Separation of methane and nitrogen using ionic liquidic zeolites by pressure vacuum swing adsorption

Abstract The separation of methane (CH 4 ) and nitrogen (N 2 ) is a significant challenge to the enrichment and utilization of low concentration CH 4 due to the similarity in the physical and chemical properties of the two molecules. In this work, we investigated the separation of CH 4 from N 2 using 100 kg of a new ionic liquidic zeolite (ILZ) material in a 6‐bed pilot‐scale pressure swing adsorption process. Feed gases with CH 4 concentrations of 5.0% and 16.1% were upgraded to 11.5% and 34.6%, respectively, with CH 4 recoveries higher than 80%. The pilot test results were used to anchor a numerical model that then allowed the efficient investigation of multiple operational parameters including desorption pressure and feed gas flow rates. The numerical model produced CH 4 concentrations for both product streams consistent with those measured in the pilot experiments, with root mean square deviations below 2%. The modeling results revealed that sufficiently low desorption pressures can unexpectedly lead to lower heavy product purities under limited feed gas flow conditions. Furthermore, the optimum feed gas flow rate under which maximum heavy product purity is achieved increases with lower desorption pressure. The maximum CH 4 concentrations increased from 31.8% to 41.5%, as desorption pressures decreased from 22.8 to 12.2 kPa for optimum feed flow rates between 78.2 and 105.5 mol/h. We also demonstrate a method of process optimization based on the bed capacity ratio, ℂ, which provides a scale‐independent measure of the degree to which the column is being used effectively. By varying feed flow rate and/or desorption pressure, ℂ values between 0.2 and 0.8 were explored, with maxima in the combined separation performance metric (methane recovery) × (methane purity) occurring for values of ℂ in the range 0.29–0.36. This separation performance optimization by adjusting ℂ provides an effective strategy for integrating and understanding the impact of multiple operating parameters.