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An optimization model for assessment of membrane-based post-combustion gas upcycling into hydrogen or syngas

Abstract: In this work, we present an optimization model and techno-economic analysis aimed at assessing the viability of employing membrane technology to recover value-added compounds from post-combustion gases of the process industry. In particular, the tail gas generated in carbon black manufacturing process is targeted. The content of hydrogen (H2) and carbon monoxide (CO) in this waste gas stream is relatively high, thus the possibility of increasing the sustainability of the process by recovering either H2 or both compounds simultaneously (syngas) is addressed. A comparison is performed between the optimal process designs for each recovery scenario based on the separation characteristics provided by state-of-the-art and prospective membrane materials. To that end, a two-stage membrane separation process using hollow-fiber membranes is implemented in the General Algebraic Modeling System (GAMS) as a nonlinear programming model (NLP). The optimal process design for each recovery scenario is found determining the feed pressure, membrane area, power consumption and composition of all process streams that meet the specified H2 recovery and product purity targets at the minimum net present value cost. Results indicate that membrane technology can drive the recovery of significant amounts of H2 from this unconventional source using the separation potential of current polymeric membranes. Moreover, novel ionic liquid-based membranes may be seen as promising candidates providing the required separation properties to obtain a syngas-rich product stream at a lower cost. In this way, the recovery of value-added products is intensified and the carbon dioxide emissions related to the conventional thermal treatment of the tail gas are partially mitigated, thus also reducing the environmental impact of carbon black manufacturing process.

 Autoría: Zarca G., Urtiaga A., Biegler L., Ortiz I.,

 Fuente: Journal of Membrane Science, 2018, 563, 83-92

 Editorial: Elsevier

 Fecha de publicación: 01/10/2018

 Nº de páginas: 37

 Tipo de publicación: Artículo de Revista

 DOI: 10.1016/j.memsci.2018.05.038

 ISSN: 0376-7388,1873-3123

 Proyecto español: CTQ2015-66078

 Url de la publicación: https://doi.org/10.1016/j.memsci.2018.05.038