| Journal of Smart Sensors and Computing
Received: 12 August 2026; Revised: 28 September 2026; Accepted: 29 September 2026; Published Online: 29 June 2026.
J. Smart Sens. Comput., 2026, 2(3), 26214 | Volume 2 Issue 3 (September 2026) | DOI: https://doi.org/10.64189/ssc.26214
© The Author(s) 2026
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
Leveraging Python for Potentiometric Titrations: An
AI-Enhanced Approach to Hydrochloric Acid
Detection
Shanthi Vunguturi
*
Department of Chemistry, Muffakham Jah College of Engineering and Technology Hyderabad, Telangana 500034, India
*Email: v.shanthi@mjcollege.ac.in (Shanthi Vunguturi)
Abstract
Potentiometric titration is a classical analytical method for determining the concentration of acids and bases
with high precision. Traditional titration techniques, however, often require manual interpretation of titration
curves, which may introduce errors and limit reproducibility. This study presents an AI-enhanced approach to
hydrochloric acid (HCl) detection through potentiometric titration using Python-based automation and
machine learning. The system integrates automated data acquisition, preprocessing, and curve fitting to identify
equivalence points with greater accuracy. By applying regression models and anomaly detection algorithms, the
proposed framework reduces subjectivity and improves consistency in endpoint determination. Experimental
trials using standard NaOH titrants demonstrated that the AI-assisted approach achieved higher precision in
detecting HCl concentrations compared to traditional manual methods. The Python framework also provides
visualization tools and real-time feedback, enabling a user-friendly interface for laboratory and educational
settings. This study highlights the potential of combining artificial intelligence and classical analytical chemistry
to advance modern titration practices.
Keywords: Potentiometric titration; Hydrochloric acid detection; Python automation; Artificial intelligence;
Chemometrics; Machine learning in chemistry.
1. Introduction
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Note: RMSE and R² refer to held-out electrode-potential prediction, not to the chemical endpoint volume.
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