Five-factor Response Surface Optimisation of Hydrochloric acid Dissolution of Alumina from a Nigerian Clay

Orugba, Henry Oghenero; Onukwuli, Dominic O.; Babayemi, Akinpelu Kamoru; Umeuzuegbu, Jonah Chukwudi

doi:10.22541/au.158023259.94936552

Cited by 1 publication

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“…In the study of how variables affect a process, the interacting effect is very important. One of the most effective and robust statistical tools for studying the interactive influence of process variables is the Response Surface Methodology [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Process Optimization for Enhancing Yield and Quality of Bio-Oil from the Pyrolysis of Cow Hooves

Chukwuneke¹,

Sinebe²,

Orugba³

et al. 2022

IJDNE

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To reduce the environmental footprints of fossil fuels, there is a need to source more biomasses to increase the renewable energy supply. However, a critical study of the energy-conversion process of biomass must be carried out to make the process economical. In this research, the optimization of the bio-oil production from the thermal conversion of novel biomass- cow hoof was carried out. Three independent variables- temperature of pyrolysis, heating rate and CaO catalyst mass were studied at 3 levels based on the rotatable central composite design (CCD) of the response surface methodology (RSM) to ascertain their influence on two responses- bio-oil yield and its HHV. The quadratic model was more suitable to fit the experimental data. At optimum values of the process variables, bio-oil yield of 50.64% and HHV of 23.86 MJ/kg were obtained. From the analysis of variance carried out, the model R2 values were 0.9949 and 0.9802 respectively for the bio-oil yield and HHV models which showed the models’ ability to predict the bio-oil yield and its HHV in the pyrolysis process is high. The characterization of the bio-oil revealed it has better fuel properties compared with most bio-oils from some biomasses hence it is a viable renewable energy source.

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Section: Introductionmentioning

confidence: 99%