Methodological Evaluation and Time-Series Forecasting for Process-Control System Efficiency Gains in Rwanda
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2021
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| _version_ | 1866901423906619392 |
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| author | Uwase, Marie Claire Uwimana, Jean de Dieu |
| author_facet | Uwase, Marie Claire Uwimana, Jean de Dieu |
| contents | <p>{ "background": "Process-control systems in industrial and infrastructure sectors are critical for operational efficiency, yet there is a paucity of robust methodological frameworks for evaluating their performance and forecasting efficiency gains in developing economies.", "purpose and objectives": "This paper aims to develop and validate a methodological framework for evaluating process-control systems, with the specific objective of constructing a time-series forecasting model to quantify potential efficiency gains.", "methodology": "A hybrid methodology integrates system diagnostics with statistical modelling. A key forecasting model, the Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX), is employed, specified as $\\phi(B)\\Phi(B^s)\\nabla^d\\nablas^D yt = \\theta(B)\\Theta(B^s)\\epsilont + \\beta Xt$, where $X_t$ represents control-system intervention variables. Model parameters are estimated using maximum likelihood, and inference is based on robust standard errors to account for heteroskedasticity.", "findings": "The application of the model to case study data from a water treatment facility demonstrated a statistically significant forecasted efficiency gain. Specifically, the model projected a 12-18% reduction in specific energy consumption following the implementation of an optimised control protocol, with a 95% confidence interval of [10.5%, 19.2%] for the mean gain.", "conclusion": "The proposed methodological framework provides a rigorous, evidence-based approach for evaluating process-control systems, confirming that time-series forecasting can reliably quantify efficiency improvements in such contexts.", "recommendations": "Adoption of this modelling framework is recommended for baseline assessments and post-intervention analysis in similar engineering projects. Further research should focus on integrating real-time data streams for adaptive forecasting.", "key words": "process control, time-series analysis, forecasting, efficiency, SARIMAX, infrastructure", "contribution statement": "This paper presents a novel application of the SARIMAX forecasting model, integrated within a systematic evaluation methodology, to quantify engineering efficiency gains from</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18968256 |
| institution | Zenodo |
| language | eng |
| publishDate | 2021 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Methodological Evaluation and Time-Series Forecasting for Process-Control System Efficiency Gains in Rwanda Uwase, Marie Claire Uwimana, Jean de Dieu Process-control systems Time-series forecasting Operational efficiency Sub-Saharan Africa Methodological evaluation Industrial automation <p>{ "background": "Process-control systems in industrial and infrastructure sectors are critical for operational efficiency, yet there is a paucity of robust methodological frameworks for evaluating their performance and forecasting efficiency gains in developing economies.", "purpose and objectives": "This paper aims to develop and validate a methodological framework for evaluating process-control systems, with the specific objective of constructing a time-series forecasting model to quantify potential efficiency gains.", "methodology": "A hybrid methodology integrates system diagnostics with statistical modelling. A key forecasting model, the Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX), is employed, specified as $\\phi(B)\\Phi(B^s)\\nabla^d\\nablas^D yt = \\theta(B)\\Theta(B^s)\\epsilont + \\beta Xt$, where $X_t$ represents control-system intervention variables. Model parameters are estimated using maximum likelihood, and inference is based on robust standard errors to account for heteroskedasticity.", "findings": "The application of the model to case study data from a water treatment facility demonstrated a statistically significant forecasted efficiency gain. Specifically, the model projected a 12-18% reduction in specific energy consumption following the implementation of an optimised control protocol, with a 95% confidence interval of [10.5%, 19.2%] for the mean gain.", "conclusion": "The proposed methodological framework provides a rigorous, evidence-based approach for evaluating process-control systems, confirming that time-series forecasting can reliably quantify efficiency improvements in such contexts.", "recommendations": "Adoption of this modelling framework is recommended for baseline assessments and post-intervention analysis in similar engineering projects. Further research should focus on integrating real-time data streams for adaptive forecasting.", "key words": "process control, time-series analysis, forecasting, efficiency, SARIMAX, infrastructure", "contribution statement": "This paper presents a novel application of the SARIMAX forecasting model, integrated within a systematic evaluation methodology, to quantify engineering efficiency gains from</p> |
| title | Methodological Evaluation and Time-Series Forecasting for Process-Control System Efficiency Gains in Rwanda |
| topic | Process-control systems Time-series forecasting Operational efficiency Sub-Saharan Africa Methodological evaluation Industrial automation |
| url | https://doi.org/10.5281/zenodo.18968256 |