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Khoshraftar Z, Ghaemi A, Hemmati A. Comprehensive investigation of isotherm, RSM, and ANN modeling of CO 2 capture by multi-walled carbon nanotube. Sci Rep 2024; 14:5130. [PMID: 38429340 PMCID: PMC10907356 DOI: 10.1038/s41598-024-55836-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/28/2024] [Indexed: 03/03/2024] Open
Abstract
Chemical vapor deposition was used to produce multi-walled carbon nanotubes (MWCNTs), which were modified by Fe-Ni/AC catalysts to enhance CO2 adsorption. In this study, a new realm of possibilities and potential advancements in CO2 capture technology is unveiled through the unique combination of cutting-edge modeling techniques and utilization of the recently synthesized Fe-Ni/AC catalyst adsorbent. SEM, BET, and FTIR were used to analyze their structure and morphology. The surface area of MWCNT was found to be 240 m2/g, but after modification, it was reduced to 11 m2/g. The modified MWCNT showed increased adsorption capacity with higher pressure and lower temperature, due to the introduction of new adsorption sites and favorable interactions at lower temperatures. At 25 °C and 10 bar, it reached a maximum adsorption capacity of 424.08 mg/g. The optimal values of the pressure, time, and temperature parameters were achieved at 7 bar, 2646 S and 313 K. The Freundlich and Hill models had the highest correlation with the experimental data. The Second-Order and Fractional Order kinetic models fit the adsorption results well. The adsorption process was found to be exothermic and spontaneous. The modified MWCNT has the potential for efficient gas adsorption in fields like gas storage or separation. The regenerated M-MWCNT adsorbent demonstrated the ability to be reused multiple times for the CO2 adsorption process, as evidenced by the study. In this study, a feed-forward MLP artificial neural network model was created using a back-propagation training approach to predict CO2 adsorption. The most suitable and efficient MLP network structure, selected for optimization, consisted of two hidden layers with 25 and 10 neurons, respectively. This network was trained using the Levenberg-Marquardt backpropagation algorithm. An MLP artificial neural network model was created, with a minimum MSE performance of 0.0004247 and an R2 value of 0.99904, indicating its accuracy. The experiment also utilized the blank spreadsheet design within the framework of response surface methodology to predict CO2 adsorption. The proximity between the Predicted R2 value of 0.8899 and the Adjusted R2 value of 0.9016, with a difference of less than 0.2, indicates a high level of similarity. This suggests that the model is exceptionally reliable in its ability to predict future observations, highlighting its robustness.
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Affiliation(s)
- Zohreh Khoshraftar
- School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, P.O. Box 16765-163, Tehran, Iran.
| | - Ahad Ghaemi
- School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, P.O. Box 16765-163, Tehran, Iran.
| | - Alireza Hemmati
- School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, P.O. Box 16765-163, Tehran, Iran
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Abuwatfa WH, AlSawaftah N, Darwish N, Pitt WG, Husseini GA. A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs). MEMBRANES 2023; 13:685. [PMID: 37505052 PMCID: PMC10383311 DOI: 10.3390/membranes13070685] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/29/2023] [Accepted: 07/11/2023] [Indexed: 07/29/2023]
Abstract
Membrane fouling is a major hurdle to effective pressure-driven membrane processes, such as microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO). Fouling refers to the accumulation of particles, organic and inorganic matter, and microbial cells on the membrane's external and internal surface, which reduces the permeate flux and increases the needed transmembrane pressure. Various factors affect membrane fouling, including feed water quality, membrane characteristics, operating conditions, and cleaning protocols. Several models have been developed to predict membrane fouling in pressure-driven processes. These models can be divided into traditional empirical, mechanistic, and artificial intelligence (AI)-based models. Artificial neural networks (ANNs) are powerful tools for nonlinear mapping and prediction, and they can capture complex relationships between input and output variables. In membrane fouling prediction, ANNs can be trained using historical data to predict the fouling rate or other fouling-related parameters based on the process parameters. This review addresses the pertinent literature about using ANNs for membrane fouling prediction. Specifically, complementing other existing reviews that focus on mathematical models or broad AI-based simulations, the present review focuses on the use of AI-based fouling prediction models, namely, artificial neural networks (ANNs) and their derivatives, to provide deeper insights into the strengths, weaknesses, potential, and areas of improvement associated with such models for membrane fouling prediction.
