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<p>Esterification reactions are essential to the production of a wide range of industrially important compounds. Therefore, this process demands efficient monitoring and control strategies based on efficient predictive models. In this study of butyl butyrate production, the rate kinetics and reactant conversion (in batch mode) are comprehended from a data-based prediction approach using Electrical Impedance Spectroscopy (EIS). The presence of acids makes the esterification reaction available for electrochemical measurements due to its solubility in aqueous solutions and ability to transfer charge. EIS corresponding to different experimental conditions (reactant molar ratio: 1–3, temperature: 60–90&#xa0;°C, and fixed catalyst concentration of 1.5 wt%) were collected at different sampling times (15, 30, 45, 60, 75, and 90&#xa0;min) during the reaction process. The EIS of esterification product samples is utilized to derive the impedimetric parameters by fitting them to an equivalent electrical circuit model. A multifunctional soft sensor based on a multiple linear regression (MLR) model was developed to forecast the reaction rate and conversion of butyric acid by utilizing extracted impedance parameters. The accuracy (R²) of the MLR ranges from 0.96 to 0.99 across different experimental conditions. The predicted rate of reaction was used to estimate the forward rate constant. Although rate constants were calculated based on EIS data collected at various time intervals, it was found to be constant with a nominal standard deviation (&lt; 2%). The errors between experimentally observed and predicted forward rate constants were found to be 1%, 1.8%, and 1.4% at 60&#xa0;°C, 75&#xa0;°C, and 90&#xa0;°C, respectively. The multifunctional software sensor thus developed may be utilized in real-time monitoring of the ester manufacturing process.</p> Graphical abstract <p></p>

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Development of a multifunction soft sensor for a chemical reaction process using impedimetric parameters

  • 期刊论文

  • 发表日期：

    2025年11月15日

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  • Ashutosh Kumar Pathak,
  • Madhusree Kundu

摘要

Esterification reactions are essential to the production of a wide range of industrially important compounds. Therefore, this process demands efficient monitoring and control strategies based on efficient predictive models. In this study of butyl butyrate production, the rate kinetics and reactant conversion (in batch mode) are comprehended from a data-based prediction approach using Electrical Impedance Spectroscopy (EIS). The presence of acids makes the esterification reaction available for electrochemical measurements due to its solubility in aqueous solutions and ability to transfer charge. EIS corresponding to different experimental conditions (reactant molar ratio: 1–3, temperature: 60–90 °C, and fixed catalyst concentration of 1.5 wt%) were collected at different sampling times (15, 30, 45, 60, 75, and 90 min) during the reaction process. The EIS of esterification product samples is utilized to derive the impedimetric parameters by fitting them to an equivalent electrical circuit model. A multifunctional soft sensor based on a multiple linear regression (MLR) model was developed to forecast the reaction rate and conversion of butyric acid by utilizing extracted impedance parameters. The accuracy (R²) of the MLR ranges from 0.96 to 0.99 across different experimental conditions. The predicted rate of reaction was used to estimate the forward rate constant. Although rate constants were calculated based on EIS data collected at various time intervals, it was found to be constant with a nominal standard deviation (< 2%). The errors between experimentally observed and predicted forward rate constants were found to be 1%, 1.8%, and 1.4% at 60 °C, 75 °C, and 90 °C, respectively. The multifunctional software sensor thus developed may be utilized in real-time monitoring of the ester manufacturing process.

Graphical abstract