Why does this project matter?

Developing tolerant processes also requires the ability to quantify and compare the trade-off between tolerance and efficiency. Without such a framework, engineers cannot determine whether a more tolerant design is worth its efficiency cost, or identify process configurations that achieve the best balance. No universal methods yet exist for assessing the tolerance-efficiency balance of bio-based processes in a way that integrates techno-economic analysis (TEA) and life cycle assessment (LCA). Standard LCA databases also lack crucial biochemical data (fermentation media, enzyme costs, downstream processing), hampering early-stage environmental assessment. P9 fills this gap with novel assessment methods applied to the integrated TALENT process chain.

What are we aiming to achieve?

The core objective is to develop methods and tools for quantifying the tolerance-efficiency balance from both techno-economic and ecological perspectives. Key goals are: (1) build flowsheet simulations of TALENT process units and integrated chains; (2) perform sensitivity analyses to identify parameters with the greatest impact on the tolerance-efficiency balance; (3) derive quantitative performance indicators for this balance; (4) develop an LCA framework adapted for tolerant processes with variable bio-based feedstocks; and (5) evaluate whether simplified proxy metrics (PMI, E-factor) can substitute full LCA at early development stages.

What will you work on as a PhD researcher?

As doctoral researcher in P9 (starting October 2027), you build the process simulation and techno-economic assessment framework for the TALENT value chain. Your start date is intentionally staggered by one year so that sufficient experimental data from P1–P8 are available as modeling inputs. You will start with reviewing TEA methodologies for bio-based processes and building unit models for different TALENT process steps in the value chain.

From year two, you perform systematic sensitivity analyses, varying feedstock composition, inhibitor loads, reaction conversions, and extraction yields, to identify which input variability most critically affects key performance indicators (KPIs). You develop quantitative performance indicators for the tolerance-efficiency trade-off and integrate units from P1–P2–P7–P3 into a connected flowsheet that propagates compositional uncertainties across the full value chain. To efficiently screen many process alternatives, you develop machine learning surrogate models (Gaussian process regression or neural networks) trained on simulation outputs – enabling fast prediction without full flowsheet runs.

Skills and methods you will develop during your doctorate:

• Process flowsheet simulation: Aspen Plus or DWSIM (steady-state simulation, mass and energy balances

• Techno-economic assessment (TEA): CAPEX/OPEX estimation, sensitivity analysis, cost KPIs

• Process sensitivity analysis: parameter variation, uncertainty propagation

• Surrogate modeling and machine learning: Gaussian process regression, neural networks for process approximation

• Python programming: scientific computing, data analysis, integration with simulation tools

• Interdisciplinary data integration: collecting and harmonizing experimental data from other projects

• Scientific communication: presenting complex process systems results to interdisciplinary audiences

Who will you work with and where?

In P9, you work across two research groups with complementary expertise. The Freund group has extensive experience in modeling, simulation, and optimization of catalytic reactors and integrated chemical processes, including pioneering work on tolerant and load-flexible process design, and works with Aspen Plus and Python-based process systems engineering tools. The Lütz group contributes established expertise and infrastructure for TEA and LCA of fermentation and enzymatic processes, including an existing database of biochemical process data rarely found in standard LCA repositories. Together, the two groups provide a unique combination of chemical and bioprocess engineering perspectives for integrated value chain evaluation. Embedded in RTG TALENT, you gain access to a structured qualification program that combines advanced scientific training with transferable skills development, active exchange with academic and industrial collaboration partners, and tailored career support including the opportunity for a three-month placement in research, industry, or a start-up aligned with your career goals.