Performance evaluation of the ISMLR package for predicting the next day's influent wastewater flowrate at Kirie WRP
| Autor: | Paul R. Anderson, Junjie Zhu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Environmental Engineering
Computer science 0208 environmental biotechnology 02 engineering and technology Wastewater 010501 environmental sciences 01 natural sciences Water Purification Linear regression Process control Data retention MATLAB Process engineering 0105 earth and related environmental sciences Water Science and Technology computer.programming_language Artificial neural network business.industry Water Missing data 020801 environmental engineering Variable (computer science) Linear Models business computer Water Pollutants Chemical |
| Zdroj: | Water Science and Technology. 80:695-706 |
| ISSN: | 1996-9732 0273-1223 |
| DOI: | 10.2166/wst.2019.309 |
| Popis: | 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 R2 up to 0.83 by integrating with a typical artificial neural network. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|