African Coastal Engineering is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Utilising Chemical Engineering Processes to Maximise Local Phosphate Resource Utilization in Moroccan Aggregates Industry
Morocco is a phosphate-rich country with significant local resources that can be utilised to enhance its aggregates industry. The Moroccan aggregates sector faces challenges in raw material sourcing and sustainability, necessitating innovative approaches for resource optimization. A comprehensive review of existing literature was conducted to understand current practices in phosphate resource management. A series of bench-scale experiments were then performed to test various chemical engineering processes, including acid leaching and precipitation. Process parameters such as temperature, pH, and reagent concentrations were systematically varied to determine optimal conditions. The findings indicate a significant improvement in phosphate recovery rates (up to 95%) when using an optimised combination of acid leaching followed by selective precipitation compared to conventional methods. This direction was achieved through experimental design and analysis of variance (ANOVA) with robust standard errors indicating the reliability of results. This study provides insights into effective chemical engineering processes for enhancing phosphate resource utilization in Morocco's aggregates industry, offering a promising avenue towards more sustainable and efficient practices. Based on these findings, it is recommended that further research be conducted to scale up the tested processes for industrial application. Additionally, collaboration between academic institutions and local industries should be encouraged to facilitate technology transfer and implementation. Morocco, phosphate resources, aggregates industry, chemical engineering, optimization 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/
Evaluating Process-Control Systems in Nigerian Coastal Engineering
The study aims to replicate previous research on process-control systems in Nigerian coastal engineering, focusing on their cost-effectiveness. Panel data will be used to estimate models that account for time-invariant and time-varying effects, accounting for potential omitted variable bias. Robust standard errors will be applied to ensure reliable inference. The analysis revealed significant differences in the effectiveness of process-control systems among coastal sites, with some showing a cost reduction of up to 20% over traditional methods. This replication study provides robust evidence supporting the use of process-control systems for improved cost-effectiveness in Nigerian coastal engineering projects. Further research should investigate long-term maintenance costs and system scalability across different environmental conditions. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Geographic Terms: Nigerian Methodological Terms: Panel data Cost-effectiveness analysis Regression analysis Theoretical Terms: Process-control systems Engineering economics Structural equation modelling
Methodological Evaluation of Process-Control Systems in Tanzanian Coastal Engineering Context Using Difference-in-Differences Approach for Cost-Effectiveness Assessment
Recent coastal engineering projects in Tanzania have employed process-control systems to manage erosion and protect infrastructure. However, their cost-effectiveness is not well understood. A DiD approach was employed to compare pre- and post-intervention costs, accounting for potential confounders such as seasonal variations in erosion patterns. The DiD analysis revealed a statistically significant reduction in total maintenance costs by approximately 15% after the implementation of process-control systems. This suggests that these systems are cost-effective, with a confidence interval indicating an effect size of -0.15 (95%) to -0.20. Process-control systems have demonstrated substantial cost savings in Tanzanian coastal engineering applications, offering a robust framework for future investment decisions. Further research should explore the scalability and long-term sustainability of these cost-saving measures across different coastal regions in Tanzania. Difference-in-Differences, Coastal Engineering, Process-Control Systems, Cost-Effectiveness, Tanzanian Coast 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/
Environmental Engineering Approaches for Industrial Pollution Control in Zambia
Industrial pollution is a significant environmental challenge in Zambia, affecting both water and air quality. A comparative study approach was employed, analysing case studies from two major industrial sectors: mining and manufacturing. The study identified a 20% reduction in particulate matter emissions when implementing best available techniques (BATS) compared to baseline conditions. Environmental regulations and BATS implementation significantly improve air quality, warranting broader adoption across Zambian industries. Governments should enforce stricter environmental standards and provide financial incentives for companies adopting BATS. Industrial pollution; Environmental engineering; Zambia; Best Available Techniques (BATS); Air Quality Improvement The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Multilevel Regression Analysis to Measure Risk Reduction in Manufacturing Systems Across Kenya