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Affiliation(s)
- Waad H Abuwatfa
- Materials Science and Engineering Ph.D. Program, College of Arts and Sciences, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
- Department of Chemical and Biological Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
| | - Nour AlSawaftah
- Materials Science and Engineering Ph.D. Program, College of Arts and Sciences, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
- Department of Chemical and Biological Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
| | - Naif Darwish
- Department of Chemical and Biological Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
| | - William G Pitt
- Chemical Engineering Department, Brigham Young University, Provo, UT 84602, USA
| | - Ghaleb A Husseini
- Materials Science and Engineering Ph.D. Program, College of Arts and Sciences, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
- Department of Chemical and Biological Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
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3
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Abdi-Khanghah M, Hamoule T, D'Agostino C, Spallina V, Wu KC. Para-xylene production from toluene methylation: Novel catalyst synthesis, fabrication and ANFIS modelling. J Taiwan Inst Chem Eng 2023. [DOI: 10.1016/j.jtice.2023.104753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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4
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Niu C, Li X, Dai R, Wang Z. Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review. WATER RESEARCH 2022; 216:118299. [PMID: 35325824 DOI: 10.1016/j.watres.2022.118299] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/11/2022] [Accepted: 03/13/2022] [Indexed: 05/26/2023]
Abstract
Membrane fouling is one of major obstacles in the application of membrane technologies. Accurately predicting or simulating membrane fouling behaviours is of great significance to elucidate the fouling mechanisms and develop effective measures to control fouling. Although mechanistic/mathematical models have been widely used for predicting membrane fouling, they still suffer from low accuracy and poor sensitivity. To overcome the limitations of conventional mathematical models, artificial intelligence (AI)-based techniques have been proposed as powerful approaches to predict membrane filtration performance and fouling behaviour. This work aims to present a state-of-the-art review on the advances in AI algorithms (e.g., artificial neural networks, fuzzy logic, genetic programming, support vector machines and search algorithms) for prediction of membrane fouling. The working principles of different AI techniques and their applications for prediction of membrane fouling in different membrane-based processes are discussed in detail. Furthermore, comparisons of the inputs, outputs, and accuracy of different AI approaches for membrane fouling prediction have been conducted based on the literature database. Future research efforts are further highlighted for AI-based techniques aiming for a more accurate prediction of membrane fouling and the optimization of the operation in membrane-based processes.
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Affiliation(s)
- Chengxin Niu
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Xuesong Li
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Ruobin Dai
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Zhiwei Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 201210, China.
