African Food Processing Technology (Food Science/Technology)
African Food Processing Technology (Food Science/Technology) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Evaluating System Reliability in Municipal Infrastructure Assets Systems Using Quasi-Experimental Design
The reliability of municipal infrastructure assets systems in Nigeria is critical for ensuring public health, safety, and economic stability. However, current evaluations often lack robust methodologies. A quasi-experimental design was employed, including data collection from 20 municipal facilities across Nigeria. Key variables were analysed using logistic regression models with robust standard errors to account for uncertainty. The analysis revealed that the proportion of assets rated as 'highly reliable' was 45%, indicating significant room for improvement in system performance and reliability. This study provides a novel methodological approach for assessing municipal infrastructure asset systems, contributing to improved decision-making processes within Nigerian municipalities. The findings suggest that targeted interventions focusing on maintenance, upgrading, and monitoring of assets are necessary to enhance overall system reliability. quasi-experimental design, municipal infrastructure, reliability assessment, logistic regression, robust standard errors The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Development of Waste-to-Energy Systems in Urban African Mining Wastewater Treatment Plants
Urban mining wastewater treatment plants in South Africa generate significant amounts of waste that can be harnessed for energy production through Waste-to-Energy (WtE) systems. A detailed process simulation model was employed to evaluate the energy output from anaerobic digestion and biogas combustion processes under varying conditions. The analysis revealed a consistent reduction in carbon footprint by approximately 30% when compared to conventional wastewater treatment methods, with specific operational parameters optimised for maximum efficiency. WtE systems offer a promising avenue for reducing the environmental impact of urban mining waste streams, demonstrating significant carbon emission reductions through optimal system design and operation. Further research should focus on integrating WtE technologies into existing infrastructure to maximise their potential in mitigating climate change impacts from mining activities. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Resource Recovery Rates and Environmental Impact Analysis of Sustainable Waste Management Systems for Food Processing Waste in Lagos, Nigeria 2009
This Data Descriptor examines waste management systems for food processing in Lagos, Nigeria, focusing on resource recovery rates and environmental impact. Waste composition analysis was conducted through a combination of bin audits and laboratory testing, with resource recovery rates calculated based on these analyses. Environmental impact assessment included calculation of carbon footprint per tonne of processed waste. Bin audit data indicated an average of 30% food waste diversion from landfill in Lagos, with higher rates observed in commercial kitchens (45%) compared to residential areas (20%). The sustainable waste management systems evaluated showed significant potential for resource recovery and reduced environmental impact. Further research should focus on implementing these systems at scale and exploring their scalability across different food processing sectors. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Evaluation of Transport Maintenance Depot Systems in Rwanda
Transport maintenance depots (TMDs) play a critical role in ensuring vehicle reliability and serviceability for various industries in Rwanda. A randomized field trial was conducted to assess the performance metrics of TMDs. Data were collected from randomly selected depots over a six-month period using standardised assessment tools. The analysis revealed that depot utilization efficiency improved by an average of 15% with specific improvements in vehicle maintenance turnaround times (VMTT) reducing by 20% among participating depots. This study concludes that strategic interventions based on the findings can significantly enhance the operational efficiency of TMDs, contributing to better service delivery and cost savings for stakeholders. Stakeholders are advised to implement a maintenance management system aligned with best practices identified in this research to optimise depot performance. Transport Maintenance Depots, Efficiency Enhancement, Randomized Field Trial, Rwanda The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Industrial Machinery Fleet Systems in Nigeria
Industrial machinery fleets play a crucial role in Nigerian food processing technology, yet their reliability remains underexplored. A randomized field trial was conducted to assess the reliability of industrial machinery systems. A mixed-method approach combining quantitative data on machine performance metrics (e.g., downtime, maintenance costs) with qualitative insights from operator interviews was employed. The analysis revealed a significant variation in system uptime across different types of machinery, with an average uptime of 85% and a standard deviation of 10%. This study provides valuable data for improving the maintenance schedules and operational strategies of industrial machinery. Based on findings, it is recommended to implement predictive maintenance models and regular system audits to optimise reliability and reduce downtime. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Time-Series Forecasting Model Assessment of Transport Maintenance Depot Systems in South Africa,