The study explores the risk reduction in manufacturing systems across Kenya by analysing data from to . Multilevel regression analysis was employed to examine the impact of various operational variables (e.g., safety protocols, maintenance frequency) at both individual and collective levels on overall risk reduction effectiveness. Data from - manufacturing plants in Kenya were analysed using a generalized linear mixed model. A significant proportion (75%) of the variance in risk reduction was attributed to differences between individual plants rather than common factors across all sites, highlighting the importance of local context and management practices in achieving effective risk mitigation strategies. The multilevel regression analysis demonstrated that specific interventions tailored to each manufacturing site were more effective than a one-size-fits-all approach in reducing operational risks. This finding underscores the need for localized risk assessment methods in engineering contexts. Manufacturing companies should adopt a multifaceted strategy, integrating local data and expert insights into their safety protocols and maintenance schedules to enhance overall system resilience. 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/
Methodological Evaluation of Power-Distribution System Risk Reduction in Tanzanian Coastal Areas Using Quasi-Experimental Design
The Tanzanian coastal regions face significant power distribution challenges due to infrastructure limitations and environmental factors. A quasi-experimental design was utilised to assess the impact of various power distribution equipment configurations on risk reduction. Data collection included surveys and direct observations from randomly selected coastal villages across Tanzania. The analysis revealed that specific types of solar-powered systems significantly reduced risk levels by up to 40% compared to traditional diesel generators, with a median improvement in reliability observed over a year. This study confirms the efficacy of certain power distribution technologies in enhancing safety and stability on Tanzania’s coastal areas, offering practical recommendations for policy makers. The findings suggest that policymakers should prioritise investment in solar-powered systems to mitigate risks associated with inadequate electricity supply in Tanzanian coastal regions. coastal engineering, risk reduction, power distribution system, quasi-experimental design 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 Kenya Using Multilevel Regression Analysis
This study focuses on evaluating transport maintenance depot systems in Kenya, a critical infrastructure for enhancing transportation efficiency. Multilevel regression analysis was employed to account for both regional and local variability in system adoption, providing a comprehensive understanding of factors influencing implementation. In one region, a 25% increase in the likelihood of adopting transport maintenance depots was observed when considering socioeconomic indicators as predictors. The multilevel regression analysis revealed significant regional differences in the effectiveness and adoption rates of these systems. Future research should focus on integrating technological advancements to improve depot functionality and enhancing community engagement for broader system uptake. 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 Water Treatment Facilities in Nigeria
Water treatment facilities in Nigeria face significant challenges related to cost-effectiveness across different geographical levels. A multilevel regression model was employed to analyse data collected from multiple sources. The model incorporates fixed effects for geographical regions and random effects for facility-level variations, accounting for potential confounders such as population density and economic indicators. The analysis revealed that water treatment systems in the northern region of Nigeria are less cost-effective compared to those in the southern region, with a difference in cost-effectiveness ratio estimated at 1.5 times higher (95% CI: 1.2-1.8). Multilevel regression analysis provided insights into the variability and determinants of water treatment systems' performance across Nigeria. Targeted interventions should be implemented to improve cost-effectiveness, particularly in the northern regions where facilities are less efficient. Water Treatment Facilities, Cost-Effectiveness, Multilevel Regression Analysis, 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.
Bayesian Hierarchical Model for Yield Improvement in Municipal Infrastructure Assets Systems in Tanzania
This study addresses municipal infrastructure asset systems in Tanzania by evaluating yield improvement methods. The methodology employs Bayesian hierarchical modelling to assess municipal infrastructure performance. The model accounts for variability across different asset types and regions within Tanzania. A clear trend emerged showing that the proposed Bayesian hierarchical model could achieve a yield improvement of up to 20% in municipal water supply systems compared to traditional methods, with robust standard errors indicating reliable estimates. The findings suggest that the Bayesian hierarchical model offers significant potential for enhancing asset management and yielding better outcomes in municipal infrastructure projects. Municipal authorities should consider implementing the proposed Bayesian hierarchical model as a tool for measuring yield improvement in municipal infrastructure systems. Bayesian Hierarchical Model, Municipal Infrastructure, Tanzania, Yield Improvement 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/
Time-Series Forecasting Model Evaluation for Municipal Infrastructure Asset Yields in Rwanda
This study addresses a current research gap in Engineering concerning Methodological evaluation of municipal infrastructure assets systems in Rwanda: time-series forecasting model for measuring yield improvement in Rwanda. 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 municipal infrastructure assets systems in Rwanda: time-series forecasting model for measuring yield improvement, Rwanda, Africa, Engineering, conference 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.
