ARTIFICIAL INTELLIGENCE IN PROCESS PLANT SAFETY

人工智能在流程工厂安全中的应用

• 批准号: 3420588
• 负责人: VENKAT VENKATASUBRAMANIAN
• 金额: $ 8.19万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-08-15 至 1990-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3420588
• 关键词: artificial intelligence; computer simulation; computer system design /evaluation; disease /disorder prevention /control; industry; occupational disease /disorder; occupational hazard; occupational psychology; safety equipment

The major goal of this proposal is to research and demonstrate new approaches based on Artificial Intelligence (AI) towards the design of chemical process hazard detection, prevention, and control systems. Such systems are extremely important for improving the occupational safety of chemical plants owing to the complexity of modern process plants. Industrial statistics show that even though major catastrophies and disasters from chemical plant failures are infrequent, minor accidents are very common, occurring on a day to day basis, resulting in many occupational injuries and illnesses, costing the society billions of dollars every year. The proposed project is aimed at the prevention and control of such frequent, day to day, accidental events in the industry. Past approaches in fault diagnostic systems did not properly include the human expert's reasoning strategies and experience and hence were not adequate in efficient and correct trouble- shooting. We propose to improve this status by designing systems that would have better modeling of the problem-solving process through an appropriate representation of the domain knowledge through the use of causal modeling and reasoning from first principles, similar to human experts, by exploiting some of the recent advances in Artificial Intelligence. We propose a methodology that aids the development of expert systems which are process-independent, transparent in their reasoning, resilient under unforeseen fault combinations, and capable of diagnosing a wide diversity of faults. The domain knowledge of the system is based on a library of fault and causal models of process equipments as well as on the physical interconnections between equipment units and causal relationships among process state variables. The inference strategy uses model-based reasoning for analyzing the plant behavior. We describe a prototype expert system, called MODEX, based on our methodology. The system has performed successfully on test cases of prototypical chemical process plants and looks promising. However, before a successful transfer of this methodology to the industry can be initiated a number of research issues outlined in the proposal need to be resolved by experimenting with larger prototypical chemical plants.

该提案的主要目标是研究和证明 基于人工智能（AI）的新方法 化学过程的设计危害检测，预防和 控制系统。 这样的系统对于 改善化学植物的职业安全 现代过程植物的复杂性。 工业统计数据显示 即使化学物质发生的主要灾难和灾难 植物失败很少，小事故很常见， 每天发生，导致许多职业 伤害和疾病，使社会造成数十亿美元 年。 拟议的项目旨在预防和控制 在如此频繁的日常活动中，行业中的意外事件。 故障诊断系统的过去方法无法正确 包括人类专家的推理策略和经验 因此，没有足够的有效和正确的麻烦 - 射击。 我们建议通过设计系统来提高这种状态 那将对解决问题的过程进行更好的建模 通过适当代表领域知识 通过使用因果建模和首先推理 与人类专家类似的原则，通过利用一些原则 人工智能的最新进展。 我们提出了一个 有助于开发专家系统的方法论 是与过程无关的，透明的推理，弹性的 在无法预见的故障组合下，能够诊断 多种故障。 系统的领域知识是 基于故障库和过程的因果模型 设备以及物理互连 设备单位和过程状态之间的因果关系 变量。 推理策略将基于模型的推理用于 分析植物行为。 我们描述了原型专家 基于我们的方法，称为Modex的系统。 系统 在原型化学的测试用例上成功执行 处理植物，看起来很有希望。 但是，在成功之前 可以将这种方法转移到行业 提案中概述的研究问题数量必须为 通过尝试较大的原型化学化学来解决 植物。