Browsing by Author "Omonhinmin CA"

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    Data on artificial neural network and response surface methodology analysis of biodiesel production
    (2020) Ayoola AA; Hymore FK; Omonhinmin CA; Babalola PO; Bolujo EO; Adeyemi GA; Babalola R; Olafadehan OA
    The biodiesel production from waste soybean oil (using NaOH and KOH catalysts independently) was investigated in this study. The use of optimization tools (artificial neural network, ANN, and response surface methodology, RSM) for the modelling of the relationship between biodiesel yield and process parameters was carried out. The variables employed in the experimental design of biodiesel yields were methanol-oil mole ratio (6 – 12), catalyst concentration (0.7 – 1.7 wt/wt%), reaction temperature (48 – 62°C) and reaction time (50 – 90 min). Also, the usefulness of both the RSM and ANN tools in the accurate prediction of the regression models were revealed, with values of R-sq being 0.93 and 0.98 for RSM and ANN respectively.
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    Dataset on rbcL-based intra-specific diversity of Gongronema latifolium Benth: (Apocynaceae) in South-East Nigeria
    (2022) Omonhinmin CA; Onuselogu CC; Olomukoro E
    Gongronema latifolium (Apocynaceae) is a versatile plant of nutritional and medicinal value and is widely distributed and endemic to the South-Eastern region of Nigeria. The plant is relatively wild and its natural habitat is threatened by deforestation, excessive exploitation and constant expansion of the urban areas into its endemic space. Hence, there is a need to understand its genetic diversity for breeding and conservation. The data consist of fourteen partial rbcL gene sequences, nucleotide compositions and amino acid profiles of G. latifolium. The data set provides insight on the species genetic diversity and evolution that is important for scientist and breeders alike as well as for conservation efforts of the species.
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    Response surface methodology and artificial neural network analysis of crude palm kernel oil biodiesel production
    (2020) Ayoola AA; Hymore FK; Omonhinmin CA; Babalola PO; Fayomi OS; Olawole OC; Olawepo AV; Babalola A
    Response surface methodology (RSM) and Artificial neural network (ANN) analysis of crude palm kernel oil (CPKO) biodiesel production, using KOH and NaOH catalysts, were carried out in this research work. The four process parameters considered during the production process and modelling stages were 6–12 mol ratio of methanol/oil, 0.7–1.7 wt/wt% catalyst concentration, 48–62 °C reaction temperature and 50–90 min reaction time. Log sigmoid function and Levenberg marquardt technique were adopted in ANN while Box-Benkhen method was utilised for RSM. The results revealed that KOH catalyst process produced higher yield of biodiesel (87 – 99%), compared to the yield obtained from NaOH catalysed process (79 – 91%). The regression coefficients for RSM models were 0.9324 for KOH catalysed process and 0.8935 for NaOH catalysed process, while the overall correlation coefficients for ANN models were 0.82921 for KOH catalysed process and 0.89396 for NaOH catalysed process, an implication that RSM is a better analytical tool (compare to ANN) in models formulation.