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

Author

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

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.

Suggested Citation

  • Zenteno-Catemaxca, Rolando & Moguel-Castañeda, Jazael G. & Rivera, Victor M. & Puebla, Hector & Hernandez-Martinez, Eliseo, 2021. "Monitoring a chemical reaction using pH measurements: An approach based on multiscale fractal analysis," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
  • Handle: RePEc:eee:chsofr:v:152:y:2021:i:c:s0960077921006901
    DOI: 10.1016/j.chaos.2021.111336
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    References listed on IDEAS

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    1. Hernandez-Martinez, Eliseo & Velasco-Hernandez, Jorge X. & Perez-Muñoz, Teresa & Alvarez-Ramirez, Jose, 2013. "A DFA approach in well-logs for the identification of facies associations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6015-6024.
    2. Gabriel-Guzmán, Mauricio & Rivera, Victor M. & Cocotle-Ronzón, Yolanda & García-Díaz, Samuel & Hernandez-Martinez, Eliseo, 2017. "Fractality in coffee bean surface for roasting process," Chaos, Solitons & Fractals, Elsevier, vol. 99(C), pages 79-84.
    3. Ramírez-Platas, Mariana & Morales-Cabrera, Miguel A. & Rivera, Victor M. & Morales-Zarate, Epifanio & Hernandez-Martinez, Eliseo, 2021. "Fractal and multifractal analysis of electrochemical noise to corrosion evaluation in A36 steel and AISI 304 stainless steel exposed to MEA-CO2 aqueous solutions," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    4. Altan, Aytaç & Karasu, Seçkin & Bekiros, Stelios, 2019. "Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 325-336.
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    1. Romero-Bustamante, Jorge A. & Velazquez-Camilo, Oscar & Garcia‐Hernandez, Ángeles & Rivera, Victor M. & Hernandez-Martinez, Eliseo, 2022. "Monitoring of cane sugar crystallization process by multiscale time-series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).

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