Temperature Forecasting as a Means of Mitigating Climate Change and Its Effects: A Case Study of Mali

Temperature forecasts and trend analyzes were carried out for several locations in Mali as an important tool for warning of potentially threatening weather events such as severe heat waves, storms, droughts and floods, which could pose a great risk to humans and their environment. Five locations (Segou, Sikasso, Kayes, Gao and Taoudenni) across Mali (170 00’N – 40 00’W) were chosen for this research work. Satellite data of annual temperature obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) database for 35 years (1985-2019) was used for this work. The Mann-Kendall trend test was carried out for various locations to observe and study the trend. Four Models including Auto Regressive and Integrated Moving Average (ARIMA), Exponential smoothening (ETS), TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components) and the linear model were employed to forecast average temperature for 10 years for all the locations. The model that produces the best forecast at the 95% confidence level is expected to have the lowest Root Mean Square Error (RMSE) value. The results showed that no significant trends were recorded at the considered locations. The linear model produced the best forecast for Segou, Kayes and Taoudenni, while the TBATS model produced the best forecast for Gao and the ARIMA model produced the best forecast for Sikasso.Citation: Billy, U., Udo, S., Ewona, I., Umoh, M., & Mfongang, A. (2023). Temperature Forecasting as a Means of Mitigating Climate Change and Its Effects: A Case Study of Mali. Trends in Renewable Energy, 9(2), 167-179. doi:http://dx.doi.org/10.17737/tre.2023.9.2.0015

MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN END MILLING OF ALUMINIUM USING LEAST SQUARE APPROXIMATION METHOD AND RESPONSE SURFACE METHODOLOGY

In end milling, accurate setting of process parameters is extremely important to obtained enhanced surface roughness (SR). Due to a recent innovation in mechanization made it possible to produce high quality manufacturing products. The perceptions of quality in mechanical products are their physical look that is the surface roughness (SR). The aim of this research work is to develop mathematical expression (M.E) and mathematical model using least square approximation method and Response Surface Methodology (RMS) to predict the SR for end milling of Al 6061 alloy. The process parameters that were selected as predictors for the SR are Spindle speed (V), axial depth of cut (a), feed rate (f) and radial depth of cut (d). 30 samples of Al 6061 alloy were carried out using SIEG 3/10/0010 CNC machines and each of the experimental result was measured using Mitutoyo surface roughness tester and Presso-firm. The minimum SR of 0.5 μm were obtained at a spindle speed of 2034.608 rpm, feed rate of 100 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.5 mm. Analysis of variances shows that the most influential parameters was feed rate. After the predicted SR has been obtained by using the two methods, average percentage deviation was calculated, the result obtained using least square approximation method (that is the mathematical expression) show the accuracy of 99% and Response Surface Methodology (RSM) mathematical model shows accuracy of 99.6% which is viable and appropriate in prediction of SR. When either of these models are applied this will enhance the rate of production

The Role of Research in Attaining Sustainable Development Goals

A study on the significant roles of research in the realization of the Sustainable Development Goals was carried out to analyze the facts and figures gotten through secondary information’s to determine the resultant effect of research in the pursuance of the SDGs. This research explores various opinions of several authors and researchers towards the concept of research in totality. This study gave clear indications that research as a full concept is not a cliché and it is revolving year-in year-out in our contemporary world of interest as the need for human survival on the planet earth gets more prioritized on the agenda of the various sustainability organizations like UNESCO, United Nations, UNEP, IISD. This is the present century’s goal and it is all NGOs and other private parastatals are enjoined to be part of this move as it is a collective effort to seeing its realization

Manufacturing of Brake Pad Using Aluminium Silicon Carbide Reinforced with Alumina for Automobile Industry

The brake pad plays an integral part in the automobile industry by controlling cars’ motion and making them safe to drive. The brake pad ensures the safety and longevity of the vehicle. In the last century, we have seen great strides in finding materials that best perform thermal conductivity, compressive strength and wear-resistant. The continual search for new composites with improved properties to substitute for the composites commonly used for the brake pad necessitated this work. This research involves reinforcing aluminium metal with small grits sizes of silicon carbide. Subsequently, the additions of alumina, zinc and calcium through mechanical stir casting were adopted to produce the samples. The samples were further tested and analysed on those particular properties. The results illustrate that the wear resistance and thermal conductivity were similar to those from the controlled specimen. The composite with 70%wt of aluminium, 20%wt of SiC and 10%wt of Al2O3 is recommended as a substitute for the conventional brake pad. It has thermal conductivity of 93.3 W/m.K, wear resistance of 43.22 cm3/cm and a compressive strength of 1343.3 MPa against 101.2 W/m.K, 8.10 cm3/cm and 1220.4 MPa, respectively, for the control sample

Noise Level Investigation and Control of Household Electric Power Generator

Electric power generator is an essential household facility particularly in developing countries. It exists in various capacities and makes in shops, offices, workshops and factories. The attendant environmental risks of this source of power supply are major challenges to users both at household and industrial level. Efforts through research and development are still on-going to mitigate the prevailing health hazards. This study investigates noise level of typical portable generator (2 kW/220 V) and the sound absorbance capacity of an acoustic enclosure developed from a multilayer panel made of locally sourced galvanized metal sheet (0.90 mm), Polyurethane acoustic foam (7.50 mm), Particle board (35.0 mm) and plywood (10.0 mm). The noise produced by the generator when on load and no load were considered for cases of it being placed inside and outside the enclosure and compared with acceptable OSHA safe thresholds noise level for the school environment where the study was carried out. The result shows a significant reduction of noise produced indicating a shift from very laud threshold (average of 86.23 dB (A)) for no load and no enclosure to moderately low region (average of 69.34 dB (A)) for the case of no load using enclosure. Likewise considering loaded scenario similar trend was recoded with average acoustic capacity of the enclosure of 23.20 dB (A). The effect of the enclosure is positive and the potential benefits of improved noise absorption panels to enhance better performance of the developed enclosure for office and household generators was established. Keywords: Electric Generator, Enclosure Panel, Noise control, Load, Household

