(392s) Development of Planning Models for the Production of Sustainable Aviation Fuels Via Hydro-Processed Ester and Fatty Acid (HEFA) Pathways

The production of Sustainable Aviation Fuels (SAF) through the HEFA (Hydro-processed Esters and Fatty Acids) pathway has gained significant attention because of its compatibility with the existing infrastructure and its potential to reduce carbon emissions. In this process, vegetable oils are converted into synthetic paraffinic kerosene (SPK) through hydrodeoxygenation (HDO) reactions. Here, hydrogen is used to break the triglyceride chains, and to remove oxygen from fatty acids. Once produced, the synthetic paraffinic kerosene (SPK) is sent to a dewaxing/isomerization reactor which improves the cold-flow properties of the fuel through hydro-isomerization and hydrocracking reactions (Yang et al.2019). Presently, HEFA is considered the most mature technology to produce SAF and has been adopted by companies like Neste, AltAir Fuels, and REG yielding a combined capacity of 4.3 MMT per year (Richter et al. 2018). However, the high hydrogen consumption, the CO₂ emissions produced in the HDO step, and the narrow range of operating temperature favoring the production of green diesel as opposed to SAF, are the major challenges of this process (Bezergianni et al. 2010). These items create complex trade-offs affecting the profitability and sustainability of the process. In this context, SAF producers require reliable (non-linear) models to capture the behavior and response of the system to make business decisions. In this work, we develop an optimization model for the planning production of sustainable aviation fuels based on the HEFA process. The model integrates surrogate models aimed at capturing the non-linear characteristics of the HDO and dewaxing stages. Here, two approaches are considered to formulate the surrogate models, namely, the base-plus-delta approach and non-linear reduced order model (ROM) one. The latter is represented by non-linear correlations between independent and dependent variables, while the former consists of first-order Taylor expansion representations of the process units around a base operating point. We create those surrogate models based on first-principles models developed by Aspen Technology (2025a), using the Aspen AI Model Builder® technology (AIMB) (Aspen Technology, 2024b) for the HDO and dewaxing units. Specifically, we considered hydrogen production, the CO₂ emissions, and the production rate of fuel gas (C3-C4), naphtha (C5-C12), SAF (C8-C16), green diesel (C10-C22), and gasoil (C10-C20) and their key properties as the key dependent variables. The major decisions of this model include: (i) feedstock selection (based on its composition and price), (ii) optimal operating parameters for HDO and dewaxing reactors, (iii) optimal product distribution including SAF-to-green diesel production ratio, (iv) properties of the main products (i.e., cloud point, cetane number, freeze point, boiling point among others), (v) total hydrogen consumption, and (vi) total CO₂ emissions. The model is formulated as non-linear (NLP) and is solved in Aspen Unified Planning (AUP) framework using the Aspen XSLP solver (Varvarezos 2008; Khor and Varvarezos 2017). To show the applicability of the model, we conducted a sensitivity analysis to know the effect of feedstock selection, among palm oil, soybean oil, and rapeseed oil on the economics, overall hydrogen consumption and the properties of the products. Results showed that the rapeseed oil yielded the highest SAF production and better cold flow properties with respect to the others but poor economic performance. Palm oil showed the best economic performance but a higher selectivity to produce green diesel.

References

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