Energy Analysis of Industrial Boilers: A Case Study of the Coca-Cola Challawa Plant in Kano, Nigeria

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Mika’il Alhaji Abdulkareem
Paul O. Udom
M. T. Jimoh

Abstract

The industrial sector heavily relies on boiler systems for various operations, making them significant energy consumers. As a result, it becomes imperative to conduct comprehensive analyses to assess their efficiency and identify areas of energy loss. This study employs energy analysis based on the first law of thermodynamics to precisely determine the efficiency and the primary contributors to energy losses within the boiler system. The research encompasses a detailed examination of both the combustion chamber and the steam production chamber of the boiler. The analysis reveals that the energy efficiency of the combustion chamber stands at an impressive 82%, signifying its effectiveness in converting fuel into usable energy. Conversely, the steam production chamber operates at a slightly lower efficiency of 69%. Taking into account both components, the overall energy efficiency of the entire boiler system is calculated at 71%. This consolidated figure serves as a critical benchmark for assessing the system's performance and initiating potential improvements. Among the key findings, it is evident that preheating the feed water using the flue gas emerges as the most promising strategy to minimize energy losses. This conclusion underscores the importance of implementing energy-efficient practices to enhance the overall productivity of industrial boiler systems. The energy efficiency and losses within an industrial boiler system, offer valuable insights into its performance. The research emphasizes the importance of energy-saving techniques, such as preheating feed water, to enhance efficiency and reduce losses, adopting a more sustainable and cost-effective in the industrial sector.

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How to Cite
Abdulkareem, M. A., Udom, P. O., & Jimoh, M. T. (2024). Energy Analysis of Industrial Boilers: A Case Study of the Coca-Cola Challawa Plant in Kano, Nigeria. African Multidisciplinary Journal of Sciences and Artificial Intelligence, 1(1), 486-508. https://doi.org/10.58578/amjsai.v1i1.3681

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