SNU CBE

Batch-Wise Nonlinear Model Predictive Control of a Gas Antisolvent Recrystallization Process for the Uniform Production of Micronized HMX with Carbon Dioxide as the Antisolvent

2015 · Industrial & Engineering Chemistry Research

저자

Shin Je Lee, Sungho Kim, Bumjoon Seo, Youn-Woo Lee, 이종민 (Jong Min Lee)

연구주제

Advanced Control Systems Optimization · Crystallization and Solubility Studies · Phase Equilibria and Thermodynamics

NTIS 과학기술표준분류

화공 > 화학공정 > 공정시스템기술

배정 근거: 지원 과제·키워드 근거가 없어 AI가 논문 제목·초록을 읽고 고른 분류(정책 연계 맵과 같은 방법).

AI 연구 키워드·방법

gas antisolvent recrystallization hmx nonlinear model predictive control 입도 분포

model reduction

키워드 (OpenAlex)

nonlinear model predictive control HMX supercritical fluid crystallization particle size distribution model reduction high-resolution methods batch process control

초록

Novel crystallization processes that use supercritical fluids have recently attracted considerable attention because they can overcome the problems associated with conventional crystallization processes. The gas antisolvent (GAS) process is one of the promising techniques and has been applied to many applications. However, control of the GAS process is a quite challenging problem and has not yet been studied due to the complex liquid–vapor equilibrium and particle formation kinetics. This work proposes a batch-wise nonlinear model predictive control (BNMPC) approach to the GAS process to obtain the desired particle size distribution (PSD) of HMX, a widely used explosive, which should be small and uniform for stability. Although a dynamic model of the GAS crystallization is required for BNMPC, the previously developed model is too complex for real-time applications. We propose a model sim…

이 논문을 지원한 국가R&D 과제 (NTIS 성과)

NTIS 성과DB(2020–24)에서 DOI 매칭 없음

인접 그래프

노드 hover = 이웃 강조 · 1회 클릭 = 고정 · 고정된 노드 다시 클릭 = 페이지 이동

이 문서와 연결된 페이지

교수 1

이종민

연구주제 1

Advanced Control Systems Optimization

정책분야 1

화학공정

키워드 1

입도 분포