Narjes Esmaeili, Fatemeh Esmaeili Khalil Saraei, Azadeh Ebrahimian Pirbazari, Fatemeh-Sadat Tabatabai-Yazdi, Ziba Khodaee, Ali Amirinezhad, Amin Esmaeili, Ali Ebrahimian Pirbazari
{"title":"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","authors":"Narjes Esmaeili, Fatemeh Esmaeili Khalil Saraei, Azadeh Ebrahimian Pirbazari, Fatemeh-Sadat Tabatabai-Yazdi, Ziba Khodaee, Ali Amirinezhad, Amin Esmaeili, Ali Ebrahimian Pirbazari","doi":"10.1515/cppm-2021-0065","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":9935,"journal":{"name":"Chemical Product and Process Modeling","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Estimation of 2,4-dichlorophenol photocatalytic removal using different artificial intelligence approaches\",\"authors\":\"Narjes Esmaeili, Fatemeh Esmaeili Khalil Saraei, Azadeh Ebrahimian Pirbazari, Fatemeh-Sadat Tabatabai-Yazdi, Ziba Khodaee, Ali Amirinezhad, Amin Esmaeili, Ali Ebrahimian Pirbazari\",\"doi\":\"10.1515/cppm-2021-0065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Photocatalytic degradation is one of the effective methods to remove various pollutants from domestic and industrial effluents. 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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. 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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.
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.