Artificial neural network modelling of As(III) removal from water by novel hybrid material
•A cost effective novel adsorbent is proposed for As(III) removal from water.•Removal efficiency of the adsorbent is tested over a wide range of pH (1–12).•Using 9g of adsorbent, 10mg As in 1L water could be reduced to 0.01mgL−1 in 30min.•The material can be regenerated and reused up to five adsorpt...
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| Published in | Process safety and environmental protection Vol. 93; pp. 249 - 264 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.01.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-5820 1744-3598 |
| DOI | 10.1016/j.psep.2014.02.016 |
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| Abstract | •A cost effective novel adsorbent is proposed for As(III) removal from water.•Removal efficiency of the adsorbent is tested over a wide range of pH (1–12).•Using 9g of adsorbent, 10mg As in 1L water could be reduced to 0.01mgL−1 in 30min.•The material can be regenerated and reused up to five adsorption cycles.•ANN modelling of AS(III) removal process reasonably predicts the removal efficiency.
The present study reported a method for removal of As(III) from water solution by a novel hybrid material (Ce-HAHCl). The hybrid material was synthesized by sol–gel method and was characterized by XRD, FTIR, SEM–EDS and TGA–DTA. Batch adsorption experiments were conducted as a function of different variables like adsorbent dose, pH, contact time, agitation speed, initial concentration and temperature. The experimental studies revealed that maximum removal percentage is 98.85 at optimum condition: pH=5.0, agitation speed=180rpm, temperature=60°C and contact time=80min using 9gL−1 of adsorbent dose for initial As(III) concentration of 10mgL−1. Using adsorbent dose of 10gL−1, the maximum removal percentage remains same with initial As(III) concentration of 25mgL−1 (or 50mgL−1). The maximum adsorption capacity of the material is found to be 182.6mgg−1. Subsequently, the experimental results are used for developing a valid model based on back propagation (BP) learning algorithm with artificial neural networking (BP-ANN) for prediction of removal efficiency. The adequacy of the model (BP-ANN) is checked by value of the absolute relative percentage error (0.293) and correlation coefficient (R2=0.975). Comparison of experimental and predictive model results show that the model can predict the adsorption efficiency with acceptable accuracy. |
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| AbstractList | The present study reported a method for removal of As(III) from water solution by a novel hybrid material (Ce-HAHCl). The hybrid material was synthesized by sol-gel method and was characterized by XRD, FTIR, SEM-EDS and TGA-DTA. Batch adsorption experiments were conducted as a function of different variables like adsorbent dose, pH, contact time, agitation speed, initial concentration and temperature. The experimental studies revealed that maximum removal percentage is 98.85 at optimum condition: pH = 5.0, agitation speed = 180 rpm, temperature = 60 [degrees]C and contact time = 80 min using 9 gL super(-1) of adsorbent dose for initial As(III) concentration of 10 mgL super(-1). Using adsorbent dose of 10 gL super(-1), the maximum removal percentage remains same with initial As(III) concentration of 25 mgL super(-1) (or 50 mgL super(-1)). The maximum adsorption capacity of the material is found to be 182.6 mgg super(-1). Subsequently, the experimental results are used for developing a valid model based on back propagation (BP) learning algorithm with artificial neural networking (BP-ANN) for prediction of removal efficiency. The adequacy of the model (BP-ANN) is checked by value of the absolute relative percentage error (0.293) and correlation coefficient (R super(2) = 0.975). Comparison of experimental and predictive model results show that the model can predict the adsorption efficiency with acceptable accuracy. •A cost effective novel adsorbent is proposed for As(III) removal from water.•Removal efficiency of the adsorbent is tested over a wide range of pH (1–12).•Using 9g of adsorbent, 10mg As in 1L water could be reduced to 0.01mgL−1 in 30min.•The material can be regenerated and reused up to five adsorption cycles.•ANN modelling of AS(III) removal process reasonably predicts the removal efficiency. The present study reported a method for removal of As(III) from water solution by a novel hybrid material (Ce-HAHCl). The hybrid material was synthesized by sol–gel method and was characterized by XRD, FTIR, SEM–EDS and TGA–DTA. Batch adsorption experiments were conducted as a function of different variables like adsorbent dose, pH, contact time, agitation speed, initial concentration and temperature. The experimental studies revealed that maximum removal percentage is 98.85 at optimum condition: pH=5.0, agitation speed=180rpm, temperature=60°C and contact time=80min using 9gL−1 of adsorbent dose for initial As(III) concentration of 10mgL−1. Using adsorbent dose of 10gL−1, the maximum removal percentage remains same with initial As(III) concentration of 25mgL−1 (or 50mgL−1). The maximum adsorption capacity of the material is found to be 182.6mgg−1. Subsequently, the experimental results are used for developing a valid model based on back propagation (BP) learning algorithm with artificial neural networking (BP-ANN) for prediction of removal efficiency. The adequacy of the model (BP-ANN) is checked by value of the absolute relative percentage error (0.293) and correlation coefficient (R2=0.975). Comparison of experimental and predictive model results show that the model can predict the adsorption efficiency with acceptable accuracy. |
| Author | Mandal, S. Patel, R.K. Mahapatra, S.S. Sahu, M.K. |
| Author_xml | – sequence: 1 givenname: S. surname: Mandal fullname: Mandal, S. organization: Department of Chemistry, National Institute of Technology, Rourkela, Odisha 769008, India – sequence: 2 givenname: S.S. surname: Mahapatra fullname: Mahapatra, S.S. organization: Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha 769008, India – sequence: 3 givenname: M.K. surname: Sahu fullname: Sahu, M.K. organization: Department of Chemistry, National Institute of Technology, Rourkela, Odisha 769008, India – sequence: 4 givenname: R.K. surname: Patel fullname: Patel, R.K. email: rkpatel@nitrkl.ac.in, rkpatelnitr@gmail.com organization: Department of Chemistry, National Institute of Technology, Rourkela, Odisha 769008, India |
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| Snippet | •A cost effective novel adsorbent is proposed for As(III) removal from water.•Removal efficiency of the adsorbent is tested over a wide range of pH... The present study reported a method for removal of As(III) from water solution by a novel hybrid material (Ce-HAHCl). The hybrid material was synthesized by... |
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| SubjectTerms | Adsorbents Adsorption Agitation Algorithms ANN Arsenic Back propagation Contact Hybrid materials Mathematical models |
| Title | Artificial neural network modelling of As(III) removal from water by novel hybrid material |
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