Tail Gas Quality Warning System in a Sulfur Recovery Unit based on H2S and SO2 Concentration Soft Sensor utilizing Multi-State-Dependent Modeling Method
The design and development of data-driven soft sensors is important to predict the concentration of perilous pollutants in industry effluents to protect environmental health. The aim of this research is to design a tail gas quality warning system in the sulfur recovery unit (SRU) based on H2S and SO...
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| Format: | Article |
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University of Sistan and Baluchestan
2023-12-01
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| Series: | International Journal of Industrial Electronics, Control and Optimization |
| Subjects: | |
| Online Access: | https://ieco.usb.ac.ir/article_8028_4dd19a474c304b42e724082bd0ce6bc9.pdf |
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| author | Fereshte Tavakoli Dastjerd Farhad Shahraki Jafar Sadeghi Mir Mohammad Khalilipour Bahareh Bidar |
| author_facet | Fereshte Tavakoli Dastjerd Farhad Shahraki Jafar Sadeghi Mir Mohammad Khalilipour Bahareh Bidar |
| author_sort | Fereshte Tavakoli Dastjerd |
| collection | DOAJ |
| description | The design and development of data-driven soft sensors is important to predict the concentration of perilous pollutants in industry effluents to protect environmental health. The aim of this research is to design a tail gas quality warning system in the sulfur recovery unit (SRU) based on H2S and SO2 concentration soft sensor utilizing multi-state-dependent modeling method. The SRU in the petrochemical plant of ERG PETROLI, located in Italy, is selected as the study region for implementation of the warning system. The generalized random walk- multi-state-dependent parameter method (GRW-MSDP) for soft sensor design is proposed. The GRW-MSDP estimation system is based on multi-state-dependent modeling method by utilizing the extension of the generalized random walk model. The method has been developed by utilizing the algorithms of extended Kalman filter (EKF) and fixed interval smoothing (FIS). The quality warning system of tail gas based on the estimated concentrations of SO2 and H2S sends instructions to adjust the ratio of air to feed flow in the reaction furnace of SRU by plant operators. The results indicate that the proposed estimation system can be efficient in dealing with process non-linearity, high-dimensional values, and random missing data. The comparative discussion of GRW-MSDP technique performance with different soft sensing methods shows that the designed soft sensor model is more reliable with fewer input variables, lower complexity and relatively higher prediction accuracy. Furthermore, the great efficiency of the designed quality warning system is obvious from the good accuracy and F1-score values of 99.4% and 0.8951, respectively. |
| first_indexed | 2024-03-08T19:17:35Z |
| format | Article |
| id | doaj.art-7a2803eed9a94153ae931bed427d62c1 |
| institution | Directory Open Access Journal |
| issn | 2645-3517 2645-3568 |
| language | English |
| last_indexed | 2024-03-08T19:17:35Z |
| publishDate | 2023-12-01 |
| publisher | University of Sistan and Baluchestan |
| record_format | Article |
| series | International Journal of Industrial Electronics, Control and Optimization |
| spelling | doaj.art-7a2803eed9a94153ae931bed427d62c12023-12-27T05:13:28ZengUniversity of Sistan and BaluchestanInternational Journal of Industrial Electronics, Control and Optimization2645-35172645-35682023-12-016430731910.22111/ieco.2023.46394.14998028Tail Gas Quality Warning System in a Sulfur Recovery Unit based on H2S and SO2 Concentration Soft Sensor utilizing Multi-State-Dependent Modeling MethodFereshte Tavakoli Dastjerd0Farhad Shahraki1Jafar Sadeghi2Mir Mohammad Khalilipour3Bahareh Bidar4Center for Process Integration and Control (CPIC), Department of Chemical Engineering, University of Sistan and Baluchestan, Zahedan 98164, IranCenter of Process Integration and Control (CPIC) Department of Chemical