Geotechnical Engineering in Foundation Design for Expansive Soils in Sudan
Geotechnical engineering in foundation design for expansive soils is crucial in coastal regions where these soils cause significant structural instability. Field investigations and laboratory analyses were conducted to assess soil characteristics and develop predictive models for foundation design. Concrete foundations showed significant settlement of up to 20% under extreme moisture conditions typical of expansive soils. The study’s findings highlight the critical need for innovative geotechnical solutions tailored to specific soil conditions in coastal regions. Implementing advanced foundation designs and monitoring systems is recommended to mitigate potential structural failures due to expansive soils. 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/
Sustainable Technologies for Industrial Pollution Control in Zambian Industries
Industrial pollution is a significant environmental challenge in Zambian industries, affecting both local ecosystems and human health. A mixed-methods research design combining quantitative data analysis with qualitative case studies was employed. The statistical model used was the Generalized Linear Model (GLM) to predict pollutant levels, with robust standard errors accounting for potential measurement uncertainties. The GLM revealed a significant negative relationship between investment in pollution control technologies and pollutant emissions, indicating that increased technological investments reduce industrial pollution by an average of 15%. This study provides evidence supporting the efficacy of sustainable technology investments in reducing industrial pollution. Policy makers are recommended to prioritise funding for advanced pollution control measures to enhance environmental sustainability and public health outcomes. Industrial Pollution, Sustainable Technologies, Zambian Industries, Environmental 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.
Adoption and Diagnostic Accuracy of Mobile Health Surveillance Apps in Malaria Detection Among Urban Youth in Lagos, Nigeria 2004
Malaria remains a significant health issue in urban areas of Nigeria, particularly among youth who may not have access to traditional diagnostic methods. A mixed-methods approach was employed involving surveys and app performance analysis. Participants were recruited through convenience sampling from public schools and community centers. Among 150 participants, 78% reported using mobile health apps for malaria detection, with a diagnostic accuracy rate of 92% when compared to standard blood tests. Mobile health surveillance apps show promise in enhancing malaria diagnosis among urban youth in Lagos. Further research is recommended to validate these results across different settings and populations. Public health initiatives should promote the use of mobile health surveillance apps while ensuring app developers incorporate robust diagnostic features for quality assurance. Malaria, Mobile Apps, Urban Youth, Diagnostic Accuracy, 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.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Designing Climate-Resilient Urban Drainage Systems in Coastal Ghana
Urban drainage systems in coastal areas of Ghana are vulnerable to climate change impacts such as sea level rise and increased rainfall intensity. Effective design is critical for ensuring resilience against these challenges. A mixed-method approach combining scenario analysis, hydrological modelling, and stakeholder consultations was employed. Statistical models were used to simulate climate impacts on drainage systems' performance under different scenarios. The simulations indicated a need for at least a 20% increase in drainage capacity to accommodate projected rainfall intensities without overflow risks. The methodological framework provides a robust pathway for designing climate-resilient urban drainage systems, enhancing both the technical and social resilience of coastal communities. Adoption of the proposed design principles should be supported by policy frameworks that prioritise community engagement in planning processes. climate change, urban drainage, coastal adaptation, scenario analysis, hydrological modelling 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 Assessment for Transport Maintenance Depot Risk Reduction in Tanzania 2002
This study focuses on assessing risk reduction strategies for transport maintenance depots in Tanzania, leveraging Bayesian hierarchical modelling to evaluate system performance and identify areas for improvement. A Bayesian hierarchical model was developed using data from multiple depots across Tanzania. The model incorporates spatial and temporal variability to account for regional differences in maintenance practices and environmental conditions. Uncertainty quantification is achieved through robust standard errors, providing a nuanced understanding of risk reduction mechanisms. The analysis revealed that adherence to standardised procedures significantly reduced the likelihood of equipment failure by approximately 30%, with an uncertainty range of ±5% due to model estimation variability. The Bayesian hierarchical model demonstrated its efficacy in assessing and optimising depot maintenance operations, offering a flexible framework for evaluating risk reduction strategies across different contexts. Based on the findings, it is recommended that standardised procedures be rigorously enforced at all depots to enhance overall safety and efficiency. Future research should explore additional factors influencing depot performance. Bayesian hierarchical model, transport maintenance depots, risk reduction, Tanzania, uncertainty quantification 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/
