1Department of Mechanical and Mechatronics Engineering, Tshwane University of Technology, Pretoria 0183, South Africa
2Department of Mechanical Engineering, Pan-Atlantic University, Lekki, 105101, Nigeria
3Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0183, South Africa
BibTex Citation Data :
@article{IJRED61190, author = {Anthony Onokwai and Udochukwu Akuru and Dawood Desai}, title = {Predictive accuracy and characterisation of bio-oil yield from pyrolysis of Cocos nucifera: A comparison of traditional RSM and hybrid models}, journal = {International Journal of Renewable Energy Development}, volume = {14}, number = {6}, year = {2025}, keywords = {Biofuels; Biomass; Characterization; Intermediate pyrolysis; Response Surface Methodology; Hybrid Models}, abstract = { The pressing demand for renewable energy has made biomass a quintessential alternative to fossil fuels. This study aims to develop and compare predictive models for optimising bio-oil yield from the intermediate pyrolysis of Cocos nucifera, utilising response surface methodology with the central composite design and hybrid models (PSO-ANFIS and GA-ANFIS). It seeks to characterize the bio-oil yield to investigate its quality for use as a biofuel. An experimental run was performed by varying pyrolysis operating parameters, namely, temperature (300–700°C), heating rate (6–30°C/min), residence time (5–25 minutes), particle size (0.5–4.5 mm), and nitrogen flow rate (10–50 mL/min). Hybrid models (PSO-ANFIS and AN-FIS-GA) were used to predict the bio-oil yield to identify the most robust model. An optimum bio-oil yield (52.17 wt.%) was attained at a temperature, heating rate, residence time, particle size, and nitrogen flow rate of 510.2°C, 10.5°C/min, 5.2 minutes, 0.3 mm, and 17.3 mL/min, respectively. The study shows that its hybrid models are scalable and outperform traditional techniques (RSM) in terms of predictive accuracy and computational efficiency. The GC-MS analysis identified over 200 compounds in bio-oil, comprising mainly phenols, esters, and oleic acids, which confirmed its suitability for producing biofuels, lubricants, and pharmaceuticals. Also, FTIR analysis confirms functional groups of biodiesel, adhesives, and resins. The PSO-ANFIS and GA-ANFIS models accurately predict the bio-oil yield, with the PSO-ANFIS model outperforming the other models with an R² of 0.994 and RMSE of 0.449 during the test phase, representing a two- to three-fold improvement over traditional RSM. Unlike conventional empirical models, the hybrid approach improves predictive accuracy and reduces the number of required experiments and computational errors, enabling real-time adjustments to the pyrolysis process, thereby advancing pyrolysis research and bio-oil optimization. This research is highly relevant for improving waste-to-energy production in regions where Cocos nucifera residues remain abundant, especially in emerging economies. }, pages = {1125--1145} doi = {10.61435/ijred.2025.61190}, url = {https://ijred.cbiore.id/index.php/ijred/article/view/61190} }
Refworks Citation Data :
The pressing demand for renewable energy has made biomass a quintessential alternative to fossil fuels. This study aims to develop and compare predictive models for optimising bio-oil yield from the intermediate pyrolysis of Cocos nucifera, utilising response surface methodology with the central composite design and hybrid models (PSO-ANFIS and GA-ANFIS). It seeks to characterize the bio-oil yield to investigate its quality for use as a biofuel. An experimental run was performed by varying pyrolysis operating parameters, namely, temperature (300–700°C), heating rate (6–30°C/min), residence time (5–25 minutes), particle size (0.5–4.5 mm), and nitrogen flow rate (10–50 mL/min). Hybrid models (PSO-ANFIS and AN-FIS-GA) were used to predict the bio-oil yield to identify the most robust model. An optimum bio-oil yield (52.17 wt.%) was attained at a temperature, heating rate, residence time, particle size, and nitrogen flow rate of 510.2°C, 10.5°C/min, 5.2 minutes, 0.3 mm, and 17.3 mL/min, respectively. The study shows that its hybrid models are scalable and outperform traditional techniques (RSM) in terms of predictive accuracy and computational efficiency. The GC-MS analysis identified over 200 compounds in bio-oil, comprising mainly phenols, esters, and oleic acids, which confirmed its suitability for producing biofuels, lubricants, and pharmaceuticals. Also, FTIR analysis confirms functional groups of biodiesel, adhesives, and resins. The PSO-ANFIS and GA-ANFIS models accurately predict the bio-oil yield, with the PSO-ANFIS model outperforming the other models with an R² of 0.994 and RMSE of 0.449 during the test phase, representing a two- to three-fold improvement over traditional RSM. Unlike conventional empirical models, the hybrid approach improves predictive accuracy and reduces the number of required experiments and computational errors, enabling real-time adjustments to the pyrolysis process, thereby advancing pyrolysis research and bio-oil optimization. This research is highly relevant for improving waste-to-energy production in regions where Cocos nucifera residues remain abundant, especially in emerging economies.
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