Coordination Funds

协调基金

• 批准号: 466461567
• 负责人: Professor Alexander Mitsos, Ph.D.
• 金额: --
• 依托单位: Lehrstuhl Systemverfahrenstechnik (SVT)
• 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/466461567?language=en
• 关键词: Coordination; Funds

Chemical Engineering (CE) is at a crossroad. Worldwide, the chemical industry has a 10% share of the total energy utilization and relies almost entirely on fossil sources. A transformation of the chemical industry to renewable energy and feedstock supply is of the utmost importance in Germany as the world's third largest chemical supplier. Renewable resources fluctuate in time and space, requiring dynamic operation and new paradigms for the design of flexible plants. Simultaneously, the chemical industry needs to continuously optimize their plant operation, increase plant availability, and shorten time-to-market to ensure competitiveness. CE is currently ill-equipped to facilitate this fundamental change by itself. CE is deeply rooted in physics and chemistry and combines models and simulation with experiments. Models cover scales from molecular to enterprise and the environment. Experiments play a major role to identify, calibrate, or validate models for process design and operation. However, developing models and suitable mathematical methods is expensive and many phenomena cannot be fully described by tractable models. To tackle the transformation of chemical production, we envision a close collaboration between Machine Learning (ML) as emerging field and CE with its wide set of methods based in physics and chemistry.ML has a great track record in working on heterogeneous and large data sets and performing creative tasks. Applications like AlphaGo and autonomous driving show impressive results highlighting ML's potential. So far, ML applications within CE focus mostly on data analytics and replacing existing physicochemical models by surrogates. Joint interdisciplinary research between ML and CE has the potential for breakthrough results. CE has a track record of working with applied mathematics and computer science and co-developing methods with applicability well beyond CE, e.g., in partial differential equations (PDE), differential algebraic equations (DAE), and deterministic (global) optimization.We identified six areas of collaborative research for this Priority Programme (PP), which open up new methods for CE, formulate new types of problems for ML, and jointly generate advances for methods in both ML and CE. These areas are #1 optimal decision making, #2 introducing / enforcing physical laws in ML models, #3 heterogeneity of data, #4 information and knowledge representation, #5 safety and trust in ML applications, and #6 creativity. Under the umbrella of these areas / topics, the PP will have collaborative projects between groups from chemical engineering and ML, which promise progress regarding process synthesis (especially regarding feedstock transformation), process flexibility, material selection, generation of alternatives, and uncovering hidden information. This PP can hence make a large contribution towards readying Germany's chemical industry for a sustainable future.

化学工程(CE)正处于十字路口。在世界范围内，化学工业占总能源利用的10%，几乎完全依赖化石能源。在作为世界第三大化学品供应国的德国，化学工业向可再生能源和原料供应的转变至关重要。可再生资源在时间和空间上波动，需要动态运行和灵活植物设计的新范式。同时，化学工业需要不断优化装置运行，提高装置利用率，缩短上市时间，以确保竞争力。目前，行政长官本身并不具备推动这项根本性转变的能力。CE深深植根于物理和化学，将模型和模拟与实验相结合。模型涵盖了从分子到企业和环境的范围。实验在确定、校准或验证工艺设计和操作的模型方面发挥着重要作用。然而，开发模型和合适的数学方法是昂贵的，而且许多现象不能用易处理的模型来完全描述。为了应对化工生产的转型，我们设想作为新兴领域的机器学习(ML)与CE之间的密切合作，以及其广泛的基于物理和化学的方法。ML在处理异质和大型数据集以及执行创造性任务方面有着良好的记录。AlphaGo和自动驾驶等应用程序显示了令人印象深刻的结果，突显了ML的潜力。到目前为止，CE中的ML应用程序主要关注数据分析和用代理替换现有的物理化学模型。ML和CE之间的联合跨学科研究具有取得突破性成果的潜力。CE在应用数学和计算机科学以及共同开发的方法方面有着远远超出CE的应用记录，例如在偏微分方程组(PDE)、微分代数方程(DAE)和确定性(全局)优化方面。我们为这个优先计划(PP)确定了六个合作研究领域，这些领域为CE开辟了新的方法，为ML制定了新类型的问题，并共同促进了ML和CE的方法进步。这些领域是#1最优决策、#2在ML模型中引入/实施物理定律、#3数据异构性、#4信息和知识表示、#5 ML应用程序的安全和信任、以及#6创造力。在这些领域/主题的保护伞下，PP将有来自化学工程和ML的小组之间的合作项目，这些项目承诺在过程合成(特别是关于原料转化)、过程灵活性、材料选择、替代品的生成和揭示隐藏的信息方面取得进展。因此，这一PP可以为德国化学工业为可持续的未来做好准备做出巨大贡献。