Matching Items (2)
Filtering by
Description
This research focused on how low-income communities in Ghana could convert Waste Vegetable Oil (WVO) into biodiesel to supplement their energy demands. The 2016 World Energy Outlook estimates that about 8 million Ghanaians do not have access to electricity while 82% of the population use biomass as cooking fuel. However, WVO is available in almost every home and is also largely produced by hotels and schools. There are over 2,700 registered hotels and more than 28,000 educational institutions from Basic to the Tertiary level. Currently, most WVOs are often discarded in open gutters or left to go rancid and later disposed of. Therefore, WVOs serve as cheap materials available in large quantities with a high potential for conversion into biodiesel and commercializing to support the economic needs of low-income communities. In 2013, a group of researchers at Kwame Nkrumah University of Science and Technology (KNUST) in Ghana estimated that the country could be producing between 82,361 and 85,904 tons of biodiesel from WVOs generated by hotels alone in 2015. Further analysis was also carried out to examine the Ghana National Biofuel Policy that was introduced in 2005 with support from the Ghana Energy Commission. Based on the information identified in the research, a set of recommendations were made to help the central government in promoting the biodiesel industry in Ghana, with a focus on low-income or farming communities. Lastly, a self-sustaining biodiesel production model with high potential for commercialization, was proposed to enable low-income communities to produce their own biodiesel from WVOs to meet their energy demands.
ContributorsAnnor-Wiafe, Stephen (Author) / Henderson, Mark (Thesis director) / Rogers, Bradley (Committee member) / Engineering Programs (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Many tasks that humans do from day to day are taken for granted in term of appreciating their true complexity. Humans are the only species on the planet that have developed such an in-depth means of auditory communication. Recreating the mechanisms in the brain that recognize speech patterns is no easy task. This paper compares and contrasts various algorithms used in modern day ASR systems, and focuses primarily on ASR systems in resource constrained environments. The Green colored blocks in Figure 1 will be focused on in greater detail throughout this paper, they are the key to building an exceptional ASR system. Deep Neural Networks (DNNs) are the clear and current leader among ASR technologies; all research in this field is currently revolving around this method. Although DNNs are very effective, many older methods of ASR are used often due to the complexities involved with DNNs; these difficulties include the large amount of hardware resources as well as development resources, such as engineers and money, required for this method.
ContributorsPetersen, Casey Alexander (Author) / Csavina, Kristine (Thesis director) / Pollat, Scott (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12