A Dynamic Model-Fitting Algorithm for Batch Laboratory Data: Application to Constant-Pressure Cake Filtration Experiments
Model fitting of laboratory-generated experimental data is a foundational task in engineering, bridging theoretical models with real-world data to enhance predictive accuracy. This process is particularly valuable in batch dynamic experiments, where mechanistic models are often used to represent com...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | ChemEngineering |
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| Online Access: | https://www.mdpi.com/2305-7084/9/1/20 |
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| author | Belmiro P. M. Duarte Maria J. Moura Lino O. Santos Nuno M. C. Oliveira |
| author_facet | Belmiro P. M. Duarte Maria J. Moura Lino O. Santos Nuno M. C. Oliveira |
| author_sort | Belmiro P. M. Duarte |
| collection | DOAJ |
| description | Model fitting of laboratory-generated experimental data is a foundational task in engineering, bridging theoretical models with real-world data to enhance predictive accuracy. This process is particularly valuable in batch dynamic experiments, where mechanistic models are often used to represent complex system behavior. Here, we propose a systematic algorithm tailored for the model fitting and parameter estimation of experimental data from batch laboratory experiments, rooted in a Process Systems Engineering framework. The paper provides an in-depth, step-by-step approach covering data collection, model selection, parameter estimation, and accuracy assessment, offering clear guidelines for experimentalists. To demonstrate the algorithm’s effectiveness, we apply it to a series of dynamic experiments on the pressure-constant cake filtration of calcium carbonate, where the pressure drop across the filter is varied as a key experimental factor. This example underscores the algorithm’s utility in enhancing the reliability and interpretability of model-based analyses in engineering. |
| format | Article |
| id | doaj-art-0ad83e2c51a24d898ceabbb7cb9eb844 |
| institution | DOAJ |
| issn | 2305-7084 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ChemEngineering |
| spelling | doaj-art-0ad83e2c51a24d898ceabbb7cb9eb8442025-08-20T03:11:59ZengMDPI AGChemEngineering2305-70842025-02-01912010.3390/chemengineering9010020A Dynamic Model-Fitting Algorithm for Batch Laboratory Data: Application to Constant-Pressure Cake Filtration ExperimentsBelmiro P. M. Duarte0Maria J. Moura1Lino O. Santos2Nuno M. C. Oliveira3Instituto Politécnico de Coimbra, Instituto Superior de Engenharia de Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, PortugalInstituto Politécnico de Coimbra, Instituto Superior de Engenharia de Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, PortugalCERES—Chemical Engineering and Renewable Resources for Sustainability, Universidade de Coimbra, Rua Sílvio Lima, Pólo II, 3030-790 Coimbra, PortugalCERES—Chemical Engineering and Renewable Resources for Sustainability, Universidade de Coimbra, Rua Sílvio Lima, Pólo II, 3030-790 Coimbra, PortugalModel fitting of laboratory-generated experimental data is a foundational task in engineering, bridging theoretical models with real-world data to enhance predictive accuracy. This process is particularly valuable in batch dynamic experiments, where mechanistic models are often used to represent complex system behavior. Here, we propose a systematic algorithm tailored for the model fitting and parameter estimation of experimental data from batch laboratory experiments, rooted in a Process Systems Engineering framework. The paper provides an in-depth, step-by-step approach covering data collection, model selection, parameter estimation, and accuracy assessment, offering clear guidelines for experimentalists. To demonstrate the algorithm’s effectiveness, we apply it to a series of dynamic experiments on the pressure-constant cake filtration of calcium carbonate, where the pressure drop across the filter is varied as a key experimental factor. This example underscores the algorithm’s utility in enhancing the reliability and interpretability of model-based analyses in engineering.https://www.mdpi.com/2305-7084/9/1/20dynamic model fittinglaboratory experimentsdata filtrationmodel quality metricscake filtrationconstant pressure |
| spellingShingle | Belmiro P. M. Duarte Maria J. Moura Lino O. Santos Nuno M. C. Oliveira A Dynamic Model-Fitting Algorithm for Batch Laboratory Data: Application to Constant-Pressure Cake Filtration Experiments ChemEngineering dynamic model fitting laboratory experiments data filtration model quality metrics cake filtration constant pressure |
| title | A Dynamic Model-Fitting Algorithm for Batch Laboratory Data: Application to Constant-Pressure Cake Filtration Experiments |
| title_full | A Dynamic Model-Fitting Algorithm for Batch Laboratory Data: Application to Constant-Pressure Cake Filtration Experiments |
| title_fullStr | A Dynamic Model-Fitting Algorithm for Batch Laboratory Data: Application to Constant-Pressure Cake Filtration Experiments |
| title_full_unstemmed | A Dynamic Model-Fitting Algorithm for Batch Laboratory Data: Application to Constant-Pressure Cake Filtration Experiments |
| title_short | A Dynamic Model-Fitting Algorithm for Batch Laboratory Data: Application to Constant-Pressure Cake Filtration Experiments |
| title_sort | dynamic model fitting algorithm for batch laboratory data application to constant pressure cake filtration experiments |
| topic | dynamic model fitting laboratory experiments data filtration model quality metrics cake filtration constant pressure |
| url | https://www.mdpi.com/2305-7084/9/1/20 |
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