Manuel Hechinger

Tailor-made fuels for future engine concepts

Increasing carbon dioxide accumulation in earth’s atmosphere and the depletion of fossil resources pose huge challenges for our society and, in particular, for all stakeholders in the transportation sector. The Cluster of Excellence ‘Tailor-Made Fuels from Biomass’ at RWTH Aachen University establishes innovative and sustainable processes for the conversion of whole plants into molecularly well-defined fuels exhibiting tailored properties for low-temperature combustion engine processes, enabling high efficiency and low pollutant emissions. The concept of fuel design, that is, considering fuel’s molecular structure to be a design degree of freedom, aims for the simultaneous optimisation of fuel production and combustion systems. In the present contribution, three examples of tailor-made biofuels are presented. For spark ignition engines, both 2-methylfuran and 2-butanone show increased knock resistance compared to RON95 gasoline, thus enabling a higher compression ratio and an efficiency gain of up to 20% at full-load operation. Moreover, both fuels comprise a good mixture formation superior to the one of ethanol, especially under difficult boundary conditions. For compression ignition engines, 1-octanol enables a remarkable reduction in engine-out soot emissions compared to standard diesel fuel due to the high oxygen content and lower reactivity. This advantage is achieved without sacrificing the high indicated efficiency and low NOX emissions.

What is Wrong with Quantitative Structure–Property Relations Models Based on Three-Dimensional Descriptors?

Quantitative structure-property relations (QSPR) employing descriptors derived from the three-dimensional (3D) molecular structure are frequently applied for property prediction in various fields of research. However, there is no common understanding of the necessary degree of detail to which molecular structure has to be known for reliable descriptor evaluation, but computational methods used vary from simplified molecular mechanics up to rigorous ab initio programs. In order to quantify the yet unknown error due to this heterogeneity, widely used 3D molecular descriptors from diverse fields of application are evaluated for molecular structures computed by different computational methods. The results clearly indicate that the widespread, exclusive use of the most stable molecular conformation as well as too simplistic computational methods yield systematically erroneous descriptor values with misleading information for the inferred structure-property relations. Thus, generating an awareness and understanding of this fundamental problem is considered an important first step to make 3D QSPR a generally accepted property prediction method.

Rigorous Generation and Model-Based Selection of Future Biofuel Candidates

Abstract Future liquid energy carriers should not only be synthesized sustainably from biomass, but they should also exhibit optimal properties for their application in future combustion engines. Due to the large amount of potential organic molecules, the identification of most promising fuel candidates in the entire molecular search space can only be realized by a model-supported approach. To this end, a novel fuel design framework is presented which combines a rigorous generation of molecular structures with a stepwise reduction to a set of promising candidates based on fuel-relevant properties. Predictive quantitative structure-property relations (QSPR) are employed to assess molecular structures in order to reduce necessary experiments to high potential fuel candidates. Although some of the property prediction models employed are still of limited predictive quality, the feasibility of the proposed approach is successfully shown for gasoline fuels. In particular, a significant reduction of the space of candidate fuel molecules is achieved, thus demonstrating the potential of the novel framework.

Towards Model-Based Design of Tailor-Made Fuels from Biomass

In face of the continuous depletion of fossil carbon resources alternative liquid energy carriers have to be identified to guarantee sustainable future mobile propulsion. In this context, the Cluster of Excellence (CoE) “Tailor-Made Fuels from Biomass” (TMFB) at RWTH Aachen University aims at identifying sustainable fossil fuel surrogates from biomass by means of a holistic approach from biomass supply to engine combustion. As the fuel identification process requires the screening of a tremendous number of possible fuel candidates, solely experimental methodologies cannot be applied. To this end, a research team at AVT.PT contributes to a model-based fuel design (MBFD) methodology which is based on an integrated product and process design approach, considering aspects of both fuel combustion and fuel production. It aims at identifying possible fossil fuel surrogates from a database of rigorously generated molecular structures. These fuel surrogates have to comply with a set of pre-defined constraints, which has been elaborated by interdisciplinary collaboration within the CoE. The present contribution illustrates the status quo and future perspectives of model-based fuel design and its integration into the research context of the TMFB cluster.

Towards Integrated Product and Process Design for Biofuels

Abstract Sustainable processes for the conversion of whole plants into fuels are investigated in the research cluster “Tailor-Made Fuels from Biomass” at RWTH Aachen University (RWTH Aachen University, 2007). In contrast to known attempts, this project not only aims at the identification of components with promising fuel properties, but also accounts for the preservation of functional biomass structures within the biofuel production process. Accordingly, both fuel design approaches and new synthesis routes need to be explored. The solution strategy for these challenges is supported by systems engineering techniques: (i) promising target fuels are predicted by Computer-Aided Molecular Design (CAMD) and (ii) possible production alternatives are identified and classified by reaction network flux analysis (RNFA). In this contribution we introduce both methodologies and show how they can be combined to an integrated product and process design.