Dataset on experimental investigation of optimum carburizing temperature and holdingtime of bi-nano additives treatment of AISI 5130 steel

Investigation of optimum carburizing temperature and holding time on bi-nano additives treatment of AISI 5130 steel was presented in this study. AISI 5130 steel of 100 kg mass of 0.35% carbon content was buried in pulverized additives consisting of palm kernel and coconut shell using egg shell as an energizer. Four sets of 150�150�150mm3 steel boxes packed with additives mixed at varying weight ratio of 50:30:20 and sixty-four pieces of 20�20�5mm3 AISI 5130 steel were case hardened using muffle furnace (2500 °C maxcapacity) at respective temperatures and time of 950,1000,1050,1100 °C and 60,90,120,180 min. The core, interface and surface hardness of the treated samples with their respective weight loss, wear volume and rate were investigated. This data set could be used in nano-composite match mixed ratio and optimization of carburizing medium and time for any industrial used case hardened steel

NUMERICAL INVESTIGATION OF STRESS AND STRAIN DISTRIBUTION IN EQUAL CHANNEL ANGULAR EXTRUSION OF AL 6063 ALLOY

This research work is aimed at studying the stress and strain distribution in the Equal Channel Angular Extrusion of Al 6063. Equal Channel Angular Extrusion (ECAE) processes enable material achieve nanoscale ultra-fine grain size without altering the physical properties. Automatic Dynamic Incremental Nonlinear Analysis (ADINA) which is a Finite Element Analysis (FEA) based solution was used to determine the stress and strain distribution in the material subjected to a single ECAE process. The model ECAE die and Al 6063 billet was developed with ADINA, the ECAE process were simulated and the data from the application was analysed. The result from the simulation showed that after a pass of ECAE of Al 6063, the average effective stress and strain were 203.08 MPa and 0.67 respectively and were highest at the inner part of the billet. Also, the average effective stress and strain were 178.02 MPa and 0.59 respectively at the mid part of the billet and the average effective stress and strain of 178.37 MPa and 0.58 respectively were lowest at the outer part of the billet. The results showing higher stress and strain distribution in the billet part closest to the die inner corner confirms that ECAE process for Al 6063 was inhomogeneous and it is an effective method in increasing the yield strength of Al 6063 alloy

Factors Affecting Ballability of Mixture Iron Ore Concentrates and Iron Oxide Bearing Wastes in Metallurgical Processing

Iron oxide bearing wastes (IOBS) are produced at every part of processing stage of sinter, molten iron and steel production. They are hard to handle and in many cases are stockpiled only to be a source of environmental pollution. However, they can be balled into pellets. Pellets characterized by good ballability values are transportable and recyclable as they can withstand stress without disintegrating back to dust. Yet, ballability is affected by certain factors like the grain sizes of the materials, the moisture and binder contents of the ball mix, wettability of the balled materials and the processing perimeters of the granulator. The objective of this research work is to investigate the factors affecting ballability of mixture of iron ore concentrates and iron oxide bearing wastes in metallurgical processing. The parameters under consideration were: grain size of materials, the moisture contents, speed of balling disc, IOBS and bentonite (binder) contents of the balled mix. The investigation was carried out by balling different volume fractions of mix containing iron oxide concentrate and IOBS using a balling disc and testing the resulting balls for green compressive strength using an universal testing machine. It was found that the ballability of the mixture of iron ore concentrate and IOBS increases as grain sizes of the materials reduce but increases as the moisture contents and IOBS content increase up to an optimum value of moisture content in the mix before it starts to reduce. The ballability also increases along with the speed of the granulator (balling disc) within the limit of this work. An increase in ballability with a slight raise in bentonite content in the mix was observed as well

Model Selection Process in Time Series Analysis of Production System with Random Output

In time series investigation of characteristics of production system, different competing models are generally obtained particularly in production settings with stochastic output attributable to bottleneck problems. Consequently, selecting the best model that describes a production system becomes challenging and critical because some models that fit observed data most accurately may not predict future values correctly on account to model complexities. This research desires to demonstrate the procedure for model selection in production system with random output via the use of Adjusted Coefficient of Determination (R2 ) , Akaike and Schwarz criteria tools. Production output measurements obtained serve as input data to Autocorrelation Function and Partial Autocorrelation Function to obtain the order of Autoregressive, Autoregressive Moving Average and Autoregressive Integrated Moving Average models. The model parameters were estimated and used for predictions and compared with original and transformed data to obtain Sum of Squared Error (SSE). Afterward, the models were subjected to adequacy evaluation and subsequently tested with Akaike and Schwarz criteria. Among the competing models, ARIMA (3, 1, 1) model explain 66% variance of the dataset and wielded the lowest Akaike and Schwarz values of 534.41m and 534.34m respectively and thus selected as the model that represents the production system under investigation. The approach establishes that Adjusted Coefficient of Determination in conjunction with Akaike and Schwarz criteria are adequate tools for model selection in time series investigation particularly in stochastic situatio