Ghana Mining Journal

Evolution of the Kenyan Geodetic Reference Frame

S. M. Matara, D. N. Siriba, J. B. K. Kiema, S. M. Musyoka — Fri, 23 Aug 2024 00:00:00 +0000

A uniform geodetic reference frame forms the spatial foundation for the creation of a Land Information System (LIS), based on which related spatial data for transport, mineral resources, power transmission, telecommunication, etc., can be overlaid to aid in operations like design, construction, commissioning, and surveillance of the infrastructure. In essence, a geodetic reference frame permits spatial referencing of all land data to identifiable positions on the earth’s surface. Further, a geodetic reference frame provides an effective language for interpreting and disseminating land information. Kenya does not have a uniform geodetic reference frame instead there is a patchwork of geodetic networks across the country established by different organizations to facilitate activities like the demarcation of international boundaries, as such, Kenya’s geodetic network consists of many coordinate systems. This paper reviews the evolution of the Kenyan geodetic network, highlights various geodetic networks established in Kenya, and identifies challenges to the development of a modern uniform Geodetic Reference Frame for Kenya (KENREF). The installation of Continuously Operating Reference Stations (CORS) by the government and private entities is not centrally coordinated, which implies that Kenya may not realize a uniform modern geodetic reference frame soon.

Pixel-Based Image Classification using a Grey Wolf Optimised Support Vector Machine*

M. B. Poku, I. Yakubu, Y. Y. Ziggah — Fri, 23 Aug 2024 00:00:00 +0000

Support Vector Machine (SVM) is one of the most effective machine learning algorithms widely employed for classification tasks. SVMs perform well in high-dimensional spaces, making them suitable for applications with a large number of features. This capability is crucial in tasks like image classification, where each pixel can represent a feature. Its effectiveness has made it a preferred choice among remote sensing experts. However, the performance of the SVM is highly dependent on the appropriate selection of the best combination of hyperparameters. Thus, optimisation is an essential step for maximising classification accuracy. This paper explores a metaheuristic optimisation algorithm, the Grey Wolf Optimisation Algorithm (GWO), to optimise the performance of the SVM by fine-tuning the optimal combination of hyperparameters that can improve the accuracy of the SVM. With an accuracy of 92%, the GWO-optimised SVM confirms its superiority compared to the standalone SVM, which obtained an accuracy of 89%. The findings of this research highlight the potential of metaheuristic algorithms in improving the effectiveness of machine learning algorithms for image classification tasks.

Prediction of Surface Deformation for Open Cast Mine Based on Pit Wall Prisms Monitoring Dataset*

I. Yakubu, Y. Y. Ziggah, F. Osei — Fri, 23 Aug 2024 00:00:00 +0000

This paper explores the application of Artificial Intelligence (AI) techniques for predicting pit wall deformation in open-cast mining operations. Four AI models, including the Patient Rule Induction Method (PRIM), Radial Basis Function Neural Network (RBFNN), Back Propagation Neural Network (BPNN), and Group Method of Data Handling (GMDH), were developed and evaluated to estimate pit wall deformation for three different monitoring locations designated as Prisms 1, 2 and 3. The AI models were statistically evaluated using dimensioned error indicators such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The coefficient of determination (R²) was also used for the model’s performance evaluation. The study concluded that for the Prism 1 monitoring point, the BPNN was the most suitable for predicting the pit wall deformation. However, for Prisms 2 and 3, the RBFNN demonstrated superior performance, with minimal errors and high R² scores, making it a suitable choice for deformation prediction. GMDH exhibited fair results, while PRIM produced significant prediction errors, rendering it less suitable for pit wall deformation estimation. In general, the study findings suggest that AI techniques can significantly enhance and automate the deformation prediction process in open-cast mining, offering opportunities for improved safety and operational efficiency.

