Process and Systems Modeling of Hydrothermal Liquefaction for Fuels and
- 1Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, 500 Bartholomew Road, Piscataway, NJ, USA, 08854
- 2Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, 94 Brett Road
- 3Department of Physics and Engineering, Washington and Lee University, 204 W Washington St, , Lexington, VA, United States, 24450
Abstract
17 Hydrothermal liquefaction (HTL) can convert wet organic waste into fuels and bioproducts, but 18 variation in feedstocks and operating conditions can propagate from product yields to system 19 performance and deployment. This study linked machine learning based HTL yield prediction with 20 process simulation, techno-economic analysis (TEA), life cycle assessment (LCA), uncertainty 21 analysis, and surrogate modeling. A literature data set of 402 experiments was used to predict 22 biocrude, aqueous, gas, and solids yields, with 71.8% of predictions within ±5 wt% of experimental 23 values. The predicted yields reproduced 77.1% of internal rate of return (IRR) and 83.2% of 24 GWP100 estimates within ±5% and ±10 kg CO2e/kg, respectively, and were integrated into two 25 deployment configurations (centralized and decentralized HTL) and two product pathways 26 (biobinder/biofuel and sustainable aviation fuel [SAF]). Process and system scale analyses 27 showed that operating conditions strongly affecting HTL performance could have weaker or 28 competing economic effects due to tradeoffs among product yields, water handling, energy 29 requirements, and capital costs. Across representative feedstocks, decentralized biobinder 30 systems achieved median IRRs up to 35.8%, compared with up to 8.0% for decentralized SAF 31 systems. Feedstock price, followed by biocrude quality, and biocrude yield emerged as the 32 leading economic drivers. Monte Carlo filtering identified waste tipping fee thresholds $25.1–33 196.7 per wet metric tonne for achieving the 10% IRR target. These results show that existing 34 tipping fee structure can support HTL deployment in some cases, while higher tipping fees or 35 additional financial incentives can expand viability and enable higher investment returns. 36
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BibTeX
@misc{ahmad2026process,
title = {{Process and Systems Modeling of Hydrothermal Liquefaction for Fuels and}},
author = {Ali Ahmad and Rohit Bhagat and Kevin Warren and Saketnath Pabba and Yalin Li},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010134/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010134/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Process and Systems Modeling of Hydrothermal Liquefaction for Fuels and
AU - Ali Ahmad
AU - Rohit Bhagat
AU - Kevin Warren
AU - Saketnath Pabba
AU - Yalin Li
PY - 2026
DA - 2026/10/09
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010134/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010134/v1
AB - 17 Hydrothermal liquefaction (HTL) can convert wet organic waste into fuels and bioproducts, but 18 variation in feedstocks and operating conditions can propagate from product yields to system 19 performance and deployment. This study linked machine learning based HTL yield prediction with 20 process simulation, techno-economic analysis (TEA), life cycle assessment (LCA), uncertainty 21 analysis, and surrogate modeling. A literature data set of 402 experiments was used to predict 22 biocrude, aqueous, gas, and solids yields, with 71.8% of predictions within ±5 wt% of experimental 23 values. The predicted yields reproduced 77.1% of internal rate of return (IRR) and 83.2% of 24 GWP100 estimates within ±5% and ±10 kg CO2e/kg, respectively, and were integrated into two 25 deployment configurations (centralized and decentralized HTL) and two product pathways 26 (biobinder/biofuel and sustainable aviation fuel [SAF]). Process and system scale analyses 27 showed that operating conditions strongly affecting HTL performance could have weaker or 28 competing economic effects due to tradeoffs among product yields, water handling, energy 29 requirements, and capital costs. Across representative feedstocks, decentralized biobinder 30 systems achieved median IRRs up to 35.8%, compared with up to 8.0% for decentralized SAF 31 systems. Feedstock price, followed by biocrude quality, and biocrude yield emerged as the 32 leading economic drivers. Monte Carlo filtering identified waste tipping fee thresholds $25.1–33 196.7 per wet metric tonne for achieving the 10% IRR target. These results show that existing 34 tipping fee structure can support HTL deployment in some cases, while higher tipping fees or 35 additional financial incentives can expand viability and enable higher investment returns. 36
ER -