Dangerous environmental consequences and market unpredictability of fossil fuels have necessitated the need for sustainable large-scale production of biofuel in Nigeria. Unrefined palm oil (UPO) is a significant product of commercially available oil palm plants in the country. This study experimentally investigates the production of biodiesel from refined, bleached and deodorised (RBD) palm olein extracted from UPO obtained from batch reactors. The transesterification process of the RBD palm olein with methanol and in the presence of potassium hydroxide (KOH) catalyst produced biodiesel with a 62.5% yield, thus confirming its feasibility for mass production. The derived biodiesel has properties equivalent to ASTM D792 standard for biodiesel fuels.

Energy from municipal solid waste is steadily being integrated into the global energy feedstock, given the huge amount of waste being generated from various sources. This study develops a Multilayer Perceptron Artificial Neural Network for the prediction of High Heating Value of municipal solid waste as a function of moisture content, carbon, hydrogen, oxygen, nitrogen, sulphur, and ash. A total of 123 experimental data were extracted from reliable database for training, testing, and validation of the model. This model was trained, validated and tested with 70%, 20%, and 10% of the municipal solid waste biomass datasets respectively. The predicted High Heating Value was compared with the experimental data for two different training functions: Levenberg Marquardt backpropagation and Resilience backpropagation, and with some correlation from the literature. The accuracy of the model was reported based on some known performance criteria. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and Coefficient of Correlation (CC) were 3.587, 2.409, 21.680, 0.970 respectively for RP and 3.095, 0.328, 22.483, 0.986 for LM respectively. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted High Heating Values (HHV). The authors concluded that these models can be a useful tool in the prediction of heating value of MSW in order to facilitate clean energy production from waste.

This study investigated the breakdown trend in an automated production with an aim to recommend the application of reliability-centered maintenance (RCM) for improved productivity via a new preventive maintenance (PM) program. An individual section-forming machine (ISM)—a glass blowing machine for making glass bottles—was used as the case study for an automated production system. The machine parts and the working mechanisms were analysed with a special focus on methods of processes and procedures. This will enable the ISM maintenance department to run more effectively and achieve its essential goal of ensuring effective machine operation and reduction in machine downtime. In this work, information is provided on the steps and procedures to identify critical components of the ISM using failure modes and effect analysis (FMEA) as a tool to come up with an optimal and efficient maintenance program using the reliability data of the equipment’s functional components. A relationship between the failure rate of the machine components and the maintenance costs was established such that using the recommended PM program demonstrates evidence of an improvement in the machine’s availability, safety, and cost-effectiveness and will result in an increase in the company’s profit margin.