Inverse Modeling of Nitrification-Denitrification Processes: A Case Study on the Blue Plains Wastewater Treatment Plant in Washington, DC.

Abstract

Evaluating the uncertainty associated with the predictions of Activated Sludge Models (ASMs) is essential in designing and optimization of biological wastewater treatment systems. The sources of ASM model prediction uncertainties can be classified into influent and environmental factor uncertainties, parameter uncertainty and epistemic uncertainty due to model abstraction. In this study, observed data obtained from nitrification-denitrification processes at the Blue Plains advanced wastewater treatment plant is used to quantifying the uncertainty associated with ASM parameter estimation. The posterior distributions obtained for parameters are then used in a Monte Carlo simulation to assess the uncertainties of predicted effluent constituents expressed as the 95% credible intervals of effluent concentrations. Using the outcome of the parameter estimation step, two scenarios of methanol loading scheme including one based on flow-rate (baseline) and the second one based on total nitrogen in the influent were evaluated. The treatment performance of the system was quantified based on the probability distributions of effluent violation with respect to certain water quality standards. The results show that methanol loading based on influent flow-rate is less effective compared to determining methanol loading based on total nitrogen loading in the influent.

Application of Bayesian Approach in the Parameter Estimation of Continuous Lumping Kinetic Model of Hydrocracking Process

“…Commonly, Monte Carlo simulations over a pre-assumed range of parameters [18] [19] [20], the autoregressive moving average for uncertainty analysis associated with the time series [21] [22] [23], the generalized likelihood uncertainty estimation (GLUE) method [24], and the Bayesian approach [25] [26] [27] [28] are used uncertainty quantification. Albrecht [29] Parameter estimation and uncertainty analysis by using Bayesian approach are widely used in different fields of science.…”

An Adaptive Time-Step Backward Differentiation Algorithm to Solve Stiff Ordinary Differential Equations: Application to Solve Activated Sludge Models

“…This system was modeled as three reactors in a series based on mass balance equation (Equation (8)) with a modified ASM1 reaction network consisting of 11 reactions and 15 constituents, as shown in Table 1 [11]. The total volume of the reactor was 17,500 m 3 , divided into three tanks in a series, as depicted in Figure 2.…”

“…In some applications, the computation time of the ASM is critical due to a long simulation period, the need for online simulation [10], and the use of parameter estimation algorithms that require numerous simulations. For example, Alikhani et al [11] modeled a large scale WWTP with an ASM by using Markov-Chain Monte Carlo (MCMC) sampling and solved a large system of ODEs for 500,000 times to obtain the ASM's parameter posterior probability distribution. Therefore, applying a fast algorithm to solve ASMs that can significantly decrease the computation time of such applications would be highly desired.…”