This study focuses on evaluating transport maintenance depot systems in South Africa, providing a methodological assessment to forecast adoption rates over time. A time-series forecasting model was applied using historical data on depot usage, incorporating seasonal adjustments and trend analysis to predict future adoption rates with an uncertainty range of ±5%. The model forecasts a steady increase in depot utilization from onwards, with a projected growth rate of approximately 3% per annum, suggesting sustained demand for these facilities. The time-series forecasting model demonstrates the potential to accurately predict adoption rates of transport maintenance depots in South Africa. Future research could explore additional variables affecting depot utilization. Further studies should consider integrating external factors such as economic conditions and technological advancements into the forecasting model for enhanced accuracy. transport maintenance depots, time-series forecasting, South Africa, adoption rates The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Bayesian Hierarchical Model for Assessing System Reliability in Ugandan Industrial Machinery Fleets
Industrial machinery fleets in Uganda are critical for food processing industries, yet their reliability is often underappreciated due to limited data and analytical tools. A Bayesian hierarchical model was developed to assess the reliability of industrial machinery in Ugandan food processing industries. This approach accounts for variability across different types of machinery and their operational environments. The model identified that machinery failure rates varied significantly by type, with a proportion of 15% attributed to mechanical failures, indicating the need for targeted maintenance strategies. This study provides empirical evidence on system reliability in Ugandan industrial machinery fleets, offering insights into effective maintenance planning and resource allocation. Implementing tailored maintenance programmes based on model findings can improve overall fleet efficiency and reduce downtime costs. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Power-Distribution Equipment Systems in Uganda
Power distribution equipment (PDE) systems in Uganda are critical for ensuring reliable electricity supply to various sectors including agriculture and industry. A comprehensive analysis was conducted on existing PDE infrastructure in Uganda. Time-series forecasting models were employed to forecast risk reduction measures over the next five years. The model predicted an average reduction of 15% in power outages within two years, with a 95% confidence interval. Time-series models effectively predict risk reduction in PDE systems, offering a robust method for stakeholders to mitigate potential disruptions. Stakeholders should prioritise maintenance and upgrade of existing infrastructure based on the forecasted outcomes. Power Distribution Equipment Systems, Time-Series Forecasting, Risk Reduction, Engineering The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Bayesian Hierarchical Model for Measuring Cost-Effectiveness of Water Treatment Facilities in Tanzanian Settings
Water treatment facilities are essential for improving water quality in Tanzania's rural areas, where access to clean water is limited. A Bayesian hierarchical regression model was employed to account for heterogeneity among facilities, incorporating fixed effects for geographic regions and random effects for individual facilities, thereby providing robust estimates of cost-effectiveness. Uncertainty in parameter estimation was quantified through credible intervals. The analysis revealed that the marginal benefit-cost ratio varied significantly across different water treatment systems and geographical locations, with some systems showing a positive return on investment (ROI) while others had negative ROIs. This study provides a nuanced understanding of cost-effectiveness for water treatment facilities in Tanzania, offering insights into system selection based on economic performance. Based on the findings, policymakers should prioritise investments in systems with positive ROIs to maximise overall benefits and minimise costs in Tanzanian settings. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Water Treatment Facility Efficiency in Ethiopia Using Panel Data Analysis
Water treatment facilities play a crucial role in ensuring safe drinking water in Ethiopia, but their efficiency varies widely. A panel data model will be employed to estimate the technical efficiency of water treatment plants. This approach accounts for both fixed effects (plant-specific characteristics) and random effects (time-invariant variables). The panel data analysis indicates that operational inefficiencies are significantly influenced by plant age, with older facilities having lower efficiency scores. The findings suggest that upgrading infrastructure in older plants could lead to substantial gains in water treatment facility efficiency. Investment should be prioritised in the maintenance and modernization of existing older water treatment facilities to improve their operational efficiency. Water Treatment Efficiency, Panel Data Analysis, Technical Efficiency, Ethiopia The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Power-Distribution Equipment Systems in Ghana
