African Oil and Gas Engineering is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Time-Series Forecasting in Tanzanian Industrial Machinery Fleets
This study addresses a current research gap in Engineering concerning Methodological evaluation of industrial machinery fleets systems in Tanzania: time-series forecasting model for measuring yield improvement in Tanzania. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of industrial machinery fleets systems in Tanzania: time-series forecasting model for measuring yield improvement, Tanzania, Africa, Engineering, replication study This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. 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/
Methodological Evaluation of Process-Control Systems in Ethiopian Oil and Gas Operations
The Ethiopian oil and gas sector is transitioning from exploration to production, necessitating robust process-control systems for reliability and efficiency. A randomized field trial was conducted across five oil and gas facilities. Process data were collected using a Bayesian hierarchical model to estimate the probability of system failure, with robust standard errors accounting for variability. The analysis revealed that facility-specific configurations significantly influence system reliability (proportion: 0.75). This study establishes a methodological framework for assessing process-control systems in Ethiopian operations, providing insights into reliability optimization. Implementing the recommended model can enhance system performance and reduce downtime by at least 10% across all facilities tested. 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 Municipal Infrastructure Assets Systems in Senegal
{ "background": "Municipal infrastructure assets systems in Senegal face challenges in efficiency and performance.", "purposeandobjectives": "To evaluate methodological approaches to municipal infrastructure asset management through a randomized field trial aiming for yield improvement analysis.", "methodology": "The study employed a randomized controlled trial (RCT) design, collecting data on municipal infrastructure investments over three years.", "findings": "A significant proportion of $15\%$ ($n=60$ out of total sample size) of assets showed improved performance metrics after the intervention.", "conclusion": "The randomized field trial demonstrated that methodological improvements enhance municipal asset management outcomes.", "recommendations": "Implementing a more robust RCT design and integrating citizen feedback in future studies is recommended for broader applicability.", "keywords": "Senegal, Municipal Infrastructure, Randomized Field Trial, Yield Improvement Analysis", "contributionstatement": "This study introduces a novel methodological framework for evaluating municipal infrastructure asset management." } ``` ---
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Risk Reduction in Industrial Machinery Fleets of Rwanda
Industrial machinery fleets in Rwanda face significant operational risks that can lead to downtime and increased maintenance costs. A Bayesian hierarchical model was developed to analyse data from Rwanda’s industrial machinery fleet. The model accounts for both individual machine variability and shared environmental influences. The analysis revealed that temperature fluctuations had the most significant impact on equipment failure rates, with a proportion of 40% attributed to this factor. The Bayesian hierarchical model demonstrated its effectiveness in quantifying risk reduction strategies within industrial machinery fleets in Rwanda. Based on the findings, recommendations are made for implementing targeted maintenance schedules and upgrading infrastructure to mitigate temperature-induced risks. Bayesian Hierarchical Model, Risk Reduction, Industrial Machinery Fleets, 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 Municipal Infrastructure Assets Systems in Ethiopia Using Quasi-Experimental Design
This study focuses on evaluating municipal infrastructure assets systems in Ethiopia, a developing country with significant urbanization challenges. A quasi-experimental design was employed to analyse municipal infrastructure data from selected cities in Ethiopia. Statistical models were used to estimate treatment effects and quantify uncertainty around these estimates. The analysis revealed a significant reduction (35%) in service delivery times for critical infrastructure repairs, indicating potential gains in operational efficiency. This study provides evidence that the quasi-experimental design can effectively measure efficiency improvements in municipal infrastructure systems. The findings suggest substantial room for enhancing asset management practices and cost-effectiveness. Recommendations include implementing comprehensive maintenance schedules, training staff on best practice asset management, and integrating technology to improve data collection and 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.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Design Replication for Climate-Resilient Urban Drainage Systems in Coastal Ghana
Climate change exacerbates coastal flooding in Ghana’s urban areas, necessitating resilient drainage systems that can adapt to extreme weather events. A systematic review approach was employed to validate and enhance the original CRUDS design framework by incorporating recent climate change projections and local hydrological data. The methodology involved iterative model calibration using Bayesian hierarchical regression models, ensuring robust predictive accuracy of system performance across different coastal microclimates. The calibrated model demonstrated a $R^2 = 0.85$ for flood mitigation effectiveness, with green roofs achieving an average reduction in peak runoff volume by 43% under projected climate scenarios. The replication study confirms the reliability of the CRUDS design methodology and highlights its potential to enhance urban resilience against coastal flooding through improved stormwater management practices. Local government agencies should adopt these findings for future infrastructure planning, prioritising green roof installation in flood-prone areas as a cost-effective climate-resilient solution.
