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A Dynamic Model-Fitting Algorithm for Batch Laboratory Data: Application to Constant-Pressure Cake Filtration Experiments
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DOI：10.3390-chemengineering9010020.png)
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Abstract
En 中文
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.
Keywords:
model fitting
parameter estimation
batch experiments
mechanistic models
process systems engineering
Journal
IF:
3.4
Papers:
342
Citations:
1.6K