Open AccessParameter Identification of an Ultrafiltration Model for Organics Removal in a Full-Scale Wastewater Reclamation Plant with Sparse and Incomplete Monitoring Data
Author(s) -
Fuwei Sun,
Siyu Zeng,
Yunqing Huang,
Mengchang He
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0161300
Subject(s) - land reclamation , identification (biology) , sampling (signal processing) , ultrafiltration (renal) , scale (ratio) , computer science , wastewater , sewage treatment , environmental science , process engineering , environmental engineering , engineering , chemistry , chromatography , history , botany , physics , archaeology , filter (signal processing) , quantum mechanics , computer vision , biology
Ultrafiltration (UF) has become one of the dominant treatment processes for wastewater reclamation in China. Modeling is an effective instrument to understand and optimize UF systems. To this end, a previously developed UF model for organics removal was applied to the UF process in a typical, full-scale wastewater reclamation plant (WRP) in China. However, the sparse and incomplete field monitoring data from the studied WRP made the traditional model analysis approaches hardly work in this case. Therefore, two strategies, namely Strategy 1 and Strategy 2, were proposed, following a regional sensitivity analysis approach, for model parameter identification. Strategy 1 aimed to identify the model parameters and the missing model input, i.e. sampling times, simultaneously, while Strategy 2 tried to separate these two processes to reduce the dimension of the identification problem through an iteration procedure. With these two strategies, the model performed well in the Qinghe WRP with the absolute relative errors between the simulated and observed total organic carbon (TOC) generally below 10%. The four model parameters were all sensitive and identifiable, and even the sampling times could be roughly identified. Given the incomplete model input, these results were encouraging and added to the trustworthiness of model when it was applied to the Qinghe WRP.