Energy efficiency improvement for industrial boilers through a flue-gas condensing heat recovery system with nonlinear MPC approach

被引:8
|
作者
Rastegarpour, Soroush [1 ]
Mariotti, Andrea [2 ]
Ferrarini, Luca [3 ]
Aminyavari, Mehdi [4 ]
机构
[1] ABB Corp Res, Dept Automat Technol, S-72178 Vasteras, Sweden
[2] Dept Energy, Politecn Milano, I-20133 Milan, Italy
[3] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy
[4] Cannon Grp Bono Energia SpA, I-20068 Peschiera Garda, Italy
关键词
Condensing heat exchanger; Constrained optimal observer; Energy efficiency; Heat accumulation system; Nonlinear MPC; MODEL-PREDICTIVE CONTROL; GLOBAL WASTE HEAT;
D O I
10.1016/j.applthermaleng.2023.120554
中图分类号
O414.1 [热力学];
学科分类号
摘要
Recovering waste heat from boiler's exhaust flue gas has proven to be an effective way to improve energy efficiency in industrial boilers. Generally, a hydraulic design based on Condensing heat exchangers (CHXs) that boosts startup efficiency by increasing feed-water temperature and condensation rate in the CHX is suggested. In this paper, an innovative coupled CHX and heat accumulation system is presented. A detailed 1-D reference model of the system is formulated and validated by experimental data. Although the mentioned innovative heat recovery system alone is able to enhance the boiler's efficiency, the optimal performance extremely depends on the way it is operated. Furthermore, accurately estimating and predicting the condensation heat of the CHX is crucial for enhancing efficiency, yet the task is challenging due to the vapor content's phase change in the flue gas. To address these issues, a Nonlinear Model Predictive Controller (NMPC) is implemented, which utilizes a constrained optimal observer to handle the system's hybrid behavior. The performance of the proposed NMPC is evaluated against Linear MPC and conventional PID control. Results show that the proposed approach provides 48 [kg/h] saving in fuel consumption, which is about 6% more than the heuristic approach and 3.5% more than linear MPC technique.
引用
收藏
页数:15
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