Model Predictive Control (MPC)-Based Supervisory Control and Design of Off-Gas Recovery Plant with Periodic Disturbances from Parallel Batch Reactors
2016 · Industrial & Engineering Chemistry Research
저자
Hyuncheol Ryu, 이종민 (Jong Min Lee)
연구주제
Advanced Control Systems Optimization · Process Optimization and Integration · Fault Detection and Control Systems
NTIS 과학기술표준분류
배정 근거: 지원 과제·키워드 근거가 없어 AI가 논문 제목·초록을 읽고 고른 분류(정책 연계 맵과 같은 방법).
AI 연구 키워드·방법
모델 예측 제어(MPC) off gas recovery process 플랜트 전체 제어(플랜트와이드 제어) polysilicon production
aspen dynamics simulation genetic algorithm
키워드 (OpenAlex)
model predictive control supervisory control polysilicon production chemical vapor deposition reactor periodic disturbances plantwide control buffer tank genetic algorithm
초록
This paper proposes a plantwide control structure and a model predictive control (MPC) scheme for the effective separation of off-gas from a polysilicon plant with periodic disturbances in the feed. The target process studied in this work produces 10 000 MT of polysilicon per year with 28 chemical vapor deposition (CVD) reactors. CVD reaction kinetic models were constructed, and their parameters were identified using a genetic algorithm. Aspen Dynamics was used to model the off-gas recovery process and implement proportional–integral (PI) controllers for regulating the inventory levels. A centralized supervisory layer based on MPC, to minimize the fluctuation of the recovered concentration, was linked with regulatory controllers through a MATLAB/SIMULINK environment. Using a rigorous plantwide dynamic model, four cases were studied with different control schemes with and without buffer t…
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교수 1
연구주제 1
Advanced Control Systems Optimization