Optimization of production process of agro-based activated carbon for sustainable cooling systems

• Developed high-performance Raphia-nut-endocarp derived activated carbon (RNEAC) using CaCl₂ and H₃PO₄ activating agents. • Applied hybrid multi-objective optimization and machine learning techniques to optimize RNEAC production parameters. • Produced chemically activated RNEAC with excellent adsorption properties. • Quantified pore structure via SEM-ImageJ analysis and compared RNEAC adsorption potential with commercial AC. • Proposed a scalable, eco-friendly and low-cost agro-organic-based adsorbent for solid adsorption refrigerators (SAR) and related technologies for off-grid and rural communities towards achieving sustainable cooling solutions and a greener environment. This study investigates the experimental production and multi-objective optimization of the production process of microporous activated carbon (AC) derived from Raphia nut endocarp (RNE) for solid adsorption refrigerators (SAR) and related systems. Using phosphoric acid (H₃PO₄) and calcium chloride (CaCl₂) as activating agents, a comprehensive optimization technique integrating genetic algorithms (GA), Pareto optimality (PO), min-max normalization (MMN), and machine learning (ML) was applied to determine the optimal RNE-derived AC (RNEAC) properties, including surface area, carbon yield, and ash content. Linear regression was used as the ML algorithm to analyze the relationship between the production variables of the RNEAC. The experimental design varied parameters such as carbonization temperature, residence time, activating agent concentration, and impregnation ratio, to statistically evaluate their impacts. Results revealed that temperature and residence time significantly influence ash content, while impregnation ratio, temperature, and residence time optimize surface area. Similarly, carbon yield was affected by temperature, residence time, and impregnation ratio. The optimized RNEAC exhibits properties that include a high surface area, low ash content, and a promising methanol adsorption capacity, highlighting its suitability for application in SAR systems. This study will contribute to achieving a greener environment and the development of high-efficiency and sustainable adsorption cooling technologies for rural communities by valorizing agricultural wastes and turning them into excellent low-cost adsorbents for the next generation of SAR.

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Journal
Next research.
Published
2025-11-25
DOI
https://doi.org/10.1016/j.nexres.2025.101132
Primary Topic
Adsorption and Cooling Systems
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article
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article

Optimization of production process of agro-based activated carbon for sustainable cooling systems

Rasheed Bolanle Ayoola, Moruf Olalekan Yusuf, Maryam Buhari, Olusegun M. Ilori et al.
Next research.
Adsorption and Cooling Systems
article

Optimization of production process of agro-based activated carbon for sustainable cooling systems

Rasheed Bolanle Ayoola, Moruf Olalekan Yusuf, Maryam Buhari, Olusegun M. Ilori, Alex Okibe Edeoja, Jacob S. Ibrahim, Sikiru A. Amidu
article en

Abstract

• Developed high-performance Raphia-nut-endocarp derived activated carbon (RNEAC) using CaCl₂ and H₃PO₄ activating agents. • Applied hybrid multi-objective optimization and machine learning techniques to optimize RNEAC production parameters. • Produced chemically activated RNEAC with excellent adsorption properties. • Quantified pore structure via SEM-ImageJ analysis and compared RNEAC adsorption potential with commercial AC. • Proposed a scalable, eco-friendly and low-cost agro-organic-based adsorbent for solid adsorption refrigerators (SAR) and related technologies for off-grid and rural communities towards achieving sustainable cooling solutions and a greener environment. This study investigates the experimental production and multi-objective optimization of the production process of microporous activated carbon (AC) derived from Raphia nut endocarp (RNE) for solid adsorption refrigerators (SAR) and related systems. Using phosphoric acid (H₃PO₄) and calcium chloride (CaCl₂) as activating agents, a comprehensive optimization technique integrating genetic algorithms (GA), Pareto optimality (PO), min-max normalization (MMN), and machine learning (ML) was applied to determine the optimal RNE-derived AC (RNEAC) properties, including surface area, carbon yield, and ash content. Linear regression was used as the ML algorithm to analyze the relationship between the production variables of the RNEAC. The experimental design varied parameters such as carbonization temperature, residence time, activating agent concentration, and impregnation ratio, to statistically evaluate their impacts. Results revealed that temperature and residence time significantly influence ash content, while impregnation ratio, temperature, and residence time optimize surface area. Similarly, carbon yield was affected by temperature, residence time, and impregnation ratio. The optimized RNEAC exhibits properties that include a high surface area, low ash content, and a promising methanol adsorption capacity, highlighting its suitability for application in SAR systems. This study will contribute to achieving a greener environment and the development of high-efficiency and sustainable adsorption cooling technologies for rural communities by valorizing agricultural wastes and turning them into excellent low-cost adsorbents for the next generation of SAR.

Next research.Vol. 3
Birmingham City University (GB), University of Lagos (NG), University of Hafr Al-Batin (SA), Joseph Sarwuan Tarka University Makurdi (NG)
Openalex Percentile: Top 62%
Adsorption and Cooling Systems
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