@inbook{839dd1d2e0a34700a9472730490312e2,
title = "Complementing Natural Gas Driven Syngas with Optimum Blends of Gasified Biomass Waste",
abstract = "Countries around the world strive to diversify their energy portfolio with a suitable and sustainable alternative to fossil fuels, whilst achieving the reduction in environmental impacts from released wastes. The co-conversion of biomass wastes and natural gas (NG) has received much attention due to the potential improvement in downstream power and production of fuels, while minimizing greenhouse gas (GHG) emissions. This study explores the optimal blending of synthesis gas generated from biomass wastes and NG feeds. Aspen Plus is utilized to develop the models of biomass and NG steam gasification considering Qatar's biomass and NG characteristics. Three types of biomass wastes; date pits, sludge and manure are gasified to generate the H2-rich syngas which is blended later with the NG-driven syngas. The simulated flowsheets have then been used to optimize the blending of downstream generated syngas by means of manipulating the biomass wastes and NG feeds. The optimization problem is constrained by the downstream quality of produced syngas to be utilized for the generation of power and fuels. Typically, the generation of syngas involves high-cost subsequent purification prior to the production of downstream value-added products. However, the optimization attained in this study lowers the requirement of further syngas purification and waste removal, through the blending of NG and biomass-driven syngas and minimization of gasifying agents. This requirement can be further reduced by manipulating reaction agents and process conditions. The result of the optimization problem demonstrates an increase in biomass wastes utilization with the increase in syngas quality constraint. Dates pits biomass dominated the biomass utilization with a lower contribution from sludge and manure wastes.",
keywords = "Biomass, Blending, Co-Conversion, Natural Gas, Optimization, Syngas",
author = "Ahmed AlNouss and Gordon Mckay and Tareq Al-Ansari",
note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",
year = "2022",
month = jan,
doi = "10.1016/B978-0-323-95879-0.50224-1",
language = "English",
series = "Computer Aided Chemical Engineering",
publisher = "Elsevier B.V.",
pages = "1339--1344",
booktitle = "Computer Aided Chemical Engineering",
address = "Netherlands",
}