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: 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=687616
• 关键词: Data; driven; hybrid; model; identification

Excellence in operation of process plants is attained by determining the best operating conditions (optimisationof operation) and controlling their real-time operation to maintain these best conditions. Such control andoptimisation require accurate process models.Since 1970s there have been two separate streams of model development: (i) rigorous models derived fromthe first principles, and (ii) empirical models identified from plant data via methods developed in theautomatic control community. The former usually require a large effort in deriving the first principlesequations and construction of efficient algorithms for solving them. The latter have proceeded along the pathof identifying empirical models from the plant operating data. Even though significant advances have beenmade, 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 forprocesses with large time delaysDuring the last decade there have been significant advances in artificial intelligence methods for speechrecognition, image recognition and classification, handwriting recognition etc. Foundation for these advancesare deep neural networks comprised on many layers. It has been found that specific neural network structuresare best at creating models for specific types of applications. This research proposes to identify very accuratemodels from operating data by developing specific model structures for specific types of processes. It willcombine some first principles equations (e.g. mass and energy balances) with deep neural networks or withmodels developed via identification methods from the automatic control field. Models predicting bothsteady-state and dynamic behaviour will be developed, with particular attention devoted to models with largetime delays (e.g. ethane/ethylene splitter). Having developed a "standard form" of the model for specificequipment 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（优化运行）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：(i) 从第一原理导出的严格模型，以及 (ii) 通过自动控制界开发的方法从工厂数据中识别的经验模型。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，大时延过程的模型识别在过去的十年中，语音识别、图像识别和分类、手写识别等人工智能方法取得了显着的进步。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）。为特定设备开发模型的“标准形式”将使这些模型能够容易地调整以代表特定设备并被重复使用。