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题名: Optimal operation mode switching of dividing wall distillation based on model-predictive control
作者: Pan H(潘昊); Yu HB(于海斌); Yuan MZ(苑明哲)
作者部门: 信息服务与智能控制技术研究室
会议名称: 27th Chinese Control and Decision Conference, CCDC 2015
会议日期: May 23-25, 2015
会议地点: Qingdao, China
会议录: Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015
会议录出版者: IEEE
会议录出版地: Piscataway, NJ, USA
出版日期: 2015
页码: 332-337
收录类别: EI ; CPCI(ISTP)
ISBN号: 978-1-4799-7016-2
关键词: Dividing Wall Distillation ; Model-Predictive Control ; Process Control ; Optimal Operation ; Fractionating Column
摘要: Dividing wall distillation(DWC) column is becoming a new standard of separating and purifying multivariate mixed liquid, because of DWC represents that forced operation will play a role in separation technology, which have high energy efficiency and compact structure. DWC is composed of a lot of equipments of diversified configurations, so it is complicated to operate process and requirements for specific or advanced control system technology. When operation objectives of DWC need to be online conversion, operation will be not smooth, etc. In order to solve these problems, we used two-layer control structure. The bottom layer was basic control layer(PID) and model-predictive control(MPC), and the top layer was predictive functional control(PFC) or fast model-predictive control(FMPC) which can realize operation optimizing function. The main objective is to achieve a smooth transition between the optimal mode switching and to ensure optimum goal can be realized. By implementation of proposed operating strategy in a four-component mixture Kaible column, the simulation results showed that this new method was feasible and concise.
语种: 英语
产权排序: 1
WOS记录号: WOS:000375232900057
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/17191
Appears in Collections:信息服务与智能控制技术研究室_会议论文

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Recommended Citation:
Pan H,Yu HB,Yuan MZ. Optimal operation mode switching of dividing wall distillation based on model-predictive control[C]. 见:27th Chinese Control and Decision Conference, CCDC 2015. Qingdao, China. May 23-25, 2015.
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