Developing and Optimizing Media Formulations Using Agro waste for the Production of Fungal Secondary Metabolites, Specifically Vitamin B12 (Published)
Vitamin B12 is a water-soluble compound that is essential for human health, typically sourced from animal products or synthesized through microbial fermentation. This study explored the formulation of mycological media from agro waste for production of Vitamin B12. The media formulated were agro waste from sugarcane and sweet potato (SSP) and sugarcane and cassava (SC). Experimental condition variables including different percentages (1% and 2%) of carbon (Na3C6H5O7) and nitrogen (NaNO3) source, hydrogen ion concentration (pH:5,6,7and 8), different ratios of 5-6 dimethylbenzimidazole (5,6-DMB) and cobalt (Co) and temperature (30oC and 37oC) were optimized using standard procedures in order to enhance microbial production of vitamin B12. The fungi Aspergillus niger (A. niger) strains screened for production of vitamin B12 were obtained from soil sample collected from hospital dumping sites. Statistical analysis of data was done using ANOVA and results were presented as means ± standard deviations. Results obtained from optimization showed that temperature, pH, and different percentages of carbon and nitrogen have significant (p ≤ 0.05) effects on fungal growth as well as its production of secondary metabolite (vitamin B12). The result also showed that the effect of different ratios of DMB and Co on vitamin B12 production was significant (P<0.05). The optimum conditions for the growth of A. niger and synthesis of its secondary metabolite were observed at temperature 30oC (0.4µg/ml±0.006), pH 7.0 (0.92µg/ml±0.007) DMB and Co ratio 80: 0.75 mg (0.94µg/ml± 0.006) in SC medium. The outcome of this study has indicated that vitamin B12 would be effectively produced by A.niger strains using formulations of agro waste: Sugarcane and cassava media supplemented with appropriate percentage of carbon and nitrogen, adequate ratio of DMB and Co and incubation at optimum temperature and pH values. These findings have significant implications for the food, pharmaceutical and biotechnology industries.
Keywords: 5-6 dimethylbenzimidazole, Agro waste, Aspergillus Niger, Cobalt, optimization, vitamin B12
Neural Network Prediction of Surface Roughness with Bearing Clearance Effect (Published)
In manufacturing industry, the quality of manufactured machine components, is determined by how well they follow a defined product’s criteria for dimensional accuracy, tool wear, and surface finish quality. For this reason, manufacturers must be able to regulate machining processes to ensure improved performance and service life of engineering components. This research work presents a study on the optimization of machining parameters for mild steel using artificial neural networks (ANNs). The focus is on developing an effective and efficient machining technique for mild steel by leveraging the capabilities of ANNs to predict optimal machining parameters. To bridge the gap between laboratory figures, model-simulated values, and real-world application, experiments were conducted to obtain data used in the research analysis. Levenberg-Marquardt method were utilized to train the ANNs, with input factors like depth of cut, bearing clearance, cutting speed, and feed rate considered, while the surface roughness of the cut, normalized within 0 to 1 range. A statistical measure of the surface roughness predicted using indicated MAPE value of 0.002% while the correlation coefficient (R) was 0.99995. The results showed that ANNs are a viable machining parameter optimization method and can improve product quality, while providing significant economic and production benefits.
Keywords: Artificial neural networks (ANN), Modelling, machining processes, optimization, predictive modelling., surface roughness, turning parameters