On-Line Adaptive and Nonlinear Process Monitoring of a Pilot-Scale Sequencing Batch Reactor

This article describes the application of on-line nonlinear monitoring of a sequencing batch reactor (SBR). Three-way batch data of SBR are unfolded batch-wisely, and then a adaptive and nonlinear multivariate monitoring method is used to capture the nonlinear characteristics of normal batches. The approach is successfully applied to an 80 L SBR for biological wastewater treatment, where the SBR poses an interesting challenge in view of process monitoring since it is characterized by nonstationary, batchwise, multistage, and nonlinear dynamics. In on-line batch monitoring, the developed adaptive and nonlinear process monitoring method can effectively capture the nonlinear relationship among process variables of a biological process in a SBR. The results of this pilot-scale SBR monitoring system using simple on-line measurements clearly demonstrated that the adaptive and nonlinear monitoring technique showed lower false alarm rate and physically meaningful, that is, robust monitoring results.     

An intelligent data collection tool for chemical safety/risk assessment

REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) is the new European chemical legislation which aims to assess risk or safety of tens of thousands of chemicals to improve the protection of human health and the environment. The chemical safety assessment process is of an iterative nature. First, an initial, worst-case assessment is conducted after which refinements are made until no risk has been estimated or the risk is adequately controlled. Wasting time and resources on additional testing and implementing risk management measures with low effect on risk conclusions should be avoided as much as possible. This paper demonstrates the usefulness of an intelligent data collection strategy based on a sensitivity (and uncertainty) analysis on the risk assessment model EUSES to identify and order the most important "within-EU-TGD-reducible" input parameters influencing the local and regional risk characterisation ratios. The ordering can be adjusted for the costs involved in additional testing (e.g. ecotoxicity, physico-chemical properties, emission estimates, etc.). The risk refinement tool therefore reduces the resources needed to obtain a realistic risk estimate (both less conservative and less uncertain) as efficient as possible.     

Carbon and hydrogen isotope fractionation by microbial methane oxidation: improved determination

Isotope fractionation is a promising tool for quantifying methane oxidation in landfill cover soils. For good quantification an accurate determination of the isotope fractionation factor (alpha) of methane oxidation based on independent batch experiments with soil samples from the landfill cover is required. Most studies so far used data analysis methods based on approximations of the Rayleigh model to determine alpha. In this study, the two most common approximations were tested, the simplified Rayleigh approach and the Coleman method. To do this, the original model of Rayleigh was described in measurable variables, methane concentration and isotopic abundances, and fitted to batch oxidation data by means of a weighted non-linear errors-in-variables regression technique. The results of this technique were used as a benchmark to which the results of the two conventional approximations were compared. Three types of batch data were used: simulated data, data obtained from the literature, and data obtained from new batch experiments conducted in our laboratory. The Coleman approximation was shown to be acceptable but not recommended for carbon fractionation (error on alpha-1 up to 5%) and unacceptable for hydrogen fractionation (error up to 20%). The difference between the simplified Rayleigh approach and the exact Rayleigh model is much smaller for both carbon and hydrogen fractionation (error on alpha-1<0.05%). There is also a small difference when errors in both variables (methane concentration and isotope abundance) are accounted for instead of assuming an error-free independent variable. By means of theoretical calculations general criteria, not limited to methane, (13)C, or D, were developed for the validity of the simplified Rayleigh approach when using labelled compounds.     

Modelling the biological performance of a side-stream membrane bioreactor using ASM1

Membrane bioreactors(MBRs) are attracting global interest but the mathematical modeling of the biological performance of MBRs remains very limited. This study focuses on the modelling of a side-stream MBR system using Activated Sludge Model No. 1 (ASM1), and comparing the results with the modelling of traditional activated sludge processes. ASM1 parameters relevant for the long-term biological behaviour in MBR systems were calibrated(i, e. YH = 0.72gCOD/gCOD, YA = 0.25gCOD/gN, bH = 0.25 d^-1, bA = 0.080 d^-1 and fp =0.06), and generally agreed with the parameters in traditional activated sludge processes, with the exception that a higher autotrophic biomass decay rate was observed in the MBR. A sensitivity analysis for steady state operation and DO dynamics suggested that the biological performance of the MBR system (the sludge concentration, effluent quality and the DO dynamics) are very sensitive to the parameters(i.e. YH, YA, bH, hA, μmaxH and μmaxA), and influent wastewater components( X1, Ss, Xs, SNH).     

Adaptive Consensus Principal Component Analysis for On-Line Batch Process Monitoring

As the regulations of effluent quality are increasingly stringent, the on-line monitoring of wastewater treatment processes becomes very important. Multivariate statistical process control such as principal component analysis (PCA) has found wide applications in process fault detection and diagnosis using measurement data. In this work, we propose a consensus PCA algorithm for adaptive wastewater treatment process monitoring. The method overcomes the problem of changing operating conditions by updating the covariance structure recursively. The algorithm does not require any estimation compared to typical multiway PCA models. With this method process disturbances are detected in real time and the responsible measurements are directly identified. The presented methodology is successfully applied to a pilot-scale sequencing batch reactor for wastewater treatment.