Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant
Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions....
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.945769/full |
| _version_ | 1811321299934380032 |
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| author | Muhammad Waqas Saif-Ul-Allah Javed Khan Faisal Ahmed Chaudhary Awais Salman Zeeshan Gillani Arif Hussain Muhammad Yasin Noaman Ul-Haq Asad Ullah Khan Asad Ullah Khan Aqeel Ahmed Bazmi Zubair Ahmad Mudassir Hasan |
| author_facet | Muhammad Waqas Saif-Ul-Allah Javed Khan Faisal Ahmed Chaudhary Awais Salman Zeeshan Gillani Arif Hussain Muhammad Yasin Noaman Ul-Haq Asad Ullah Khan Asad Ullah Khan Aqeel Ahmed Bazmi Zubair Ahmad Mudassir Hasan |
| author_sort | Muhammad Waqas Saif-Ul-Allah |
| collection | DOAJ |
| description | Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author’s knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models’ development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant. |
| first_indexed | 2024-04-13T13:14:30Z |
| format | Article |
| id | doaj.art-c1def8e9953c4819a2d20f3d4402e8e9 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-13T13:14:30Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-c1def8e9953c4819a2d20f3d4402e8e92022-12-22T02:45:31ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2022-08-011010.3389/fenrg.2022.945769945769Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power PlantMuhammad Waqas Saif-Ul-Allah0Javed Khan1Faisal Ahmed2Chaudhary Awais Salman3Zeeshan Gillani4Arif Hussain5Muhammad Yasin6Noaman Ul-Haq7Asad Ullah Khan8Asad Ullah Khan9Aqeel Ahmed Bazmi10Zubair Ahmad11Mudassir Hasan12Process and Energy Systems Engineering Center-PRESTIGE, Department of Chemical Engineering, COMSATS University Islamabad, Lahore, PakistanProcess and Energy Systems Engineering Center-PRESTIGE, Department of Chemical Engineering, COMSATS University Islamabad, Lahore, PakistanProcess and Energy Systems Engineering Center-PRESTIGE, Department of Chemical Engineering, COMSATS University Islamabad, Lahore, PakistanSchool of Business, Society and Engineering, Mälardalen University, Västerås, SwedenDepartment of Computer Science, COMSATS University Islamabad, Lahore, PakistanProcess and Energy Systems Engineering Center-PRESTIGE, Department of Chemical Engineering, COMSATS University Islamabad, Lahore, PakistanDepartment of Chemical Engineering, COMSATS University Islamabad, Lahore, PakistanDepartment of Chemical Engineering, COMSATS University Islamabad, Lahore, PakistanDepartment of Chemical Engineering, COMSATS University Islamabad, Lahore, PakistanDepartment of Chemical Engineering, SCME, National University of Science and Technology, Islamabad, PakistanProcess and Energy Systems Engineering Center-PRESTIGE, Department of Chemical Engineering, COMSATS University Islamabad, Lahore, PakistanSchool of Chemical Engineering, Yeungnam University, Gyeongsan, South KoreaDepartment of Chemical Engineering, College of Engineering, King Khalid University, Abha, Saudi ArabiaCoal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author’s knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models’ development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant.https://www.frontiersin.org/articles/10.3389/fenrg.2022.945769/fullNOX predictionmachine learning1D-convolutional neural networkLSTMGRUcoal-fired power plant |
| spellingShingle | Muhammad Waqas Saif-Ul-Allah Javed Khan Faisal Ahmed Chaudhary Awais Salman Zeeshan Gillani Arif Hussain Muhammad Yasin Noaman Ul-Haq Asad Ullah Khan Asad Ullah Khan Aqeel Ahmed Bazmi Zubair Ahmad Mudassir Hasan Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant Frontiers in Energy Research NOX prediction machine learning 1D-convolutional neural network LSTM GRU coal-fired power plant |
| title | Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant |
| title_full | Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant |
| title_fullStr | Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant |
| title_full_unstemmed | Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant |
| title_short | Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant |
| title_sort | computationally inexpensive 1d cnn for the prediction of noisy data of nox emissions from 500 mw coal fired power plant |
| topic | NOX prediction machine learning 1D-convolutional neural network LSTM GRU coal-fired power plant |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.945769/full |
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