Transformation Parameters Derivation between Global, National and Mine Grid Reference Systems*

Y. Y. Ziggah, I. Yakubu — Fri, 23 Aug 2024 00:00:00 +0000

In mining operations, coordinate transformation plays a key role in transforming coordinates acquired in the Global Navigation Satellite System (GNSS) into the national and local mine grid systems. It has often been known that most mining sites have transformation parameters determined using only a few common points or the minimum co-located points. However, these determined parameters only fit within a limited extent of the mine concession. Hence, allowing for extrapolation and incorrect transformation results when the existing transformation parameters are utilised beyond the existing co-located points. As the mine expands beyond it operationalised zones, there is the need to redefine a new set of transformation parameters that are devoid of extrapolation and apply to a wider coverage of the mine concession. This study applied, evaluated, and compared the Two-dimensional (2D) conformal similarity model and 2D affine model to facilitate the transformation of the Local Mine Grid (LMG) coordinates to the Ghana National Projected Grid (GNG), Universal Transverse Mercator (UTM), and vice versa. To guarantee the consistency of the transformation results between the models tested on all the grid systems, similar transformation performance was revealed. In transforming between GNG and LMG, the 2D conformal results vary from the 2D Affine by 0.0079 m, -0.0128 m, 0.0079 m and 0.0261 m in RMSE_HPE, SD_HPE, Max_HPE, and Min_HPE. Similar observation was made for transforming between UTM and LMG, and UTM and GNG, respectively. Based on the results obtained it can be stated that the two models are applicable in connecting mine grid system into a national grid system (non-geocentric), and UTM.

Design and Implementation of a Smart Data Model for Geospatial Addressing Systems for Urban Areas in Ghana Using an Area Zone Identifier: A Case Study*

P. E. Baffoe, S. Fiatonu — Fri, 23 Aug 2024 00:00:00 +0000

For effective processing of taxes and bills, recording of crime events, and dispatching of emergency services in Ghana, many organisations are maintaining large databases; unfortunately, within an organization, various units and sections of the same organization may be using varying address formats for streets and properties, whereby staff of the same organization often disagree on what address system to be adopted for a particular area. This often leads to stakeholders using different address systems for the same property. This brings about much confusion in the communities, especially with the city authorities, the utility companies and the Post Office and others. This usually poses significant challenges in sharing information, locating and extracting information that may be keyed to a situs address, or simply communicating about an address’s location. Therefore, one of the principal needs for this standard is to make addresses more uniform and, thereby, facilitate the sharing of address information. With technological advancements, addresses can be directly associated with features like points, polylines or polygons. In order to combine all formats of address systems into a unique and uniform standard system, a smart data model for a geospatial addressing system has been designed and implemented, using sub-Metros of Accra Metropolitan Assembly as case study, for efficient courier system and communication in urban areas in Ghana. The results provide a system for naming existing and new roads, thereby directly associating addresses with graphic features such as points, polygons and line through GIS. The system can aid in the proper delivery of parcels without delays or lost parcels, efficient and timely responses by ambulance, fire services, and law enforcement agents, and effective revenue collection for the Metropolitan authorities.

Performance Evaluation of Extreme Learning Machine Techniques for Prediction of Noise Pollution*

P. E. Baffoe, C. B. Boye — Fri, 23 Aug 2024 00:00:00 +0000

Urban planning, epidemiology research, and environmental management have significant challenges when predicting intraurban noise levels in communities, particularly in developing nations. To accurately predict changes in noise levels during intraurban development and the resulting noise pollution, the majority of existing noise-predicting models are limited. In this study, two noise prediction models namely, Classical Extreme Learning Machine (C-ELM) and Bi-directional Extreme Learning (B-ELM) were developed for Tarkwa Nsuaem Municipality, and their performances were evaluated using statistical indicators. Using statistical measures to compare the models' performances, the B-ELM outperformed the C-ELM. The indications show the difference, with the RMSE of B-ELM being 0.87 dB and that of C-ELM being 3.67 dB. Additionally, the B-Standard ELM's Deviation and Mean Square Error were 0.80 dB and 0.1399 dB, respectively, while for the C-ELM, they were 3.73 dB and 0.06 dB. The findings of the B-ELM were used to create a map that depicts the distribution of the expected noise levels. It was discovered that there is a hazard, meaning persons who live in that region are at a high risk of experiencing adverse health impacts from noise levels above 65 dB when comparing the expected noise levels to the EPA limits.

