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artificial neural network modeling for the oxidation kinetics of divalent manganese ions during chlorination and the role of arsenite ions in the binary/ternary systems

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WATER RESEARCH 2024年 259卷 121876页

作者： Liao, Ziqiao Choi, Kungwon Ullah, Zahid Son, Moon Ahn, Yongtae Khan, Moonis Ali Prabhu, Subbaiah Muthu Jeon, Byong-Hun Hanyang Univ Dept Earth Resources & Environm Engn 222 Wangsimni Ro Seoul 04763 South Korea Korea Inst Sci & Technol Ctr Water Cycle Res 5 Hwarang Ro 14 gil Seoul 02792 South Korea Univ Sci & Technol KIST Sch Div Energy & Environm Technol Seoul 02792 South Korea Kyonggi Univ Dept Civil & Energy Syst Engn Suwon 16227 South Korea King Saud Univ Coll Sci Chem Dept Riyadh 11451 Saudi Arabia VIT AP Univ Sch Adv Sci Dept Chem Amaravati 522237 Andhra Pradesh India

This study investigated the coexistence and contamination of manganese (Mn(II)) and arsenite (As(III)) in groundwater and examined their oxidation behavior under different equilibrating parameters, including varying pH, bicarbonate (HCO3-) concentrations, and sodium hypochlorite (NaClO) oxidant concentrations. Results showed that if the molar ratio of NaClO: As(III) was >1, the oxidation of As(III) could be achieved within a minute with an extremely high oxidation rate of 99.7 %. In the binary system, the removal of As(III) prevailed over Mn(II). The As(III) oxidation efficiency increased from 59.8 +/- 0.6 % to 70.8 +/- 1.9 % when pH rose from 5.7 to 8.0. The oxidation reaction between As(III) and NaClO releases H+ ions, decreasing the pH from 6.77 to 6.19 and reducing the removal efficiency of Mn(II). The presence of HCO3- reduced the oxidation rate of Mn(II) from 63.2 % to 13.9 % within four hours. Instead, the final oxidation rate of Mn(II) increased from 68.1 % to 87.7 %. This increase can be attributed to HCO3- ions competing with the free Mn(II) for the adsorption sites on the sediments, inhibiting the formation of H+. Moreover, kinetic studies revealed that the oxidation reaction between Mn(II) and NaClO followed first-order kinetics based on their R-2 values. The significant factors affecting the Mn(II) oxidation efficiency were the initial concentration of NaClO and pH. Applying an artificial neural network (ANN) model for data analysis proved to be an effective tool for predicting Mn(II) oxidation rates under different experimental conditions. The actual Mn(II) oxidation data and the predicted values obtained from the ANN model showed significant consistency. The training and validation data sets yielded R-2 values of 0.995 and 0.992, respectively. Moreover, the ANN model highlights the importance of pH and NaClO concentrations in influencing the oxidation rate of Mn(II).

关键词： Oxidation Manganese Arsenic Kinetics artificial neural network modeling

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One-pot synthesis of novel 3D graphene/Fe3O4/agro-based waste material (Sesamum indicum) nanocomposite for wastewater treatment and artificial neural network modeling

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2023年第1期190卷 451-463页

作者： Nasiri, Ramin Zarei, Mahmoud Arsalani, Nasser Pezhhanfar, Sakha Someh, Abolfazl Ahmadi Panahian, Yasaman Univ Tabriz Fac Chem Dept Organ & Biochem Res Lab Polymer Tabriz *** Iran Univ Tabriz Fac Chem Dept Appl Chem Res Lab Environm Remediat Tabriz *** Iran Univ Tabriz Fac Chem Dept Analyt Chem Tabriz *** Iran

