Production and Characterization of Hybrid Briquette from Biomass

Imoisili, Patrick Ehi; Ukoba, Kingsley; Daniel, B.E.; Ibegbulam, M. C.

doi:10.9734/bjast/2014/7091

Cited by 12 publications

(11 citation statements)

References 4 publications

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“…3,4 These are major challenges confronting bioenergy production technologies. These challenges could, however, be improved upon by applying biomass blending 5,6 and a thermochemical process called mild pyrolysis (also known as torrefaction) 3,7 before biomass can be used to co-fire boilers. Nonetheless, some attempts have been performed using torrefied biomass.…”

Section: Introductionmentioning

confidence: 99%

“…The fuel properties of torrefied biomass is often close to that of low rank coal 3 and blending improves the qualities of raw biomasses making them suitable for many domestic and industrial applications. 5,6 Torrefied biomass could be blended with coal 23 but the resulting effect of coal for fuel applications is not environmental friendly, hence, the blending of two torrefied biomass with another is recommended.…”

Section: Introductionmentioning

confidence: 99%

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Quality enhancement of fuel briquette from cornhusk and cassava peel blends for co‐firing in coal thermal plant

Waheed

Akogun

2020

Int J Energy Res

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Summary This study was carried out to evaluate the physico‐mechanical and combustion indices of briquettes made from blends of cornhusk (CH) and cassava peel (CP). The CH and CP was torrefied at 200°C to 300°C and were mixed in the ratio 90/10 to 10/90 for briquette production using D‐optimal crossed design. The fuel application for co‐firing in coal engines was evaluated with fuel ratio, combustibility index and volatile ignitability. The findings revealed that the density, durability index and compressive strength of briquettes blend at 10/90 (CH/CP) torrefied at 300°C and water preconditioned, were respectively improved by 18.19, 2.98, and 32.25% compared with those for raw briquettes at 10/90 blend ratio. Furthermore, the fixed carbon and calorific value increased with the increase in the torrefaction temperature and time. The produced briquettes at the mixing ratios of 10/90 and 30/70 (CH/CP) torrefied at 300°C for at least 1 hour is sufficient for co‐firing in coal engines. The CH and CP wastes with low calorific value which are hitherto discarded could be used to produce briquettes with appropriate qualities attributes of alternative fuel source for heating applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quality enhancement of fuel briquette from cornhusk and cassava peel blends for co‐firing in coal thermal plant

Waheed

Akogun

2020

Int J Energy Res

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“…AI helps optimize the selection and management of biomass feedstocks by analysing factors like crop growth patterns, soil conditions, and transportation logistics (Rane, 2023). This ensures a sustainable and efficient supply chain for biomass energy production (Imoisili et al, 2014). AI is applied to biomass conversion processes, improving the efficiency of bioenergy production.…”

Section: Economic and Social Implications Of Limited Electrificationmentioning

confidence: 99%

Role of Artificial Intelligence in Electrification of Africa

Ibegbulam C.M,

Aigbovbiosa, O.J,

Olowonubi, J.A

et al. 2023

Eng. sci. technol. j.

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The research explores the relationship between Artificial Intelligence (AI) and electrification in Africa, focusing on the challenges and emerging trends. The electrification deficit in Africa poses a significant impediment to economic development and social progress. This paper explores the pivotal role that Artificial Intelligence (AI) plays in addressing the challenges associated with electrification initiatives across the African continent. With its capacity for innovation and optimization, AI emerges as a transformative force capable of revolutionizing the planning, deployment, and management of electrification projects in a region characterized by diverse geographical landscapes and economic constraints. The paper investigates the potential of AI in addressing financial barriers associated with electrification projects. By facilitating innovative financing models, reducing operational costs, and attracting investments, AI contributes to creating sustainable and economically viable electrification solutions. The focus extends to decentralized energy systems and microgrids, exploring how AI can empower remote and underserved communities with reliable access to electricity. The socio-economic impact of AI-driven electrification initiatives is also scrutinized, emphasizing the potential for job creation, economic growth, and improved living standards. The paper discusses the importance of capacity building and local empowerment to ensure that AI technologies are effectively integrated into electrification projects while fostering inclusive and sustainable development. It highlights the role of AI in revolutionizing electrification, predicting electricity consumption and enabling decentralized solutions. AI-driven electrification has shown economic and social benefits, including enhanced productivity, improved quality of life, and increased market access. However, it also raises ethical concerns and privacy implications. The research emphasizes the need for proactive mitigation strategies and collaborations across sectors to drive regulatory frameworks, technological innovations, and global impact. The research envisions a future where AI plays a central role in fostering inclusive growth, connecting communities, and illuminating a digitally transformed and sustainable continent. Keywords: Artificial Intelligence, Electrification, Africa, Electricity, Energy

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“…Biomass conversion without oxygen produces a solid product (biochar) [5][6][7]. Stability of biochar is responsible for carbon sequestration [8].…”

Section: Introductionmentioning

confidence: 99%

Biochar and Application of Machine Learning: A Review

Ukoba¹,

Jen²

2023

Biochar - Productive Technologies, Properties and Applications

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This study discusses biochar and machine learning application. Concept of biochar, machine learning and different machine learning algorithms used for predicting adsorption onto biochar were examined. Pyrolysis is used to produce biochar from organic materials. Agricultural wastes are burnt in regulated conditions to produce charcoal-like biochar using pyrolysis. Biochar plays a major role in removing heavy metals. Biochar is eco-friendly, inexpensive and effective. Increasing interest in biochar is due to stable carbon skeleton because of ease of sourcing the precursor feedstock and peculiar physicochemical. However, artificial intelligence is a process of training computers to mimic and perform duties human. Artificial intelligence aims to enable computers to solve human challenges and task like humans. A branch of artificial intelligence that teaches machine to perform and predict task using previous data is known as machine learning. It uses parameters called algorithms that convert previous data (input) to forecast new solution. Algorithms that have been used in biochar applications are examined. It was discovered that neural networks, eXtreme Gradient Boosting algorithm and random forest for constructing and evaluating the predictive models of adsorption onto biochar have all been used for biochar application. Machine learning prevents waste, reduces time and reduces cost. It also permits an interdisciplinary means of removing heavy metals.

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Production and Characterization of Hybrid Briquette from Biomass

Cited by 12 publications

References 4 publications

Quality enhancement of fuel briquette from cornhusk and cassava peel blends for co‐firing in coal thermal plant

Quality enhancement of fuel briquette from cornhusk and cassava peel blends for co‐firing in coal thermal plant

Role of Artificial Intelligence in Electrification of Africa

Biochar and Application of Machine Learning: A Review

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