Subproject SP7: Self-learning control of the catalytic conversion of olefins to α-amino acids and β-amino alcohols

该提案是FOR5538研究单位的一部分。在这个子项目中，我们的目标是开发用于在线优化烯烃催化转化为α-氨基酸和β-氨基醇的自学习控制系统。这个子项目是对SP6中进行的综合分子、材料和工艺设计的补充。它通过在单个批次和/或批次之间的重复在线优化来解决工厂模型失配和不可预见的干扰，以使用可用的测量信息和所谓的混合数学模型自动重新调整操作变量。混合建模将在其他子项目中获得的物理化学知识与来自机器学习的数据驱动方法相结合。这一方法的基本步骤包括：(1)单个工艺步骤的混合数学模型，(2)适用于在线模型调整的方法，(3)有效的在线优化策略，(4)将上述方法融入自学习控制概念，(5)系统地对所开发的方法进行计算机测试，以及(6)与本研究单位涉及的相应分项目合作，对其进行实验验证。除了非线性模型，还将考虑一族局部线性化的过程描述，并将其与非线性方法进行比较。为了在存在不确定性的情况下安全地满足迭代优化过程中的关键操作约束，提出了一种在下层直接控制关键变量的递阶方法。第一个资助期的重点将是将α-酮羧酸转化为α-氨基酸，特别是高苯丙氨酸和α-羟基酮，转化为β-氨基醇，特别是高苯丙氨醇，如第三阶段所研究的那样，然后是用于催化剂回收和溶剂分离的膜分离，如第四阶段所研究的那样。由于这一分项目的方法还依赖于将在SP3中产生的先验知识，因此在启动阶段，将首先开发酶反应结晶过程的方法，并对模型系统进行测试，该模型系统已在本研究单位的筹备工作中进行研究。第二个资助期的可能延长包括将进一步的反应步骤纳入总体自学控制战略，特别强调“全局性”方面，即必须以何种方式控制哪些步骤才能实现总体进程目标。此外，我们的目标是将SP6中的设计方法与本子项目中考虑的控制方法相结合。

This proposal is part of the research unit FOR5538. In this subproject we aim at the development of self-learning control systems for online optimization of catalytic conversions of olefins to α-amino acids and β-amino alcohols. This subproject complements the integrated molecular, material and process design performed in SP6. It accounts for plant-model mismatch and unforeseen disturbances by repetitive online optimization on the single batch and/or the batch-to-batch level to automatically re-adjust the operational variables using available measurement information and so-called hybrid mathematical models. Hybrid modeling combines physical chemical knowledge obtained in the other subprojects with data-driven approaches from machine learning. Essential steps of this approach comprise the development of: (i) hybrid mathematical models of the individual process steps, (ii) suitable methods for online model adaption, (iii) efficient strategies for online optimization, (iv) the integration of the aforementioned methods into self-learning control concepts (v) systematic in silico testing of the developed methods, and finally (vi) their experimental validation in cooperation with the corresponding subprojects involved in this research unit. Besides nonlinear models also families of locally linearized process descriptions will be considered and compared to the nonlinear approach. To safely satisfy crucial operational constraints during the iterative optimization in the presence of uncertainties, a hierarchical approach with direct control of critical variables on the lower level is suggested. Focus in the first funding period will be on the enzymatic conversion of α-keto carboxylic acids to α-amino acids, especially homophenylalanine, and α-hydroxy ketones to β-amino alcohols, especially homophenylalaninol with integrated product crystallization as studied in SP3, and afterwards on membrane separation for catalyst recycle and solvent separation, as studied in SP4. Since the approach of this subproject relies also on a priori knowledge to be generated in SP3, during the startup phase, the methodology for the enzymatic reactive crystallization process will be first developed and tested for a model system which has been studied in the preparatory work of this research unit. Possible extensions for a second funding period comprise the integration of further reaction steps into an overall self-learning control strategy with special emphasis on ‘plantwide’ aspects, i.e. which steps have to be controlled in which way to achieve the overall process objectives. Further, we aim at an integration of the design approach in SP6 with the control approach considered in this subproject.