Research

Cluster

SmartProSys

We will need  #biomass for the transition to #NetZero. But does the public support the use of forest biomass for a #sustainable chemical industry? How can policy support this transformation?
The defossilization of the chemical industry requires renewable carbon sources, including forest resources. We analyze policy processes and different governance approaches regarding the sustainable #transformation of the chemical industry. We identify drivers and obstacles to innovation; we investigate policy instruments and design options to transforming the chemical industry’s carbon metabolism.

In a recent study, we examined the changes in the forest policy subsystem of the forest-based #bioeconomy that may result from the transformation of the chemical industry in Europe. The study is the result of an innovative collaboration between political scientists, environmental psychologists and chemical engineers at Otto-von-Guericke-Universität Magdeburg and Max Planck Institute for Dynamics of Complex Technical Systems as part of the SmartProSys Cluster of Excellence initiative.

The main findings are:

➡ The demand to use forest biomass to de-carbonize the chemical industry has implications for forest policy,
➡ New actors from the chemical sector are trying to gain access to the political subsystem, which could lead to new political conflicts with traditional actors in forest policy.
➡ New chemical uses for forest carbon are not yet sufficiently accepted by consumers, who have little knowledge of new innovative technologies for climate protection and are more likely to support forest protection than the use of forest resources for economic benefits.

The study is currently under review (status: Revise & Resubmit) and can be found here as PrePrint: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4904040

Dr. Katrin Beer | Prof. Michael Böcher | Dr. Caroline Ganzer | Dr. Anke Blöbaum | Lukas Engel | Theresa de Paula Sieverding | Prof. Kai Sundmacher | Prof. Ellen Matthies

Picture: Symbolic photo of forestry, FotoRieth on Pixabay // Overview figure: c Dr. Katrin Beer

 Dynamical systems often exhibit a variety of temporal scales. In a recent study we examined the slow degradation of H₂ #electrolyzers under highly oscillatory load.

#Modelling and #simulation is not feasible for long-term predictions spanning months if the fastest chemical scales are also taken into account. Through our #mathematical #analysis, we have been able to separate the scales and achieve accelerations of over 1:1000. This not only reduces the time required for simulations, it also allows to solve optimization and control problems.

Our approach extends beyond the specific challenge of green H₂ production, offering a general tool with broad applicability. As part of the SmartProSys Cluster of #Excellence initiative we are tackling related dynamical problems describing complex chemical conversion processes having the transformation to green carbon-based processes in mind.

Original publications:

➡ [1] https://doi.org/10.48550/arXiv.2410.06863

➡ [2] https://doi.org/10.1137/19M1258396

Dayron Chang Dominguez I An Phuc Dam I Prof. Thomas Richter I Prof. Kai Kai Sundmacher I Shaun M. Alia I Stefan Frei

Otto-von-Guericke-Universität Magdeburg I Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg I National Renewable Energy Laboratory, Colorado, United States of America

#GreenCarbonbasedProcesses #GreenH2Production #ClusterofExcellence

Effective research data management (RDM) is increasingly becoming a central element of all research collaborations due to the rapidly increasing amounts of available and generated data. RDM varies across disciplines, each with unique needs and tools.

Here is a snapshot of how we approach #RDM across our four research areas at the SmartProSys research initiative:

➡ Process Labs (PL): LabFolder ELN (Labforward) for streamlined data capture and sharing in experimental workflows

➡ Molecular Labs (ML): eLabFTW for precise documentation and collaboration in chemistry-focused research].

➡ System Level studies & questionnaires (SL): Leibniz-Institut für Psychologie (ZPID)’s Research Data Center supports psychological data management.

➡ Computational and Mathematical Programming (CMA): GitLab for secure, versioned code and model development.

By tailoring RDM to each field, we ensure data that is Findable, Accessible, Interoperable, and Reusable (FAIR) over the entire research range.

➡ See also RDM@OVGU