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Estimation and sensitivity analysis of fouling resistance in phosphoric acid/steam heat exchanger using artificial neural networks and regression methods
One of the most frequent problem in phosphoric acid concentration plant is the heat exchanger build-up. This problem causes a reduction of the performance of this equipment and an increase of energy losses which lead to damage the apparatus. In this study, estimation of fouling resistance in a cross...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10587106/ https://www.ncbi.nlm.nih.gov/pubmed/37857645 http://dx.doi.org/10.1038/s41598-023-44516-6 |
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author | Jradi, Rania Marvillet, Christophe Jeday, Mohamed Razak |
author_facet | Jradi, Rania Marvillet, Christophe Jeday, Mohamed Razak |
author_sort | Jradi, Rania |
collection | PubMed |
description | One of the most frequent problem in phosphoric acid concentration plant is the heat exchanger build-up. This problem causes a reduction of the performance of this equipment and an increase of energy losses which lead to damage the apparatus. In this study, estimation of fouling resistance in a cross-flow heat exchanger was solved using a linear [Partial Least Squares (PLS)] and non linear [Artificial Neural Network (ANN)] methods. Principal Component Analysis (PCA) and Step Wise Regression (SWR) were preceded the modeling in order to determine the highest relation between operating parameters with the fouling resistance. The values of correlation coefficient (r(2)) and predictive ability which are equal to 0.992 and 87%, respectively showed a good prediction of the developed PLS model. In order to improve the results obtained by PLS method, an ANN model was developed. 361 experimental data points was used to design and train the network. A network containing 6 hidden neurons trained with Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm and hyperbolic tangent sigmoid transfer function for the hidden and output layers was selected to be the optimal configuration. The Garson’s equation was applied to determine the sensitivity of input parameters on fouling resistance based on ANN results. Results indicated that acid inlet and outlet temperatures were the high relative important parameters on fouling resistance with importance equal to 56% and 15.4%, respectively. |
format | Online Article Text |
id | pubmed-10587106 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105871062023-10-21 Estimation and sensitivity analysis of fouling resistance in phosphoric acid/steam heat exchanger using artificial neural networks and regression methods Jradi, Rania Marvillet, Christophe Jeday, Mohamed Razak Sci Rep Article One of the most frequent problem in phosphoric acid concentration plant is the heat exchanger build-up. This problem causes a reduction of the performance of this equipment and an increase of energy losses which lead to damage the apparatus. In this study, estimation of fouling resistance in a cross-flow heat exchanger was solved using a linear [Partial Least Squares (PLS)] and non linear [Artificial Neural Network (ANN)] methods. Principal Component Analysis (PCA) and Step Wise Regression (SWR) were preceded the modeling in order to determine the highest relation between operating parameters with the fouling resistance. The values of correlation coefficient (r(2)) and predictive ability which are equal to 0.992 and 87%, respectively showed a good prediction of the developed PLS model. In order to improve the results obtained by PLS method, an ANN model was developed. 361 experimental data points was used to design and train the network. A network containing 6 hidden neurons trained with Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm and hyperbolic tangent sigmoid transfer function for the hidden and output layers was selected to be the optimal configuration. The Garson’s equation was applied to determine the sensitivity of input parameters on fouling resistance based on ANN results. Results indicated that acid inlet and outlet temperatures were the high relative important parameters on fouling resistance with importance equal to 56% and 15.4%, respectively. Nature Publishing Group UK 2023-10-19 /pmc/articles/PMC10587106/ /pubmed/37857645 http://dx.doi.org/10.1038/s41598-023-44516-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Jradi, Rania Marvillet, Christophe Jeday, Mohamed Razak Estimation and sensitivity analysis of fouling resistance in phosphoric acid/steam heat exchanger using artificial neural networks and regression methods |
title | Estimation and sensitivity analysis of fouling resistance in phosphoric acid/steam heat exchanger using artificial neural networks and regression methods |
title_full | Estimation and sensitivity analysis of fouling resistance in phosphoric acid/steam heat exchanger using artificial neural networks and regression methods |
title_fullStr | Estimation and sensitivity analysis of fouling resistance in phosphoric acid/steam heat exchanger using artificial neural networks and regression methods |
title_full_unstemmed | Estimation and sensitivity analysis of fouling resistance in phosphoric acid/steam heat exchanger using artificial neural networks and regression methods |
title_short | Estimation and sensitivity analysis of fouling resistance in phosphoric acid/steam heat exchanger using artificial neural networks and regression methods |
title_sort | estimation and sensitivity analysis of fouling resistance in phosphoric acid/steam heat exchanger using artificial neural networks and regression methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10587106/ https://www.ncbi.nlm.nih.gov/pubmed/37857645 http://dx.doi.org/10.1038/s41598-023-44516-6 |
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