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 **）并控制其实时操作以保持这些最佳条件，可实现过程工厂的卓越操作。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，有两个独立的模型开发流：（i）严格的模型来自 ** 的第一原则，和（ii）经验模型确定的工厂数据通过在 ** 自动控制社区开发的方法。前者通常需要大量的努力，推导第一原理方程和构造求解它们的有效算法。后者已经沿着从工厂运行数据中识别经验模型的路径 ** 前进。尽管已经取得了显著的进步，并且有许多非常成功地分别使用这两种类型的模型进行优化和控制的例子，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重大进展。已经发现，特定的神经网络结构 ** 最适合为特定类型的应用程序创建模型。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 ** 联合收割机一些第一原理方程（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，特别注意具有大 ** 时间延迟的模型（例如乙烷/乙烯分离器）。为具体 ** 设备制定一个“标准形式”的模型，将使这些模型能够随时调整，以代表具体设备，并重新使用。

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.