Monitoring a chemical reaction using pH measurements: An approach based on multiscale fractal analysis

Rolando Zenteno-Catemaxca, Jazael G. Moguel-Castañeda, Victor M. Rivera, Hector Puebla and Eliseo Hernandez-Martinez

Chaos, Solitons & Fractals, 2021, vol. 152, issue C

Abstract: The monitoring of (bio)-chemical reactions is mandatory to determine the operating conditions that maximize the performance of the processes and guarantee the process safety of chemical and biotechnological processes. However, it is not always possible to have monitoring systems that determine the reactive/product concentration in real-time. Therefore, offline analysis methods are commonly used, which are usually expensive and can generate considerable lag times. In this work, an indirect monitoring approach based on the fractal analysis of fluctuations of pH time series to monitor a chemical saponification reaction is presented. First, an experimental design was implemented to set the validation of the proposed monitoring system, evaluating the temperature and stirring speed effects on the conversion rate. Then, pH time series were analyzed using two multiscale methodologies (i.e., DFA and R/S analysis), which show that the pH series fluctuations exhibit fractal behavior. Also, two distinct regions can be identified, suggesting that the pH signals may contain information on the phenomena that intervene in the reaction process (i.e., transport phenomena). The dynamic scaling exponents show direct correlations with the reagent concentration, obtaining coefficients of determination R2> 0.98 in all experiments carried out. pH measurement is inexpensive, easy to implement, and can be obtained in real-time. The results suggest that multiscale analysis of pH series has a high potential for online monitoring of a class of reactive processes with low economic and computational costs.

Keywords: Indirect monitoring; Multiscale analysis; pH time-series (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:152:y:2021:i:c:s0960077921006901

DOI: 10.1016/j.chaos.2021.111336

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