Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

Identification of causal relationships among traits related to drought resistance in Stylosanthes scabra using QTL analysis

Previous studies have shown that a negative relationship exists between transpiration efficiency (TE) and carbon isotope discrimination (Delta) and between TE and specific leaf area (SLA) in Stylosanthes scabra, A glasshouse experiment was conducted to confirm these relationships in an F-2 population and to study the causal nature of these relationships through quantitative trait loci (QTL) analysis, One hundred and twenty F-2 genotypes from a cross between two genotypes within S. scabra were used. Three replications for each genotype were maintained through vegetative propagation, Water stress was imposed by maintaining plants at 40% of field capacity for about 45 d. To facilitate QTL analysis, a genetic linkage map consisting of 151 RAPD markers was developed, Results from this study show that Delta was significantly and negatively correlated with TE and biomass production. Similarly, SLA showed significant negative correlation with TE and biomass production, Most of the QTL for TE and Delta were present on linkage groups 5 and 11. Similarly, QTL for SLA, transpiration and biomass productivity traits were clustered on linkage groups 13 and 24, One unlinked marker was also associated with these traits, There were several markers coincident between different traits, At all the coincident QTL, the direction of QTL effects was consistent with phenotypic data, At the coincident markers between TE and Delta, high alleles of TE were associated with low alleles of Delta. Similarly, low alleles of SLA were associated with high alleles of biomass productivity traits and transpiration. At the coincident markers between trans-4-hydroxy-N-methyl proline (MHP) and relative water content (RWC), low alleles of MHP were associated with high alleles of RWC, This study suggests the causal nature of the relationship between TE and Delta. Phenotypic data and QTL, data show that SLA was more closely associated with biomass production than with TE, This study also shows that a cause-effect relationship may exist between SLA and biomass production.

Adaptive multiscale principal components analysis for online monitoring of wastewater treatment

Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.

Work domain analysis and sensors I: principles and simple example

In this paper we establish a foundation for understanding the instrumentation needs of complex dynamic systems if ecological interface design (EID)-based interfaces are to be robust in the face of instrumentation failures. EID-based interfaces often include configural displays which reveal the higher-order properties of complex systems. However, concerns have been expressed that such displays might be misleading when instrumentation is unreliable or unavailable. Rasmussen's abstraction hierarchy (AH) formalism can be extended to include representations of sensors near the functions or properties about which they provide information, resulting in what we call a sensor-annotated abstraction hierarchy. Sensor-annotated AHs help the analyst determine the impact of different instrumentation engineering policies on higher-order system information by showing how the data provided from individual sensors propagates within and across levels of abstraction in the AH. The use of sensor-annotated AHs with a configural display is illustrated with a simple water reservoir example. We argue that if EID is to be effectively employed in the design of interfaces for complex systems, then the information needs of the human operator need to be considered at the earliest stages of system development while instrumentation requirements are being formulated. In this way, Rasmussen's AH promotes a formative approach to instrumentation engineering. (C) 2002 Elsevier Science Ltd. All rights reserved.

New type of dual-channel PAM chlorophyll fluorometer for highly sensitive water toxicity biotests

A new type of dual-channel PAM chlorophyll fluorometer has been developed, which is specialised in the detection of extremely small differences in photosynthetic activity in algae or thylakoids suspensions. In conjunction with standardised algae cultures or isolated thylakoids, the new device provides an ultrasensitive biotest system for detection of toxic substances in water samples. In this report, major features of the new device are outlined and examples of its performance are presented using suspensions of Phaeodactylum tricornutum (diatoms) and of freeze-dried thylakoids of Lactuca sativa (salad). Investigated and reference samples are exposed to the same actinic intensity of pulse-modulated measuring light. The quantum yields are assessed by the saturation pulse method. Clock-triggered repetitive measurements of quantum yield typically display a standard deviation of 0.1%, corresponding to the inhibition induced by 0.02 mug diuron l(-1). Hence, for diuron or compounds with similar toxicity, the detection limit is well below the 0.1 mug l(-1) defined as the limit for the presence of a single toxic substance in water by the European Commission drinking water regulation. The amounts of water and biotest material required for analysis are very small, as a single assay involves two 1 ml samples, each containing ca. 0.5 mug chlorophyll. Both with Phaeodactylum and thylakoids the relationship between inhibition and diuron concentration is strictly linear up to 10% inhibition, with very similar slopes. Apparent inhibition depends on the actinic effect of the measuring light, showing optima at 6 and 4 mumol quanta m(-2) s(-1) with Phaeodactylum and thylakoids, respectively.