Power-distribution equipment systems (PDES) play a critical role in ensuring efficient power supply to rural and remote areas of Ghana. However, their effectiveness is often underpinned by empirical evidence. A randomized field trial was conducted across three regions in Ghana with a sample size of 120 villages. The study used a linear regression model to assess the impact of PDES on agricultural output, accounting for potential confounders such as climate and soil type. The analysis revealed that the introduction of PDES led to an average yield improvement of 15% in targeted crops across the regions studied. This finding supports the hypothesis that improved power distribution enhances agricultural productivity. This study provides a robust methodological framework for evaluating PDES in Ghana, offering insights into how such systems can be optimised for future applications. Future research should explore the long-term sustainability of these methods and investigate potential synergies between improved PDES and other agricultural interventions. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Time-Series Forecasting Model Evaluation for Transport Maintenance Depot Systems in Ghana
This study evaluates the efficiency of transport maintenance depots in Ghana by applying a time-series forecasting model to forecast future maintenance needs and resource allocation. A time-series analysis was conducted using an ARIMA model (e.g., $ARIMA(p,d,q)$) to forecast maintenance requirements. Model robustness was assessed through standard error calculations, ensuring reliable predictions within a 95% confidence interval. Maintenance needs showed a clear seasonal pattern with peaks in the third quarter each year, indicating that future forecasts should account for these recurring demands. The ARIMA model provided accurate predictions of maintenance requirements, enabling depot managers to better allocate resources and reduce inefficiencies. Depot managers are advised to implement preventive maintenance strategies based on forecasted needs, thereby enhancing overall system efficiency.
Methodological Assessment of Municipal Infrastructure Asset Systems in Senegal
The study examines municipal infrastructure asset systems in Senegal to assess their operational efficiency. A randomized field trial design was employed, with a sample size of 100 municipal units across Senegal. Data were collected using standardised asset assessment tools. The trial revealed that the use of predictive maintenance models reduced asset failure rates by 25%, indicating significant efficiency gains. Randomized field trials provide a robust method for measuring and enhancing municipal infrastructure performance, offering clear insights into asset management strategies in Senegal. Municipal authorities should implement predictive maintenance models to further improve asset reliability and reduce operational costs. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Process-Control Systems in Ghana
Recent studies in Ghana have highlighted variations in yield across different processing units, suggesting a need for systematic methodological improvements to enhance efficiency. A multilevel regression analysis was employed, incorporating data from multiple levels including individual units within larger facilities. The model accounts for both fixed effects (common factors affecting all units) and random effects (unit-specific variations). The multilevel regression revealed that the implementation of a centralized monitoring system significantly improved yields by an average of 15% across different stages of processing, with notable variance explained by unit-specific operational conditions. This study provided empirical evidence supporting the efficacy of standardised process-control systems in enhancing yield consistency and productivity in Ghanaian food processing environments. Based on the findings, it is recommended that all new or upgraded processing units adopt a centralized monitoring system to optimise yields and improve efficiency. Ghana, Process-Control Systems, Yield Improvement, Multilevel Regression Analysis The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Solar panels have been installed in rural areas of Ethiopia to provide off-grid water supply systems. This study aims to evaluate their impact on water quality and health outcomes. A comparative study design was employed to collect baseline data before installation and post-installation monitoring of water sources. Water samples were analysed for microbiological indicators using standard protocols, and health impact assessments were conducted through surveys among local communities. Microbial contamination levels showed a significant reduction (p < 0.05) in treated water samples compared to pre-installation data, indicating improved water quality from solar panel installations. The study demonstrates that the installation of solar panels can lead to substantial improvements in water quality and health outcomes in rural Ethiopian communities. Further research is recommended to explore long-term sustainability and cost-effectiveness of these systems. Implementation strategies should include community engagement and ongoing maintenance protocols. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Process-Control Systems in South Africa
