인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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개인구독
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
In this paper, in order to evaluate the availability of the boiler combustion optimization of MPC technology for the current method of thermal power generation operation, two cases of experiment were implemented. Firstly, an experiment was performed using simulator linkage and actual process operation information. As a result, it was possible to obtain effects such as reducing overshooting when the target load was reached when linking the simulator. Secondly, an experiment was performed using actual operation information for optimizing combustion-related variables. However. the result of optimization by MPC solution was difficult to apply to actual process control. This is because the mechanical power generation control system is designed for the purpose of following the demand load, ① it is difficult to define a clear causal relationship between the process operation variables and nitrogen oxides and carbon monoxide, and ② data preprocessing techniques for handling disturbances and time delays. It could be confirmed that this was due to the inadequacy of the optimization technique among the multiple conflicting variables. To supplement this, soft-computing approaches including ① data linearization applying Bayesian optimization method, ② generalization of process situation prediction algorithm through application of multi-stage machine learning prediction structure and incremental data utilization, ③ global optimization method through combination of empirical-heuristic optimization algorithm are introduced to optimize the boiler combustion of thermal power plant.
본문·목차
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