Browsing by Author "Omoruwou, Felix"

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    Production of Biogas from Co-Digestion of Cow Dung, Saw Dust and Maize Husk
    (Advances in Chemical Engineering and Science, 2018-08) Okewale, Akindele Oyetunde; Anih, Christiana Edward; Omoruwou, Felix
    The co-digestion of cow dung, with maize husk for biogas production at laboratory scale was investigated. The study was carried out at a temperature range of 24˚C - 30˚C and pH range of 5.5 - 6.5 for a period of 60 days with a total solid concentration of 7.4% in the digester sample (fermentation slurry). Water displacement method was used to collect the biogas produced which was subsequently measured. 444.8 mL was the cumulative biogas yield at the end of 60 days retention time in the digester 1, which comprised of cow dung, maize husk, and water. Digester 2, which is made up of sawdust, cow dung, and water produced negligible biogas at the end of 60 days of the experiment. X-RF analysis revealed high presence of elements like silica, aluminium oxides, and aluminium oxides in cow dung, maize husk, and sawdust respectively. The preponderance of alkanes and methyl group inmaize husk makes it to produce biogas compared to saw dust as shown by the Fourier transform infrared spectroscopy (FTIR) that was carried out to identify the various functional groups. The potential of maize husk to produced biogas was also established. The kinetic modeling shows that there was an increase in biogas yield as the retention time increases as depicted by the linear model
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    Strategic Feature Selection for Enhanced Scorch Prediction in Flexible Polyurethane Form Manufacturing
    (Journal of Computing Theories and Application, 2024) Omoruwou, Felix; Ojug, Arnold Adimabua; Ilodigwe, Solomon Ebuka
    The occurrence of scorch during the production of flexible polyurethane is a significant issue that negatively impacts foam products' resilience and generally jeopardizes their integrity. The likelihood of foam product failure can be decreased by optimizing production variables based on machine learning algorithms used to predict the occurrence of scorch. Investigating technology is required because prevention is the best approach to dealing with this problem. Hence, machine learning algorithms were trained to predict the occurrence of scorch using the thermodynamic profile of polyurethane foam, which is made up of recorded production variables. A variety of heuristics algorithms were trained and assessed for how well they performed, namely XGBoost, Decision trees, Random Forest, K-nearest neighbors, Naive Bayes, Support Vector Machines, and Logistic Regression. The XGboost ensemble was found to perform best. It outperformed others with an accuracy of 98.3% (i.e., 0.983), followed by logistic regression, decision tree, random forest, K-nearest neighbors, and naïve Bayes, yielding a training accuracy of 88.1%, 66.7%, 84.2%, 87.5%, and 67.5% respectively. The XGBoost was finally used, yielding 2-distinct cases of non(occurrence) of scorch. Ensemble demonstrates that it is quite capable and is an effective way to predict the occurrence of scorch.
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    Unmasking effects of feature selection and SMOTE-Tomek in tree-based random forest for scorch occurrence detection
    (Bulletin of Electrical Engineering and Informatics, 2025) Omoruwou, Felix; Okpor, Margaret Dumebi; et. al
    Scorch occurrence during the production of flexible polyurethane foam has been a menace that consistently, jeopardize a foam’s integrity and resilience. It leads to foam suppression and compactness integrity failure due to scorch. There is always the increased likelihood of scorching, and makes crucial the utilization of methods that seek to avert it. Studies predict that the formation of foam constituent processes via optimization using machine learning have adequately trained models to effectively identify scorch occurrence during the profiling in the polyurethane foam production. Our study utilizes the random forest (RF) ensemble with feature selection (FS) and data balancing technique to identify production predictors. Study yields accuracy of 0.9998 with F1-score of 0.9819. Model yields 2-distinct cases for (non)-occurrence of scorch respectively, and the ensemble demonstrates that it can effectively and efficiently predict the occurrence of scorch in the production of flexible polyurethane foam manufacturing process.