Mining Methods Employed in Artisanal and Small-Scale Gold Mining and their Contribution Towards Sustainable Development of the Sector*

A. B. Yalley — Fri, 23 Aug 2024 00:00:00 +0000

The selection of a suitable mining method for a particular ore deposit is an important attribute required for the environmental, safety and economic sustainability of a mining project. These multi-criteria decision-making process is a complex task for mining engineers as the selected method must suit the technical, economic, environmental and safety requirements of the mine. In Large-Scale Gold Mining (LSGM) operations, several mining methods have been developed and designed based on engineering principles to match the attributes of the various deposits exploited. However, in Artisanal and Small-Scale Gold Mining (ASGM), mining method development, selection and its implications on the safety, economic and environmental sustainability of the operations have been given little attention. This paper evaluates the mining methods employed in ASGM in Ghana and the contribution of the employed mining methods towards the sustainable development of the sector. The study employed a combination of literature review and field studies to ascertain the mining methods employed in ASGM. Relevant literature was retrieved from 2003 to 2022 from 4 scientific database namely, Academia, ResearchGate, Google Scholar and Google in August 2023. The field work involved the study of 4 ASGM sites in Tarkwa and Prestea. A total of 14 mining methods were identified to be employed in ASGM in Ghana. It was revealed that the identified mining methods were not designed or selected based on any engineering principles but rather the miners employed any abecedarian methods which result in ground failures subsequently leading to fatalities, environmental degradation and low ore recovery. The associated environmental degradation, poor safety and low productivity were indications that the mining methods employed in ASGM were hindering the sustainable development of the sector. Moreover, the study identified limited literature on mining methods employed by ASGM. It is therefore recommended that for sustainable development of ASGM in Ghana, researchers and experts should focus on developing and designing sustainable mining methods that will suit the various deposits exploited. It is also recommended that mining engineering and allied professionals should engage in ASGM to ensure effective implementation of the designed mining methods.

Predicting Abrasive Wear in Excavator Bucket Teeth using Cutting-Edge Vector Machines and Artificial Neural Networks*

G. Ativor, V. A. Temeng, Y. Y. Ziggah — Fri, 23 Aug 2024 00:00:00 +0000

The mining industry faces a growing need to accurately predict abrasive wear of excavator bucket teeth in order to establish effective maintenance policies. Hence, developing robust predictive models that can effectively track the deterioration of ground cutting tools in harsh operational environments is a logical strategy to address this challenge. This research compared the effectiveness of three vector machine models for predicting abrasive wear of excavator bucket teeth: the least squares support vector machine (LS-SVM), the relevant vector machine (RVM), and the support vector machine (SVM). The research included a comparison of the LS-SVM model's predictive performance to that of three reference ANN techniques: radial basis function network, backpropagation neural network, and generalised regression neural network. To determine the most effective technique, the prediction results of these methods were assessed using metrics such as mean square error (MSE), root-mean-square error (RMSE), correlation coefficient (R), coefficient of determination (R²), mean absolute error (MAE), and Nash-Sutcliffe Efficiency (NSE). Additionally, the Bayesian Information Criterion (BIC) was employed to select the best performing predictive method. According to the evaluation results, the LS-SVM model outperformed both the RVM and SVM methods and the reference ANN techniques, as it achieved the lowest values for MSE, RMSE, and MAE (0.025726, 0.160394, and 0.131220) respectively, along with the highest values for R, R², and NSE (0.999900, 0.999800 and 0.999794) respectively. Moreover, LS-SVM attained the lowest BIC value (-300.5774), demonstrating its superior ability to predict on-site wear of excavator bucket teeth.

Tracking the Usage Patterns of Explosives Devices from Birth to Death: A Case of the Inspectorate Division of Minerals Commission, Ghana*

D. Boahen, B. O. Affum, S. Vondee, S. Akpah — Fri, 23 Aug 2024 00:00:00 +0000

The Inspectorate Division of the Minerals Commission in Ghana plays a pivotal role in overseeing the safe and responsible usage of explosive devices within the Ghanaian mining industry. The proper management of explosives devices in the Ghanaian mining industry is a challenge. Lack of absolute control has caused several incidents and accidents in mining communities of Ghana. This study sought to develop a comprehensive tracking system, called Harmonised Explosive Management System (HEMS), for the usage patterns of explosive devices throughout their lifecycle within the jurisdiction of the Inspectorate Division of Ghana. The implementation of the robust tracking system will enhance regulatory oversight, ensure compliance with safety standards, and contribute to the sustainable and secure management of explosive materials in the mining sector. The procedure used involved a multi-faceted approach including data collection, analysis, stakeholder engagement and system design. Data was garnered from regulatory records, industry reports, and field inspections for developing the HEMS. A systematic software development approach was adopted to design and deploy the management system. The study investigated the complete lifecycle of explosive devices, starting from the point of acquisition by mining, quarrying, civil and oil and gas companies to their eventual disposal or demilitarization. Factors influencing usage patterns, such as geological conditions, mining practices, and regulatory compliance, were considered and analyzed to identify trends and potential areas for improvement. After deploying the software, the results revealed that, the proposed system was able to take inventory of all explosive devices imported or manufactured in the country, track their usage patterns, ensure compliance, and effectively extend the monitoring arm of the Inspectorate Division.