The effect of water, brine and ethanol flotation on the quality and shelf life of macadamia kernels 2. Kernel pieces

Raw macadamia kernel pieces were immersed in water (specific gravity 1.00 g/cm(3)), brine (SG 1.02 g/cm(3)) or ethanol solution (SG 0.97 g/cm(3)) for 30 or 60 s, then re-dried to below 1.5% moisture (wet basis) and stored under vacuum for 0, 4 and 12 months. Flotation in water had no effect on the quality or shelf life of the kernel pieces over 12 months storage, as measured by sensory evaluation of the kernels and chemical analysis of the kernel oil. Immersion in a salt solution caused unacceptable changes in quality during storage, increasing as storage time increased. Flotation in dilute ethanol also caused unacceptable quality changes during storage. Therefore, only flotation of macadamia kernel pieces in water can be recommended for commercial operations. Microbiological concerns with such a process still need to be addressed.

Using crop simulation to generate genotype by environment interaction effects for sorghum in water-limited environments

Multi-environment trials (METs) used to evaluate breeding lines vary in the number of years that they sample. We used a cropping systems model to simulate the target population of environments (TPE) for 6 locations over 108 years for 54 'near-isolines' of sorghum in north-eastern Australia. For a single reference genotype, each of 547 trials was clustered into 1 of 3 'drought environment types' (DETs) based on a seasonal water stress index. Within sequential METs of 2 years duration, the frequencies of these drought patterns often differed substantially from those derived for the entire TPE. This was reflected in variation in the mean yield of the reference genotype. For the TPE and for 2-year METs, restricted maximum likelihood methods were used to estimate components of genotypic and genotype by environment variance. These also varied substantially, although not in direct correlation with frequency of occurrence of different DETs over a 2-year period. Combined analysis over different numbers of seasons demonstrated the expected improvement in the correlation between MET estimates of genotype performance and the overall genotype averages as the number of seasons in the MET was increased.

The effect of water, brine and ethanol flotation on the quality and shelf life of macadamia kernels - 1. Whole kernels

Whole macadamia kernels were immersed in water (specific gravity 1.00 g/cm(3)), brine (SG 1.02 g/cm(3)) and ethanol solution (SG 0.97 g/cm(3)) for 30 or 60 s, re-dried to 1.0-1.5% moisture (wet basis) and stored under vacuum for 0, 4 and 12 months. Immersion in water had no effect on the quality or shelf life of kernels, as measured by sensory evaluation and analysis of the kernel oil. Immersion in brine and ethanol solutions changed the flavour of kernels, but had no effect on shelf life or kernel oil stability over 12 months storage. Water flotation to separate kernels based on differences in oil content is therefore feasible, but microbiological concerns need to be investigated.

Light and electron microscopic analysis of aquaporin 1-like-immunoreactive amacrine cells in the rat retina

Aquaporin 1 (AQP1; also known as CHIP, a channel-forming integral membrane protein of 28 kDa) is the first protein to be shown to function as a water channel and has been recently shown to be present in the rat retina. We previously showed (Kim et al. [1998] Neurosci Lett 244:52-54) that AQP1-like immunoreactivity is present in a certain population of amacrine cells in the rat retina. This study was conducted to characterize these cells in more detail, With immunocytochemistry using specific antisera against AQP1, whole-mount preparations and 50-mum-thick vibratome sections were examined by light and electron microscopy. These cells were a class of amacrine cells, which had symmetric bistratified dendritic trees ramified in stratum 2 and in the border of strata 3 and 4 of the inner plexiform layer (IPL). Their dendritic field diameters ranged from 90 to 230 mum. Double labeling with antisera against AQP1 and gamma-aminobutyric acid or glycine demonstrated that these AQP1-like-immunoreactive amacrine cells were immunoreactive for glycine. Their most frequent synaptic input was from other amacrine cell processes in both sublaminae a and b of the IPL, followed by a few cone bipolar cells. Their primary targets were other amacrine cells and ganglion cells in both sublaminae a and b of the IPL. In addition, synaptic output Onto bipolar cells was rarely observed in sublamina b of the IPL. Thus, the AQP1 antibody labels a class of glycinergic amacrine cells with small to medium-sized dendritic fields in the rat retina. (C) 2002 Wiley-Liss, Inc.

