Experience and extensions to the ASM2 family of models

Abstract

The development of ASM2 has created a complex model for biological phosphorus removal. Most of the published work on calibrating this model has focused on the design of experiments to maximise information with which to calibrate the model, or the use of hourly data collected around and within an aeration tank. But many sewage works do not collect such data, nor have such instrumentation. The application of ASM2 with sparse data collected at a low frequency, and mostly only input-output, is considered in this paper, based on data collected at a Swedish sewage works. This paper shows that ASM2 can be calibrated with such measurements. This paper also looks at a modification to ASM2d to better handle heterotrophic usage of volatile fatty acids, and the use of this model to study the effects of large increases in in-sewer storage on sewage treatment works. Concern about the generation of large quantities of VFAs, and their effect on the sewage treatment processes, was unfounded.

The Use of Simulation Modelling for Optimisation of Phosphorus Removal in Sewage Treatment under Varying Influent Loading

“…Thus, in addition to yield coefficient for heterotrophic biomass (Y H 0.67) which was transferred from the Changzhou WWTP, the oxygen half-saturation constant for autotrophic organisms (K OA ) was reduced from the default value of 0.5 to 0.4 at the NWWTP. This change is in accordance with Petersen et al (2003) and Dudley et al (2002). Table 2 presents the operating parameters and the wastewater characteristics of the Nanjing WWTP (Table 2).…”

Process stress, stability and resilience in wastewater treatment processes: A novel conceptual methodology

“…The first variable BOD5 has a removal efficiency (Rpst), which is dependent on processes retention time (HRT) and is influenced by diurnal flow variations (Eq 5). This dynamic variation of a wastewater treatment process, instead of the exhaustive detail of PWM, E-PWM and simulators based on ASM methodologies similar to Dudley et al (2002), can be visualised as shown in Fig. 5 to Fig.…”

Parameter Identification of an Ultrafiltration Model for Organics Removal in a Full-Scale Wastewater Reclamation Plant with Sparse and Incomplete Monitoring Data

“…Data sparsity is not uncommon in environmental modeling studies. This could result from lack of appropriate monitoring instruments or capacity as well as inadequate temporal/spatial resolution, poor representativeness or low accuracy of the monitoring instruments [ 19 – 22 ]. There are also cases where model parameters or input are difficult to monitor, e.g.…”