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Artificial Neural Network Modelling of Reverse Electrodialysis

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Emerging Materials and Technologies in Water Remediation and Sensing (ICWT 2022)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 439))

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Abstract

Reverse electrodialysis (RED) is an emerging technology with the potential to generate energy from salinity power gradients with limited environmental pollution. In a salinity-gradient power generator (SGP-RED), cation and anion exchange membranes are stacked alternately and due to the difference in concentrations of river and seawater, the diffusion of ions generates an electrochemical potential. In this work, a data-driven approach has been adopted for predicting the performance (Power Density) of SGP-RED power generators over different operating conditions and membrane types, using Artificial Neural Network (ANN) models. Experimental data was gathered and mined from 130 research publications from the last ten years to predict the output Power density of the system against a set of nine input parameters, e.g. membrane types, thickness, resistance, current density, perm selectivity, temperature, etc. A simple mathematical model to estimate the Power density is proposed and used to validate the experimental data before using them with ANN. We used ANN models using Bayesian Regularisation as the backpropagation algorithm (ANN-BR) along with a combination of six sets of pair-wise activation functions amongst Tan-sigmoid, Log-Sigmoid and Linear, in hidden and output layers. The ANN-BR model with Tan-Sigmoid activation functions in both hidden and output layers is shown to predict most accurately the Power density of the SGP-RED system.

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Acknowledgements

I would like to thank Prof. Anirban Roy for guiding us throughout and Rudra Rath for helping me in every step of the way. This project would not have been possible without their help.

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Authors and Affiliations

  1. Department of Chemical Engineering Goa, BITS Pilani, Pilani, Goa, 403726, India

    Shruti Sen, Rudra Rath & Anirban Roy

  2. Process Engineering and Technology Transfer Department, CSIR-Indian Institute of Chemical Technology, Hyderabad, 500007, India

    Reddi Kamesh

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  1. Shruti Sen
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  2. Rudra Rath
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  3. Reddi Kamesh
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  4. Anirban Roy
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Correspondence to Shruti Sen .

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Editors and Affiliations

  1. Department of Metallurgical Engineering and Materials Science, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Sumit Saxena

  2. Department of Metallurgical Engineering and Materials Science, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Shobha Shukla

  3. Department of Metallurgical Engineering and Materials Science, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Prasanna Kumar S. Mural

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Sen, S., Rath, R., Kamesh, R., Roy, A. (2024). Artificial Neural Network Modelling of Reverse Electrodialysis. In: Saxena, S., Shukla, S., Mural, P.K.S. (eds) Emerging Materials and Technologies in Water Remediation and Sensing. ICWT 2022. Lecture Notes in Civil Engineering, vol 439. Springer, Singapore. https://doi.org/10.1007/978-981-99-6762-9_4

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  • DOI: https://doi.org/10.1007/978-981-99-6762-9_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6761-2

  • Online ISBN: 978-981-99-6762-9

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