/TiO2/Ag) were synthesized, characterized by various analytical techniques and applied to degradation of 2,4-dichlorophenol (2,4-DCP). In this work, a series of models, including stochastic gradient boosting (SGB), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), the improvement of ANFIS with genetic algorithm (GA-ANFIS), and particle swarm optimization (PSO-ANFIS) were developed to estimate the removal percentage of 2,4-DCP. The model inputs comprised of catalyst dosage, radiation time, initial concentration of 2,4-DCP, and various volumes of AgNO3. Evaluating the developed models showed that all models can predict the occurring phenomena with good compatibility, but the PSO-ANFIS and the SGB models gave a high accuracy with the coefficient of determination (R2) of 0.99. Moreover, the relative contributions, and the relevancy factors of input parameters were evaluated. The catalyst dosage and radiation time had the highest (32.6%), and the lowest (16%) relative contributions on the predicting of removal percentage of 2,4-DCP, respectively.
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来源期刊
Chemical Product and Process Modeling
Chemical Product and Process Modeling ENGINEERING, CHEMICAL-
CiteScore
2.10
自引率
11.10%
发文量
27
期刊介绍: Chemical Product and Process Modeling (CPPM) is a quarterly journal that publishes theoretical and applied research on product and process design modeling, simulation and optimization. Thanks to its international editorial board, the journal assembles the best papers from around the world on to cover the gap between product and process. The journal brings together chemical and process engineering researchers, practitioners, and software developers in a new forum for the international modeling and simulation community. Topics: equation oriented and modular simulation optimization technology for process and materials design, new modeling techniques shortcut modeling and design approaches performance of commercial and in-house simulation and optimization tools challenges faced in industrial product and process simulation and optimization computational fluid dynamics environmental process, food and pharmaceutical modeling topics drawn from the substantial areas of overlap between modeling and mathematics applied to chemical products and processes.
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