Co-design of alternative fuels and future spark-ignition (SI) engines allows very high engine efficiencies to be achieved. To tailor the fuel’s molecular structure to the needs of SI engines with very high compression ratios, computer-aided molecular design (CAMD) of renewable fuels has received considerable attention over the past decade. To date, CAMD for fuels is typically performed by computationally screening the physicochemical properties of single molecules against property targets. However, achievable SI engine efficiency is the result of the combined effect of various fuel properties, and molecules should not be discarded because of individual unfavorable properties that can be compensated for. Therefore, we present an optimization-based fuel design method directly targeting SI engine efficiency as the objective function. Specifically, we employ an empirical model to assess the achievable relative engine efficiency increase compared to conventional RON95 gasoline for each candidate fuel as a function of fuel properties. For this purpose, we integrate the automated prediction of various fuel properties into the fuel design method: Thermodynamic properties are calculated by COSMO-RS; combustion properties, indicators for environment, health and safety, and synthesizability are predicted using machine learning models. The method is applied to design pure-component fuels and binary ethanol-containing fuel blends. The optimal pure-component fuel tert-butyl formate is predicted to yield a relative efficiency increase of approximately 8% and the optimal fuel blend with ethanol and 3,4-dimethyl-3-propan-2-yl-1-pentene of 19%.

The conventional design of organic Rankine cycle (ORC) power systems starts with the selection of the working fluid and the subsequent optimization of the corresponding thermodynamic cycle. More recently, systematic methods have been proposed integrating the selection of the working fluid into the optimization of the thermodynamic cycle. However, in both cases, the turbine is designed subsequently. This procedure can lead to a suboptimal design, especially in the case of mini- and small-scale ORC systems, since the preselected combination of working fluid and operating conditions may lead to infeasible turbine designs. The resulting iterative design procedure may end in conservative solutions after multiple trial-and-error attempts due to the strong interdependence of the many design variables and constraints involved. In this work, we therefore present a new design and optimization method integrating working fluid selection, thermodynamic cycle design, and preliminary turbine design. To this purpose, our recent 1-stage continuous-molecular targeting (CoMT)-computer-aided molecular design (CAMD) method for the integrated design of the ORC process and working fluid is expanded by a turbine meanline design procedure. Thereby, the search space of the optimization is bounded to regions where the design of the turbine is feasible. The resulting method has been tested for the design of a small-scale high-temperature ORC unit adopting a radial-inflow turbo-expander. The results confirm the potential of the proposed method over the conventional iterative design practice for the design of small-scale ORC turbogenerators.