A Simulation Study on Biogas Upgrading and Liquefaction to Biomethane

Abstract

The bioenergy contribution to the global energy consumption is 14% of total renewable energy consumption and it is estimated that global production capacity of biogas energy will be escalated from 14.5 GW to 29.5 GW in time span of 2012 to 2022. Biogas will be main part of future energy system and will attain a similar significance as that of natural gas. The purity of biogas is of utmost importance for being utilized in different sectors, therefore, biogas needs to be upgraded using a viable upgrading technology. Among available technologies of upgrading including water scrubbing, organic scrubbing, chemical absorption, membrane separation and cryogenic distillation; absorption of carbon dioxide through deep eutectic solvent is the most viable technology because of its higher capacity of production and almost no damage to the equipment. Moreover, for the transportation of biomethane (upgraded biogas) to far reaching areas, liquefaction is an indispensable step for better handling of biomethane. Due to lesser liquefaction capacity of biomethane (compared to natural gas) in a single liquefaction plant, single mixed refrigerant liquefaction technology is used because of its low energy consumption for a production capacity of less than 1 million tons per annum. Owing to these reasons, chemical absorption using deep eutectic solvent was selected for biogas upgrading among various methods available, and single mixed refrigerant liquefaction technology was utilized to liquify the upgraded biogas in this work. The optimization of chemical processes to reduce the overall specific energy consumption is crucial for efficient operation. In previous studies, biogas upgrading process is optimized through case studies and best operating parameters are selected. The biomethane obtained at these selected parameters is liquified and only the liquefaction process is optimized through evolutionary algorithm. To the best of our knowledge, there has been no integrated optimization carried out for biogas upgrading and biomethane liquefaction processes so far. In addition, to optimize the integrated process, this study employed a recently developed evolutionary algorithm, i.e., teaching, learning self-study optimization (TLSO) which has not been utilized for the integrated process under consideration so far. Aspen HYSYS was used as a modeling tool for this research and MATLAB programing was used for coding of evolutionary process optimization. The upgraded biogas obtained will be liquefied with single mixed refrigerant. This upgrading and liquefaction processes will be optimized using MATLAB programing for minimizing the process energy. Two integrated models were developed, one with JT-valves and other one with JT-valves replaced with C-turbines to further recover energy loss in the integrated upgrading and liquefaction process. At first for the integrated process with JT-valves, an integrated optimization of biogas upgrading and biomethane liquefaction was carried out by inter-phasing the Aspen HYSYS (used for modeling and simulating the integrated process) and MATLAB (used for programing of TLSO). The results were compared with a base case in terms of upgrading section parameters (flash drum pressure, flash drum temperature, stripper pressure, air flow rate and solvent flow rate) and liquefaction section (refrigerants flow rates, suction pressure and discharge pressure). It is found that the specific energy consumption of upgrading process is reduced by 27% while there is an energy reduction of 6% in liquefaction section. Due to this integrated optimization, the overall specific energy is reduced by 15%. At the second stage, the integrated optimization was performed with the JT-valves replaced with the C-turbines. The overall specific energy was further reduced by 20% compared with the base case. The values of process parameters obtained by integrated optimization of the process may be set as setpoints of the controllers in the plant and operational cost may be reduced.