Sensitivity Analysis to Evaluate Haulage Systems Selection for Underground Mines Using Discrete Event Simulation and Mixed Integer Programming*

A. Salama, M. Nehring — Fri, 23 Aug 2024 00:00:00 +0000

As deposits located in near surface continue to be mined out, the mines will focus on deeper operations whereby the haulage distance to the mine surface is going to be one of the main challenges. Several haulage systems such as diesel and electric-powered trucks, shaft, and conveyors can be utilized for hauling operations at shallow or great mine depth. When they are to be used for longer haul distances to the mine surface, the haulage methods with less operating costs will be beneficial towards cost reduction. To improve the decision making, the haulage selection processes require knowledge of the mine planning, scheduling under variable parameters. Therefore, this paper presents a sensitivity analysis to analyse the effects of changes on selected parameters on the net present value (NPV) of the mine plans generated for an orebody at 1000- and 3000-meters levels for diesel and electric-powered trucks, shaft, and belt conveyor haulage methods using discrete event simulation and Mixed Integer Programming to evaluate the most sensitive parameters on the selection of haulage operations as mine depth increases. The simulation analysis aimed to assess the performance of these haulage options in achieving the production target of 100,000 tonnes of ore per month for each option at each depth. The simulation results were used to determine the energy cost per tonne of each haulage scenario for each depth and then exported to the mixed integer programming to calculate the operating cash flows to generate an operating NPV. The results show that, in hauling of material from underground to mine surface, diesel trucks are only feasible at shallow depth, while electric trucks, shaft, and conveyor belt systems can be used for longer haul distance. It also indicated that, diesel-powered trucks are observed to have low NPVs compared to other haulage options, while shaft and belt conveyor generated high NPVs with increasing mine depth. It is therefore concluded that, mine planners can minimize risks when evaluating haul methods, and use sensitivity to guide a suitable choice and flexibility on the hauling operations.

Electrowinning of Silver from Non-circulated Silver Nitrate Electrolyte using Graphite Electrode Recovered from Spent Zinc Carbon Batteries as Anode*

J. R. Dankwah — Fri, 23 Aug 2024 00:00:00 +0000

The electrowinning of silver from non-circulated aqueous silver nitrate electrolytic bath was investigated using cylindrical graphite rods recovered from spent zinc-carbon batteries (TigerHead Brand, Guangzhou-China) as anode and a slab of ultrapure silver metal as cathode. The effects of current density and temperature on product quality, current efficiency, and specific energy consumption were also investigated at voltages ranging from 1.2 to 2.5 V. Visual examination of electrodeposited product at the cathode was combined with analysis of the cathodic product by X-Ray Diffraction (XRD) and Scanning Electron Microscopy-Energy Dispersive X-Ray Spectroscopy (SEM/EDS), followed by measurement of current efficiency and specific energy consumption. The results indicated that graphite rods recovered from spent zinc-carbon batteries are effective at functioning as anodes for the electrowinning of silver from non-circulated nitrate baths, even at room temperatures. Increasing the current density resulted in an increase in current efficiency, increase in energy consumption and deterioration in electrodeposited product quality, evidenced by the formation of dendrites. Utilisation of voltages above 2.0 V resulted in physical deterioration of the cylindrical graphite at the anode. Analysis by XRD revealed distinct and well-defined peaks of silver metal with minor impurities at high current densities. Finally, high concentrations of silver metal (above 99.0 wt%) were revealed by SEM/EDS with minor contamination of aluminium and gold at an applied potential of 1.8 V and silver ion concentration of 30.0 g/l.