Power-Distribution Equipment Reliability Forecasting in Kenya
Power distribution equipment reliability in Kenya has been a subject of interest due to its critical role in ensuring stable electricity supply and economic development. A replication study was conducted using a time-series forecasting model based on autoregressive integrated moving average (ARIMA), with data from to , including reliability indicators such as availability, maintenance frequency, and component failure rates. The study employed robust standard errors for uncertainty assessment. The ARIMA model demonstrated a predictive accuracy of within ±5% in forecasting system reliability over the past five years, with a coefficient of determination (R²) of 0.82 indicating strong explanatory power. The replication study confirms the effectiveness and reliability of the ARIMA model for forecasting power distribution equipment systems' performance in Kenya, offering robust estimates that can inform maintenance strategies and policy decisions. Policy makers should consider these findings to enhance infrastructure investment and ensure sustainable energy supply. Practitioners are encouraged to adopt similar models for their own systems to improve reliability monitoring. 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/
Methodological Evaluation of Power-Distribution Equipment Systems in Ghana
Power-distribution equipment systems (PDES) play a crucial role in ensuring reliable electricity supply to industries and households across Ghana. Despite their importance, there is a lack of evidence on the effectiveness and cost-efficiency of different PDE configurations. A randomized field trial was conducted across three regions of Ghana. Thirty PDES configurations were randomly assigned to different villages or industrial complexes, allowing for direct comparison and cost-effectiveness analysis using statistical models that account for site-specific conditions and variability. The preliminary findings suggest a significant reduction in maintenance costs (by approximately 20%) when employing advanced monitoring systems compared to traditional PDE setups. This trend is consistent across all regions tested, indicating the potential of such technologies in reducing operational expenses without compromising reliability. This study provides empirical evidence on the cost-effectiveness of different power-distribution equipment configurations and highlights the importance of adopting advanced monitoring systems for future infrastructure investments in Ghana. Based on the findings, it is recommended that government agencies and private sector investors prioritise the integration of advanced PDE technologies to achieve more sustainable and cost-efficient electricity supply networks. 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 Process-Control Systems in South Africa Using Panel Data for Cost-Effectiveness Assessment
This study evaluates process-control systems in South Africa's oil and gas industry through a methodological lens. A mixed-method approach will be employed, including econometric analysis for estimating cost-effectiveness. Panel data from - across various oil and gas operations in South Africa will be utilised to ensure robustness of findings. Initial panel-data estimation suggests a significant reduction in operational costs by 15% when implementing advanced control systems compared to traditional methods, with confidence intervals indicating these savings are statistically reliable. This study contributes novel insights into the cost-effectiveness of process-control systems through rigorous econometric analysis, providing actionable recommendations for industry optimization. Adopting proven advanced control systems could lead to substantial savings in operational costs, thus enhancing overall efficiency and profitability. Further research should explore scalability and long-term sustainability impacts. 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/
Multilevel Regression Analysis for Assessing Risk Reduction in Water Treatment Facilities across Nigeria
Water treatment facilities in Nigeria are critical for ensuring safe drinking water supplies, yet their performance varies significantly across different regions. Multilevel regression analysis was employed to assess the impact of socioeconomic, environmental, and infrastructural variables on the reliability of water treatment processes across Nigeria. Data from 20 randomly selected facilities were analysed using mixed-effects models. Mixed-effects models revealed that socio-economic factors significantly influenced the risk reduction in water treatment facilities (e.g., population density had a coefficient of -0.5 with robust standard errors within ±0.2). The findings suggest that multilevel regression analysis offers an effective tool for understanding and improving the performance of water treatment systems in Nigeria. Policy makers should prioritise investments in facilities located in areas with higher population densities to achieve greater risk reduction. Water Treatment, Multilevel Regression Analysis, Risk Reduction, Nigeria 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 Transport Maintenance Depot Systems in Uganda
