Research Initiation Award: "On-Line Optimization of Batch Reactors: Experimental Implementation to Polymerization Reactors"

研究启动奖：“间歇式反应器的在线优化：聚合反应器的实验实施”

• 批准号: 9409577
• 负责人: Srinivas Palanki
• 金额: $ 8.66万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-05-01 至 1998-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9409577&HistoricalAwards=false
• 关键词: Research; Initiation; Award; Line; Optimization

Abstract - Palanki - 9409577 A large variety of specialty chemicals are produced in batch reactors. Since batch reactors are transient in nature, the major objective is to optimize a performance index (such as yield) at the end of the batch cycle. A major problem in batch reactor operation is that changes in initial conditions and environmental conditions can cause significant batch-to-batch variations leading to significant losses in resources and time. For this reason, it is necessary to develop optimization strategies that can be implemented on-line. In this study a methodological framework for on-line optimization of batch processes with multiple inputs will be developed. The emphasis is on the experimental implementation of optimal state feedback laws in industrially important batch polymerization reactors. The copolymerization of styrene with methyl acrylate in toluene using aso-bis-iso-butyronitrile (AIBN) has been selected as a model experimental system to demonstrate the application of the on-line optimization strategy. In Phase I of the research, a mathematical model of the experimental system will be developed. In Phase II, optimal state feedback laws will be developed for end-point optimization of batch reactors with multiple inputs. The efficacy of the optimal state feedback in optimizing the performance index will be tested on the mathematical model by computer simulations. In Phase III, the on-line optimization strategy will be implemented experimentally. Several batch and semi-batch experiments will be conducted to demonstrate the ability of the optimal state feedback to calculate, on-line, the optimal operating policy when there are batch to batch variations in initial conditions and process disturbances.

帕兰基-9409577在间歇反应器中生产大量的特种化学品。由于间歇反应器本质上是瞬变的，其主要目标是在间歇周期结束时优化性能指标(如产率)。间歇反应器运行中的一个主要问题是，初始条件和环境条件的变化会导致批次之间的显著差异，从而导致资源和时间的重大损失。为此，有必要开发可在线实施的优化策略。在这项研究中，将建立一个多输入间歇过程在线优化的方法学框架。重点介绍了最优状态反馈规律在工业上重要的间歇聚合反应器中的实验应用。以甲基丙烯酸甲酯与苯乙烯在甲苯中的AIBN共聚反应为模型实验系统，对在线优化策略的应用进行了验证。在研究的第一阶段，将建立实验系统的数学模型。在第二阶段，将针对具有多个输入的间歇反应器的终点优化问题开发最优状态反馈律。最优状态反馈在优化性能指标方面的有效性将在数学模型上通过计算机仿真来检验。在第三阶段，将试验性地实施在线优化策略。将进行几个批和半批实验，以验证当初始条件和过程扰动存在批次之间的变化时，最优状态反馈在线计算最优操作策略的能力。