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Hydrothermal liquefaction (HTL) is set to become a breakthrough technology for producing advanced biofuels, particularly suitable for hard-to-abate sectors. However, HTL faces significant challenges such as high heating demands and aqueous phase (AP) management. This study investigates, from an energy efficiency perspective, the integration of HTL with wet oxidation (WO), an exothermic process which oxidizes organic pollutants in water. The goal is to utilize the heat generated by WO to sustain the high-temperature energy requirement of the HTL process, aiming for autothermal operation. Batch HTL experiments were performed under various temperatures and residence times with wheat straw and cattle manure as feedstocks. The biocrude yield ranged from 24.8 % to 31.4 %, with a HHV between 28.7 and 32 MJ/kg while the AP exhibited a chemical oxygen demand ranging from 45 to 56 g/L. WO was simulated in Aspen Plus® V9 to assess heat generation and AP output composition. The HTL-WO energy integration and standalone HTL processes were then simulated and compared using MATLAB®, which incorporated experimental data, WO simulation results, and an optimized heat exchanger network to assess the corresponding energy expenditures. The integration achieved autothermal operation in several scenarios and demonstrated clear energy efficiency enhancements even when autothermicity was not fully reached, highlighting its robustness. Remarkably, the energy return on investment (EROI) increased by 1.3–3.0 times compared to standalone HTL, reducing the overall energy expenditures from 2.74 to 2.92 MJ/kg dryfeedstock in the HTL standalone process to 0.99–2.29 MJ/kg dryfeedstock in HTL-WO integration. • Batch HTL of straw and manure yielded 37–45 % carbon to biocrude, highest at 350 °C. • WO can create heat by oxidizing the HTL-AP, which contains 20–29 % of initial carbon. • An optimal HTL-WO integrated process configuration was identified. • Exothermic heat from WO is sufficient to achieve integrated autothermal HTL-WO process. • Energy return on investment is improved from HTL standalone from ∼3 to ∼7.
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Ceragioli et al. (2025) studied this question.
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