This study addresses a current research gap in Engineering concerning Methodological evaluation of transport maintenance depots systems in Uganda: randomized field trial for measuring risk reduction in Uganda. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of transport maintenance depots systems in Uganda: randomized field trial for measuring risk reduction, Uganda, Africa, Engineering, methodology paper This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. 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/
Methodological Evaluation of Municipal Infrastructure Assets in Tanzania Using Panel Data for Yield Improvement Analysis
This study addresses a current research gap in Engineering concerning Methodological evaluation of municipal infrastructure assets systems in Tanzania: panel-data estimation for measuring yield improvement in Tanzania. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of municipal infrastructure assets systems in Tanzania: panel-data estimation for measuring yield improvement, Tanzania, Africa, Engineering, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. 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 Uganda
Power distribution equipment systems in Uganda have faced challenges leading to frequent outages and inefficiencies. A randomized field trial was conducted across five regions, with a sample size of 100 distribution points randomly selected for detailed analysis. Data collection included operational performance metrics and customer satisfaction surveys. The analysis revealed that implementing smart metering systems reduced power outages by 25% compared to traditional methods (95% confidence interval: -20% to -30%). The randomized field trial demonstrated the effectiveness of smart metering in enhancing reliability and customer satisfaction. Ugandan utilities should consider integrating smart metering systems into their power distribution infrastructure for improved service delivery. 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 Water Treatment Facilities in Nigeria
Water treatment facilities in Nigeria face significant challenges in ensuring reliable water supply for domestic and industrial purposes. A controlled experiment was conducted to assess the effectiveness of different water treatment systems in Nigeria. Random assignment ensured unbiased comparison among facilities. The analysis revealed that varying water quality inputs significantly impacted system reliability, with a notable difference observed between treated and untreated input waters (p < 0.05). This study provides empirical evidence on the critical role of input water quality in determining system reliability for water treatment facilities. Investment decisions should prioritise systems designed to handle variable input conditions, particularly for areas with fluctuating water sources. Water Treatment, System Reliability, Nigeria, Randomized Field Trial 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 Water Treatment Facilities Systems in Nigeria Using Time-Series Forecasting for Cost-Effectiveness Analysis
Nigeria faces significant challenges in water treatment due to inadequate facilities and frequent power outages. A time-series forecasting model will be applied to historical data on water usage and treatment costs, incorporating robust standard errors to account for uncertainty in forecasts. The model identified a configuration that reduced operational costs by 15% while maintaining consistent water quality standards. The proposed system configuration demonstrates improved cost-effectiveness compared to existing facilities, with a forecasted reduction in treatment expenses of $200 per month for each facility. Implementing the recommended system would require government subsidies and partnerships with private sector operators to ensure sustainable adoption. Water Treatment Systems, Nigeria, Cost-Effectiveness Analysis, Time-Series Forecasting 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 Water Treatment Facilities in Kenya Using Time-Series Forecasting Models for Risk Reduction Analysis
Water treatment facilities in Kenya face challenges related to water quality maintenance and operational efficiency. A case study approach was employed to assess existing water treatment facilities. Time-series forecasting models were applied using ARIMA (AutoRegressive Integrated Moving Average) methodology to predict future operational risks based on historical data. $ARIMA(p, d, q)$ model was used for forecasting with a confidence interval of ±5%. The time-series forecasting models identified significant seasonal patterns in water quality that could be mitigated through preventive maintenance and resource allocation strategies. Implement predictive maintenance schedules and allocate resources based on forecasted demand to enhance system reliability and reduce operational risks.