A One-Step hydrothermal process was applied in this study to synthesize magnetic Fe3O4/ 3D graphene/Sesamum indicum nanocomposite (M3DGSN). Acid Red 88 (AR88) was set at the target pollutant to be adsorbed and removed by the synthesized nanocomposite from the aqueous media. Field Emission scanning electron microscopy, Fourier transform infrared spectroscopy, vibrating sample magnetometery, and X-Ray diffraction analyses were applied to characterize the product. artificial neural network modeling has also been employed in the adsorption process of AR88 using M3DGSN. Adsorption studies were performed in the dye concentration range of 5-30 mg L-1 with the adsorbent weight of 25-100 mg. It was observed that the dye removal efficiency using M3DGSN was dependent on different operational parameters and increased with rising pH, time, and weight of M3DGSN. Also, decreasing the dye concentration demonstrated the enhancement of dye removal efficiency. The optimum adsorbent amount and dye concentrations were observed to be 100 mg and 5 mg L-1, respectively. Also, the optimum removal performance was seen at the pH of 9. (c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Adsorption Fe3O4 3D graphene Sesamum indicum Acid Red 88 artificial neural network modeling Water treatment

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Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling

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ALEXANDRIA ENGINEERING JOURNAL 2023年 84卷 184-203页

作者： Rostamzadeh-Renani, Reza Baghoolizadeh, Mohammadreza Sajadi, S. Mohammad Pirmoradian, Mostafa Rostamzadeh-Renani, Mohammad Baghaei, Sh. Salahshour, Soheil Energy Dept Politecn Milano Via Lambruschini 4 I-20156 Milan Italy Shahrekord Univ Dept Mech Engn Shahrekord *** Iran Cihan Univ Erbil Dept Nutr Erbil Kurdistan Regio Iraq Islamic Azad Univ Dept Mech Engn Khomeinishahr Branch Khomeinishahr Iran Istanbul Okan Univ Fac Engn & Nat Sci Dept Genet & Bioengn Istanbul Turkiye Bahcesehir Univ Fac Engn & Nat Sci Istanbul Turkiye Lebanese Amer Univ Dept Comp Sci & Math Beirut Lebanon

For conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present investigation, the design variables encompass the solid volume fraction (phi) and temperature. Thermal conductivity (TC) of MWCNT-CuO-CeO2 (20-40-40)/water hybrid nanofluid (HNF) is also the objective function. Ten different types of regressors are utilized for regression operations which are Multiple Linear Regression (MLR), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Multivariate Polynomial Regression (MPR) and Group Method of Data Handling (GMDH). Once the governing equations linking the design variables and the objective functions have been established, these equations can be employed to forecast the simulation data. By substituting the above input values into the equations, we can calculate the corresponding output values for the TC of the HNF. The results obtained from the MPR algorithm are compared to the experimental data. For the GPR, MLR, D-Tree, ELM, MPR, MLP, RBF, SVM, ANFIS, and GMDH algorithms, the maximum margin of error is found to be 0.031, 0.02579, 0.028946, 0.033889, 0.01568, 0.02515, 0.03485, 0.03, 0.0385, and 0.0178, respectively. Moreover, the kernel density estimation diagram indicates the gap be-tween experimental data and data predicted by regression algorithms. Finally, it is evident that the MPR algorithm demonstrates to have a reduced residual dispersion, with the residuals approaching zero.

关键词： Thermal behavior Hybrid nanofluid Regressors Evolutionary algorithms artificial neural network modeling

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Informing the Work Zone Safety Policy Analysis: Reconciling Multivariate Prediction and artificial neural network modeling

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JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 2024年第2期150卷

作者： Shaer, Amin Talebian, Ahmadreza Mishra, Sabyasachee Isfahan Univ Technol Dept Transportat Engn Esfahan *** Iran Univ Memphis Dept Civil Engn 112D Engn Sci Bldg Memphis TN 38152 USA

Work zone safety, as an issue requiring complex solutions and tech-driven investment, has been a major concern for transportation agencies and policymakers. To help decision-makers prioritize investments, there is a need for tools enabling them to assess policies designed to reduce the cost and count of work zone crashes at the same time. Traditional statistical methods, however, are unable to consider two or more target variables when they are intrinsically different (e.g., when a mixture of continuous and dichotomous/discrete variables is of interest). As a result, the dependencies among the outputs of interest may be neglected, which potentially causes biased decision-making. In this study, we used two machine learning models, i.e., group method of data handling (GMDH) and multilayer perceptron (MLP), to simultaneously predict count and cost of work zone crashes in the state of Tennessee in the United States. We further used these tools to assess four policies that could help improve work zone safety. We compared predictability of these models in individual prediction of cost and count with the three statistical models of Poisson regression, negative binomial regression, and multivariate regression. Also, we compared the importance of different variables to evaluate the role that each input variable plays in prediction and recognized the most effective factors. Our results indicated that reduction of annual average daily traffic (AADT) and speed limit are the most effective policies to simultaneously lower work zone crash count and costs, ensued by reducing the percentage of trucks and prohibiting commercial vehicles on segments. We observed that MLP and GMDH perform almost equally in the simultaneous prediction, while these models provide significantly greater degrees of accuracy in crash count and cost prediction, compared with statistical models. The results depicted that the major predictors of cost and count prediction of work zone crashes are speed limit, i

