Assessment of some mechanical properties and microstructure of particulate periwinkle shell-aluminium 6063 metal matrix composite (PPS-ALMMC) produced by two-step casting

This work investigates some mechanical properties and microstructures of PPS-AlMMC and compares the properties of the composites and those of the aluminium 6063 (AA6063) alloy. Periwinkle shells were milled to particle sizes of 75μm and 150μm and used to produce PPS-AlMMC at 1,5,10 and 15wt% filler loadings using two-step casting technique. The mechanical properties and microstructures of the composite materials were compared with those of the AA6063 alloy. It was observed that the filler distributes uniformly in the matrix due to the two-step casting technique. Improved strength, ductility, hardness and modulus were obtained when the filler was used to reinforce the alloy. However, using a filler of bigger particle size resulted to reduced tensile strength, ductility and toughness of composites.Key words: Composites, Periwinkle shell, Aluminum, Mechanical properties, Microstructur

Extreme gradient boosting: A machine learning technique for daily global solar radiation forecasting on tilted surfaces

Enhancing solar irradiance and accurate forecasting is required for improved performance of photovoltaic and solar thermal systems. In this study, Extreme Gradient Boosting (XGBoost) model was developed using three input parameters (time, day number, and horizontal solar radiation) and was utilized to forecast daily global solar radiation on tilted surfaces. The proposed model was built using XGBRegressor with five generations, 100 n estimators, and a learning rate of 0.1. Three statistical metrics, such as the coefficient of determination (R2 ), root mean square error (RMSE), and mean absolute error (MAE), were used to compare the model’s results to observed solar radiation data from the Nation Centre for Energy, Research and Development, University of Nigeria, Nsukka. The results showed improved prediction accuracy and XGBoost capability to estimate daily global solar radiation on tilted surfaces. In the training section, the proposed model had a statistical performance of R2 = 0.9977, RMSE = 1.6988, and MAE = 1.081, and in the testing section, R2 = 0.9934, RMSE = 2.8558, and MAE = 2.033. XGBoost model demonstrated a better performance when compared with other models in the literature. As a result, the proposed model provides an effective approach for estimating solar radiation

Machine learning approach for solar irradiance estimation on tilted surfaces in comparison with sky models prediction

In this study, two supervised machine learning models (Extreme Gradient Boosting and K-nearest Neighbour) and four isotropic sky models (Liu and Jordan, Badescu, Koronakis, and Tian) were employed to estimate global solar radiation on daily data measured for one year period at the National Center for Energy, Research and Development (NCERD) at the University of Nigeria, Nsukka. Two solarimeters were employed to measure solar radiation: one measured solar radiation on a tilted surface at a 15° angle of tilt, facing south, and the other measured global horizontal solar radiation. The measured global horizontal solar radiation and the time and day number were used as input for the prediction process. Python computational software was used for model prediction, and the performance of each model was assessed using statistical methods such as mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE) (RMSE). Compared to the measured data, it was discovered that the Extreme Gradient Boosting (XGBoost) algorithm offered the best performance with the least inaccuracy to sky models

Analyses of Use of Improved Beekeeping Equipment among Agricultural Development Programme Registered Bee Farmers in Imo State, Nigeria

The study examined improved beekeeping in Imo State, Nigeria. A sample of 30 registered bee farmers participated in the study. Data were elicited from the farmers using structured questionnaire and analysed using percentages and means. Results showed that the major sources of information on modern bee keeping equipment were farmers’ association (96%), extension agents (76%) and friends/relatives (70%). Available bee products in the area were honey (97%), bee wax (83%), bee venom (70%) and propolis (63%). Improved beekeeping equipment used in the area were foot wears (100%), gloves (100%), smokers (93%), bee veil (96%) and bee suits (87%). Constraints to improved beekeeping in the area include; lack of favourable agricultural policies (87%), lack of standard market for the products (77%), inadequate training and information on beekeeping (67%) and high cost of equipment (70%). The study therefore recommends the need for efforts aimed at promoting modern bee farming in the area, especially targeted at younger and educated farmers

A review on the factors affecting the properties of natural fibre polymer composites

Improved quality natural fibre composites cannot be achieved without considering certain factors such as the degree of uniformity of the fibre, wettability of the fibre, fibre length, fibre volume fraction, type of matrix, interfacial bond strength, fibre orientation, compatibility of the fibre with the resin, processing parameters and manufacturing techniques among others. Their influences on the properties of the composites with typical examples from previous works were highlighted. The exact or approximate volume fractions of specific fibres in specific resins for optimal performance in composites are lacking. Epoxy, low density polyethylene, polystyrene and polyester resins were mostly used as matrix for natural fibre composites. Epoxy resins possess higher tensile and flexural strengths than polyester resins. Significant differences in the tensile strength and Young’s modulus of natural fibre polymer composites were observed with changes in the orientation and length of the fibres particularly when the differences in length are significant. Other relevant issues affecting natural fibre composites were buttressed with the aim of improving the properties of natural fibre polymer composites for advanced applications

Tensile and microstructural properties of unidirectional coir bio-derived epoxy composites

The study investigates the possibilities of reinforcing bio-derived epoxy (bio-epoxy) polymer with unidirectional arrays of coir fibre using hand layup. The coir fibres were unidirectionally prepared in two layers. The tensile properties such as strength, Young’s modulus and elongation at break were found to be 43.83 MPa, 2.4 GPa and 2.72 % respectively. The tensile strength of coir/bio-epoxy composite was higher than the unreinforced bio-epoxy resin. The fibre volume fraction of the composites obtained using ImageJ on an 8-bit grey scale was 34%. The optical microstructure of the composites shows the distribution of the fibres within the bio-epoxy matrix