Green Energy Efficiency Diagnosis of Ethylene Oxide Dehydration and Refining Unit Based on Dynamic Modeling
Ethylene oxide, a crucial ethylene derivative, is synthesized through direct ethylene oxidation, yet its dehydration and refining unit grapples with significant challenges including excessive energy consumption, high CO2 emissions, and substantial wastewater discharge. These environmental and energy efficiency concerns underscore the urgent need for innovative diagnostic and optimization approaches to enhance the sustainability of industrial processes. Traditional energy efficiency analyses, while effective for pinpointing bottlenecks in steady-state operations, fall short in capturing the complexities of dynamic processes during disturbances such as feed fluctuations or temperature variations. These conventional methods often fail to provide detailed operational insights or account for nuanced efficiency shifts and environmental impacts under such conditions. To overcome these limitations, this study introduces a pioneering unit-equipment hierarchical green efficiency diagnostic framework, integrating dynamic modeling with a slack-based measure network data envelopment analysis model. This novel approach enables precise tracking of efficiency transitions during dynamic disturbances by incorporating multidimensional indicators─energy consumption, CO2 emissions, and wastewater discharge─offering a comprehensive evaluation from the unit to the equipment level. Unlike traditional methods, this framework excels in identifying optimization potential under varying operational conditions, establishing a groundbreaking pathway for energy conservation and emission reduction in dynamic industrial processes. Results indicate that disturbances involving 10% feed increase or 5 °C temperature reduction significantly degrade unit efficiency. Detailed equipment-level diagnostics identified T-310 as a critical inefficiency source with substantial optimization potential. Its liquid level regulation via cascade control modification demonstrated measurable improvements: During 5 °C temperature reduction and 10% feed increase, the utility saving potential was released by 92.35 and 117.62 kgce, respectively. The CO2 emission reduction potential was released by 4.99 and 90.40 kg, respectively.
Authors
- Jian Du (ORCID: https://orcid.org/0000-0001-7667-4835)
- Linlin Liu (ORCID: https://orcid.org/0000-0002-9904-5174)
- Dejun Ma (ORCID: https://orcid.org/0000-0001-8800-5356)
- Lihong Bao (ORCID: https://orcid.org/0000-0002-1259-7444)
- Yu Zhuang (ORCID: https://orcid.org/0000-0003-0886-4607)
- Yimin Wang (ORCID: https://orcid.org/0000-0002-2701-0714)
- Chao Wang (ORCID: https://orcid.org/0009-0009-3888-0561)
Institutions
- Dalian University of Foreign Languages (CN)
- Dalian University of Technology (CN)
- Dalian University (CN)
- Smart Material (Germany) (DE)
Publication Details
- Journal
- Industrial & Engineering Chemistry Research
- Published
- 2025-10-11
- DOI
- https://doi.org/10.1021/acs.iecr.5c01944
- Primary Topic
- Process Optimization and Integration
- Type
- article
- Field-Weighted Citation Impact
- 0.00