In this study, the effect of H2SO4 on viscosity of methyl esters from Jatropha oil (JCME), palm kernel oil (PKOME) from Elaeis guineensis species, and coconut oil (COME) has been studied. Effect of methanol to oil molar mass ratio on yield of the three feedstocks has also been studied. Methyl ester yield was decreased by esterification process using sulphuric acid anhydrous (H2SO4). Jatropha methyl ester experienced a viscosity reduction of 24% (4.1–3.1mm2/s) with the addition of 1% sulphuric acid. In this work palm kernel oil (PKOME), coconut oil (COME) and Jatropha oil (JCME) were used as feedstocks for the production of biodiesel to investigate optimum parameters to obtain high yield. For each of the feedstock, the biodiesel yield increased with increase in NaOH concentration. The highest yield was obtained with 1% NaOH concentration for all. The effect of methanol in the range of 4:1–8:1 (molar ratio) was investigated, keeping other process parameters fixed. Optimum ratios of palm kernel oil and coconut oil biodiesels yielded 98% each at methanol:oil molar ratio of 8:1. The physiochemical properties obtained for each methyl showed superior properties compared with those reported in published data

This study seeks to char and briquette various agricultural wastes to be used as fuel in cookstove. To realize this, charred briquettes were produced from various agricultural wastes such as rice husk, maize cobs, palm kernel shell, and sawdust using the fast pyrolysis method at three different particle sizes (0.6 mm, 1.8 mm, and 2.36 mm) and compacting pressures (10 MPa, 20 MPa, and 30 MPa). Fuel tests such as physical test, proximate, and ultimate analysis, combustion test, and mechanical test were determined to assess the quality and suitability of the charred briquettes for use as fuel in cookstoves. The findings show that for all the three factors considered, palm kernel shell briquettes recorded the highest calorific value of 20,836.32 kJ/kg, volatile matter content of 17.3%, and fixed carbon of 76.5% while recording the least amounts of ash content of 2.7% and moisture content 3.95% compared to the other briquettes. The water boiling test results revealed that Palm kernel shell and sawdust briquette fuels reached their boiling point of 100 ℃ at the fastest time of 10 minutes. Furthermore, palm kernel shell briquette of particle size of 0.60 mm and compacting pressure of 30 MPa exhibited an outstanding performance than the other briquettes with regard to the values of specific fuel consumption of 2.38 kg/liters, burning rate of 0.00428 kg/min, shatter index of 96.93%, impact resistance of 500%, relax density of 753,291 kg/m³ and water resistance of 92.9%.

Recent developments in artificial intelligence (AI) modeling tools allows for envisaging that AI will remove elements of human mechanical effort from welding operations. This paper contributes to this development by proposing an AI tungsten inert gas (TIG) welding algorithm that can assist human welders to select desirable end factors to achieve good weld quality in the welding process. To demonstrate its feasibility, the proposed model has been tested with data from 27 experiments using current, arc length and welding speed as control parameters to predict weld bead width. A fuzzy deep neural network, which is a combination of fuzzy logic and deep neural network approaches, is applied in the algorithm. Simulations were carried out on an experimental test dataset with the AI TIG welding algorithm. The results showed 92.59% predictive accuracy (25 out of 27 correct answers) as compared to the results from the experiment. The performance of the algorithm at this nascent stage demonstrates the feasibility of the proposed method. This performance shows that in future work, if its predictive accuracy is improved with human input and more data, it could achieve the level of accuracy that could support the human welder in the field to enhance efficiency in the welding process. The findings are useful for industries that are in the welding trade and serve as an educational tool.