Oxygen Demand Forecasting and Optimal Scheduling of the Oxygen Gas Systems in Iron- and Steel-Making Enterprises
Due to the imbalance between the supply and demand of oxygen, the oxygen systems of iron- and steel-making enterprises in China have problems with high oxygen emissions and high pressure in the pipelines, resulting in the energy consumption of oxygen production being high. To reduce the energy consu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/21/11618 |
| _version_ | 1797632238439890944 |
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| author | Zhen Cheng Peikun Zhang Li Wang |
| author_facet | Zhen Cheng Peikun Zhang Li Wang |
| author_sort | Zhen Cheng |
| collection | DOAJ |
| description | Due to the imbalance between the supply and demand of oxygen, the oxygen systems of iron- and steel-making enterprises in China have problems with high oxygen emissions and high pressure in the pipelines, resulting in the energy consumption of oxygen production being high. To reduce the energy consumption of oxygen systems, this study took a large-scale iron- and steel-making enterprise as a case study and developed a two-stage forecasting and scheduling model. The novel aspect and progressiveness of this work are as follows: First, an oxygen demand forecasting model was developed based on the backpropagation neural network with genetic algorithm optimization (GABP) and is driven only by historical data. Compared with some complex models in the literature, although the accuracy of this model has been reduced, the model does not need to consider production plans for other process steps, making it more practical and feasible. Second, different from the existing literature, an oxygen production scheduling model was developed for load-variable ASUs with an internal compression process, and both the oxygen emissions and pipeline pressure are included in the objective function. The case study showed that based on the oxygen demand forecast and optimal scheduling, the oxygen emissions and pipeline pressure in the studied iron- and steel-making enterprise can be significantly reduced, thereby achieving considerable energy-saving effects and economic benefits. Specifically, the following conclusions were obtained: (1) For the oxygen demand forecast, the prediction accuracy of the GABP model was better than that of the ARIMA model. The average MAPE of the 12 sets of data of the ARIMA and GABP models was 23.8% and 20.2%, respectively. (2) By comparing the scheduling results and the field data, it was found that after scheduling, the amount of oxygen emissions decreased by 6.32%, the pipeline pressure decreased by 0.61%, and the energy consumption of oxygen compression decreased by 1.6%. Considering both the oxygen emission loss and the energy consumption of oxygen compression, the total power consumption of the studied oxygen system was reduced by 1.38%, resulting in electricity cost savings of approximately 9.03 million RMB per year. |
| first_indexed | 2024-03-11T11:35:10Z |
| format | Article |
| id | doaj.art-e43ab29b664e41fe86bf1d819e816377 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T11:35:10Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e43ab29b664e41fe86bf1d819e8163772023-11-10T14:58:09ZengMDPI AGApplied Sciences2076-34172023-10-0113211161810.3390/app132111618Oxygen Demand Forecasting and Optimal Scheduling of the Oxygen Gas Systems in Iron- and Steel-Making EnterprisesZhen Cheng0Peikun Zhang1Li Wang2School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaDue to the imbalance between the supply and demand of oxygen, the oxygen systems of iron- and steel-making enterprises in China have problems with high oxygen emissions and high pressure in the pipelines, resulting in the energy consumption of oxygen production being high. To reduce the energy consumption of oxygen systems, this study took a large-scale iron- and steel-making enterprise as a case study and developed a two-stage forecasting and scheduling model. The novel aspect and progressiveness of this work are as follows: First, an oxygen demand forecasting model was developed based on the backpropagation neural network with genetic algorithm optimization (GABP) and is driven only by historical data. Compared with some complex models in the literature, although the accuracy of this model has been reduced, the model does not need to consider production plans for other process steps, making it more practical and feasible. Second, different from the existing literature, an oxygen production scheduling model was developed for load-variable ASUs with an internal compression process, and both the oxygen emissions and pipeline pressure are included in the objective function. The case study showed that based on the oxygen demand forecast and optimal scheduling, the oxygen emissions and pipeline pressure in the studied iron- and steel-making enterprise can be significantly reduced, thereby achieving considerable energy-saving effects and economic benefits. Specifically, the following conclusions were obtained: (1) For the oxygen demand forecast, the prediction accuracy of the GABP model was better than that of the ARIMA model. The average MAPE of the 12 sets of data of the ARIMA and GABP models was 23.8% and 20.2%, respectively. (2) By comparing the scheduling results and the field data, it was found that after scheduling, the amount of oxygen emissions decreased by 6.32%, the pipeline pressure decreased by 0.61%, and the energy consumption of oxygen compression decreased by 1.6%. Considering both the oxygen emission loss and the energy consumption of oxygen compression, the total power consumption of the studied oxygen system was reduced by 1.38%, resulting in electricity cost savings of approximately 9.03 million RMB per year.https://www.mdpi.com/2076-3417/13/21/11618iron- and steel-making enterpriseoxygen systemforecasting modelscheduling modelenergy-saving |
| spellingShingle | Zhen Cheng Peikun Zhang Li Wang Oxygen Demand Forecasting and Optimal Scheduling of the Oxygen Gas Systems in Iron- and Steel-Making Enterprises Applied Sciences iron- and steel-making enterprise oxygen system forecasting model scheduling model energy-saving |
| title | Oxygen Demand Forecasting and Optimal Scheduling of the Oxygen Gas Systems in Iron- and Steel-Making Enterprises |
| title_full | Oxygen Demand Forecasting and Optimal Scheduling of the Oxygen Gas Systems in Iron- and Steel-Making Enterprises |
| title_fullStr | Oxygen Demand Forecasting and Optimal Scheduling of the Oxygen Gas Systems in Iron- and Steel-Making Enterprises |
| title_full_unstemmed | Oxygen Demand Forecasting and Optimal Scheduling of the Oxygen Gas Systems in Iron- and Steel-Making Enterprises |
| title_short | Oxygen Demand Forecasting and Optimal Scheduling of the Oxygen Gas Systems in Iron- and Steel-Making Enterprises |
| title_sort | oxygen demand forecasting and optimal scheduling of the oxygen gas systems in iron and steel making enterprises |
| topic | iron- and steel-making enterprise oxygen system forecasting model scheduling model energy-saving |
| url | https://www.mdpi.com/2076-3417/13/21/11618 |
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