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; 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（操作的优化**）并控制其实时操作以维持这些最佳条件，从而实现了过程植物的卓越运营。这种控制和**优化需要准确的过程模型。前者通常需要付出巨大的努力来得出第一个原则**方程和构建有效算法以解决它们。后者已经沿着从工厂操作数据中识别经验模型的路径**进行。尽管已经取得了重大进展，并且分别非常成功地使用两种模型进行优化和控制，但仍有许多改进的机会。例如，在过去的十年中，识别具有大时延迟的过程的模型**在人工智能方法的语音方法**识别，图像识别和分类，手写识别等方面取得了重大进展。这些进步的基础**是深层的神经网络，在许多层上构成了深层的神经网络。已经发现，特定的神经网络结构**最好是为特定类型的应用程序创建模型。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来确定非常准确的**模型。它将**将一些第一原理方程（例如质量和能量平衡）与深神经网络或通过自动控制场的识别方法开发的**模型相结合。将开发预测**稳态和动态行为的模型，特别注意具有较大时间延迟的模型（例如乙烷/乙烯分离器）。为特定**设备开发了模型的“标准形式”，将使此类模型容易调整以表示特定的设备并重新使用。