Development of a novel predictive controller synthesis method for complex reaction systems

复杂反应系统新型预测控制器综合方法的开发

• 批准号: 1264902
• 负责人: Antonios Armaou
• 金额: $ 20.49万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1264902&HistoricalAwards=false
• 关键词: Development; novel; predictive; controller; synthesis

PI: Armaou, AntoniosInstitutions: Pennsylvania State UniversityProposal Number: 1264902Title: Development of a novel predictive controller synthesis method for complex reaction systemsIntellectual Merit: Continuous pressure on profit margins and ever stringent environmental limits have ledto a need to develop advanced control structures that force optimal process behavior and simultaneouslysatisfy strict process and product quality constraints. Over the past twenty years model predictivecontrol (MPC) has become a powerful tool that is extensively used by the chemical industry. The controlaction in MPC is calculated by repeatedly solving online a finite-horizon open-loop optimization problem.As the control action is computed during process evolution, MPC has the capability to suppress the externaldisturbances and tolerate model inaccuracies during the course of forcing the system to follow a certain optimalpath that respects the process constraints. Issues that significantly limit the practical implementationof MPCs is their computational requirements, the need for development of specialized search algorithms andperformance degradation due to model uncertainty.Motivated by the above, this work aims to extend the applicability of MPC designs to complexprocesses and address their computational requirements which have so far prevented their implementation tofast evolving and unstable processes. The intellectual objective is to develop ageneral and systematic MPC synthesis methodology that is specifically tailored for processes in the chemicaland energy fields. The work will resolve fundamental computational issues associated with a)the dynamic nature of optimal control problems, and b) spatial variations due to the interplay of transportphenomena and reaction. Individual project aims include:o Development of a computationally efficient algorithm to derive nonlinear low-order, approximatealgebraic models that describe the dynamic process behavior.o Characterization of error and enforcement of user-defined error bounds.o Construction of practically implementable MPC designs via reformulation of the underlying dynamicoptimization problem as an algebraic one that is amenable to standard search algorithms.o Computational acceleration of the MPC designs. Characterization of model nonlinearity and uncertaintyeffects on MPC results.o Integration of the research results into the graduate curriculum and dissemination of softwaretools; involvement of undergraduate students into selected parts of the research and revision ofundergraduate curriculum.Broader Impact: A wide range of complex industrial processes could benefit from the results of this research. Examples include both batch reactors and reactive distillation columns for the production of unsaturated polyesters, and microelectronics manufacturing processes (e.g. vapor phase epitaxy, chemical vapor deposition, etching & electrodeposition). The latter processes are extensively used forthe production of both organic and inorganic photovoltaic systems. The PI aims to implement the research results in real-life industrial processes focusing on economic operation and tight control of key process and product characteristics.The predictive controllers will be used in collaborative efforts with NTUA and PSU experimentaliststo design more efficient experiments, discover crucial process parameters and identify optimaltime dependent operating conditions. In return, the developed methodologies will be evaluated in real-lifeexperimental reactors; relevant weaknesses will be identified and addressed.To transfer the results and insight of the research to the industrial sector, the PI will also activelyseek collaborations with industry and will develop and disseminate software tools with a transparentuser-machine interaction interface. In addition, The PI plans a number of activities to integrate theresearch with education including incorporation of research results in optimization and control courses,undergraduate student participation in research through the honors program, and the development of educationaltools. Finally, the PI will employ current available venues to efficiently disseminate the software toother research groups within Penn State, other educational institutions and industries.

PI：Armaou，Antonios 机构：宾夕法尼亚州立大学 提案编号：1264902 标题：开发一种用于复杂反应系统的新型预测控制器合成方法 智力优点：利润率的持续压力和日益严格的环境限制导致需要开发先进的控制结构，以强制实现最佳过程行为，同时满足严格的过程和产品质量约束。在过去的二十年里，模型预测控制 (MPC) 已成为化工行业广泛使用的强大工具。 MPC中的控制作用是通过在线重复求解有限范围开环优化问题来计算的。由于控制作用是在过程演化过程中计算的，MPC有能力在迫使系统遵循一定的尊重过程约束的最优路径的过程中抑制外部干扰并容忍模型的不准确性。显着限制 MPC 实际实现的问题是其计算要求、开发专门搜索算法的需要以及由于模型不确定性而导致的性能下降。受上述启发，这项工作旨在将 MPC 设计的适用性扩展到复杂过程，并解决其计算要求，这些要求迄今为止阻碍了其在快速发展和不稳定过程中的实现。其智力目标是开发一种通用且系统的 MPC 合成方法，该方法专门针对化学和能源领域的过程而定制。这项工作将解决与以下相关的基本计算问题：a）最优控制问题的动态性质，b）由于传输现象和反应的相互作用而导致的空间变化。各个项目的目标包括： o 开发一种计算高效的算法，以导出描述动态过程行为的非线性低阶近似代数模型。 o 误差表征和用户定义误差界限的执行。 o 通过将底层动态优化问题重新表述为适合标准搜索算法的代数问题，构建实际可实现的 MPC 设计。 o 计算 加速 MPC 设计。模型非线性和不确定性对 MPC 结果影响的表征。 o 将研究成果纳入研究生课程和软件工具的传播；本科生参与本科课程的研究和修订的选定部分。更广泛的影响：广泛的复杂工业过程可以从这项研究的结果中受益。例子包括用于生产不饱和聚酯的间歇式反应器和反应蒸馏塔，以及微电子制造工艺（例如气相外延、化学气相沉积、蚀刻和电沉积）。后一种工艺广泛用于有机和无机光伏系统的生产。该 PI 旨在将研究成果应用到现实工业过程中，重点关注经济运行以及对关键过程和产品特性的严格控制。预测控制器将与 NTUA 和 PSU 实验人员合作使用，以设计更高效的实验，发现关键过程参数并确定最佳的时间相关操作条件。作为回报，所开发的方法将在现实实验反应器中进行评估；为了将研究结果和见解转移到工业部门，PI还将积极寻求与工业界的合作，并开发和传播具有透明的人机交互界面的软件工具。此外，PI还计划开展一系列将研究与教育相结合的活动，包括将研究成果纳入优化和控制课程、本科生通过荣誉计划参与研究以及开发教育工具。最后，PI 将利用当前可用的场所向宾夕法尼亚州立大学、其他教育机构和行业内的其他研究小组有效地传播该软件。