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2 results for “renewable natural gas”
Data for: Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid, Iowa, USA, 2022-2023.
This dataset compiles model outputs, parameter sets, and documentation supporting a techno‑economic analysis (TEA) and life‑cycle assessment (LCA) of co‑digesting beef cattle manure with pretreated mixed prairie biomass to produce renewable natural gas (RNG), with hydroxycinnamic acids (HCA) and digestate‑derived biochar co‑products. It accompanies the study by Katherine Wild, Elmin Rahic, Lisa A Schulte Moore, and Mark Mba Wright "Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid," in Biofuels, Bioproducts, & Biorefining, 2024 (https://doi.org/10.1002/bbb.2710). The integrated simulation and assessment framework quantifies process performance, economics, and greenhouse‑gas intensity across five scenarios representing combinations of alkaline‑ethanol pretreatment for HCA extraction, liquid recirculation fractions, and biochar addition. This data collection includes: stream‑level mass flow/composition tables for each scenario; RNG, biochar, and HCA annual production summaries; literature‑based methane/biogas yield benchmarks; equipment‑level capital costs; TEA assumptions; emission‑factor inventories and displacement credits; and full sensitivity/uncertainty matrices for MFSP and GWP.
Integrated experimental and techno-economic modeling of renewable natural gas production from prairie biomass
This study coupled experimental and techno-economic modeling to evaluate the economic prospects of utilizing prairie biomass as a feedstock for anaerobic digestion. Anaerobic digestion experiments were performed using 15 lab-scale bioreactors under semi-continuous operation, designed based off a box-Behnken design with three factors. Response variables included biogas and biomethane yields, in addition to numerous digestate physico-chemical characteristics. Statistical models were developed from the experimental data to predict these responses and were subsequently incorporated into a techno-economic model developed in Python using BioSTEAM. In addition to optimizing key anaerobic digestion parameters, four scenarios were evaluated investigating liquid digestate recirculation, as well as methane recovery from the liquid digestate in a two-stage anaerobic digestion process.
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