Cw Suh

Variation of ADM1 by using temperature-phased anaerobic digestion (TPAD) operation

The objective of the study was to examine the application of the Anaerobic Digestion Model No. 1 (ADM1) developed by the IWA task group for mathematical modelling of anaerobic process. Lab-scale temperature-phased anaerobic digestion (TPAD) process were operated continuously, and were fed with co-substrate composed of dog food and flour. The model platform implemented in the simulation was a derivative of the ADM1. Sensitivity analysis showed that k(m.process) (maximum specific uptake rate) and K(S.process) (half saturation value) had high sensitivities to model components. Important parameters including maximum uptake rate for propionate utilisers (k(m.pro)) and half saturation constant for acetate utilisers (K(S.ac)) in the thermophilic digester and maximum uptake rate for acetate utilisers (k(m.ac)) in the mesophilic digester were estimated using iterative methods, which optimized the parameters with experimental results. Simulation with estimated parameters showed good agreement with experimental results in the case of methane production, uptake of acetate, soluble chemical oxygen demand (SCOD) and total chemical oxygen demand (TCOD). Under these conditions, the model predicted reasonably well the dynamic behavior of the TPAD process for verifying the model.

Sequential modeling of fecal coliform removals in a full-scale activated-sludge wastewater treatment plant using an evolutionary process model induction system

We propose an evolutionary process model induction system that is based on the grammar-based genetic programming to automatically discover multivariate dynamic inference models that are able to predict fecal coliform bacteria removals using common process variables instead of directly measuring fecal coliform bacteria concentration in a full-scale municipal activated-sludge wastewater treatment plant. A sequential modeling paradigm is also proposed to derive multivariate dynamic models of fecal coliform removals in the evolutionary process model induction system. It is composed of two parts, the process estimator and the process predictor. The process estimator acts as an intelligent software sensor to achieve a good estimation of fecal coliform bacteria concentration in the influent. Then the process predictor yields sequential prediction of the effluent fecal coliform bacteria concentration based on the estimated fecal coliform bacteria concentration in the influent from the process estimator with other process variables. The results show that the evolutionary process model induction system with a sequential modeling paradigm has successfully evolved multivariate dynamic models of fecal coliform removals in the form of explicit mathematical formulas with high levels of accuracy and good generalization. The evolutionary process model induction system with sequential modeling paradigm proposed here provides a good alternative to develop cost-effective dynamic process models for a full-scale wastewater treatment plant and is readily applicable to a variety of other complex treatment processes.

A comparative study of perchlorate degradation in acute toxicity and chronic toxicity.

Since the discovery of perchlorate in water system, the public has been concerned about its human health effect. In practice it was reported that chronic exposure to perchlorate may lead to damage in thyroid hormone activity. This study introduced a method of perchlorate reduction, using autotrophic bacteria which utilise hydrogen as an electron donor. Two experiments were conducted to compare the effects of acute and chronic perchlorate toxicity on bacterial perchlorate reduction potential. One was a batch-fed operation generating acute toxicity and another was a continuous-fed operation generating chronic toxicity. Acclimation period of the batch-fed operation was 14 days while that of the continuous-fed operation was 31 days as commensurate with double. Lots of batch tests using the mixed culture passing through acclimation were conducted to figure out kinetics of biological perchlorate reduction. The maximum perchlorate utilisation rate (q(max)) of the mixed culture acclimated by acute toxicity was 2.92 mg ClO(4)(-)/mg dry-weight (DW)/d, while that of chronic toxicity was 0.27 mg ClO(4)(-)/mg DW/d. Half-maximum rate constants (K(s)) of acute and chronic toxicity were 567.3 and 25.6 mg ClO(4)(-)/L respectively. This result showed that acute toxicity acclimated the mixed culture more rapidly and produced a higher activity for biological perchlorate reduction than chronic toxicity.

Online nonlinear sequential Bayesian estimation of a biological wastewater treatment process

Online estimation of unknown state variables is a key component in the accurate modelling of biological wastewater treatment processes due to a lack of reliable online measurement systems. The extended Kalman filter (EKF) algorithm has been widely applied for wastewater treatment processes. However, the series approximations in the EKF algorithm are not valid, because biological wastewater treatment processes are highly nonlinear with a time-varying characteristic. This work proposes an alternative online estimation approach using the sequential Monte Carlo (SMC) methods for recursive online state estimation of a biological sequencing batch reactor for wastewater treatment. SMC is an algorithm that makes it possible to recursively construct the posterior probability density of the state variables, with respect to all available measurements, through a random exploration of the states by entities called ‘particle’. In this work, the simplified and modified Activated Sludge Model No. 3 with nonlinear biological kinetic models is used as a process model and formulated in a dynamic state-space model applied to the SMC method. The performance of the SMC method for online state estimation applied to a biological sequencing batch reactor with online and offline measured data is encouraging. The results indicate that the SMC method could emerge as a powerful tool for solving online state and parameter estimation problems without any model linearization or restrictive assumptions pertaining to the type of nonlinear models for biological wastewater treatment processes.