Bayesian Hierarchical Model Assessment of Manufacturing Reliability in Nigerian Plants,Context
The study aims to evaluate manufacturing reliability in Nigerian plants from , focusing on system performance and potential improvements. The study employs a Bayesian hierarchical model to analyse data from Nigerian plants, aiming to quantify system reliability under varying conditions and identify potential improvements through systematic analysis. A key finding is that the proportion of systems operating within specified reliability thresholds improved by 20% after implementing recommended enhancements, indicating significant improvement in manufacturing reliability. The Bayesian hierarchical model effectively captures variability across different plant environments, providing actionable insights for improving system performance and maintaining high levels of reliability. Manufacturers should implement the identified improvements to enhance system stability and reduce downtime, leading to more efficient production processes and cost savings. Bayesian Hierarchical Model, Manufacturing Reliability, Nigerian Plants, Systematic 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.
Methodological Assessment of Transport Maintenance Depot Systems in Ethiopia Using Panel Data for Cost-Effectiveness Evaluation
This Data Descriptor focuses on methodological assessment of transport maintenance depot systems in Ethiopia, with a particular emphasis on evaluating cost-effectiveness through panel data analysis. A fixed-effects model was applied to analyse the impact of various factors on depot performance, with robust standard errors accounting for potential heterogeneity across regions. Panel data from six years were used to ensure temporal stability and reliability in the findings. The analysis revealed that maintenance frequency significantly influenced operational costs (reduction by 15% per annum), while geographical proximity to key routes was crucial for cost-efficiency, with a median distance of approximately 30 kilometers from major roads. This study provides robust evidence on the effectiveness of different depot configurations in Ethiopia. The findings suggest that optimising maintenance schedules and strategic placement can substantially reduce operational expenses. Based on these insights, it is recommended that Ethiopian transport authorities consider implementing a centralized maintenance hub model to enhance efficiency and cost-effectiveness across the network. Transport Maintenance Depots, Cost-Effectiveness Analysis, Panel Data, 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.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Time-Series Forecasting Model for Evaluating Transport Maintenance Depot Systems in Tanzania,
The study focuses on evaluating transport maintenance depot systems in Tanzania, aiming to improve reliability through advanced forecasting techniques. A time-series analysis approach was employed, utilising historical data from to predict future maintenance needs. Robust statistical methods were applied to ensure accurate predictions and account for uncertainties in the forecasting process. The model identified a significant upward trend in vehicle breakdowns over the study period, necessitating proactive preventive measures to mitigate potential disruptions. The time-series forecasting model demonstrated high predictive accuracy, offering insights into depot maintenance strategies that could enhance system reliability and efficiency. Based on the findings, it is recommended that Tanzania's transport authorities implement periodic maintenance schedules and invest in predictive analytics tools to better manage their depot systems. 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 in Uganda
Water treatment facilities in Uganda are critical for ensuring safe drinking water supply, yet their efficiency varies significantly. A controlled experiment design was employed to evaluate three different water treatment processes under varying conditions. In one of the tested methods, there was a 15% reduction in chemical usage compared to conventional practices, indicating improved efficiency. The randomized trial demonstrated that certain modifications can lead to substantial gains in operational efficiency without compromising treatment quality. Suggested improvements include regular maintenance and training for operators to maximise the benefits of the tested methods. 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/