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: 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=697517
• 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.

通过确定最佳操作条件（优化），可以实现流程工厂的卓越运营 操作）并控制其实时操作以保持这些最佳条件。这样的控制和 优化需要准确的过程模型。 自 20 世纪 70 年代以来，出现了两个独立的模型开发流：（i）源自 第一原则，以及（ii）通过在植物数据中开发的方法确定的经验模型 自动控制社区。前者通常需要付出巨大的努力来推导第一原理 方程以及构建求解方程的有效算法。后者已经沿着这条路前进了 从工厂运行数据中识别经验模型。尽管已经取得了重大进展 制作，并且有许多非常成功地使用这两种模型进行优化和控制的实例， 分别还有很多改进的机会。例如，模型的识别 具有较大时间延迟的过程 在过去的十年中，人工智能语音方法取得了重大进展 识别、图像识别和分类、手写识别等。这些进步的基础 是由多层组成的深度神经网络。研究发现特定的神经网络结构 最擅长为特定类型的应用程序创建模型。这项研究建议非常准确地识别 通过为特定类型的流程开发特定的模型结构，从操作数据中建立模型。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。模型预测两者 将开发稳态和动态行为，特别关注具有大的模型 时间延迟（例如乙烷/乙烯分离器）。为特定的模型开发了“标准形式” 设备将使此类模型能够轻松调整以代表特定设备并重复使用。