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Yaqub M, Lee SH, Lee W. Investigating micellar-enhanced ultrafiltration (MEUF) of mercury and arsenic from aqueous solution using response surface methodology and gene expression programming. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2021.119880] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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6
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Estimation of Heavy Metals in Agricultural Soils Using Vis-NIR Spectroscopy with Fractional-Order Derivative and Generalized Regression Neural Network. REMOTE SENSING 2021. [DOI: 10.3390/rs13142718] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
With the development of industrialization and urbanization, heavy metal contamination in agricultural soils tends to accumulate rapidly and harm human health. Visible and near-infrared (Vis-NIR) spectroscopy provides the feasibility of fast monitoring of the variation of heavy metals. This study explored the potential of fractional-order derivative (FOD), the optimal band combination algorithm and different mathematical models in estimating soil heavy metals with Vis-NIR spectroscopy. A total of 80 soil samples were collected from an agriculture area in Suzi river basin, Liaoning Province, China. The spectra for mercury (Hg), chromium (Cr), and copper (Cu) of the samples were obtained in the laboratory. For spectral preprocessing, FODs were allowed to vary from 0 to 2 with an increment of 0.2 at each step, and the optimal band combination algorithm was applied to the spectra after FOD. Then, four mathematical models, namely, partial least squares regression (PLSR), adaptive neural fuzzy inference system (ANFIS), random forest (RF) and generalized regression neural network (GRNN), were used to estimate the concentration of Hg, Cr and Cu. Results showed that high-order FOD had an excellent effect in highlighting hidden information and separating minor absorbing peaks, and the optimal band combination algorithm could remove the influence of spectral noise caused by high-order FOD. The incorporation of the optimal band combination algorithm and FOD is able to further mine spectral information. Furthermore, GRNN made an obvious improvement to the estimation accuracy of all studied heavy metals compared to ANFIS, PLSR, and RF. In summary, our results provided more feasibility for the rapid estimation of Hg, Cr, Cu and other heavy metal pollution areas in agricultural soils.
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Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques. MEMBRANES 2021; 11:membranes11010070. [PMID: 33478084 PMCID: PMC7835737 DOI: 10.3390/membranes11010070] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 01/10/2021] [Accepted: 01/15/2021] [Indexed: 12/05/2022]
Abstract
The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.
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Vaziri H, Hedayati Moghaddam A, Mirmohammadi SA. Optimization of distillation column in phenol production process for increasing the isopropyl benzene concentration using response surface methodology and radial basis function (RBF) coupled with leave-one-out validation method. CHEMICAL PAPERS 2020. [DOI: 10.1007/s11696-020-01162-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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9
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Hosseinzadeh A, Zhou JL, Altaee A, Baziar M, Li X. Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network. BIORESOURCE TECHNOLOGY 2020; 310:123391. [PMID: 32344239 DOI: 10.1016/j.biortech.2020.123391] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/11/2020] [Accepted: 04/13/2020] [Indexed: 06/11/2023]
Abstract
Osmotic Membrane Bioreactor (OMBR) is an emerging technology for wastewater treatment with membrane fouling as a major challenge. This study aims to develop Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models in simulating and predicting water flux in OMBR. Mixed liquor suspended solid (MLSS), electrical conductivity (EC) and dissolved oxygen (DO) were used as model inputs. Good prediction was demonstrated by both ANFIS models with R2 of 0.9755 and 0.9861, and ANN models with R2 of 0.9404 and 0.9817, for thin film composite (TFC) and cellulose triacetate (CTA) membranes, respectively. The root mean square error for TFC (0.2527) and CTA (0.1230) in ANFIS models was lower than in ANN models at 0.4049 and 0.1449. Sensitivity analysis showed that EC was the most important factor for both TFC and CTA membranes in ANN models, while EC (TFC) and MLSS (CTA) are key parameters in ANFIS models.
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Affiliation(s)
- Ahmad Hosseinzadeh
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - John L Zhou
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia.
| | - Ali Altaee
- Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
| | - Mansour Baziar
- Ferdows School of Paramedical and Health, Birjand University of Medical Sciences, Birjand, Iran
| | - Xiaowei Li
- School of Environmental and Chemical Engineering, Organic Compound Pollution Control Engineering, Ministry of Education, Institute for the Conservation of Cultural Heritage, Shanghai University, Shanghai 200444, PR China
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10
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Micellar-Enhanced Ultrafiltration to Remove Nickel Ions: A Response Surface Method and Artificial Neural Network Optimization. WATER 2020. [DOI: 10.3390/w12051269] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Nickel ions from aqueous solutions were removed by micellar-enhanced ultrafiltration (MEUF), using the surfactant sodium dodecyl sulfate (SDS) as a chelating agent. Process variables and indicators were modeled and optimized by a response surface methodology (RSM), using the Box–Behnken design (BBD). The generated quadratic models described the relationship between a performance indicator (nickel rejection rate or permeate flux) and process variables (pressure, nickel concentration, SDS concentration, and molecular weight cut-off (MWCO)). The analysis of variance (ANOVA) showed that both models are statistically significant. To remove 1 mM of nickel ions, the optimal condition for maximum nickel removal and flux were: pressure = 30 psi, CSDS = 10.05 mM, and MWCO = 10 kDa, resulting in a rejection rate of 98.16% and a flux of 119.20 L/h∙m2. Experimental verification indicates that the RSM model could adequately describe the performance indicators within the examined ranges of the process variables. An artificial neural network (ANN) modelling followed to predict the MEUF performance and validate the RSM results. The obtained ANN models showed good fitness to the experimental data.
