Solving Sensor Reading Drifting Using Denoising Data Processing Algorithm (DDPA) for Long-Term Continuous and Accurate Monitoring of Ammonium in Wastewater
Xingyu Wang, Yingzheng Fan, Yuankai Huang, Jing Ling, Abby Klimowicz, Grace Pagano, Baikun Li
Abstract
Sensor reading drifting caused by sensor property deterioration is a major problem of long-term continuous monitoring in wastewater and hinders wide-range application of online wastewater management. This study aims to tackle this problem by developing denoising data processing algorithm (DDPA) for a typical electrochemical sensor, solid-state ion-selective membrane (S-ISM) sensor. Based on data mining and electrochemical principles, DDPA was designed by combining digital filter and outlier analysis to differentiate actual sensor readings from background noise when the S-ISM sensitivity declined over time. The sensor sensitivity was raised from 21 mV/dec to 55 mV/dec after the reading processing, without compromising the detection limit (7 × 10–6 mol/L). Furthermore, long-term accuracy of S-ISM sensors in wastewater was enhanced by adding hydrophobic polytetrafluoroethylene (PTFE) into polymer matrix. The sensitivity (57 mV/dec) of PTFE-loaded S-ISM sensors was the near-theoretical value on the first day and still higher than 35 mV/dec after 24 days in wastewater, providing an excellent stable baseline for DDPA. Combination of sensor material enhancement (adding PTFE) with sensor reading processing (using DDPA) assured the stable and high sensitivity (55 mV/dec after 24 days) and high detection limit (<5 × 10–5 mol/L) for wastewater monitoring. The study demonstrates a new route toward long-term accurate wastewater monitoring and smart wastewater sensor networks by establishing a strong correlation between multiorder derivatives of sensor readings and electrochemical responses with DDPA as an efficient data analysis approach.