{"title":"Sb (III) Removal from Aqueous Solutions by the Mesoporous Fe3O4/GO Nanocomposites: Modeling and Optimization Using Artificial Intelligence","authors":"X. L. Wu, R. Cao, J. W. Hu, C. Zhou, X. H. Wei","doi":"10.3808/jei.202300495","DOIUrl":"https://doi.org/10.3808/jei.202300495","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77379772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Rapid Prototyping of An Automated Sensor-to-Server Environmental Data Acquisition System Adopting A FAIR-Oriented Approach","authors":"P. Célicourt, R. Sam, M. Piasecki","doi":"10.3808/jei.202300483","DOIUrl":"https://doi.org/10.3808/jei.202300483","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76842888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Z. Ning, C. Chen, S. Zhang, A. Wang, Q. Wang, T. Xie, J. Bai, B. Cui
{"title":"Lateral Hydrological Connectivity Driven by Tidal Flooding Regulates Range-Expansion of Invasive Spartina alterniflora in Tidal Channel-Salt Marsh Systems","authors":"Z. Ning, C. Chen, S. Zhang, A. Wang, Q. Wang, T. Xie, J. Bai, B. Cui","doi":"10.3808/jei.202300484","DOIUrl":"https://doi.org/10.3808/jei.202300484","url":null,"abstract":"","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84044574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid emergence of the global greening phenomenon under remote sensing monitoring, the prevailing trend of phenomenon analysis and traceability research is self-evident. However, identifying characteristics is basic research of the greening phenomenon, which sometimes subverts research results. The choice of method may directly affect the difference in the greening-browning range, which is easily overlooked. At the same time, influenced by the regional vegetation state’s basic value, the greening contribution’s spatialization still needs to be further verified. Based on the enhanced vegetation index results at the global kilometer-grid scale, this research chose to use the maximum value composite and the simple average method to explore the differences in China’s characteristic identification process initially. While paying attention to results and phenomena, scholars’ attention to basic research needs further improvement. The results show that the widely used two groups of basic methods have shown noticeable differences in greening and browning, and are affected by human activities, climate, geographical environment, etc. And this directional error and the phenomenon of hasty generalization are the most easily ignored in much basic research. The vegetation information considering the inherent stock and changing flux has quantified the greening contribution between regions. China, Brazil, and India dominate global greening, and Canada significantly contributes to browning. Some regions must promote the greening trend of changing flux while maintaining the inherent stock advantage.
{"title":"Differences in China Greening Characteristics and its Contribution to Global Greening","authors":"X. Zhang, D. H. Yan, T. L. Qin, C. H. Li, H. Wang","doi":"10.3808/jei.202300502","DOIUrl":"https://doi.org/10.3808/jei.202300502","url":null,"abstract":"With the rapid emergence of the global greening phenomenon under remote sensing monitoring, the prevailing trend of phenomenon analysis and traceability research is self-evident. However, identifying characteristics is basic research of the greening phenomenon, which sometimes subverts research results. The choice of method may directly affect the difference in the greening-browning range, which is easily overlooked. At the same time, influenced by the regional vegetation state’s basic value, the greening contribution’s spatialization still needs to be further verified. Based on the enhanced vegetation index results at the global kilometer-grid scale, this research chose to use the maximum value composite and the simple average method to explore the differences in China’s characteristic identification process initially. While paying attention to results and phenomena, scholars’ attention to basic research needs further improvement. The results show that the widely used two groups of basic methods have shown noticeable differences in greening and browning, and are affected by human activities, climate, geographical environment, etc. And this directional error and the phenomenon of hasty generalization are the most easily ignored in much basic research. The vegetation information considering the inherent stock and changing flux has quantified the greening contribution between regions. China, Brazil, and India dominate global greening, and Canada significantly contributes to browning. Some regions must promote the greening trend of changing flux while maintaining the inherent stock advantage.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135497515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Doped graphitic carbon nitride (g-C3N4) has been investigated as the visible light photocatalyst for photocatalytic H2 production and organic pollution removal. The elements doping could change the nanostructures, surface composition, and electronic structurescompared to pure g-C3N4. Such changes will provide better light-harvesting, more active sites and enhanced charge separation. In this work, we built the La-B co-doped g-C3N4 by an in-situ growth of g-C3N4 on LaB6. The effect of La-B co-doping on the phase, morphology, light absorption and porous structures is fully characterized to clearly understand the differences in the photocatalytic activities clearly. La and B co-doping introduced defect states and redistribution with suitable redox potentials, benefiting charge separation and photocatalytic reactions. So, the optimal co-doped samples process a higher photocatalytic performance in H2 production and Rhodamine B (RhB) degradation than the pure g-C3N4. The possible valence and conduction band edge positions and photocatalytic mechanism are discussed at last.