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11
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Asghari M, Dashti A, Rezakazemi M, Jokar E, Halakoei H. Application of neural networks in membrane separation. REV CHEM ENG 2018. [DOI: 10.1515/revce-2018-0011] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Artificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.
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Affiliation(s)
- Morteza Asghari
- Separation Processes Research Group (SPRG), Department of Engineering , University of Kashan , Kashan 8731753153 , Iran
- Energy Research Institute , University of Kashan , Ghotb–e–Ravandi Avenue , Kashan , Iran
| | - Amir Dashti
- Separation Processes Research Group (SPRG), Department of Engineering , University of Kashan , Kashan 8731753153 , Iran
| | - Mashallah Rezakazemi
- Faculty of Chemical and Materials Engineering , Shahrood University of Technology , Shahrood , Iran
| | - Ebrahim Jokar
- Separation Processes Research Group (SPRG), Department of Engineering , University of Kashan , Kashan 8731753153 , Iran
| | - Hadi Halakoei
- Separation Processes Research Group (SPRG), Department of Engineering , University of Kashan , Kashan 8731753153 , Iran
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12
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Thermodynamical and artificial intelligence approaches of H2S solubility in N-methylpyrrolidone. Chem Phys Lett 2018. [DOI: 10.1016/j.cplett.2018.07.032] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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13
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Efficient prediction of water vapor adsorption capacity in porous metal–organic framework materials: ANN and ANFIS modeling. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2018. [DOI: 10.1007/s13738-018-1476-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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14
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Fu HY, Zhao D, Xu M, Li YP, Liu J, Zhang ZB, Yan HZ, Zhu HD. Research on the Ultrafiltration and Removal of Aniline via the Compound of Sophorolipid and Rhamnolipid. ACTA ACUST UNITED AC 2018. [DOI: 10.1088/1755-1315/146/1/012071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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15
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Indolean C, Măicăneanu A, Cristea VM. Prediction of Cu(II) biosorption performances on wild mushroomsLactarius piperatususing Artificial Neural Networks (ANN) model. CAN J CHEM ENG 2016. [DOI: 10.1002/cjce.22703] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Cerasella Indolean
- Department of Chemical Engineering, Faculty of Chemistry and Chemical Engineering; Babeş-Bolyai University; 11 Arany Janos st. RO-400028 Cluj-Napoca Romania
| | - Andrada Măicăneanu
- Department of Chemical Engineering, Faculty of Chemistry and Chemical Engineering; Babeş-Bolyai University; 11 Arany Janos st. RO-400028 Cluj-Napoca Romania
- Department of Chemistry; Indiana University of Pennsylvania; Indiana PA 15705 USA
| | - Vasile-Mircea Cristea
- Department of Chemical Engineering, Faculty of Chemistry and Chemical Engineering; Babeş-Bolyai University; 11 Arany Janos st. RO-400028 Cluj-Napoca Romania
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16
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Determination of optimum conditions in forward osmosis using a combined Taguchi–neural approach. Chem Eng Res Des 2016. [DOI: 10.1016/j.cherd.2016.01.030] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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17
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Evaluation of the response surface and hybrid artificial neural network-genetic algorithm methodologies to determine extraction yield of Ferulago angulata through supercritical fluid. J Taiwan Inst Chem Eng 2016. [DOI: 10.1016/j.jtice.2015.11.003] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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18
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Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Balas VE. Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2190-2] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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19
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Neural network and neuro-fuzzy modeling to investigate the power density and Columbic efficiency of microbial fuel cell. J Taiwan Inst Chem Eng 2016. [DOI: 10.1016/j.jtice.2015.06.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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20
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Zhang W, Liang W, Huang G, Wei J, Ding L, Jaffrin MY. Studies of membrane fouling mechanisms involved in the micellar-enhanced ultrafiltration using blocking models. RSC Adv 2015. [DOI: 10.1039/c5ra06063j] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Micellar-enhanced ultrafiltration (MEUF) is a promising technology to remove organic contaminants from wastewater.