Conversion of End-of-Life Vehicle Tyres as Chemical Feedstock for the Production of Diesel Range Liquid Fuel and Char*

E. A. Arthur — Fri, 23 Aug 2024 00:00:00 +0000

Globally, over 780 billion plastics are used and discarded annually, with no proven avenues for value recovery. End-of-life vehicle tyres from the mining industry which cannot be re-used are burnt (as a disposal method) at isolated areas by scrap dealers and steel wires retrieved for sale to the iron and steel making industry. The huge amounts of gaseous pollutants released into the atmosphere as a result promote greenhouse effect and global warming. The focus of this research was investigating the feasibility of creating a diesel-compatible liquid fuel and char from end-of-life vehicle tyres (EVTs) using a temperature range of 300 °C – 670 °C from a charred palm kernel shell-fired reactor-blower assembly as a sustainable method of disposing EVTs. Samples of EVTs were collected from Tarkwa Municipality in the Western Region of Ghana and were shredded and dried in the open. Approximately 2 kg of samples of the shredded EVTs were introduced into a stainless-steel reactor and subjected to pyrolysis for 55 minutes at a heating rate of 40 °C/min. The gas exiting the reactor was collected, cooled and liquefied in a plastic container, after which its mass was measured, and its properties were analyzed by FT-IR and GC-MS. The findings show that diesel-range liquid fuel (density 0.936 g/cm³) can be generated with a yield of approximately 0.33 – 0.40 litres/kg from EVTs. Results from FT-IR and GC-MS analyses revealed that the liquid fuel is primarily composed of aromatic hydrocarbons, specifically alkyl benzenes, as well as aliphatic hydrocarbons and minor amounts of naphthenes. SEM-EDS analysis of the chars revealed partly carbonaceous material (67.18 ~ 86.67 wt.% C) that could burn to yield an ash rich in CaO, ZnO and TiO₂. It was concluded that the feasibility of end-of-life vehicle tyres pyrolysis offers a viable solution to end-of-life vehicle tyres disposal challenges faced by the mining industry and Ghana.

Characterisation of a Rehabilitated Tailings Dam Mine Water as a Potential Acid Mine Drainage and Removal of Heavy Metals Using Locally Produced Activated Carbon*

S. Fosu — Fri, 23 Aug 2024 00:00:00 +0000

Mine waters can be acidic or circumneutral and may often contain concentrations of heavy metals, which are produced from mining base and precious metals. In this work, reddish-brown mine water from an old, rehabilitated tailings storage facility (OTSF) of a mine in Ghana was investigated in terms of physical and chemical characteristics. GARD guide and Hill geochemical classification frameworks (GCF) were also used to ascertain the characteristics of the OTSF mine water in terms of acid mine drainage (AMD). The potential for using locally produced Palm Kernel Shell (PKS) activated carbon for heavy metal adsorption in OTSF mine water was also investigated. The kinetics of the adsorption process were tested using pseudo-first-order (PFO) and second-order (PSO) kinetic models. The results showed that the physicochemical parameters of the OTSF mine water measured were all below the Ghana Standard Authority (GSA) effluent discharge limit for general mining except pH = 5.00-5.40. In terms of heavy metals, except As and Cd, Mn (0.75 > 0.2 mg/L GSA), Fe (25.2 > 10 mg/L GSA), and Pb (0.31 > 0.1 mg/L GSA) were all above GSA effluent discharge limit. The OTSF mine water had low chloride but a high sulphate concentration (395 > 250 mg/L GSA). The drainage, which measured Eh of 460 mV, showed a moderate oxidising environment of OTSF mine water. The GCF revealed that the OTSF mine water is between partially oxidised to low neutralised AMD with potentially less toxic constituents. Locally produced PKS-activated carbon reduced the concentration of As, Pb, Fe, and Mn of the OTSF mine water below the GSA effluent discharge limit. The adsorption kinetics of potential constituents like Fe and Mn fitted well to PFO and PSO, indicating adsorption as diffusion controlled and chemisorption, respectively. This work has demonstrated the potential of using locally produced PKS-activated carbon as an excellent and cost-effective adsorbent for the treatment of heavy metals from the OTSF mine water.

