Extension of Real-Time Optimisation Using a MultiModel Approach in Conjunction with Evolutionary Algorithms as to Adapt the Structure of These Models

流程优化是调整流程的输入和参数，以便在满足特定约束的同时提高性能的学科。这是过程控制的一个分支，它是确保一个过程达到预期操作的学科，它是化学工程的一个分支。工艺优化用于工艺的设计阶段，在此阶段，某些参数，如流量或反应器体积被修改，以达到最佳操作条件。为了进行这种优化，需要一个过程模型。化工过程通常非常复杂，即使有为这些过程开发模型的最佳实践，它们也往往不能完全满足对真实过程的准确预测。这就是实施实时优化(RTO)的地方。这是一种技术，它使用过程的现场测量以及不准确的模型来修改过程的输入，以使它们接近真正的最优。该最优被定义为满足约束函数的成本函数的最小值。这些函数取决于过程输入和模型参数。RTO主要有3种方法：第一种是模型参数自适应，可以迭代地调整模型的某些参数以更好地适应对象，但这种技术不能在存在结构上的对象-模型不匹配的情况下正确地修改对象以达到真正的最优。第二种技术是直接输入自适应，其中输入以反馈控制启发的方式被直接修改。该方法不需要迭代模型重新优化，而是利用模型来设计控制器。第三种方法是修正自适应(MA)，它在代价和约束函数中加入一个线性项，以满足真实过程的梯度和值。这项技术的主要困难是估计对象的梯度，然而，如果做得足够好，MA将在收敛时达到对象的最佳点(受某些模型充分性条件的约束，这些条件比模型参数自适应要严格得多)。因此，MA将作为我建议的研究的一部分而被推进和推广。MA的最新进展发展了利用过程的暂态数据并在达到稳态之前重新优化的框架，这改进了收敛到最优点的时间。另一项进步是在只有开环模型可用的优化闭环系统问题的框架方面。我打算将这两个框架合并为一个框架。接下来，我将研究多模型技术的使用，该技术已经在流程设计中实现，但尚未在RTO环境中实现。最后，我将通过研究通过实施进化算法对模型群体进行结构修改的可能性来结束我的研究。

Process optimisation is the discipline of adjusting the inputs and parameters of a process as to improve the performance whilst meeting certain constraints. This is a sub-branch of process control, which is the discipline of ensuring that the desired operation of a process is achieved, which is a branch of chemical engineering. Process optimisation is used in the design stage of a process, where certain parameters, such as flowrates or reactor volumes are modified as to reach the optimum operating conditions. In order to carry out this optimisation, a model of the process is required. Chemical processes are often very complex and even with the best practices of developing models for these processes they are often not fully satisfactory for the accurate prediction of the true processes.This is where real-time optimisation (RTO) is implemented. This is a technique which uses in situ measurements of the process, along with the inaccurate model, to modify the inputs to the processes as to drive them towards the true optimum. This optimum is defined as the minimum of the cost function, subject to satisfying constraint functions. These functions depend on the process inputs and the model parameters. There are 3 main methods of RTO: The first being model parameter adaption, where certain parameters of the model can be iteratively adjusted as to better fit the plant, however this technique cannot correctly modify the plant as to reach the true optimum in the presence of a structural plant-model mismatch. The second technique is direct input adaption, where the inputs are modified directly in a feed-back control-inspired fashion. This method does not require an iterative model re-optimisation, rather it uses the model to design the controller. The third method is modifier adaption (MA), which adds a linear term to the cost and constraint function as to meet the gradients and values of the true process. This technique's main difficulty is in estimating the gradients of the plant, however if done well enough, MA will reach, upon convergence, the optimum point of the plant (subject to certain model-adequacy conditions which are much less stringent than model parameter adaption).For these reasons MA will be taken forward and extended as part of my proposed research. Recent advances in MA have developed frameworks to use transient data of the processes and reoptimize before steady state is achieved, which improves the convergence time to the optimal point. Another advancement is in frameworks to optimise closed-loop problems where only open-loop models are available. I intend to combine these two frameworks into a single framework. Next, I will investigate the use of a multiple model technique that has already been implemented in process design, but has yet to be implemented in the context of RTO. Finally, I will conclude my research by investigating the possibility of structural modifications of the population of models via the implementation of an evolutionary algorithm.