{"title":"In-Situ Construction of La-B Co-Doped g-C3N4 for Highly Efficient Photocatalytic H2 Production and RhB Degradation","authors":"L. N. Wang, L. H. Xiao, Q. Jin, Q. Chang","doi":"10.3808/jei.202200481","DOIUrl":"https://doi.org/10.3808/jei.202200481","url":null,"abstract":"Doped graphitic carbon nitride (g-C3N4) has been investigated as the visible light photocatalyst for photocatalytic H2 production and organic pollution removal. The elements doping could change the nanostructures, surface composition, and electronic structurescompared to pure g-C3N4. Such changes will provide better light-harvesting, more active sites and enhanced charge separation. In this work, we built the La-B co-doped g-C3N4 by an in-situ growth of g-C3N4 on LaB6. The effect of La-B co-doping on the phase, morphology, light absorption and porous structures is fully characterized to clearly understand the differences in the photocatalytic activities clearly. La and B co-doping introduced defect states and redistribution with suitable redox potentials, benefiting charge separation and photocatalytic reactions. So, the optimal co-doped samples process a higher photocatalytic performance in H2 production and Rhodamine B (RhB) degradation than the pure g-C3N4. The possible valence and conduction band edge positions and photocatalytic mechanism are discussed at last.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. M. Hu, C. X. Yang, Z. M. Liang, X. Y. Luo, Y. X. Huang, C. Tang
Change-point analysis of time-series data plays a vital role in various fields of earth sciences under changing environments. Most of the analysis approaches were usually designed to detect the change-point in the level of time-series mean. In this study, we aimed to propose a non-parametric approach to detect the change-point of different parameters of time-series data. In this approach, the Boot- strap method, coupling with Kernel density estimation, was first used to estimate the probability distribution function (pdf) of a parameter before and after any potential change-points. Second, the Ar-index based on the uncross area of the two pdfs was designed to quantify the difference of the parameter before and after each potential change-point. Finally, the potential change-point owning the largest Ar-index value was determined as the locations of the change-point of the parameter. The hydrological extreme series from four stations in the Hanjiang basin were used to demonstrate this approach. The Pettitt test method commonly used in hydrology was employed as a comparison to indirectly analyze the reliability of the proposed approach. The results show that change-point detected by the proposed approach in the four stations are identified with those detected by the Pettitt approach in the level of time-series mean. But in comparison with the Pettitt test, the proposed approach can provide more detection information for other parameters, such as coefficient of variation (Cv) and coefficient of skewness (Cs) of the series. The results also show that the degree of change in the series mean is greater than its Cv and Cs, while the degree of change in series Cv is greater than its Cs.