Real-Time Yield Point Prediction for Water-Based Drilling Mud using Particle Swarm Optimised Neural Network*

S. Asante-Okyere, H. Osei — Fri, 23 Aug 2024 00:00:00 +0000

Yield point (YP) is an essential rheological property of drilling mud that influences the ability of the mud to lift well cuttings from the annulus to the surface, impacting the overall drilling efficiency. Despite its significance, YP is typically measured only once or twice a day using complex rheometers. On the other hand, a simple field equipment such as the Marsh funnel is used to constantly monitor the drilling fluid's behaviour up to about 144 times daily. This only provides an indication of the drilling fluid condition and not detailed rheological characteristics. There have been previous attempts to infer the rheological characteristics from the constantly monitored Marsh funnel parameters. One of the widely used approach to estimate rheological properties from Marsh funnel parameters has been the implementation of Back Propagation Neural Network (BPNN). BPNN algorithm exhibits some drawbacks such as poor generalisation. Based on that, the present study improved the performance of BPNN in predicting YP using particle swarm optimsation (PSO) based BPNN. It was identified from the study that PSO-BPNN outperformed BPNN in the estimation of YP in terms of correlation coefficient (R) and mean square error (MSE) and variance accounted for (VAF). During testing PSO-BPNN attained 0.929, 1.129 and 92.29 % as R, MSE and VAF score, respectively, while BPNN had 0.868, 1.235 and 83.78 % for R, MSE and VAF score, respectively. These findings suggest that PSO-BPNN offers a more reliable and efficient approach to predicting drilling fluid yield point from Marsh funnel experimentation.

Optimization of Sluice Box for Small-Scale Mining Using Computational Fluid Dynamics (CFD)*

E. Yin — Fri, 23 Aug 2024 00:00:00 +0000

The issue of gold losses to tailings leading to low recovery in the artisanal and small-scale mining industries, which mostly rely on traditional sluicing techniques to concentrate gold particles from gangue minerals, remains critical. This research contributes to improving gold recovery by optimizing the sluice box design to improve its efficiency. Based on the multi-stage sluice box system proposed in prior work done, Computational Fluid Dynamics (CFD) simulations was performed by varying the slope angles of the sluice box to determine the optimal angle that will produce the highest recovery of gold concentrates from the gangue minerals. The results obtained demonstrated that the optimal slope angle of the sluice box that retained most gold particles was 10°. At this slope angle, the gold retention rate, flow velocity, and the number of gold particles trapped on the sluice box were 98.81 %, 0.311 m/s, and 7905 particles, respectively. This study, therefore, provides the foundation for numerical technique (CFD) to be used for analyzing fluid flow on a sluice box to complement experimental and empirical techniques.

Predicting Natural Gas Heating Value using Supervised Machine Learning Models*

S. A. Marfo — Fri, 23 Aug 2024 00:00:00 +0000

Heating value (HHV) is an essential parameter for evaluating natural gas quality. The existence of supervised machine learning models is therefore necessary for HHV prediction to ensure the eco-friendly and efficient utilisation of natural gas. This study aims to develop machine learning models based on the gas composition to accurately predict the HHV of natural gas. Three predictive models namely; decision tree, AdaBoost, and XGBoost models were used in the evaluation. Data samples from Jubilee, TEN, and SGN fields in Ghana were used. The study considered 721 data sets and the performance of each model was evaluated using R², RMSE, and MAE. Results obtained highlighted XGBoost model performs better than the other models. This was backed with an R² value of 92.9 % and RMSE and MAE error values of 2.002 and 1.195 respectively.

Diesel Fuel Quality Assessment – Case Study of Tarkwa, Ghana*

S. A. Marfo — Fri, 23 Aug 2024 00:00:00 +0000

This paper considered the quality of diesel fuel used in Tarkwa and its impact on health and the environment when used in mining operations and other commercial purposes. A total of 25 diesel fuel samples were sampled from selected fuel farms and Oil Marketing Companies (OMCs) in Tarkwa over a period of eleven (11) months. These samples were tested to check their quality and compared their conformance with Ghana Standard Authority (GSA) diesel fuel requirement (GS 141: 2022, 5th Edition). Test parameters such as density, viscosity, flash point, water content, total sulphur, total contamination, cetane index and distillation via ASTM and ISO methods were employed to check the quality of diesel fuel used in Tarkwa. The results showed that all 25 samples passed the tests for density, total sulphur, viscosity, cetane index, and distillation conforming to the GS 141:2022. With regards to flashpoint, 8% of diesel samples failed the tests, 4% failed the water content and 32% failed the total contamination tests which is not required in the GS 141:2022. In general, the quality of diesel fuel assessed indicated that diesel fuel used in Tarkwa is good especially those supplied for mining operations.

