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工厂实施线性模型预测控制应用时所需的时间和精力。 目前，高技能的控制工程师进行耗时的增益调节研究，然后对在线线性规划 (LP) 和动态矩阵控制 (DMC) 计算中使用的过程增益矩阵进行手动调整。这些研究用于检测在调整多个操纵变量（MV）（例如流量和加热器设置）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公司和其他公司的工程师生成具有改进调节的增益矩阵，从而提高过程自动化。所提出的增益调节工具将能够有效地结合来自两种类型模型的信息：i）从动态过程数据中识别的经验传递函数或阶跃响应模型，以及 ii）用于实时优化（RTO）计算的稳态基本模型。 这些模型的预测存在误差（由于参数不确定性、干扰和结构缺陷），但 RTO 模型可确保遵守物质和能量平衡，从而为增益调节问题提供重要见解。 还将开发该算法的替代版本，以在 RTO 模型不可用时帮助进行增益调节。将开发并使用代表性案例研究来测试所提出的方法是否有助于防止当模型预测与真实过程行为不匹配时出现不良控制行为。