{"title":"A Non-Parametric Approach for Change-Point Detection of Multi-Parameters in Time-Series Data","authors":"Y. M. Hu, C. X. Yang, Z. M. Liang, X. Y. Luo, Y. X. Huang, C. Tang","doi":"10.3808/jei.202200478","DOIUrl":"https://doi.org/10.3808/jei.202200478","url":null,"abstract":"Change-point analysis of time-series data plays a vital role in various fields of earth sciences under changing environments. Most of the analysis approaches were usually designed to detect the change-point in the level of time-series mean. In this study, we aimed to propose a non-parametric approach to detect the change-point of different parameters of time-series data. In this approach, the Boot- strap method, coupling with Kernel density estimation, was first used to estimate the probability distribution function (pdf) of a parameter before and after any potential change-points. Second, the Ar-index based on the uncross area of the two pdfs was designed to quantify the difference of the parameter before and after each potential change-point. Finally, the potential change-point owning the largest Ar-index value was determined as the locations of the change-point of the parameter. The hydrological extreme series from four stations in the Hanjiang basin were used to demonstrate this approach. The Pettitt test method commonly used in hydrology was employed as a comparison to indirectly analyze the reliability of the proposed approach. The results show that change-point detected by the proposed approach in the four stations are identified with those detected by the Pettitt approach in the level of time-series mean. But in comparison with the Pettitt test, the proposed approach can provide more detection information for other parameters, such as coefficient of variation (Cv) and coefficient of skewness (Cs) of the series. The results also show that the degree of change in the series mean is greater than its Cv and Cs, while the degree of change in series Cv is greater than its Cs.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Every manufacturing system produces toxic by-products that cause a hazardous impact on society and the environment. As a result, pollution control authorities’ role has gained importance for the betterment of society and the preservation of a clean and green environment. As a result, one of the goals of this research is to develop a sustainable smart manufacturing model with less waste and controlled pollution. Here, a flexible production process is discussed under imprecise market conditions with partial backlogging and rework. Two different sustainable production models are presented here by considering pollution control costs. A sustainable production model with variable pollution costs is examined under the influence of three pollution control mechanisms to improve the model’s applicability. A solution methodology, including three critical theorems, is provided to obtain the optimal production rate, length, and total cost per cycle. The paper’s novelty lies in introducing pollution control costs and pollution control mechanisms together in a flexible, sustainable production system with uncertainty. In comparison to the other models, the model with a variable pollution cost appears to be more sustainable as, in this case, there is a 25.5% reduction in the pollution level compared to the other models. Implementing three pollution-controlling strategies, such as pollution cap, pollution cap and trade, and pollution tax, resulted in reductions of 34.37, 0.83, and 0.62% in pollution levels, respectively. A sensitivity analysis of the obtained results is carried out to show the model’s strength and robustness.
{"title":"Reduction of Pollution through Sustainable and Flexible Production by Controlling By-Products","authors":"D. Yadav, R. Singh, A. Kumar, B. Sarkar","doi":"10.3808/jei.202200476","DOIUrl":"https://doi.org/10.3808/jei.202200476","url":null,"abstract":"Every manufacturing system produces toxic by-products that cause a hazardous impact on society and the environment. As a result, pollution control authorities’ role has gained importance for the betterment of society and the preservation of a clean and green environment. As a result, one of the goals of this research is to develop a sustainable smart manufacturing model with less waste and controlled pollution. Here, a flexible production process is discussed under imprecise market conditions with partial backlogging and rework. Two different sustainable production models are presented here by considering pollution control costs. A sustainable production model with variable pollution costs is examined under the influence of three pollution control mechanisms to improve the model’s applicability. A solution methodology, including three critical theorems, is provided to obtain the optimal production rate, length, and total cost per cycle. The paper’s novelty lies in introducing pollution control costs and pollution control mechanisms together in a flexible, sustainable production system with uncertainty. In comparison to the other models, the model with a variable pollution cost appears to be more sustainable as, in this case, there is a 25.5% reduction in the pollution level compared to the other models. Implementing three pollution-controlling strategies, such as pollution cap, pollution cap and trade, and pollution tax, resulted in reductions of 34.37, 0.83, and 0.62% in pollution levels, respectively. A sensitivity analysis of the obtained results is carried out to show the model’s strength and robustness.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, a simulation-based multi-objective optimization method is developed for optimizing the structural design of hydrodynamic cavitation (HC) reactor and improving the cavitation effect of HC reactor. The developed method integrates simulation technique of computational fluid dynamics (CFD) and optimization techniques of surrogate model and nondominated sorting genetic algorithm II (NSGA-II) into a general framework. The effect of structure parameters and their interactions on the cavitation effect of the self-excited oscillation cavity (SEOC) are analyzed. Results demonstrate that optimization techniques of surrogate model and NSGA-II can effectively improve the structure and the capacity of SEOC. Simulation results show that the internal vapor volume fraction and outlet vapor volume fraction of SEOC (based on the optimized structure) increase by 13.46 and 38.01%, respectively. The optimized structure of SEOC is also verified experimentally through the degradation experiment of methylene blue solution. The degrees of degra-dation before and after optimization respectively are 10.12 and 16.14%, and the degradation capacity increases by 59.5%. This study will play a significantly guiding role on the optimization design of HC reactor for advanced oxidation processes (AOPs) to obtain the preferable cavitation effect.