An Exploration of Emerging Technology as a Blessing or Bane to Society*

Y. Ofori, B. Arthur — Fri, 23 Aug 2024 00:00:00 +0000

Emerging technologies have become integral to contemporary society, yet the debate over their societal impact remains contentious. This study aims to explore public perceptions of emerging technology, investigating whether it is viewed as a blessing or a curse. Employing a quantitative approach, data was collected through a structured questionnaire from a diverse sample of respondents. The survey instrument was meticulously designed to address research questions regarding the societal impacts and ethical considerations associated with emerging technology. Purposive sampling was utilised to select participants with relevant knowledge and experience in the field. Data analysis involved both descriptive statistics and thematic analysis of qualitative responses, providing insights into the diverse perspectives on emerging technology. The findings reveal a nuanced understanding of the dual nature of emerging technology. While participants acknowledged its potential benefits in driving innovation and improving quality of life, concerns were raised regarding issues such as privacy breaches, algorithmic bias, and job displacement. Cultural, political, and economic factors were found to shape perceptions of emerging technology, highlighting the complexity of the issue. This study contributes to a better understanding of the societal implications of emerging technology and underscores the importance of proactive approaches to address its challenges. Policymakers should formulate policies promoting responsible and ethical use, addressing issues like privacy, security, equity, and social justice. Educational institutions should integrate digital literacy into curricula and encourage continuous learning to prepare the workforce for evolving job markets. Public awareness campaigns are needed to educate about the benefits and risks of emerging technologies, fostering informed opinions and responsible usage.

The Impact of Emerging Technologies on Sustainable Development Goals and Millenium Development Goals Achievement*

Y. Ofori, B. Arthur — Fri, 23 Aug 2024 00:00:00 +0000

This study investigates the impact of emerging technologies on the achievement of Sustainable Development Goals (SDGs) and Millennium Development Goals (MDGs), focusing on themes of environmental sustainability and social inclusivity with digital equality. The study explores how technology adoption either facilitates or hinders progress toward global development goals. The study employs a comprehensive Meaning Oriented Content Analysis drawing from reputable sources, reports, and scholarly works. The study also critically examines the role of emerging technology in achieving environmental sustainability, particularly in smart city initiatives contributing to resource efficiency and ecological footprint reduction, aligning with SDG 11. However, challenges such as electronic waste (e-waste) generation are acknowledged, emphasising the importance of a holistic life cycle approach in technology adoption. In addressing social inclusivity and digital equality, the study reviews other scholarly works, highlighting the necessity to bridge digital divides and ensure meaningful participation, echoing SDG 10. Insights from the International Telecommunication Union (ITU) stress the importance of policies promoting digital inclusion, emphasizing accessibility for all socio-economic strata. The World Economic Forum's 2022 report underscores the pivotal role of social inclusivity and digital equality in unlocking the full potential of emerging technologies for global development. To address challenges related to social inclusivity and digital equality, robust digital inclusion policies are recommended. Governments and organizations should collaborate to bridge the digital divide by implementing initiatives that provide affordable and accessible technology, digital literacy programs, and targeted efforts to include marginalized populations. the complexity of challenges and opportunities presented by emerging technologies necessitates fostering multi-stakeholder collaboration.

Exploring the Impact of Emerging Technologies on Information Accessibility Enhancement*

Y. Ofori, B. Arthur — Fri, 23 Aug 2024 00:00:00 +0000

This study explores the impact of emerging technologies on information accessibility enhancement, encompassing historical perspectives, technological evolution, and theoretical frameworks. The objective of this paper is to investigate the ramifications of emerging technologies on information accessibility enhancement, focusing on historical perspectives, technological advancements, and theoretical frameworks. A quantitative research approach was employed, utilising structured surveys to probe the impact of emerging technologies on information accessibility. Surveys were disseminated online to 500 participants in Ghana, featuring multiple-choice questions, Likert scale items, and open-ended queries. Data analysis primarily employed descriptive statistics to extract insights into the nexus between emerging technologies and information accessibility enhancement. The study revealed significant improvements in information accessibility due to emerging technologies, with respondents acknowledging the profound influence of Wi-Fi, 5G networks, and AI on access to information. However, challenges such as the digital divide and regulatory barriers were identified as hindrances to equitable access. The Diffusion of Innovations theory provided a comprehensive framework for understanding the adoption and dissemination of emerging technologies, highlighting the need for strategies to promote widespread adoption and effective use. The study underscores the importance of addressing existing barriers to fully realise the benefits of emerging technologies for enhancing information accessibility across diverse populations. A robust information and communication technology infrastructure should be prioritised to bridge the digital divide.