Data driven hybrid model identification for control and optimisation of petrochemical and refining plants

用于石化和炼油厂控制和优化的数据驱动混合模型识别

• 批准号: 523634-2018
• 负责人: Mahalec, Vladimir
• 金额: $ 2.91万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=658436
• 关键词: Data; driven; hybrid; model; identification

Excellence in operation of process plants is attained by determining the best operating conditions (optimisation**of operation) and controlling their real-time operation to maintain these best conditions. Such control and**optimisation require accurate process models.**Since 1970s there have been two separate streams of model development: (i) rigorous models derived from**the first principles, and (ii) empirical models identified from plant data via methods developed in the**automatic control community. The former usually require a large effort in deriving the first principles**equations and construction of efficient algorithms for solving them. The latter have proceeded along the path**of identifying empirical models from the plant operating data. Even though significant advances have been**made, and there are many instances of very successful use of both types of models for optimisation and control,**respectively, there are still many opportunities for improvement. For instance, identification of models for**processes with large time delays**During the last decade there have been significant advances in artificial intelligence methods for speech**recognition, image recognition and classification, handwriting recognition etc. Foundation for these advances**are deep neural networks comprised on many layers. It has been found that specific neural network structures**are best at creating models for specific types of applications. This research proposes to identify very accurate**models from operating data by developing specific model structures for specific types of processes. It will**combine some first principles equations (e.g. mass and energy balances) with deep neural networks or with**models developed via identification methods from the automatic control field. Models predicting both**steady-state and dynamic behaviour will be developed, with particular attention devoted to models with large**time delays (e.g. ethane/ethylene splitter). Having developed a "standard form" of the model for specific**equipment will enable such models to be readily adjusted to represent specific equipment and be re-used.

通过确定最佳运行条件（运行优化**）并控制其实时运行以维持这些最佳条件，可以实现流程工厂的卓越运行。这种控制和**优化需要精确的过程模型。**自 20 世纪 70 年代以来，出现了两个独立的模型开发流程：(i) 源自**第一原理的严格模型，以及 (ii) 通过方法从工厂数据中识别的经验模型在**自动控制社区开发。前者通常需要付出巨大的努力来推导第一原理**方程并构建有效的算法来解决它们。后者沿着从工厂运行数据中识别经验模型的道路**前进。尽管已经取得了重大进展，并且有许多非常成功地使用这两种模型分别进行优化和控制的实例，但仍然有许多改进的机会。例如，识别**具有大时间延迟的过程的模型**在过去十年中，用于语音**识别、图像识别和分类、手写识别等的人工智能方法取得了重大进展。这些进步的基础**是由多层组成的深度神经网络。人们发现，特定的神经网络结构**最适合为特定类型的应用创建模型。这项研究建议通过为特定类型的流程开发特定的模型结构，从操作数据中识别非常准确的**模型。它将**将一些第一原理方程（例如质量和能量平衡）与深度神经网络或通过自动控制领域的识别方法开发的**模型相结合。将开发预测稳态和动态行为的模型，特别关注具有大**时间延迟的模型（例如乙烷/乙烯分离器）。为特定**设备开发出模型的“标准形式”将使此类模型能够轻松调整以代表特定设备并重复使用。