Artificial intelligence-based forecasting model for incinerator in sulfur recovery units to predict SO2 emissions

Pollutant emissions from chemical plants are a major concern in the context of environmental safety. A reliable emission forecasting model can provide important information for optimizing the process and improving the environmental performance. In this work, forecasting models are developed for the...

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Veröffentlicht in:Environmental research Jg. 249; S. 118329
Hauptverfasser: Thameem, Muhammed, Raj, Abhijeet, Berrouk, Abdallah, Jaoude, Maguy A., AlHammadi, Ali A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 15.05.2024
Schlagworte:
Claus process
CNN-LSTM
Convolutional neural network
data collection
Emission forecasting
environmental performance
incinerators
Long short-term memory
neural networks
pollutants
prediction
sulfur
Sulfur recovery unit
Claus process
Long short-term memory
Sulfur recovery unit
Convolutional neural network
CNN-LSTM
Emission forecasting
ISSN:0013-9351, 1096-0953, 1096-0953
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Zusammenfassung:Pollutant emissions from chemical plants are a major concern in the context of environmental safety. A reliable emission forecasting model can provide important information for optimizing the process and improving the environmental performance. In this work, forecasting models are developed for the prediction of SO2 emission from a Sulfur Recovery Unit (SRU). Since SRUs incorporate complex chemical reactions, first-principle models are not suitable to predict emission levels based on a given feed condition. Accordingly, artificial intelligence-based models such as standard machine learning (ML) algorithms, multi-layer perceptron (MLP), long short-term memory (LSTM), one-dimensional convolution (1D-CNN), and CNN-LSTM models were tested, and their performance was evaluated. The input features and hyperparameters of the models were optimized to achieve maximum performance. The performance was evaluated in terms of mean squared error (MSE) and mean absolute percentage Error (MAPE) for 1 h, 3 h and 5 h ahead of forecasting. The reported results show that the CNN-LSTM encoder-decoder model outperforms other tested models, with its superiority becoming more pronounced as the forecasting horizon increased from 1 h to 5 h. For the 5-h ahead forecasting, the proposed model showed a MAPE advantage of 17.23%, 4.41%, and 2.83%, respectively over the 1D-CNN, Deep LSTM, and single-layer LSTM models in the larger dataset. •Pollutant emissions from chemical plants are a major environmental concern.•Emission forecasting models help in decision making to reduce emissions.•First principle models are unsuitable due to complexity of the system.•Machine learning models are tested for predicting SO2 emissions.•CNN-LSTM Encoder-Decoder model is suggested for SO2 emission forecasting.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0013-9351
1096-0953
1096-0953
DOI:10.1016/j.envres.2024.118329