Crystallisation optimisation and control using surrogate modelling and adaptive model predictive control

使用代理建模和自适应模型预测控制进行结晶优化和控制

• 批准号: 2746484
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2746484
• 关键词: Crystallisation; optimisation; control; using; surrogate

Crystallisation is inherently a challenging process to control as a result of a number of interacting parameters including nucleation, agglomeration, breakage, hydrodynamics, and so on. Due to molecular nature of the underlying mechanisms, robust modelling, control and optimisation is not straightforward. This can result in inconsistent batches, higher downstream processing costs, and difficulties controlling quality long-term. Population Balance Models (PBMs) and Model Predictive Control (MPC) exist for these systems but have limitations. PBMs can be computationally complex and as such real-time control is difficult. Due to the lack of detailed understanding of the full scope of interactions in crystallisation, PBMs will be of reduced complexity than the real system. This project proposes the use of surrogate models such as physics informed neural networks and reinforcement learning, utilising their nonlinear and adaptive predictive capabilities, to capture the full scope of behaviour contained within crystallisation system data. By combining PBMs and experimental data in the training of these surrogate models, it is proposed that a robust control and optimisation method can be developed that can be applied to a range of crystallisation systems. The models can enable real-time control and optimisation of a desired crystal size distribution, whilst balancing other constraints and objectives through multi-objective optimisation. The final goal is a control system that realises an established digital twin, with limited experimental data required.

结晶是一个具有挑战性的控制过程，因为许多相互作用的参数，包括成核，团聚，破碎，流体力学等。由于潜在机制的分子性质，鲁棒建模，控制和优化并不简单。这可能导致批次不一致，下游加工成本更高，并且难以长期控制质量。人口平衡模型（PBM）和模型预测控制（MPC）存在这些系统，但有局限性。PBM在计算上可能是复杂的，因此实时控制是困难的。由于缺乏对结晶中相互作用的全面详细了解，PBM将比真实的系统具有降低的复杂性。该项目提出使用替代模型，如物理学通知神经网络和强化学习，利用其非线性和自适应预测能力，以捕获结晶系统数据中包含的全部行为范围。通过结合PBM和实验数据在这些代理模型的训练，它提出了一个强大的控制和优化方法，可以开发，可以应用到一系列的结晶系统。该模型可以实现所需晶体尺寸分布的实时控制和优化，同时通过多目标优化来平衡其他约束和目标。最终的目标是一个控制系统，实现一个既定的数字孪生，所需的实验数据有限。