Data Reconciliation for Wastewater Treatment Plant Simulation Studies-Planning for High-Quality Data and Typical Sources of Errors

Headnote

ABSTRACT:

Model results are only as good as the data fed as input or used for calibration. Data reconciliation for wastewater treatment modeling is a demanding task, and standardized approaches are lacking. This paper suggests a procedure to obtain high-quality data sets for model-based studies. The proposed approach starts with the collection of existing historical data, followed by the planning of additional measurements for reliability checks, a data reconciliation step, and it ends with an intensive measuring campaign. With the suggested method, it should be possible to detect, isolate, and finally identify systematic measurement errors leading to verified and qualitative data sets.

To allow mass balances to be calculated or other reliability checks to be applied, few additional measurements must be introduced in addition to routine measurements. The intensive measurement campaign should be started only after all mass balances applied to the historical data are closed or the faults have been detected, isolated, and identified. In addition to the procedure itself, an overview of typical sources of errors is given. Water Environ. Res., 82, 426 (2010).

KEYWORDS: data quality, data reconciliation, fault detection, diagnosis, activated sludge model, modeling, measuring campaigns, sources of measuring errors.

doi:10.2175/106143009X12529484815511

Introduction

In the last decade, wastewater treatment modeling has become a standard engineering tool for wastewater treatment plant (WWTP) design, process optimization, operator training, and developing control strategies (Rieger et al., 2008). However, model predictions can only be as good as the data fed as model input or otherwise used for calibration. Data reconciliation procedures include fault detection, fault isolation, fault identification (for definitions, see Isermann and Bailé, 1997), and preparation of a data set suitable for the modeling objective.

Data reconciliation for wastewater treatment modeling consumes time (and money), and the applied techniques are seldom straightforward and reliable. In this paper, the process of data collection and reconciliation is discussed, and a procedure is suggested regarding how to plan for and finally obtain reliable data sets for simulation studies in an efficient way.

Dependent on the objectives (e.g., steady-state versus dynamic simulations), a typical simulation study starts with the collection of historical data and may be complemented by an intensive measuring campaign. Typical reasons to pursue a dynamic simulation are, among others, blower sizing, evaluation of control concepts,...