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

通过确定最佳操作条件（操作优化**）并控制其实时运行以保持这些最佳条件，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，有两种独立的模型开发流：(i)从**第一原理推导出的严格模型，以及（ii）通过**自动控制社区开发的方法从工厂数据确定的经验模型。前者通常需要在推导第一原理方程和构造求解它们的有效算法方面付出很大的努力。后者沿着从工厂运行数据中识别经验模型的路径**前进。尽管已经取得了重大进展，并且有许多实例非常成功地分别使用这两种类型的模型进行优化和控制，但仍有许多改进的机会。例如，识别具有大时间延迟的过程的模型在过去十年中，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重大进展。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创建模型。本研究建议通过为特定类型的过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。它将把一些第一性原理方程（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结合起来。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”将使这些模型易于调整以表示特定设备并可重复使用。

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.