{"title":"Development of A Simulation-Based Multi-Objective Optimization Method for Improving the Advanced Oxidizing Capacity of Hydrodynamic Cavitation Reactor - A Case Study of Self-Excited Oscillation Cavity","authors":"S. L. Nie, J. K. Zhou, H. Ji, Z. Y. Dai, Z. H. Ma","doi":"10.3808/jei.202200474","DOIUrl":"https://doi.org/10.3808/jei.202200474","url":null,"abstract":"In this study, a simulation-based multi-objective optimization method is developed for optimizing the structural design of hydrodynamic cavitation (HC) reactor and improving the cavitation effect of HC reactor. The developed method integrates simulation technique of computational fluid dynamics (CFD) and optimization techniques of surrogate model and nondominated sorting genetic algorithm II (NSGA-II) into a general framework. The effect of structure parameters and their interactions on the cavitation effect of the self-excited oscillation cavity (SEOC) are analyzed. Results demonstrate that optimization techniques of surrogate model and NSGA-II can effectively improve the structure and the capacity of SEOC. Simulation results show that the internal vapor volume fraction and outlet vapor volume fraction of SEOC (based on the optimized structure) increase by 13.46 and 38.01%, respectively. The optimized structure of SEOC is also verified experimentally through the degradation experiment of methylene blue solution. The degrees of degra-dation before and after optimization respectively are 10.12 and 16.14%, and the degradation capacity increases by 59.5%. This study will play a significantly guiding role on the optimization design of HC reactor for advanced oxidation processes (AOPs) to obtain the preferable cavitation effect.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2022-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Floods are the most common and among the most severe natural disasters in many countries around the world. As global warming continues to exacerbate sea level rise and extreme weather, governmental authorities and environmental agencies are facing the pressing need of timely and accurate evaluations and predictions of flood risks. Current flood forecasts are generally based on historical measurements of environmental variables at monitoring stations. In recent years, in addition to traditional data sources, large amounts of information related to floods have been made available via social media. Members of the public are constantly and promptly posting information and updates on local environmental phenomena on social media platforms. Despite the growing interest of scholars towards the usage of online data during natural disasters, the majority of studies focus exclusively on social media as a stand-alone data source, while its joint use with other type of information is still unexplored. In this paper we propose to fill this gap by integrating traditional historical information on floods with data extracted by Twitter and Google Trends. Our methodology is based on vine copulas, that allow us to capture the dependence structure among the marginals, which are modelled via appropriate time series methods, in a very flexible way. We apply our methodology to data related to three different coastal locations on the South coast of the United Kingdom (UK). The results show that our approach, based on the integration of social media data, outperforms traditional methods in terms of evaluation and prediction of flood events.
{"title":"Social Media Integration of Flood Data: A Vine Copula-Based Approach","authors":"L. Ansell, L. Dalla Valle","doi":"10.3808/jei.202200471","DOIUrl":"https://doi.org/10.3808/jei.202200471","url":null,"abstract":"Floods are the most common and among the most severe natural disasters in many countries around the world. As global warming continues to exacerbate sea level rise and extreme weather, governmental authorities and environmental agencies are facing the pressing need of timely and accurate evaluations and predictions of flood risks. Current flood forecasts are generally based on historical measurements of environmental variables at monitoring stations. In recent years, in addition to traditional data sources, large amounts of information related to floods have been made available via social media. Members of the public are constantly and promptly posting information and updates on local environmental phenomena on social media platforms. Despite the growing interest of scholars towards the usage of online data during natural disasters, the majority of studies focus exclusively on social media as a stand-alone data source, while its joint use with other type of information is still unexplored. In this paper we propose to fill this gap by integrating traditional historical information on floods with data extracted by Twitter and Google Trends. Our methodology is based on vine copulas, that allow us to capture the dependence structure among the marginals, which are modelled via appropriate time series methods, in a very flexible way. We apply our methodology to data related to three different coastal locations on the South coast of the United Kingdom (UK). The results show that our approach, based on the integration of social media data, outperforms traditional methods in terms of evaluation and prediction of flood events.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Valikhan Anaraki, F. Mahmoudian, F. Nabizadeh Chianeh, S. Farzin
