Distributed/boundary model predictive control for transport-reaction systems (crystal growth processing)

传输反应系统的分布式/边界模型预测控制（晶体生长处理）

• 批准号: 386508-2011
• 负责人: Dubljevic, Stevan
• 金额: $ 1.89万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=550465
• 关键词: Distributed; boundary; model; predictive; control

The state-of-the-art semiconductor processing, pharmaceutical, petrochemical and chemical process plants operational improvement lies within the realm of optimization and controller synthesis which can meet tighter environmental regulations, provide desired product specifications and ensure profit at the time of increasing energy costs and worldwide competition. In all these processes, the goal is to design a ``best'' possible controller, a model predictive controller (MPC), which should: 1) provide an optimal solution for the process plant regulation; 2) include naturally present constraints (e.g., limitations on available energy used for process regulation-valves opening to heat up or cool down the process, limitations due to the performance requirements imposed by the product specifications, safety or hazard considerations); 3) consider the moving and scanning capability of an actuator/sensor so as to outperform standard fixed actuator/sensor configurations. The theoretical and industrial application related objectives of this research program are to explore three relevant examples which account for 95% of the processes present in the current semiconductor crystal processing industry: 1) Czochralski crystal growth process, 2) Bridgman crystal growth process, 3) melted zone refining process, and within each of the above processes to explore dominant transport-phenomena and to design relevant model based model predictive controllers which should improve upon the current industrially applied controllers. The expected outcome of this program is to provide an ultimate optimal controller that can handle high cost crystal growth processing, and at the same time result in capital cost savings, chemical cost reduction, improved quality of products, and provide for more efficient use of power. Amongst other benefits, the program will provide for the training of highly qualified personnel by active involvement of PhD and MS students capable of mastering the control of semiconductor crystal growth processing, and it will draw attention of current members of the semiconductor industry interested in the process improvements.

最先进的半导体加工、制药、石油化工和化学加工厂的操作改进属于优化和控制器综合领域，可以满足更严格的环境法规，提供所需的产品规格，并在能源成本增加和全球竞争的同时确保利润。在所有这些过程中，目标是设计“最佳”可能的控制器，即模型预测控制器（MPC），其应该：1）为过程工厂调节提供最优解决方案; 2）包括自然存在的约束（例如，对用于过程调节的可用能量的限制-打开阀门以加热或冷却过程，由于产品规格、安全或危险考虑所强加的性能要求的限制）; 3）考虑致动器/传感器的移动和扫描能力，以便优于标准的固定致动器/传感器配置。本研究计划的理论和工业应用相关目标是探索三个相关的例子，这些例子占当前半导体晶体加工行业中95%的工艺：1）Czochralski晶体生长工艺，2）Bridgman晶体生长工艺，3）熔融区精炼工艺，并且在上述过程的每一个中，探索主要的传输现象，并且设计基于相关模型的模型预测控制器，该模型预测控制器应当改进当前工业应用的控制器。该计划的预期结果是提供一种最终的最佳控制器，该控制器可以处理高成本的晶体生长处理，并且同时导致资本成本节约、化学成本降低、产品质量提高，并且提供更有效的电力使用。除其他好处外，该计划将通过能够掌握半导体晶体生长过程控制的博士和硕士学生的积极参与来提供高素质人才的培训，并将引起对工艺改进感兴趣的半导体行业当前成员的注意。