Our research focus
The International Max Planck Research School for Systems and Process Engineering for a Sustainable Chemical Production focuses on cutting-edge research to develop intelligent, resource-efficient, and sustainable process systems. By integrating engineering fundamentals, molecular insights, and computational methods, the program aims to address key challenges in chemical production and energy efficiency.
We clustered our research into three areas: process level, molecular level and computational mathematics & algorithms.
Process Level
In the process level (PL), the aim is to develop intelligent process systems by creating resource-efficient steps for decomposing raw materials and synthesizing products, supported by data from the molecular level. The second task is to enhance material and energy integration in process systems, focusing on efficiency, environmental compatibility, and safety. This involves rational design and intelligent operation strategies, aided by predictive models and process knowledge bases. With the help of computational methods and algorithms we develop hybrid models for system synthesis, analysis, and control. Data is collected from miniaturized production plants.
Key areas include:
- Mechanical, mechano-chemical, and thermal processing methods, along with particle sorting for complex waste mixtures.
- Solvent-based depolymerization and selective pyrolysis processes for mixed plastics and related separation processes.
- Electro(chemical) (Re-)synthesis processes for new chemicals, microbial production of biopolymers.
- Sustainability analysis of carbon metabolism in the chemical industry.
Research groups with a main focus on PL:
Molecular Level
Research at the molecular level (ML) focuses on the chemical and physical principles of polymer degradation and depolymerization, recycling plastic waste into valuable chemical building blocks. We provide essential data and models for designing and operating processes at the process level. We identify catalyst-solvent systems for new catalytic pathways in polymer decomposition and synthesis, supported by advanced measurement techniques, chemical analytics, and computer-aided molecular design (CAMD). With computational methods and algorithms (CMA), machine learning aids in identifying reaction network structures and building hybrid models for molecular properties.
Focus areas include:
- Catalyst-solvent systems for polymer decomposition and (re-)synthesis.
- Prediction of thermodynamic properties.
- Molecular materials for CO₂ capture and energy storage.
- Computational design of novel depolymerization catalysts.
Research groups with a main focus on ML:
Computational Methods & Algorithms
The future of chemical production is complex, but digitalization and machine learning can help manage it. The computational methods and algorithms area (CMA) creates virtual process models (VPM) and develops mathematical and computational methods (MCM) to tackle complexity. Process level research uses VPM to optimize processes and ensure stability, involving complex nonlinear equations. Surrogate models are needed for real-time tasks. Molecular level research uses VPM and MCM to discover new reactions and design molecules, facing vast design possibilities.
Research includes:
- Uncertain multi-objective mixed-integer nonlinear optimization and optimal control of recurrent processes.
- Efficient simulation of particle-laden flows and learning hybrid models from data.
- Model and complexity reduction.
The program synergizes expertise from Max Planck researchers and the Otto von Guericke University (OVGU) faculty, fostering interdisciplinary approaches to process and molecular engineering. With a focus on sustainability, IMPRS SysProSus empowers future researchers to pioneer innovations in process design and optimization.
