Theoretical estimation of energy potential and environmental emissions mitigation for major livestock manure in Bangladesh

Md. Sanowar Hossain, Nahid Imtiaz Masuk, Barun K. Das, Arnob Das, Md. Golam Kibria, Miftahul Mobin Chowdhury and Imtiaz Ahmed Shozib

Renewable Energy, 2023, vol. 217, issue C

Abstract: This study investigates the potential energy resources from livestock manure to evaluate the country's energy potential from domestic animals (e.g., cows, chickens, sheep, and goats). A comparative analysis of four distinct energy routes, such as process I: anaerobic digestion and co-generation, process II: gasification and co-generation, process III: combustion and co-generation, and process IV: direct combustion to heat energy generation is exclusively examined. In addition to that, a simulation using a Biogas calculator was run to get a better idea of how the technical potential of the mathematical models stacks up. Results from this analysis reveal that the gasification process with heat recovery options (process II) has the greatest energy estimation (26,564.64 TJ) among the four energy-producing paths, whereas process IV (combustion) has the lowest energy potential (4419.75 TJ). The maximum electricity generation potential has been found for process II (1630.77 GW h/year), whereas the minimum from process I (27361.45 MW h/year). Moreover, CO2 reduction potential results show that the maximum potential was obtained for process II (3,78,941.57 ton/year), whereas the minimum reduction potential (63,047.23 ton/year) was obtained from process IV, compared to coal combustion. The annual revenue obtained through process II is the maximum among different techniques, which is 141.87 million dollars.

Keywords: Livestock manure; Chemical analysis; Mathematical modeling; Energy potential; CO2 mitigation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:217:y:2023:i:c:s0960148123012697

DOI: 10.1016/j.renene.2023.119354

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