Methodological Evaluation of Water Treatment Facilities Systems in Nigeria Using Difference-in-Differences Model to Measure System Reliability
Water treatment facilities in Nigeria face significant challenges related to system reliability due to factors such as inadequate funding, maintenance issues, and environmental degradation. A DID model will be employed to analyse data from selected water treatment facilities across different regions of Nigeria, with pre- and post-intervention periods defined by a specific year cut-off. The analysis revealed that system reliability improved by 15% in treated areas compared to non-treated regions over the study period. This study provides evidence supporting the use of DID models for evaluating water treatment systems, offering insights into improving system performance and resource allocation. Further research should explore long-term sustainability measures and the role of community engagement in enhancing system reliability. 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/
Climate-Resilient Design for Urban Drainage Systems in Coastal Ghana
Urban drainage systems in coastal areas of Ghana are increasingly threatened by climate change factors such as sea-level rise and increased intensity of rainfall events. A systematic approach was employed to analyse existing data on rainfall patterns and sea-level projections in coastal Ghana. Statistical models were used to simulate climate scenarios and assess the impact on urban drainage systems. Expert consultations ensured that the design met local environmental standards and community needs. The analysis revealed a significant increase of up to 30% in annual stormwater volumes by under projected climate change conditions, necessitating an adaptation strategy for existing infrastructure. A robust climate-resilient design incorporating green infrastructure elements such as permeable pavements and retention ponds was proposed to manage increased stormwater volumes effectively. Implementing the recommended design will require collaboration between urban planners, engineers, and local communities to ensure long-term sustainability and resilience of urban drainage systems in coastal Ghana. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Time-Series Forecasting Model for Risk Reduction in Kenyan Manufacturing Plants Systems
The manufacturing sector in Kenya is critical to the country's economic growth, yet it faces significant operational risks that can disrupt productivity and profitability. A hybrid ARIMA-GARCH model was employed to analyse historical data from several Kenyan manufacturing companies. Time-series analysis techniques were used to forecast future risk levels based on identified patterns and trends. The model demonstrated a predictive accuracy of 85% in forecasting the direction and magnitude of risks over one-year periods, with confidence intervals indicating robust uncertainty management strategies could reduce potential losses by up to 40%. The time-series forecasting model has proven effective in identifying and mitigating risks within Kenyan manufacturing systems. This study contributes a novel method for risk reduction that can be applied across various industries. Manufacturing companies should integrate the proposed forecasting tool into their risk management frameworks to enhance operational stability and resilience. manufacturing, time-series forecasting, risk reduction, ARIMA-GARCH model 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/
Methodological Evaluation of Industrial Machinery Fleets in Rwanda Using Multilevel Regression Analysis for Cost-Effectiveness Assessment
Industrial machinery fleets play a critical role in Rwanda's economic development, particularly in sectors such as manufacturing and construction. A multilevel regression model will be employed to analyse data from multiple levels, including individual machines within fleets and overall fleet operations. Uncertainty in estimates will be addressed through robust standard errors. The analysis reveals that operational maintenance costs significantly influence the cost-effectiveness of machinery fleets, with a proportion as high as 40% attributed to these expenses. This study provides insights into optimising industrial machinery fleet operations for better financial outcomes in Rwanda. Based on the findings, targeted improvements in maintenance strategies and possibly the integration of predictive maintenance systems are recommended to enhance cost-effectiveness. Industrial Machinery Fleets, Multilevel Regression Analysis, Cost-Effectiveness, Maintenance Costs, 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.