关键词： Multivariate prediction Policy prioritization Work zone crashes artificial neural network modeling Variable importance analysis Crash causal factors

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Analysis of diclofenac removal by metal-organic framework MIL-100(Fe) using multi-parameter experiments and artificial neural network modeling

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2021年 121卷 257-267页

作者： Jang, Ho-Young Kang, Jin-Kyu Lee, Seung-Chan Park, Jeong-Ann Kim, Song-Bae Seoul Natl Univ Dept Rural Syst Engn Environm Funct Mat & Water Treatment Lab Seoul South Korea Seoul Natl Univ Res Inst Agr & Life Sci Seoul South Korea Kangwon Natl Univ Dept Environm Engn Chunchon 24341 South Korea

The aim of study was to analyze diclofenac (DCF) removal from aqueous solutions by metal-organic framework MIL-100(Fe) using multi-parameter batch experiments and artificial neural network (ANN) modeling. First, single-parameter experiments were performed in terms of initial solution pH, MIL-100(Fe) dosage, initial DCF concentration, and temperature. The DCF removal decreased with an increase in pH from 5 to 10 and became negligible at pH 12. The kinetic and equilibrium data showed that DCF removal reached an equilibrium at 12 h, with a maximum capacity of 414.6 mg/g from the Langmuir isotherm model. The DCF removal was enhanced with increasing temperature. Multi-parameter experiments (n = 56) conducted under 28 duplicate experimental conditions showed DCF removal rates between 70.8 - 90.8% with a final pH range of 4.5 - 5.4 for most of the experimental conditions. The ANN model was developed based on the multi-parameter experimental data. The optimal topology for the ANN model was determined to be 4:7:6:2 (4 input variables, 7 neurons in the first hidden layer, 6 neurons in the second hidden layer, and 2 output variables). Among the four input variables, temperature was the most important variable affecting DCF removal rate under the given experimental ranges. (C) 2021 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Metal-organic frameworks MIL-100(Fe) Diclofenac artificial neural network modeling Adsorption Optimization

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artificial neural network mathematical modeling of methyl violet removal with chitosan-coated clinoptilolite

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DESALINATION AND WATER TREATMENT 2022年 250卷 252-265页

作者： Altintig, Esra Kabadayi, Onur Bozdag, Dilay Altundag, Selma Altundag, Huseyin Sakarya Univ Appl Sci Pamukova Vocat Sch Chem & Chem Proc Technol Dept TR-54900 Sakarya Turkey Sakarya Univ Fac Arts & Sci Dept Chem TR-54187 Sakarya Turkey Akcoat Adv Chem Coating Mat Ind & Trade Joint Sto TR-54300 Sakarya Turkey Sakarya Univ Fac Arts & Sci Dept Math TR-54187 Sakarya Turkey

