Machine Learning for Explainable Roundtrip Polymer Reaction Engineering

用于可解释的往返聚合物反应工程的机器学习

• 批准号: 466601458
• 负责人: Professorin Dr. Sabine Beuermann
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/466601458?language=en
• 关键词: Machine; Learning; Explainable; Roundtrip; Polymer

Polymers are important materials in everybody’s daily life and in numerous technical applications. They have a wide range of properties that can be tailored by the type and the conditions of the production process, ideally by modeling of the polymerization process. The project ML-PRE aims to bridge the gap between state-of-the-art machine learning (ML) methods and their application in modeling and optimization in polymer reaction engineering (PRE). This will enable a novel approach referred to as roundtrip PRE, covering integrated polymerization process modeling and reverse engineering of the polymerization process. The reverse engineering aspect is particularly novel to the field. The overall approach aims at designing new sustainable production processes and developing polymers with better or even new properties.The baseline of the project is the modeling of polymerization processes using kinetic Monte Carlo (KMC) methods. An open-source KMC simulator will be used to generate data sets for training and testing the ML methods. The overall goal is broken down into the following scientific objectives of ML-PRE: (1) To create a coherent and validated suite of scalable ML-based models for polymerization modeling, facilitating fast and efficient simulation-supported learning of ML models, extending the KMC simulator for novel types of problems (e.g. acrylate polymerizations at high temperature, diffusion-controlled termination). (2) To create ML-based approaches for reverse engineering of polymerization processes with modeling and optimization capabilities. (3) To create ML-based models for learning controllers of semi-batch polymerization processes. (4) To create a general and transferable methodology for increasing the transparency of ML created in the project by means of suitable and validated explainability techniques. From the ML perspective, the main innovations are provided by the first and the fourth objective: Objective 1 is about creating a coherent suite of validated ML models with interfaces designed to support the flexibility to support the complex bi-directional workflows in roundtrip PRE. The second main innovation is that through Objective 4 above, we aim at a general and transferable methodology to bring about and maintain transparency and explainability of the ML methods created and validated for roundtrip PRE.Referring to the collaboration matrix of the SPP call we mainly address target area  #1 (optimal decision making); through the work on ML-supported simulation we also cover some aspects of target area #2 (introducing/enforcing physical laws in machine learning models). W.r.t. the collaboration matrix we expect and work towards that results for mechanistic models in the 1st column (Phenomena / Micro-scale) will be transferable to the areas  mechanistic models, experiments (real process), and optimization of the 3rd column (Flowsheet / Process) and vice versa.

聚合物是每个人日常生活和众多技术应用中的重要材料。它们具有广泛的特性，可以根据生产工艺的类型和条件进行定制，理想情况下可以通过聚合工艺的建模进行定制。ML-PRE项目旨在弥合最先进的机器学习（ML）方法及其在聚合物反应工程（PRE）建模和优化中的应用之间的差距。这将使一种新的方法称为往返PRE，涵盖集成的聚合过程建模和聚合过程的逆向工程。逆向工程方面对于本领域是特别新颖的。总体方法旨在设计新的可持续生产工艺，开发具有更好甚至新性能的聚合物。该项目的基线是使用动力学蒙特卡罗（KMC）方法对聚合过程进行建模。开源KMC模拟器将用于生成用于训练和测试ML方法的数据集。ML-PRE的总体目标分为以下科学目标：（1）创建一套一致且经过验证的可扩展ML模型，用于聚合建模，促进ML模型的快速有效模拟支持学习，扩展KMC模拟器以解决新型问题（例如，高温下的丙烯酸酯聚合，扩散控制终止）。(2)创建具有建模和优化功能的聚合过程逆向工程的ML方法。(3)为半间歇聚合过程的学习控制器建立基于ML的模型。(4)创建一个通用的和可转移的方法，通过合适的和验证的可解释性技术来增加项目中创建的ML的透明度。从机器学习的角度来看，主要的创新是由第一个和第四个目标提供的：目标1是关于创建一套一致的经验证的机器学习模型，其接口旨在支持灵活性，以支持往返PRE中复杂的双向工作流。 第二个主要创新是，通过上述目标4，我们旨在提供一种通用且可转移的方法，以实现并保持为往返PRE创建和验证的ML方法的透明度和可解释性。参考SPP调用的协作矩阵，我们主要解决目标领域#1（最优决策）;通过ML支持的仿真工作，我们还涵盖了目标领域#2的某些方面（在机器学习模型中引入/执行物理定律）。W.r.t.我们所期望合作矩阵以及为第一列（现象/微观尺度）中的机理模型的结果所做的工作将可转移到机理模型、实验（真实的过程）和第三列（流程图/过程）的优化领域，反之亦然。