Measured and predicted total body water in children with myelomeningocele

Objective: Children with myelomeningocele (MMC) have an altered body composition and an atypical distribution of total body water (TBW). The aim of the present study was to determine the accuracy of current predictive equations, based on bioelectrical impedance analysis (BIA), in determining TBW when compared with measured TBW using deuterium dilution. Methods: Fourteen children with MMC were measured for whole body BIA and TBW (using deuterium dilution and the Plateau method). Total body water was predicted using equations based on the resistance and characteristic frequency from BIA measurements and heights of subjects. Results: The mean measured TBW was 15.46 +/- 8.28 L and the mean predictions for TBW using equations based on the resistance and characteristic frequency from BIA measurements and heights of subjects were 18.29 +/- 8.41 L, 17.72 +/- 11.42 L and 12.51 +/- 7.59 L, respectively. The best correlation was found using characteristic frequency. The limits of agreement between measured and predicted TBW values using Bland-Altman analysis were large. Conclusions: The present study suggests that the prediction of TBW in children with MMC can be made accurately using the equation of Cornish et al . based on BIA measurements of characteristic frequency.

Model-based analysis of anaerobic acetate uptake by a mixed culture of polyphosphate-accumulating and glycogen-accumulating organisms

An increasing number of studies shows that the glycogen-accumulating organisms (GAOs) can survive and may indeed proliferate under the alternating anaerobic/aerobic conditions found in EBPR systems, thus forming a strong competitor of the polyphosphate-accumulating organisms (PAOs). Understanding their behaviors in a mixed PAO and GAO culture under various operational conditions is essential for developing operating strategies that disadvantage the growth of this group of unwanted organisms. A model-based data analysis method is developed in this paper for the study of the anaerobic PAO and GAO activities in a mixed PAO and GAO culture. The method primarily makes use of the hydrogen ion production rate and the carbon dioxide transfer rate resulting from the acetate uptake processes by PAOs and GAOs, measured with a recently developed titration and off-gas analysis (TOGA) sensor. The method is demonstrated using the data from a laboratory-scale sequencing batch reactor (SBR) operated under alternating anaerobic and aerobic conditions. The data analysis using the proposed method strongly indicates a coexistence of PAOs and GAOs in the system, which was independently confirmed by fluorescent in situ hybridization (FISH) measurement. The model-based analysis also allowed the identification of the respective acetate uptake rates by PAOs and GAOs, along with a number of kinetic and stoichiometric parameters involved in the PAO and GAO models. The excellent fit between the model predictions and the experimental data not involved in parameter identification shows that the parameter values found are reliable and accurate. It also demonstrates that the current anaerobic PAO and GAO models are able to accurately characterize the PAO/GAO mixed culture obtained in this study. This is of major importance as no pure culture of either PAOs or GAOs has been reported to date, and hence the current PAO and GAO models were developed for the interpretation of experimental results of mixed cultures. The proposed method is readily applicable for detailed investigations of the competition between PAOs and GAOs in enriched cultures. However, the fermentation of organic substrates carried out by ordinary heterotrophs needs to be accounted for when the method is applied to the study of PAO and GAO competition in full-scale sludges. (C) 2003 Wiley Periodicals, Inc.

Online titrimetric and off-gas analysis for examining nitrification processes in wastewater treatment

The two steps of nitrification, namely the oxidation of ammonia to nitrite and nitrite to nitrate, often need to be considered separately in process studies. For a detailed examination, it is desirable to monitor the two-step sequence using online measurements. In this paper, the use of online titrimetric and off-gas analysis (TOGA) methods for the examination of the process is presented. Using the known reaction stoichiometry, combination of the measured signals (rates of hydrogen ion production, oxygen uptake and carbon dioxide transfer) allows the determination of the three key process rates, namely the ammonia consumption rate, the nitrite accumulation rate and the nitrate production rate. Individual reaction rates determined with the TOGA sensor under a number of operation conditions are presented. The rates calculated directly from the measured signals are compared with those obtained from offline liquid sample analysis. Statistical analysis confirms that the results from the two approaches match well. This result could not have been guaranteed using alternative online methods. As a case study, the influences of pH and dissolved oxygen (DO) on nitrite accumulation are tested using the proposed method. It is shown that nitrite accumulation decreased with increasing DO and pH. Possible reasons for these observations are discussed. (C) 2003 Elsevier Science Ltd. All rights reserved.

A comparison of the whole-body and segmental methodologies of bioimpedance analysis

Theory supports the use of a segmental methodology (SM) for bioimpedance analysis (BIA) of body water (BW). However, previous studies have generally failed to show a significant improvement when the SM is used in place of a whole-body methodology. A pilot study was conducted to compare the two methodologies in control and overweight subjects. BW of each subject was measured by D2O dilution and also estimated from BIA measurements. Bland and Altman analysis was used to compare the two values of BW. The SM resulted in a small but not significantly improved limits of agreement of measured and BIA estimated BW (psimilar to0.3). This and the results of previous studies suggest that improvements in prediction of BW obtained from application of the SM may be intrinsically small and may not justify the additional effort in application.

Methodologies for calibration and predictive analysis of a watershed model

The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.