Probabilistic predictive warning system for process operations

过程操作的概率预测预警系统

• 批准号: RGPIN-2019-04122
• 负责人: Ahmed, Salim
• 金额: $ 2.04万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714640
• 关键词: Probabilistic; predictive; warning; system; process

This program aims to develop a probabilistic predictive approach to the design and management of early warning systems used in process industries. The special features and novelty of the proposed approach is in the use of probabilistic models for alarm annunciation and the use of risk in every stage of warning design, namely, allocation, annunciation, prioritization and management. In addition, predictions will be used for issuing warnings. Industries significantly benefit from using predictions for control. On the other hand, considerable resources are used for risk assessment. However, current alarm systems are not risk-based; use of predictions is also restricted. Research efforts are required to make plant alarm systems an effective tool in preventing accidents. We plan to develop a comprehensive early warning system based on quantified risk and prediction of events from plant data. The current plant alarm systems use a deterministic setting where abnormality is described without any reference to its likelihood. Our objective is to develop tools to describe unwanted events in terms of likelihood and perform diagnosis by considering a set of possible combinations of failures that explain the observations. Methodologies will be developed to quantify uncertainties with plant models which arise from assumptions on model structures and noises in process data. Uncertainties associated with risk models will also be developed. Correlations and dependencies among events as well as their consequences will be quantified and taken into account in the design of warning system. We also want to propose an alarm design philosophy to complement the existing standards in incorporating risk. A prototype alarm system will be developed that will be integrate-able with the existing plant alarm systems. The new system will be tested in pilot plants and industrial settings. Process industries are in dire need of an improved alarm system. Operators are often overwhelmed by alarms and are unable to perform their main responsibilities. Risk is a measure that is well understood and highly valued. Hence, the developed methodology is expected to find widespread utility in process industries. By developing technologies and tools that ensure smooth, accident-free operation of industrial plants, this program will contribute directly to the productivity of the industrial sector. Thus it will help to save billions of dollars in losses caused by abnormal situations in industries. Along with the development of tools and methods, a number of highly qualified personnel will be trained in this program. The HQP will be trained on developing the tools as well as its implementation to real plants; thus the HQP will be valuable assets for the concerned industry. Finally, the developed methods will be useful for risk assessment in general with applications outside process facilities, e.g. for health monitoring, economic activities and natural disasters.

该计划旨在开发一种概率预测方法来设计和管理流程工业中使用的早期预警系统。该方法的特点和创新之处在于，在警报设计的每个阶段，即分配、报告、优先次序和管理中，都使用了警报警报的概率模型和风险的使用。此外，预报还将用于发布警告。 各行各业从使用预测进行控制中受益匪浅。另一方面，相当多的资源被用于风险评估。然而，目前的警报系统不是基于风险的；预测的使用也受到限制。需要研究努力，使工厂警报系统成为预防事故的有效工具。 我们计划开发一个全面的预警系统，基于量化的风险和对工厂数据的事件预测。当前的工厂报警系统使用确定性设置，其中描述异常而不参考其可能性。我们的目标是开发工具，根据可能性描述不想要的事件，并通过考虑一组可能的故障组合来执行诊断，以解释观察到的情况。将开发方法来量化工厂模型中的不确定性，这些不确定性源于对模型结构的假设和过程数据中的噪声。还将开发与风险模型相关的不确定性。在警报系统的设计中，将量化事件之间的相关性和相关性及其后果，并将其考虑在内。我们还希望提出一种警报设计理念，以补充现有的标准，以纳入风险。将开发一个原型警报系统，该系统将与现有的工厂警报系统集成。新系统将在试点工厂和工业环境中进行测试。 流程工业迫切需要改进的警报系统。操作员经常被警报淹没，无法履行他们的主要责任。风险是一种被充分理解和高度重视的衡量标准。因此，开发的方法有望在流程工业中得到广泛应用。 通过开发确保工业装置平稳、无事故运行的技术和工具，该计划将直接促进工业部门的生产率。因此，这将有助于挽救行业异常情况造成的数十亿美元的损失。 随着工具和方法的发展，将在这个项目中培养一批高素质的人员。HQP将接受有关开发工具以及将其应用于实际工厂的培训；因此，HQP将成为相关行业的宝贵资产。最后，所开发的方法将有助于在流程设施之外进行一般风险评估，例如用于健康监测、经济活动和自然灾害。