In the present study, a new approach by coupling the interpolation method with computation-based technique (data-mining algorithms and an optimization algorithm) is introduced for modeling and optimization removal of Reactive Orange 7 (RO7) dye removal from synthetic wastewater. To this end, four significant factors like pH, electrolyte concentration, current density, and electrolysis time are considered as input variables. Thus, modeling of RO7 removal is implemented using eight data mining algorithms including multi- variate linear regression (MLR), ridge regression (RR), multivariate nonlinear regression (MNLR), artificial neural network (ANN), classification and regression tree (CART), k nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). These al- gorithms require a large data set for creating reliable results. However, creating a large number of experimental data request consuming high cost and time. Hence, the interpolation methods of kriging (KRG) and inverse distance weight (IDW) are applied for generating more data, whereas KRG has more accuracy than IDW by increasing the 47.080, 36.914, and 1.77% in MAE, RMSE, and R values, res- pectively. Then, the data mining algorithms are used for modeling the decolorization efficiency (DE) based on the original data and new data from KRG. It is found that using new data leads to significantly increasing accuracy (94.47, 96.43, 1.52, and 2.77% for MAE, RMSE, R and R2, respectively) of DE modeling. Also, SVM has demonstrated the highest accuracy out of all data mining algorithms (by in- creasing the 97.13, 98.30, and 14.42% in MAE, RMSE, and R2 values, respectively). Another challenge in the removal of RO7 from synthetic wastewater is predicting the maximum removal amount and optimal input variables. For this purpose, the hybrid of SVM and whale optimization algorithm (WOA) is employed. Finally, SVM-WOA has predicted the maximum of DE (91%) by optimal values of 4.2, 1.5 g/L, 4.2 mA/cm2, and 18 min for pH, C, I, and Time, respectively. In light of the high performance of the introduced approach for modeling removal process and predicting optimal conditions of removal process, this approach can be suggested for the removal of other pollutants from wastewater when the number of experimental data set is limited.
{"title":"Dye Pollutant Removal from Synthetic Wastewater: A New Modeling and Predicting Approach Based on Experimental Data Analysis, Kriging Interpolation Method, and Computational Intelligence Techniques","authors":"M. Valikhan Anaraki, F. Mahmoudian, F. Nabizadeh Chianeh, S. Farzin","doi":"10.3808/jei.202200473","DOIUrl":"https://doi.org/10.3808/jei.202200473","url":null,"abstract":"In the present study, a new approach by coupling the interpolation method with computation-based technique (data-mining algorithms and an optimization algorithm) is introduced for modeling and optimization removal of Reactive Orange 7 (RO7) dye removal from synthetic wastewater. To this end, four significant factors like pH, electrolyte concentration, current density, and electrolysis time are considered as input variables. Thus, modeling of RO7 removal is implemented using eight data mining algorithms including multi- variate linear regression (MLR), ridge regression (RR), multivariate nonlinear regression (MNLR), artificial neural network (ANN), classification and regression tree (CART), k nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). These al- gorithms require a large data set for creating reliable results. However, creating a large number of experimental data request consuming high cost and time. Hence, the interpolation methods of kriging (KRG) and inverse distance weight (IDW) are applied for generating more data, whereas KRG has more accuracy than IDW by increasing the 47.080, 36.914, and 1.77% in MAE, RMSE, and R values, res- pectively. Then, the data mining algorithms are used for modeling the decolorization efficiency (DE) based on the original data and new data from KRG. It is found that using new data leads to significantly increasing accuracy (94.47, 96.43, 1.52, and 2.77% for MAE, RMSE, R and R2, respectively) of DE modeling. Also, SVM has demonstrated the highest accuracy out of all data mining algorithms (by in- creasing the 97.13, 98.30, and 14.42% in MAE, RMSE, and R2 values, respectively). Another challenge in the removal of RO7 from synthetic wastewater is predicting the maximum removal amount and optimal input variables. For this purpose, the hybrid of SVM and whale optimization algorithm (WOA) is employed. Finally, SVM-WOA has predicted the maximum of DE (91%) by optimal values of 4.2, 1.5 g/L, 4.2 mA/cm2, and 18 min for pH, C, I, and Time, respectively. In light of the high performance of the introduced approach for modeling removal process and predicting optimal conditions of removal process, this approach can be suggested for the removal of other pollutants from wastewater when the number of experimental data set is limited.\u0000","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":7.0,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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