Coordination Funds

化学工程（CE）正处于一个十字路口。在世界范围内，化学工业占能源利用总量的10%，几乎完全依赖化石能源。作为世界第三大化学品供应国，德国的化学工业向可再生能源和原料供应转型至关重要。可再生资源在时间和空间上波动，需要动态运行和灵活电厂设计的新范式。同时，化工行业需要不断优化工厂运营，提高工厂可用性，缩短产品上市时间，以确保竞争力。行政长官目前没有能力独自推动这一根本变革。CE深深植根于物理和化学，并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尺度。实验在识别、校准或验证工艺设计和操作模型方面起着重要作用。然而，开发模型和合适的数学方法是昂贵的，许多现象不能用可处理的模型完全描述。为了应对化工生产的转型，我们设想机器学习（ML）作为新兴领域与基于物理和化学的广泛方法的CE之间的密切合作。ML在处理异构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和自动驾驶等应用显示出令人印象深刻的结果，凸显了机器学习的潜力。到目前为止，机器学习应用主要集中在数据分析和替代现有的物理化学模型。ML和CE的跨学科联合研究具有突破性成果的潜力。CE在应用数学和计算机科学方面有着良好的工作记录，并共同开发了适用性远远超出CE的方法，例如偏微分方程（PDE），微分代数方程（DAE）和确定性（全局）优化。我们确定了该优先计划（PP）的六个合作研究领域，这些领域为机器学习开辟了新的方法，为机器学习制定了新的问题类型，并共同在机器学习和机器学习方面取得了进展。这些领域是#1最佳决策，#2在ML模型中引入/执行物理定律，#3数据的异质性，#4信息和知识表示，#5 ML应用程序的安全性和信任，#6创造力。在这些领域/主题的框架下，PP将有化学工程和机器学习小组之间的合作项目，这些项目承诺在工艺合成（特别是原料转化）、工艺灵活性、材料选择、替代品生成和发现隐藏信息方面取得进展。因此，这种PP可以为德国化学工业的可持续发展做出巨大贡献。

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.