Bayesian Hierarchical Model Evaluation of Process-Control Systems in Nigerian Contexts, 2014

This study focuses on evaluating process-control systems in Nigerian contexts, particularly examining their cost-effectiveness through a Bayesian hierarchical model. A Bayesian hierarchical model was employed to analyse data from multiple Nigerian facilities, accounting for variability across sites while estimating the cost-effectiveness of different control systems. This approach allows for robust inferences with uncertainty quantification. The analysis revealed that a specific combination of sensors and control algorithms led to an average reduction in operational costs by 20% compared to baseline conditions, with 95% confidence interval indicating significant savings. The Bayesian hierarchical model provided insights into the cost-effectiveness of process-control systems in Nigerian settings, offering practical recommendations for improving efficiency and reducing expenses. Based on the findings, it is recommended that companies in Nigeria adopt a tailored approach to sensor placement and control strategies to maximise cost savings. Additionally, ongoing monitoring and maintenance are crucial to maintain optimal performance. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.