Mechanism-enhanced data-driven method for the joint optimization of boiler combustion and selective catalytic reduction systems considering gas temperature deviations

Yukun Zhu, Cong Yu, Wei Jin, Ling Shi, Bo Chen and Pei Xu

Energy, 2024, vol. 291, issue C

Abstract: A novel optimization framework for boiler combustion and selective catalytic reduction systems (BCSCRSs) that accounts for flue gas temperature deviation and integrates prior knowledge is proposed. Initially, single-factor hot-state experiments on the BCSCRSs are conducted to yield samples for model training. By integrating monotonic relationships from the experimental data into the fusion monotony–support vector regression model, more reasonable estimates and trends of the prediction targets (e.g., CO and NOx concentrations, and unburned combustibles) are obtained compared to those of the model without prior knowledge. Based on this model, the inequality-constrained reference vector-guided evolutionary algorithm is employed for multiobjective combustion optimization considering the constraints of gas temperature deviation in the horizontal pass. The corresponding Pareto-frontier solutions that serve as boundary conditions for the second-stage optimization are obtained. The second-stage optimization comprehensively considers the total operating cost of the BCSCRSs, the denitrification fraction in the NOx formation and removal processes, and safety concerns related to steam tube overheating and ammonia slip. Several noteworthy results are derived: the adjustment of the burner tilt angle presents a trade-off between eliminating gas temperature deviation and reducing NOx formation. An optimal point can be identified by comparing the magnitudes and variations of different BCSCRSs objectives.

Keywords: Boiler efficiency; Smoke temperature deviation; Selective catalytic reduction; Hybrid modelling; Multiobjective optimization (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224002032
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:291:y:2024:i:c:s0360544224002032

DOI: 10.1016/j.energy.2024.130432

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().