Engineering University of Sistan and Baluchestan Zahedan, PoBox: 98135-987 IranCenter of Process Integration and Control (CPIC), Department of Chemical Engineering, University of Sistan and Baluchestan, Zahedan, PoBox: 98135-987 IranCenter for Process Integration and Control (CPIC) Department of Chemical Engineering University of Sistan and BaluchestanCenter for Process Integration and Control (CPIC), Department of Chemical Engineering, University of Sistan and Baluchestan, Zahedan, IranThe design and development of data-driven soft sensors is important to predict the concentration of perilous pollutants in industry effluents to protect environmental health. The aim of this research is to design a tail gas quality warning system in the sulfur recovery unit (SRU) based on H2S and SO2 concentration soft sensor utilizing multi-state-dependent modeling method. The SRU in the petrochemical plant of ERG PETROLI, located in Italy, is selected as the study region for implementation of the warning system. The generalized random walk- multi-state-dependent parameter method (GRW-MSDP) for soft sensor design is proposed. The GRW-MSDP estimation system is based on multi-state-dependent modeling method by utilizing the extension of the generalized random walk model. The method has been developed by utilizing the algorithms of extended Kalman filter (EKF) and fixed interval smoothing (FIS). The quality warning system of tail gas based on the estimated concentrations of SO2 and H2S sends instructions to adjust the ratio of air to feed flow in the reaction furnace of SRU by plant operators. The results indicate that the proposed estimation system can be efficient in dealing with process non-linearity, high-dimensional values, and random missing data. The comparative discussion of GRW-MSDP technique performance with different soft sensing methods shows that the designed soft sensor model is more reliable with fewer input variables, lower complexity and relatively higher prediction accuracy. Furthermore, the great efficiency of the designed quality warning system is obvious from the good accuracy and F1-score values of 99.4% and 0.8951, respectively.https://ieco.usb.ac.ir/article_8028_4dd19a474c304b42e724082bd0ce6bc9.pdfpollutantsdata-driven soft sensorsulfur recovery unitmulti-state-dependent parametergeneralized random walk |
| spellingShingle | Fereshte Tavakoli Dastjerd Farhad Shahraki Jafar Sadeghi Mir Mohammad Khalilipour Bahareh Bidar Tail Gas Quality Warning System in a Sulfur Recovery Unit based on H2S and SO2 Concentration Soft Sensor utilizing Multi-State-Dependent Modeling Method International Journal of Industrial Electronics, Control and Optimization pollutants data-driven soft sensor sulfur recovery unit multi-state-dependent parameter generalized random walk |
| title | Tail Gas Quality Warning System in a Sulfur Recovery Unit based on H2S and SO2 Concentration Soft Sensor utilizing Multi-State-Dependent Modeling Method |
| title_full | Tail Gas Quality Warning System in a Sulfur Recovery Unit based on H2S and SO2 Concentration Soft Sensor utilizing Multi-State-Dependent Modeling Method |
| title_fullStr | Tail Gas Quality Warning System in a Sulfur Recovery Unit based on H2S and SO2 Concentration Soft Sensor utilizing Multi-State-Dependent Modeling Method |
| title_full_unstemmed | Tail Gas Quality Warning System in a Sulfur Recovery Unit based on H2S and SO2 Concentration Soft Sensor utilizing Multi-State-Dependent Modeling Method |
| title_short | Tail Gas Quality Warning System in a Sulfur Recovery Unit based on H2S and SO2 Concentration Soft Sensor utilizing Multi-State-Dependent Modeling Method |
| title_sort | tail gas quality warning system in a sulfur recovery unit based on h2s and so2 concentration soft sensor utilizing multi state dependent modeling method |
| topic | pollutants data-driven soft sensor sulfur recovery unit multi-state-dependent parameter generalized random walk |
| url | https://ieco.usb.ac.ir/article_8028_4dd19a474c304b42e724082bd0ce6bc9.pdf |
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