Evaluation of a fuzzy logic–based pH control system: response time and accuracy under simulated fermentation conditions

Junita Tarigan; Muhammad Atqa Adzkia Zaldi; Umaya Ramadhani Putri Nasution

doi:10.18011/bioeng.2026.v20.1402

Authors

Junita Tarigan Department of Agricultural and Biosystem Engineering, Universitas Sumatera Utara, Medan, Indonesia https://orcid.org/0009-0000-7242-2788
Muhammad Atqa Adzkia Zaldi Department of Information Technology, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia https://orcid.org/0009-0006-0476-9767
Umaya Ramadhani Putri Nasution Department of Information Technology, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia

DOI:

https://doi.org/10.18011/bioeng.2026.v20.1402

Keywords:

pH control, fuzzy logic control, biohydrogen fermentation, sensor calibration, dynamic response, Arduino-based system

Abstract

pH is a critical parameter in anaerobic fermentation processes, particularly in biohydrogen production, where microbial activity is highly sensitive to environmental conditions. This study aimed to evaluate the performance of a fuzzy logic–based pH control system using standardized buffer solutions to simulate fermentation conditions. The system was developed using an Arduino Uno R4 WiFi microcontroller integrated with a pH sensor, peristaltic dosing pumps, and a mixing unit. Experiments were conducted at laboratory scale with working volumes of 300 mL under acidic (pH 4.5) and alkaline (pH 8.0) initial conditions, targeting an optimal pH range of 5.5–7.0. The pH sensor calibration showed good repeatability but limited accuracy, with a root mean square error (RMSE) of approximately 1.12 pH units. A systematic error pattern was observed, with overestimation in acidic conditions and underestimation in neutral to alkaline ranges, indicating limitations of the linear calibration model. Despite this, the sensor demonstrated stable and consistent measurements. The fuzzy logic control system successfully regulated pH toward the desired set points under both acidic and alkaline conditions. The system exhibited stable and monotonic responses without oscillation or overshoot, demonstrating robust control performance. However, differences in response dynamics were observed: the system responded faster under alkaline conditions than under acidic conditions, reflecting the nonlinear characteristics of the pH scale and mixing dynamics. Overall, the developed system demonstrates the potential of fuzzy logic control for pH regulation in fermentation processes. However, improvements in sensor calibration and response speed, particularly under acidic conditions, are necessary to enhance system accuracy and efficiency.

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