https://journaljenrr.com/index.php/JENRR <p style="text-align: justify;"><strong>Journal of Energy Research and Reviews (ISSN: 2581-8368)</strong>&nbsp; aims to publish high-quality papers (<a href="/index.php/JENRR/general-guideline-for-authors">Click here for Types of paper</a>) in all areas&nbsp;of energy generation, distribution, storage, management, production, conversion, conservation, systems, technologies and applications, and their impact on the environment and sustainable development. Articles related to the environmental, societal, and economic impacts of energy use and policy will also be considered. By not excluding papers based on novelty, this journal facilitates the research and wishes to publish papers as long as they are technically correct and scientifically motivated. The journal also encourages the submission of useful reports of negative results. This is a quality controlled, OPEN peer-reviewed, open-access INTERNATIONAL journal.</p> SCIENCEDOMAIN international en-US Journal of Energy Research and Reviews 2581-8368 https://journaljenrr.com/index.php/JENRR/article/view/303 <p>The major factor faced by Agricultural activities is water scarcity. Water is very essential in Agricultural activities (plantation), crop acquires water naturally by precipitation and subsurface moisture, when the supply of water is inadequate for crop use, mostly results to irrigation. This present study estimates and compares six various universally accepted models for estimating reference evapotranspiration (ET₀) for Kano situated in the Sahelian climatic zone of Nigeria using measured meteorological parameters of monthly average daily global solar radiation, sunshine hour, wind speed, minimum and maximum temperatures and relative humidity covering a period of thirty one years (1988 – 2018). Four different statistical validation indices of Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE) and coefficient of correlation (R) were carried out to test the accuracy of the evaluated models. The result indicated that high value of ET_o was found in the month of April with 10.0256 mm/day for Kano and a low value was found to be in August with 5.0804 mm/day for Kano. The Blaney – Morin Nigeria model was found more accurate for Kano with RMSE, MBE, MAE and R values as 1.5078 mm/day, -1.4634 mm/day, 1.4634 mm/day and 0.9790 respectively.</p> M. K. Abdulsalam D. O. Akpootu S. Aliyu A. K. Isah Copyright (c) 2023 Abdulsalam et al.; This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2023-09-11 2023-09-11 15 2 12 25 10.9734/jenrr/2023/v15i2303 https://journaljenrr.com/index.php/JENRR/article/view/304 <p>The combustion characteristics of two carbonized biomass briquettes fuels samples (sample A and B) from two bio-wastes have been presented. Sample A is comprised of Agro-waste (residues) mainly rice husk, groundnut shell, corn cob, bagasse, rice straw, coconut coir, branches, wheat straw, maize husk, grass from the field, cassava stem while Sample B comprised of decaying domestic wastes material from wastes disposal/collection points mainly garbage, vegetables/fruits, protein, groundnut shell, maize cob, waste papers, all sorts of discarded foods etc. Analysis of briquettes fuels samples in terms of proximate analysis, ultimate analysis and calorific values were necessary to determine suitability of such material as reliable and sustainable energy sources. These parameters analyzed are unique fundamental code that characterizes and determines the properties, quality, potential applications and environmental problems related to any fuel. The result of the analysis placed sample B above sample A in terms of heating value. The proximate analysis indicated that the sample B had a better ignition characteristic at 77% volatile matter against 44% of sample A. Also, sample B have better heating value with fixed carbon 32% against 21% of sample A. This was also confirmed by ultimate analysis where sample B recorded a higher value of percentage Carbon and percentage Oxygen at 47.04% and 41.6% respectively. The bulk densities were in the range of 499kg/m² to 502kg/m²which is very good in terms of handling and transportation of the fuels. Moreso, calorific value of both samples were appreciably high at 18704 KJ/Kg for sample A and 18901.3 Kj/Kg by sample B. The ratio FC:VM for sample A is 0.477 which is higher than that of sample B, indicating that sample A will have a better yield and formation of biochar. Therefore the carbonised biomass briquettes are of good quality and exhibited good combustion properties as an alternative energy feedstock for domestic and industrial applications. It is a better source of energy for cooking, replacing fuel wood (firewood) and it is eco friendly.</p> Obi, O. E. Mong, O. O. Nleonu, E. C. Kalu, P. N. Onyeocha, C. E. Ndubuisi, C. O. Onwukwe, I. E. Copyright (c) 2023 Obi et al.; This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2023-09-12 2023-09-12 15 2 26 34 10.9734/jenrr/2023/v15i2304 https://journaljenrr.com/index.php/JENRR/article/view/305 <p><strong>Aim:</strong> To investigate wind characteristics and estimate wind power density of Narok weather station in Narok county using Weibull distribution.</p> <p><strong>Research Design:</strong> Historical hourly wind direction and speed data recorded by the Kenya Meteorological Department in Narok weather station was analyzed.