Research on Intelligent Coupling SNCR/SCR Denitrification Technology for Thermal Power Station Boilers Based on Artificial Intelligence
This study presents an exploration of the implementation of intelligent coupling SNCR/SCR denitrification technology for thermal power station boilers, addressing the global imperatives of carbon peaking and carbon neutrality. By leveraging artificial intelligence (AI) techniques and advanced control strategies, the project has developed an innovative technical solution that integrates multi-objective optimization algorithms, data-driven predictive models, and fuzzy control algorithms. This solution not only tackles the complexities inherent in the denitrification process but also optimizes operational efficiency and reduces environmental impact. The technology is expected to significantly reduce nitrogen oxide (NOx) emissions, meeting or exceeding national environmental standards, while yielding substantial economic benefits through optimized ammonia consumption and reduced operational costs. Furthermore, the integration of intelligent control strategies enhances operational stability and reliability. Future prospects for the technology include the exploration of advanced AI techniques, integration with the Internet of Things (IoT) and big data analytics, efforts towards standardization and scalability, collaboration with policymakers and regulatory bodies, and raising public awareness. The intelligent coupling SNCR/SCR denitrification technology represents a significant step towards achieving carbon peaking and carbon neutrality goals, with immense potential to transform the thermal power industry.
Authors
- Gang Huang (ORCID: https://orcid.org/0000-0001-8111-6694)
- Hao Yu (ORCID: https://orcid.org/0000-0003-1091-9259)
- Shidong Cai (ORCID: https://orcid.org/0009-0003-8906-0762)
- Chunguang Zhou (ORCID: https://orcid.org/0000-0002-9760-9298)
- Zhiqiang Chen (ORCID: https://orcid.org/0000-0001-9946-8049)
- Huaina Qiu (ORCID: https://orcid.org/0000-0003-3319-0528)
Publication Details
- Journal
- Journal of Computer Science and Artificial Intelligence
- Published
- 2025-04-27
- DOI
- https://doi.org/10.54097/cackz695
- Primary Topic
- Advanced Data Processing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00