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The objective of this paper is to demonstrate the importance of incorporating more realistic energy cost models (based on current energy tariff structures) into existing water resource recovery facilities (WRRFs) process models when evaluating technologies and cost-saving control strategies. In this paper, we first introduce a systematic framework to model energy usage at WRRFs and a generalized structure to describe energy tariffs including the most common billing terms. Secondly, this paper introduces a detailed energy cost model based on a Spanish energy tariff structure coupled with a WRRF process model to evaluate several control strategies and provide insights into the selection of the contracted power structure. The results for a 1-year evaluation on a 115,000 population-equivalent WRRF showed monthly cost differences ranging from 7 to 30% when comparing the detailed energy cost model to an average energy price. The evaluation of different aeration control strategies also showed that using average energy prices and neglecting energy tariff structures may lead to biased conclusions when selecting operating strategies or comparing technologies or equipment. The proposed framework demonstrated that for cost minimization, control strategies should be paired with a specific optimal contracted power. Hence, the design of operational and control strategies must take into account the local energy tariff.

The use of process models to simulate the fate of micropollutants in wastewater treatment plants is constantly growing. However, due to the high workload and cost of measuring campaigns, many simulation studies lack sufficiently long time series representing realistic wastewater influent dynamics. In this paper, the feasibility of the Benchmark Simulation Model No. 2 (BSM2) influent generator is tested to create realistic dynamic influent (micro)pollutant disturbance scenarios. The presented set of models is adjusted to describe the occurrence of three pharmaceutical compounds and one of each of its metabolites with samples taken every 2-4h: the anti-inflammatory drug ibuprofen (IBU), the antibiotic sulfamethoxazole (SMX) and the psychoactive carbamazepine (CMZ). Information about type of excretion and total consumption rates forms the basis for creating the data-defined profiles used to generate the dynamic time series. In addition, the traditional influent characteristics such as flow rate, ammonium, particulate chemical oxygen demand and temperature are also modelled using the same framework with high frequency data. The calibration is performed semi-automatically with two different methods depending on data availability. The 'traditional' variables are calibrated with the Bootstrap method while the pharmaceutical loads are estimated with a least squares approach. The simulation results demonstrate that the BSM2 influent generator can describe the dynamics of both traditional variables and pharmaceuticals. Lastly, the study is complemented with: 1) the generation of longer time series for IBU following the same catchment principles; 2) the study of the impact of in-sewer SMX biotransformation when estimating the average daily load; and, 3) a critical discussion of the results, and the future opportunities of the presented approach balancing model structure/calibration procedure complexity versus predictive capabilities.

The growing awareness of the relevance of organic microcontaminants on the environment has led to a growing number of studies on attenuation of these compounds in wastewater treatment plants (WWTP) and rivers. However, the effects of the sampling strategies (frequency and duration of composite samples) on the attenuation estimates are largely unknown. Our goal was to assess how frequency and duration of composite samples influence uncertainty of the attenuation estimates in WWTPs and rivers. Furthermore, we also assessed how compound consumption rate and degradability influence uncertainty. The assessment was conducted through simulating the integrated wastewater system of Puigcerdà (NE Iberian Peninsula) using a sewer pattern generator and a coupled model of WWTP and river. Results showed that the sampling strategy is especially critical at the influent of WWTP, particularly when the number of toilet flushes containing the compound of interest is small (≤100 toilet flushes with compound day), and less critical at the effluent of the WWTP and in the river due to the mixing effects of the WWTP. For example, at the WWTP, when evaluating a compound that is present in 50 pulses·d using a sampling frequency of 15-min to collect a 24-h composite sample, the attenuation uncertainty can range from 94% (0% degradability) to 9% (90% degradability). The estimation of attenuation in rivers is less critical than in WWTPs, as the attenuation uncertainty was lower than 10% for all evaluated scenarios. Interestingly, the errors in the estimates of attenuation are usually lower than those of loads for most sampling strategies and compound characteristics (e.g. consumption and degradability), although the opposite occurs for compounds with low consumption and inappropriate sampling strategies at the WWTP. Hence, when designing a sampling campaign, one should consider the influence of compounds' consumption and degradability as well as the desired level of accuracy in attenuation estimations.

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