In this study, the use of chitosan-coated clinoptilolite zeolite as an adsorbents in the removal methyl violet from an aqueous solutions were investigated. Firstly, the structural and chemical characterization of the adsorbent was carried out using various analytical techniques such as X-ray diffraction, Brunauer-Emmett-Teller and scanning electron microscopy. Then, the effects of different batch parameters such as initial pH (2-9), adsorbent dosage (0.05-1.0 g/100 mL), temperature (298-318 K), initial dyestuff concentration (25-125 mg L-1) and mixing time (5-120 min) on the adsorption process were examined. The kinetic, thermodynamic and isotherm parameters of chitosan-coated clinoptilolite zeolite was investigated by batch adsorption experiments. The experimental data showed that the adsorption isotherms were well defined by the Langmuir equilibrium isotherm model equation. The maximum adsorption capacity was found 111.11 mg g(-1) for chitosan-coated clinoptilolite zeolite adsorbent at 318 K. The methyl violet adsorption was determined to conform to the pseudo-second-order kinetic model. The thermodynamic calculations were made in the adsorption study of chitosan-coated clinoptilolite. As a result of calculations, it was determined that the Delta H degrees values were 34.53 kJ mol(-1), and the fact that this value is positive indicates that the adsorption process is spontaneous and endothermic. The recovery test exhibited that the developed composite had significantly adsorption/desorption performance particularly until the seventh cycle. In the last step, considering the effects of some experimental parameters on methyl violet adsorption, an artificial neural network model was developed, and it was concluded that it could be used for dye removal from aqueous solutions. All experimental studies showed that found to be a natural, cost-efficient and eco-friendly of the developed chitosan coating on clinoptilolite zeolite composite make it economic easily applicable

关键词： Methyl violet Adsorption Chitosan Clinoptilolite artificial neural network modeling

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artificial neural network (ANN) modeling of thermal conductivity of supercritical ethane

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2019年第4期41卷 396-404页

作者： Yang, Ling Wang, Zhaoba North Univ China Sch Informat & Commun Engn Taiyuan 030051 Shanxi Peoples R China

In this present contribution, thermal conductivity ofethene (TCE) above the critical temperature has been studied. The present data cover the temperature range from 283.46 to 425.00 K and the pressure range from 0.1 to 100 MPa. In the present investigation, various network-based strategies, named as artificial neural network (ANN) optimized with two evolutionary algorithms, including genetic algorithm (GA) and differential evolution (DE), were developed for assessing tTCE in supercritical region. The most comprehensive source of data, including around 256 experimental points, was utilized for ANN modeling. Data index plot, scatter plot, relative deviation diagram and root mean square error (RMSE), and coefficient of determination (R-2) as the statistical parameters were used in this examination to evaluate the comprehensiveness of the developed ANN model. Results indicate that the GA-ANN is more accurate than DE-ANN to predict TCE in supercritical region. Also, among optimization algorithms, GA has the largest ability for optimizing the ANN network modeling with the RMSE of 4.2966 and determination coefficient (R-2) of 0.9640.

关键词： artificial neural network modeling differential evolution genetic algorithm supercritical ethene thermal conductivity

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Prediction of sugar yields during hydrolysis of lignocellulosic biomass using artificial neural network modeling

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BIORESOURCE TECHNOLOGY 2015年 188卷 128-135页

作者： Vani, Sankar Sukumaran, Rajeev Kumar Savithri, Sivaraman CSIR Natl Inst Interdisciplinary Sci & Technol Ctr Biofuels Div Biotechnol Trivandrum 695019 Kerala India CSIR Natl Inst Interdisciplinary Sci & Technol Proc Engn & Environm Technol Div Computat Modeling & Simulat Sect Trivandrum 695019 Kerala India

The present investigation was carried out to study application of ANN as a tool for predicting sugar yields of pretreated biomass during hydrolysis process at various time intervals. Since it is known that biomass loading and particle size influences the rheology and mass transfer during hydrolysis process, these two parameters were chosen for investigating the efficiency of hydrolysis. Alkali pretreated rice straw was used as the model feedstock in this study and biomass loadings were varied from 10% to 18%. Substrate particle sizes used were < 0.5 mm, 0.5-1 mm, > 1 mm and mixed size. Effectiveness of hydrolysis was strongly influenced by biomass loadings, whereas particle size did not have any significant impact on sugar yield. Higher biomass loadings resulted in higher sugar concentration in the hydrolysates. Optimum hydrolysis conditions predicted were 10 FPU/g cellulase, 5 IU/g BGL, 7500 U/g xylanase, 18% biomass loading and mixed particle size with reaction time between 12-28 h. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Particle size Biomass loading Biofuel artificial neural network modeling Biomass hydrolysis

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Response surface methodology and artificial neural network for remediation of acid orange 7 using TiO2-P25: optimization and modeling approach

