Orthogonalization-based manipulated-variable ranking for identifying and addressing gain-conditioning problems in multivariable control systems

基于正交化的操纵变量排序，用于识别和解决多变量控制系统中的增益调节问题

• 批准号: 567501-2021
• 负责人: McAuley, KimberleyKB
• 金额: $ 1.46万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=747057
• 关键词: Orthogonalization; based; manipulated; variable; ranking

The aim is to develop and test a new software tool that will reduce time and effort required by advanced control engineers when they implement linear model-predictive control applications at refineries and chemical plants. Currently, highly-skilled control engineers undertake time-consuming gain-conditioning investigations and then make manual adjustments to process gain matrices used in online Linear Programming (LP) and Dynamic Matrix Control (DMC) calculations. These investigations are used to detect potential undesirable interactions that may occur when multiple manipulated variables (MVs), such as flow rates and heater settings, are adjusted to achieve safe and economical process operation. The proposed tool will help engineers at Imperial Oil and other companies to generate gain matrices with improved conditioning that will lead to improved process automation. The proposed gain-conditioning tool will be able to effectively combine information from two types of models: i) empirical transfer-function or step-response models identified from dynamic process data and ii) steady-state fundamental models used in real-time optimization (RTO) calculations. Predictions from these models contain errors (due to parameter uncertainties, disturbances and structural imperfections), but RTO models ensure that material and energy balances are obeyed, providing key insights into the gain-conditioning problem. An alternative version of the algorithm will also be developed to aid gain conditioning when RTO models are not available. Representative case studies will be developed and used to test whether the proposed methods help to prevent undesirable control actions when there is mismatch between model predictions and the true process behaviour.

目的是开发和测试一种新的软件工具，该工具将减少高级控制工程师在炼油厂和化学工厂实施线性模型预测性控制应用时所需的时间和精力。 当前，高技能的控制工程师正在进行耗时的增益调节调查，然后进行手动调整以在线线性编程（LP）和动态矩阵控制（DMC）计算中使用的过程增益矩阵。这些研究用于检测当多个操纵变量（MV）（例如流速和加热器设置）调整以实现安全且经济的过程操作时，可能发生的潜在不良相互作用。拟议的工具将帮助帝国石油和其他公司的工程师通过改进的调理产生矩阵，从而改善过程自动化。所提出的增益条件工具将能够有效地结合两种模型的信息：i）从动态过程数据和ii）实时优化（RTO）计算中使用的稳态基本模型确定的经验转移函数或阶梯响应模型。 这些模型的预测包含错误（由于参数不确定性，干扰和结构缺陷），但是RTO模型确保遵守材料和能量平衡，从而为增益条件问题提供了关键的见解。 当RTO模型不可用时，还将开发算法的替代版本以帮助增益。将开发代表性的案例研究并用于测试当模型预测与真实过程行为之间存在不匹配时，所提出的方法是否有助于防止不良控制动作。