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Affiliation(s)
- Wenxiang Zhang
- EA 4297 TIMR
- Technological University of Compiegne
- 60205 Compiegne Cedex
- France
- MOE Key Laboratory of Regional Energy and Environmental Systems Optimization
| | - Wenzhong Liang
- South China Institute of Environmental Sciences
- Ministry of Environmental Protection
- Guangzhou 510655
- China
| | - Guohe Huang
- MOE Key Laboratory of Regional Energy and Environmental Systems Optimization
- Resources and Environmental Research Academy
- North China Electric Power University
- Beijing 102206
- China
| | - Jia Wei
- Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering
- Beijing University of Technology
- Beijing 100124
- China
| | - Luhui Ding
- EA 4297 TIMR
- Technological University of Compiegne
- 60205 Compiegne Cedex
- France
| | - Michel Y. Jaffrin
- UMR7338
- Technological University of Compiegne
- 60205 Compiegne Cedex
- France
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21
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Garg MC, Joshi H. A new approach for optimization of small-scale RO membrane using artificial groundwater. ENVIRONMENTAL TECHNOLOGY 2014; 35:2988-2999. [PMID: 25189847 DOI: 10.1080/09593330.2014.927928] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The present study aims at evaluating a small-scale brackish water reverse osmosis (RO) process using parameter optimization. Experiments were carried out using formulated artificial groundwater, and a predictive model was developed by using response surface methodology (RSM) for the optimization of input process parameters of brackish water RO process to simultaneously maximize water recovery and salt rejection while minimizing energy demand. The result of multiple response optimization along with analysis of variance for RSM predictions showed that the optimal water recovery (19.18%), total dissolved solids rejection (89.21%) and specific energy consumption (17.60 kWh/m³) occurred at 31.94 °C feed water temperature, 0.78 MPa feed pressure, 1500 mg/L feed salt concentration and 6.53 pH. Furthermore, confirmation of RSM predictions was carried out by an artificial neural network (ANN) model trained by RSM experimental data. Predicted values by both RSM and ANN modelling methodologies were compared and found within the acceptable range. Finally, a membrane validation experiment was carried out successfully at proposed optimal conditions, which proves the accuracy of employed RSM and ANN models. Present methodology can be used as a generalized way for the optimization of different RO membranes available in the market in terms of increased water recovery and salt rejection with least energy consumption to make it commercially competent.
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Affiliation(s)
- Manoj Chandra Garg
- a Department of Hydrology , Indian Institute of Technology Roorkee , Roorkee , India
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22
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Experimental analysis, modeling and optimization of chromium (VI) removal from aqueous solutions by polymer-enhanced ultrafiltration. J Memb Sci 2014. [DOI: 10.1016/j.memsci.2014.01.016] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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23
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Abghari SZ, Sadi M. Application of adaptive neuro-fuzzy inference system for the prediction of the yield distribution of the main products in the steam cracking of atmospheric gasoil. J Taiwan Inst Chem Eng 2013. [DOI: 10.1016/j.jtice.2012.11.020] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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