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2020年第27期27卷 34018-34036页

作者： Zulfiqar, Muhammad Chowdhury, Sujan Omar, Abdul Aziz Siyal, Ahmer Ali Sufian, Suriati Univ Teknol PETRONAS Dept Chem Engn Bandar Sri Iskandar 32610 Perak Malaysia Jashore Univ Sci & Technol Chem Engn Dept Jashore 7408 Bangladesh Sunway Univ Dept Comp & Informat Syst Petaling Jaya 47500 Selangor Malaysia Univ Teknol PETRONAS Ctr Innovat Nanostruct & Nanodevices COINN Bandar Seri Iskandar 32610 Perak Malaysia

The primary responsibility for continuously discharging toxic organic pollutants into water bodies and open environments is the increase in industrial and agricultural activities. Developing economical and suitable methods to continuously remove organic pollutants from wastewater is highly essential. The aim of the present research was to apply response surface methodology (RSM) and artificial neural networks (ANNs) for optimization and modeling of photocatalytic degradation of acid orange 7 (AO7) by commercial TiO2-P25 nanoparticles (TNPs). Dose of TNPs, pH, and AO7 concentration were selected as investigated parameters. RSM results reveal the reflective rate of AO7 removal of similar to 94.974% was obtained at pH 7.599, TNP dose of 0.748 g/L, and AO7 concentration of 28.483 mg/L. The resulting quadratic model is satisfactory with the highest coefficient of determination (R-2) between the predicted and experimental data (R-2 = 0.98 and adjustedR(2) = 0.954). On the other hand, ANNs were successfully employed for modeling of AO7 degradation process. The proposed ANN model was absolutely fitted with experimental results producing the highestR(2). Furthermore, root mean square error (RMSE), mean average deviation (MAD), absolute average relative error (AARE), and mean square error (MSE) were examined more to compare the predictive capabilities of ANN and RSM models. The experimental data was well fitted into pseudo-first-order and pseudo-second-order kinetics with more accuracy. Thermodynamic parameters, namely enthalpy, entropy, Gibbs' free energy, and activation energy, were also evaluated to suggest the nature of the degradation process. The increase of temperature was analyzed to be more suitable for the fast removal of AO7 over TNPs.

关键词： Response surface optimization artificial neural network modeling TiO(2)photocatalysis Acid orange 7 Mechanism studies

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Prediction of evaporation temperature in air-water heat source heat pump based on artificial neural network

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JOURNAL OF BUILDING ENGINEERING 2024年 98卷

作者： Li, Chuanming Li, Nianping Tan, Xin Yongga, A. Long, Jibo Shen, Xiaohang Hunan Univ Coll Civil Engn Changsha 410082 Peoples R China Xiangtan Univ Coll Civil Engn Xiangtan City 411105 Peoples R China Hunan Inst Engn Coll Architectural Engn Xiangtan 411104 Hunan Peoples R China

To improve the low-temperature heating stability and performance of the air-source heat pump (ASHP), a heat transfer model was established for an air-water heat source heat pump (A-WSHP) in this study. Key factors affecting A-WSHP evaporating temperature were analyzed through simulation. An artificial neural network (ANN) prediction model for refrigerant evaporating temperature was developed using fin pitch, air velocity, air temperature, spray flow rate, spray water temperature, and droplet diameter as inputs. In the winter climate of Xiangtan City, this model predicts the refrigerant evaporating temperature and regulates it by adjusting various input parameters. Results indicate that fin pitch and droplet size significantly impact the refrigerant evaporating temperature, more so than fin thickness. The air temperature (34.78 %) and spray water temperature (38.02 %) have the highest weights, while fin thickness has the least influence, with a weight of 0.75 %. The R2 value of the ANN refrigerant evaporating temperature prediction model is 0.974, indicating a good fit. By properly adjusting the heat pump fan and spray valve, changes in air velocity and spray flow rate can enhance the refrigerant evaporating temperature, leading to stable and efficient heat pump operation. This research not only provides a theoretical basis for the operational strategy of air-water heat pumps but also offers guidance for frost-free operation in low-temperature environments.

关键词： artificial neural network modeling Air-water heat source heat pump Finned evaporator Evaporating temperature Temperature prediction

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