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

过程优化是调整过程的输入和参数，以提高性能，同时满足某些限制的学科。这是过程控制的一个分支，它是确保过程的预期操作得以实现的学科，是化学工程的一个分支。工艺优化用于工艺的设计阶段，其中某些参数，如流量或反应器体积进行修改，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。化学过程通常是非常复杂的，即使使用了为这些过程开发模型的最佳实践，它们通常也不能完全令人满意地准确预测真实过程。这就是实现实时优化（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，以使它们达到真正的最佳状态。这个最优被定义为成本函数的最小值，服从于满足约束函数。这些函数依赖于流程输入和模型参数。RTO有三种主要方法：第一种是模型参数自适应，其中模型的某些参数可以迭代调整，以更好地适应工厂，但是这种技术不能正确地修改工厂，以达到真正的最优，因为存在结构工厂-模型不匹配。第二种技术是直接输入自适应，即以反馈控制方式直接修改输入。这种方法不需要迭代模型再优化，而是使用模型来设计控制器。第三种方法是修正自适应（MA），它在成本和约束函数中加入线性项，以满足真实过程的梯度和值。该技术的主要困难在于估计植物的梯度，但是如果做得足够好，MA将在收敛后达到植物的最佳点（受到某些模型充分性条件的限制，这些条件比模型参数自适应严格得多）。由于这些原因，MA将作为我提议的研究的一部分向前推进和扩展。最近的MA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，从而提高了收敛到最优点的时间。另一个进步是在框架上优化了只有开环模型可用的闭环问题。我打算将这两个框架合并为一个框架。接下来，我将研究已经在流程设计中实现，但尚未在RTO上下文中实现的多模型技术的使用。最后，我将通过研究通过实现进化算法对模型种群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.