PREDICTION OF SCALE REMOVAL WEIGHT DEPOSITED ON SURFACE OF HEAT EXCHANGER USING ARTIFICIAL NEURAL NETWORK
Scale is a term generally used in industry refers to any deposit on equipment surface. Usually the deposition of scale is undesirable because it is uncontrolled and a build-up of scale on metal surfaces may act as insulation causing decreased efficiency. So removal of scale has gained special atten...
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| Format: | Article |
| Language: | English |
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University of Diyala
2015-12-01
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| Series: | Diyala Journal of Engineering Sciences |
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| Online Access: | https://djes.info/index.php/djes/article/view/421 |
| _version_ | 1828104333986627584 |
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| author | Suheila Abd Al-Reda Akkar |
| author_facet | Suheila Abd Al-Reda Akkar |
| author_sort | Suheila Abd Al-Reda Akkar |
| collection | DOAJ |
| description |
Scale is a term generally used in industry refers to any deposit on equipment surface. Usually the deposition of scale is undesirable because it is uncontrolled and a build-up of scale on metal surfaces may act as insulation causing decreased efficiency. So removal of scale has gained special attention in the last few years due to its significance, when predicting removal scale weight. However, the complexity and variability makes it hard to model its effects. This study evaluates the usefulness of Artificial Neural Networks (ANN) to predict the scale removal weight as a function of several of their properties which have been related in previous studies i.e. time, concentration of organic acid salts, Temperature, density, viscosity. Results showed that neural networks are a powerful tool and that the validity of the results is closely linked to the amount of data available and the experience and knowledge that accompany the analysis. The structure of ANN models is [5-18-1] the best because reach MSE 0.001 with AARE%, S.D%, and R (0.12, 0.46, 0.9) respectively. The training of network use MATLAB program.
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| first_indexed | 2024-04-11T09:45:19Z |
| format | Article |
| id | doaj.art-4fccd49750fe44bcac262200e3e61b0c |
| institution | Directory Open Access Journal |
| issn | 1999-8716 2616-6909 |
| language | English |
| last_indexed | 2024-04-11T09:45:19Z |
| publishDate | 2015-12-01 |
| publisher | University of Diyala |
| record_format | Article |
| series | Diyala Journal of Engineering Sciences |
| spelling | doaj.art-4fccd49750fe44bcac262200e3e61b0c2022-12-22T04:31:06ZengUniversity of DiyalaDiyala Journal of Engineering Sciences1999-87162616-69092015-12-01PREDICTION OF SCALE REMOVAL WEIGHT DEPOSITED ON SURFACE OF HEAT EXCHANGER USING ARTIFICIAL NEURAL NETWORKSuheila Abd Al-Reda Akkar0Department of Chemical Engineering, College of Engineering, University of Baghdad Scale is a term generally used in industry refers to any deposit on equipment surface. Usually the deposition of scale is undesirable because it is uncontrolled and a build-up of scale on metal surfaces may act as insulation causing decreased efficiency. So removal of scale has gained special attention in the last few years due to its significance, when predicting removal scale weight. However, the complexity and variability makes it hard to model its effects. This study evaluates the usefulness of Artificial Neural Networks (ANN) to predict the scale removal weight as a function of several of their properties which have been related in previous studies i.e. time, concentration of organic acid salts, Temperature, density, viscosity. Results showed that neural networks are a powerful tool and that the validity of the results is closely linked to the amount of data available and the experience and knowledge that accompany the analysis. The structure of ANN models is [5-18-1] the best because reach MSE 0.001 with AARE%, S.D%, and R (0.12, 0.46, 0.9) respectively. The training of network use MATLAB program. https://djes.info/index.php/djes/article/view/421Back propagation networksTraining networkHeat exchanger piping system |
| spellingShingle | Suheila Abd Al-Reda Akkar PREDICTION OF SCALE REMOVAL WEIGHT DEPOSITED ON SURFACE OF HEAT EXCHANGER USING ARTIFICIAL NEURAL NETWORK Diyala Journal of Engineering Sciences Back propagation networks Training network Heat exchanger piping system |
| title | PREDICTION OF SCALE REMOVAL WEIGHT DEPOSITED ON SURFACE OF HEAT EXCHANGER USING ARTIFICIAL NEURAL NETWORK |
| title_full | PREDICTION OF SCALE REMOVAL WEIGHT DEPOSITED ON SURFACE OF HEAT EXCHANGER USING ARTIFICIAL NEURAL NETWORK |
| title_fullStr | PREDICTION OF SCALE REMOVAL WEIGHT DEPOSITED ON SURFACE OF HEAT EXCHANGER USING ARTIFICIAL NEURAL NETWORK |
| title_full_unstemmed | PREDICTION OF SCALE REMOVAL WEIGHT DEPOSITED ON SURFACE OF HEAT EXCHANGER USING ARTIFICIAL NEURAL NETWORK |
| title_short | PREDICTION OF SCALE REMOVAL WEIGHT DEPOSITED ON SURFACE OF HEAT EXCHANGER USING ARTIFICIAL NEURAL NETWORK |
| title_sort | prediction of scale removal weight deposited on surface of heat exchanger using artificial neural network |
| topic | Back propagation networks Training network Heat exchanger piping system |
| url | https://djes.info/index.php/djes/article/view/421 |
| work_keys_str_mv | AT suheilaabdalredaakkar predictionofscaleremovalweightdepositedonsurfaceofheatexchangerusingartificialneuralnetwork |