Numerical simulation of microbial biohydrogen production under high-pressure, high-temperature conditions for enhanced recovery from depleted reservoirs
The growing demand for sustainable energy has intensified interest in biohydrogen production under extreme environmental conditions. This study presents a numerical simulation of hydrogen production in a high-temperature, high-pressure batch bioreactor system designed to mimic depleted hydrocarbon r...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
KeAi Publishing Communications Ltd.
2026
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46726/7/Numerical%20simulation%20of%20microbial%20biohydrogen%20production%20under.pdf https://umpir.ump.edu.my/id/eprint/46726/ https://doi.org/10.1016/j.ptlrs.2025.07.006 |
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| Summary: | The growing demand for sustainable energy has intensified interest in biohydrogen production under extreme environmental conditions. This study presents a numerical simulation of hydrogen production in a high-temperature, high-pressure batch bioreactor system designed to mimic depleted hydrocarbon reservoir environments. The model captures microbial fermentation dynamics using dual substrates glucose and n-hexadecane under varying temperature (35–70 °C), pressure (1–40 atm), salinity (0–50 g/L NaCl), and surfactant-enhanced availability (α = 1–12). Monod kinetics with product (Hydrogen) and salinity inhibition terms govern biomass growth and substrate consumption. Gas accumulation is modeled using Henry's and ideal gas laws, while thermal behavior is represented using lumped heat transfer and Arrhenius-based growth scaling. Initial conditions include 10 mmol/L glucose, 1 mmol/L hexadecane, and 0.1 g/L biomass in a 1.0 L batch system at 55 °C. Over 24 h, the model predicts a maximum hydrogen accumulation of 4.34 L, with a final H2 purity of 89.6% and biomass concentration of 1.20 g/L. Parametric simulations reveal that 55 °C yields the highest hydrogen output (3.31 mol/mol substrate), while increasing salinity and pressure reduce biomass and gas evolution. Surfactant addition significantly improves hydrocarbon utilization, reducing hexadecane from 1.0 to 0.12 mmol/L at α = 12, with up to 72.8% yield enhancement. Glucose drives early hydrogen evolution, while hexadecane contributes to sustained production under co-metabolic conditions. All equations were solved using Python with parametric looping and ODE integration. This simulation framework provides a robust tool for evaluating microbial-enhanced hydrogen recovery (MEHR) and optimizing reactor design conditions for biohydrogen generation in repurposed subsurface reservoirs. |
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