Mathematical and statistical tools for chemical process modeling and performance improvement

用于化学过程建模和性能改进的数学和统计工具

• 批准号: 105367-2007
• 负责人: McLellan, James
• 金额: $ 2.19万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=390845
• 关键词: Mathematical; statistical; tools; chemical; process

Chemical engineers use mathematical models for a variety of needs - to summarize understanding about how chemical processes work, for predicting how they will behave if changes are made, for learning how to improve the design of products or manufacturing processes, for designing controllers to ensure safe, reliable and profitable operation, and to monitor the operation of the processes to watch for deteriorating equipment and failures. When building models engineers have to decide what type of mathematical equations to use, and how much physical detail to model. The goal is to balance the need for complicated models against the benefits from using them. Mathematical models also contain constants which have to be estimated by running experiments and collecting experimental information from operating plants. These constants are estimated using statistical techniques.

化学工程师使用数学模型来满足各种需求-总结对化学过程如何工作的理解，预测如果发生变化，它们将如何表现，学习如何改进产品或制造过程的设计，设计控制器以确保安全，可靠和有利可图的操作，并监控过程的操作以观察设备的恶化和故障。在构建模型时，工程师必须决定使用哪种类型的数学方程，以及要建模多少物理细节。我们的目标是平衡对复杂模型的需求和使用它们的好处。数学模型还包含常数，这些常数必须通过运行实验和从运行的工厂收集实验信息来估计。这些常数使用统计技术估计。