Journal
WATER SCIENCE AND TECHNOLOGY
Volume 80, Issue 4, Pages 695-706Publisher
IWA PUBLISHING
DOI: 10.2166/wst.2019.309
Keywords
artificial neural network; flowrate; iterated stepwise multiple linear regression; missing data; soft sensor; variable selection
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Soft-sensor applications for wastewater management can provide valuable information for intelligent monitoring and process control above and beyond what is available from conventional hard sensors and laboratory measurements. To realize these benefits, it is important to know how to manage gaps in the data time series, which could result from the failure of hard sensors, errors in laboratory measurements, or low-frequency monitoring schedules. A robust soft-sensor system needs to include a plan to address missing data and efficiently select variable(s) to make the most use of the available information. In this study, we developed and applied an enhanced iterated stepwise multiple linear regression (ISMLR) method through a MATLAB-based package to predict the next day's influent flowrate at the Kirie water reclamation plant (WRP). The method increased the data retention from 77% to 93% and achieved an adjusted R-2 up to 0.83 by integrating with a typical artificial neural network.
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