Biological upgrading of biogas assisted with membrane supplied hydrogen gas in a three-phase upflow reactor

Hythane production from brewery wastewater-generated biogas using a membrane electrochemical cell

Converting organic wastes into hythane, a blend of hydrogen (5% to 25%) and methane (75% to 95%), will not only reduce waste discharge but also maximize energy recovery. Herein, a membrane electrochemical cell was investigated to produce hythane from biogas generated in anaerobic digestion of brewery wastewater (BW). The key parameters including current densities, electrolyte concentrations, and biogas flow rates were examined in batch tests. Under an optimal condition (210 mA, 100 mM electrolyte, 1 mL min-1 of biogas flow), the system achieved the production of hythane containing 70.6% ± 1.1% CH4, 27.3% ± 0.5% H2, and 2.1% ± 1.6% CO2 (corresponding to 91.1% ± 6.4% CO2 removal). Meanwhile, the H2S concentration was decreased from 513 to 2 ppm, 99.9% ± 0.2% removal. Energy efficiency of this system was estimated 61.8% ± 10.7%, and energy output increased by 54.4% ± 10.6% with biogas upgrading to hythane. These results encourage further exploration of electrochemical approach for simultaneous biogas upgrading and hythane production. PRACTITIONER POINTS: An innovative approach is demonstrated for efficient simultaneous biogas upgrading and electrochemical hythane production. Hythane composition is affected by the applied current and biogas flow rate. Biogas from brewery wastewater is converted into hythane with composition of 70.6% ± 1.1% CH4, 27.3% ± 0.5% H2, and 2.1% ± 1.6% of CO2. Electrochemical treatment achieves significant removal of H2S from the produced hythane.

Machine learning-aided inverse design for biogas upgrading through biological CO2 conversion

The biogas upgrading process through bioconversion of CO2 to CH4 by hydrogenotrophic methanogens is an attractive strategy for energy decarbonation. Many studies have optimized operational parameters to improve key performance indicators such as CH4% and H2 utilization efficiency. However, inconsistent laboratory conditions make it challenging to compare results. Existing models for analyzing operating conditions can only assess the impact of individual conditions and lack the ability to simultaneously optimize multiple conditions. To address this, two XGBoost models were built with R2 of 0.779 and 0.903 with data collected from literatures and were embedded into multi-objective partitive swarm optimization algorithm to optimal operating conditions. Predictions were compared with experimental validations under optimized conditions, revealing an 8.50% and 2.95% relative error in CH4% and H2 conversion rate, respectively. This approach streamlines biogas upgrading processes, offering a data-driven solution to enhance efficiency and consistency in the pursuit of sustainable methane production.