Process data analytics

流程数据分析

• 批准号: RGPIN-2017-04012
• 负责人: Shah, Sirish
• 金额: $ 3.42万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689554
• 关键词: Process; data; analytics

Objectives***The fusion of information from disparate sources of data is the key step in devising strategies for the foundation of a smart analytics platform. In the context of application of analytics in the process industry, this grant proposal is to develop a theoretical framework and a tool for seamless integration of information from process and alarm databases complimented with process connectivity information. ***The discovery of information from such diverse and complex data sources can be subsequently used for process and performance monitoring including alarm rationalization, root cause diagnosis of process faults, HAZard and OPerability (HAZOP) analysis, safe and optimal process operation. Such multivariate process data analytics involves information extraction from routine process data, that is typically non-categorical (as in numerical process data from sensors), plus categorical (or non-numerical or qualitative and binary) data from Alarm and Event (A&E) logs combined with process connectivity or topology information that can be inferred from the data through causality analysis or as obtained from piping and instrument diagrams of a process. The later refers to the capture of material flow streams in process units as well information flow-paths in the process due to control loops. ***Novelty***Highly interconnected process plants are now common and the analysis of root causes of process abnormality including predictive risk analysis is non-trivial. The thrust of this proposal is to develop a theoretical framework for extracting information and knowledge from archived process data using statistical inference schemes and integrating and validating such models with alarm data and process connectivity information. The unique aspect of this proposal is the inclusion of data-based process connectivity information for process monitoring and thus represents a major paradigm shift in process data analytics. Such a methodology would serve as an enabling tool for predictive and pro-active process asset maintenance and safe and optimal process operation.***Expected significance***There is currently an explosion of applications of analytics in diverse areas (e.g. engineering, medicine, etc). In the same vein the volume of data currently archived by the process industry is massive (BIG Data) and the key aim of this proposal is to find value in this data and use this on-line for safe and optimal process operation.***The socio-economic significance of this proposal will be pro-active, as opposed to reactive, management combined with highly productive and energy efficient process operation of plants that dot the Canadian landscape including pulp&paper, petro-chemical, food processing , power generation, mineral processing and oil and gas exploration. An equally important aspect of this project is the education and training of manpower with statistical data mining skills that are in high demand.********

目标*融合来自不同数据源的信息是制定智能分析平台基础战略的关键一步。在流程工业应用分析的背景下，这项赠款提案旨在开发一个理论框架和工具，用于无缝集成来自流程和警报数据库的信息，并补充流程连接信息。*从如此多样和复杂的数据源中发现的信息随后可用于过程和性能监控，包括警报合理化、过程故障的根本原因诊断、危险和可操作性(HAZOP)分析、安全和最佳过程操作。这种多变量过程数据分析涉及从常规过程数据中提取信息，这些数据通常是非分类的(如来自传感器的数值过程数据)，加上来自警报和事件(A&E)日志的分类(或非数字或定性和二进制)数据，该分类数据与过程连通性或拓扑信息相结合，所述过程连通性或拓扑信息可以通过因果分析从数据中推断或从过程的管道和仪表图中获得。后者指的是捕获过程单元中的物流以及由于控制循环而导致的过程中的信息流路径。*新颖性*高度相互关联的加工厂现在很常见，对过程异常的根本原因的分析，包括预测风险分析，不是微不足道的。这项建议的主旨是开发一个理论框架，用于使用统计推理方案从存档的过程数据中提取信息和知识，并将这些模型与警报数据和过程连通性信息相结合并进行验证。这一提议的独特之处在于纳入了基于数据的流程连通性信息，用于流程监测，从而代表了流程数据分析的重大范式转变。这样的方法将成为预测性和主动式过程资产维护以及安全和最佳过程操作的使能工具。*预期意义*目前分析在不同领域(如工程、医学等)的应用呈爆炸式增长。同样，目前流程工业存档的数据量是海量的(大数据)，这项建议的主要目的是从这些数据中发现价值，并将其用于安全和最佳的过程操作。*这项建议的社会经济意义将是积极的，而不是被动的管理，以及点缀在加拿大景观中的工厂的高效和节能的过程操作，包括纸浆和造纸、石油化工、食品加工、发电、选矿和石油和天然气勘探。该项目的一个同样重要的方面是对具有统计数据挖掘技能的人力进行教育和培训，这些人才的需求量很大。