Reconstruction of corrupted datasets from ammonium-ISE sensors at WRRFs through merging with daily composite samples

Abstract Long-term, continuous datasets of high quality are important for instrumentation, control, and automation efforts of wastewater resources recovery facility (WRRFs). This study presents a methodology to increase the reliability of measurements from ammonium ion-selective electrodes (ISEs). This is done by correcting corrupted ISE data with a data source that often is available at WRRFs (volume-proportional composite samples). A yearlong measurement campaign showed that the existing standard protocols for sensor maintenance might still create corrupted dataset, with poor sensor recalibrations responsible for abrupt and unrealistic jumps in the measurements. The proposed automatic correction methodology removes both recalibration jumps and signal drift by using information from composite samples that already are taken for reporting to legal authorities. Results showed that the developed methodology provided a continuous, high-quality time series without the major data quality issues of the original signal. In fact, the signal was improved for 87% of days when a reference sample was available. The effect of correcting the data before use in a data-driven software sensor was also investigated. The corrected dataset led to noticeably smaller day-to-day variations in estimated NH4+ loads, and to large improvements on both median estimates and prediction bounds. The long time series allowed for an investigation of how much training data that is required to fit a software sensor, which provides estimates that are representative for the entire study period. The results showed that 8 weeks of data allowed for a good median estimate, while 16 weeks are required for obtaining good 80% prediction bounds. Overall, the proposed method can increase the applicability of relatively cheaper ISE sensors for ICA application within WRRFs.