</p> <p>Place and duration: The study utilized data samples collected at Narok weather station over a period spanning from 2011 to 2021.</p> <p><strong>Methods: </strong>To assess the temporal characteristics, a statistical average technique was employed. The spatial aspect, specifically wind speed variation with height, was evaluated through wind speed extrapolation using the power law. The dominant wind direction was determined by plotting a polar chart based on a frequency distribution table prepared using both wind direction and wind speed data. The turbulence intensity of the wind was calculated using the turbulence intensity equation. The Weibull parameters were estimated using the maximum likelihood estimation method. The Weibull probability distribution was used to analyze wind speed distribution and power density. The extrapolated Weibull parameters were utilized to calculate wind power density at various heights. The accuracy of the wind regime distribution in Narok was assessed by employing the R2 technique.</p> <p><strong>Results:</strong> The wind regime in Narok exhibited an average annual wind speed of 4.3 m/s and a mean wind power density of 126 W/m2. Analysis of diurnal wind speed variation revealed peak wind speeds around noon, with wind speeds exceeding the cut-in wind threshold (3 m/s) between 0430hrs and 2100hrs. March and October were identified as the windiest months, exhibiting the highest wind power densities, while June and December demonstrated the lowest values. Wind speed and, consequently, wind power density increased exponentially with height. The prevailing wind directions in Narok were primarily from the East, followed by the North and North West. The wind regime in Narok exhibited turbulence, as indicated by average turbulence intensities exceeding 0.25. The wind regime in Narok was accurately described by the Weibull distribution, with an approximation accuracy of 0.94 based on the R2 error.</p> <p><strong>Conclusion:</strong> The wind regime in Narok is generally suitable for extracting wind power at heights above 15 m, regardless of the scale of the wind power extraction.</p> Steven Okoth Otieno Fredrick Isaac Motochi Copyright (c) 2023 Okoth et al.; This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2023-09-15 2023-09-15 15 2 35 46 10.9734/jenrr/2023/v15i2305 https://journaljenrr.com/index.php/JENRR/article/view/306 <p>Reservoir geomechanics is a crucial aspect of optimising and developing oil and gas activities, especially in maximising production. Recent technological advancements have revolutionised reservoir geomechanics studies, including integrating data-driven approaches. This review examines and integrates machine learning, data science, and data twin in reservoir studies. The primary aim is to identify the benefits, limitations, significant advancements, potential challenges, opportunities, and research gaps of data-driven approaches to reservoir geomechanics. Additionally, this study aims to create opportunities for further research to address these challenges. The review identifies cost-effectiveness, improved reservoir characterisation, and reduced operational risks as the benefits of integrating data-driven approaches in reservoir geomechanics. However, the review also highlights the significant challenges of data-driven approaches, such as insufficient datasets, limited interpretability, and limited transferability of models. By shedding light on these issues, this review provides a foundation for future research toward finding solutions to these challenges.</p> Izuchukwu Josephmartin Korie Chudi-Ajabor Ogochukwu Onwuagba Kenechi Innocent Copyright (c) 2023 Korie et al.; This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2023-09-19 2023-09-19 15 2 47 56 10.9734/jenrr/2023/v15i2306 https://journaljenrr.com/index.php/JENRR/article/view/302 <p>&nbsp;</p> <p>Air pollution is a serious global issue that threatens human life and health, as well as the environment. Machine learning algorithms can be used to predict air pollution level data from both natural and anthropogenic activities. Environmental and government agencies can use these speculations to issue air pollution alerts. This review work is an attempt at the recent status and development of scientific studies on the use of machine learning algorithms to model air pollution challenges. This study uses the scientific web as a primary search engine and covers over 100 highly peer-reviewed articles from 2000-2022. Therefore, this review paper aims to highlight the various application methods of machine learning, notably data mining, in air pollution control and monitoring. It also comprehensively analyses published works by renowned scholars and authors worldwide, discussing how machine learning has been used in mitigating air pollution. By examining the chronological trends of machine learning in air pollution, this review paper provides an up-to-date account of the successes achieved in regulating air pollution using machine learning techniques. Additionally, it identifies areas that require further research, critically analyzing the current state of knowledge and potential research directions.</p> Marvelous Ukachukwu Nnemeka Uzoamaka Nnama Elochukwu Copyright (c) 2023 Ukachukwu et al.; This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2023-09-05 2023-09-05 15 2 1 11 10.9734/jenrr/2023/v15i2302