NEURAL NETWORKS FOR PROCESS FAULT DIAGNOSIS AND SAFETY

用于过程故障诊断和安全的神经网络

• 批准号: 2277594
• 负责人: VENKAT VENKATASUBRAMANIAN
• 金额: $ 8.29万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-01-01 至 1994-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2277594
• 关键词: artificial intelligence; chemicals; industry; injury prevention; occupational hazard; occupational health /safety

The major goal of this proposal is to research and demonstrate a new approach based on the emerging technology of Neural Networks in the discipline of 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. 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. In this project, we propose a novel methodology using Neural Networks that address the important issues which are central to the development of knowledge-based systems for process fault diagnosis and hazard control. Our methodology for developing knowledge-based systems rests on the important characteristics of neural networks, namely, their ability to automatically classify and learn associations between input and output data and to be able to handle noisy data. We have already successfully tested our approach on a prototypical case study based on real-plant data, that of fluidized catalytic cracking unit (FCCU) in operation in an Exxon refinery. We plan to investigate this approach further by experimenting with larger prototypes of chemical process plants with more realistic, complex, models for the various units such as reactors, heat exchangers, distillation columns etc. We also plan to explore the robustness of such systems for incomplete and uncertain data.

该提案的主要目标是研究并证明一个新的 基于神经网络的新兴技术的方法 人工智能（AI）的纪律 化学过程危害检测，预防和控制系统。 这样的系统对于改善职业非常重要 由于现代过程的复杂性，化学植物的安全 植物。 拟议的项目旨在预防和控制 如此频繁的，日常的行业意外事件。 过去的 故障诊断系统中的方法未正确地包括 人类专家的推理策略和经验，因此不是 足够有效，正确的故障射击。 在这个项目中，我们 提出了一种使用神经网络来解决的新方法 重要问题，这对于发展基于知识的发展至关重要 过程故障诊断和危害控制的系统。 我们的方法论 用于开发基于知识的系统取决于重要的 神经网络的特征，即它们的能力 自动对输入和输出之间的关联进行分类和学习 数据并能够处理嘈杂的数据。 我们已经成功 测试了我们基于房地产植物的原型案例研究的方法 数据，在运行中运行的流化催化开裂单元（FCCU） 埃克森美食炼厂。 我们计划通过 尝试使用较大的化学加工厂原型 更现实，复杂的模型，例如反应堆等各个单元， 热交换器，蒸馏柱等 此类系统的鲁棒性，用于不完整和不确定的数据。