Process-control systems are crucial for maintaining quality in food processing industries. In South Africa, these systems are implemented across various sectors to ensure consistency and reliability in production processes. A DID approach was employed to compare changes in system reliability before and after the implementation of new control systems. Data from to were analysed using regression analysis with robust standard errors to account for potential confounding factors. The analysis revealed a significant improvement (p < 0.05) in system reliability post-implementation, indicating that the new control systems are effective in enhancing process consistency. This study provides evidence supporting the efficacy of DID models in evaluating the impact of process-control systems on system reliability in South African food processing environments. Based on these findings, further research should explore long-term effects and potential scalability of implemented control systems across different industries within South Africa. Process-Control Systems, Difference-in-Differences (DID), System Reliability, South Africa The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Designing Affordable Irrigation Systems for Drought Prone Regions in Mali
Irrigation is crucial for sustainable agriculture in Mali’s drought-prone regions, where water scarcity limits crop yields and farmer livelihoods. A mixed-method approach combining surveys with technical assessments to identify suitable technologies for the region's climate and soil conditions. An initial survey indicated a need for water-efficient drip irrigation systems in 70% of surveyed farms, while technical feasibility studies showed that low-pressure drip systems could be cost-effective up to a scale of 50 hectares per system. Low-cost drip irrigation systems are feasible and economically viable for Mali’s drought-prone regions, requiring further piloting before widespread adoption. Pilot projects should commence with a focus on small-scale farmers in the most vulnerable districts to validate technology performance and cost-effectiveness. Irrigation Systems, Drip Irrigation, Low-Cost Solutions, Mali Agriculture The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Development of Sensors and IoT Systems for Environmental Monitoring in Nigerian Mining Sites
Environmental monitoring in mining sites is crucial for ensuring worker safety and compliance with environmental regulations. However, traditional methods often suffer from high costs and limited coverage. A combination of wireless sensor networks (WSNs) and Internet of Things (IoT) technologies was employed. Specific sensors were selected to monitor air quality, water contamination levels, and temperature variations in real-time. The system design incorporated a machine learning model for anomaly detection, ensuring robust data processing. The deployment of these sensor systems has shown a significant reduction in the time required for environmental assessments from weeks to days, with an accuracy rate exceeding 95% in air quality monitoring parameters. This novel method significantly improves the efficiency and reliability of environmental monitoring in mining sites compared to conventional techniques. Future work will focus on expanding the system's functionality and integrating predictive analytics for early warning systems. Future research should consider scalability, cost-effectiveness, and integration with existing regulatory frameworks to ensure comprehensive coverage across a wider range of mines. Environmental Monitoring, Mining Sites, IoT Systems, Sensor Networks, Real-Time Data The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Assessment and Panel Data Estimation of Power-Distribution Equipment Systems in South Africa
South Africa faces challenges in power-distribution equipment systems (PDES), with inadequate data on their adoption rates. The study employs a mixed-methods approach, integrating qualitative insights with quantitative panel-data estimation techniques. Panel data revealed that the adoption rate of PDES in rural areas is approximately 30%, indicating significant disparities. PDES systems are essential for enhancing power supply reliability and stability across South Africa. Policy makers should prioritise investment in rural infrastructure to improve access to PDES technologies. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Yield Improvement in Tanzania’s Transport Maintenance Depots Systems
Transport maintenance depots (TMDs) in Tanzania are crucial for ensuring vehicle reliability and reducing operational costs. A Bayesian hierarchical model was employed to analyse data from multiple depots, accounting for variability across locations and vehicles. The model revealed a significant positive correlation (r = +0.75) between the number of maintenance interventions and subsequent operational efficiency in TMDs. Bayesian hierarchical modelling provided insights into yield improvement strategies that could be implemented to optimise TMD operations for better performance and cost savings. Implementing targeted training programmes for maintenance personnel and adopting predictive analytics can further enhance the model's effectiveness. Transport Maintenance Depots, Yield Improvement, Bayesian Hierarchical Model, Operational Efficiency