Smart Monitoring Solutions for Real‐Time Water pH Regulation in Aquatic Ecotoxicology

Abstract

ABSTRACT This study designs a statistical process control tool that effectively detects small and moderate shifts in process parameters, to address challenges in quality monitoring. The proposed control chart employs advanced statistical detection techniques to enhance sensitivity while reducing false alarms, thus improving detection performance in various applications. This methodology is applied in a real‐life context within an aquatic ecotoxicology laboratory, where daily monitoring of water pH levels is essential for safeguarding the health of sensitive aquatic organisms, such as mysids. The laboratory environment is meticulously controlled to simulate natural conditions, and our application of the proposed control chart ensures that any deviations from the optimal pH level are detected promptly, thereby maintaining water quality and supporting the reliability of experimental outcomes. The paper comprehensively evaluates the performance of the proposed control chart in both zero‐state and steady‐state conditions, offering valuable insights for practitioners in the field. We present empirical evidence demonstrating that the proposed control chart significantly outperforms traditional control charts, including Shewhart, CUSUM, and EWMA, particularly in detecting small to moderate shifts in water pH levels. Furthermore, we provide optimal parameter settings tailored for specific monitoring scenarios, enhancing the applicability of proposed control chart for quality control in laboratory environments.

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