Kris Villez

Wastewater treatment modelling: dealing with uncertainties

This paper serves as a problem statement of the issues surrounding uncertainty in wastewater treatment modelling. The paper proposes a structure for identifying the sources of uncertainty introduced during each step of an engineering project concerned with model-based design or optimisation of a wastewater treatment system. It briefly references the methods currently used to evaluate prediction accuracy and uncertainty and discusses the relevance of uncertainty evaluations in model applications. The paper aims to raise awareness and initiate a comprehensive discussion among professionals on model prediction accuracy and uncertainty issues. It also aims to identify future research needs. Ultimately the goal of such a discussion would be to generate transparent and objective methods of explicitly evaluating the reliability of model results, before they are implemented in an engineering decision-making context.

Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes

A methodology based on Principal Component Analysis (PCA) and clustering is evaluated for process monitoring and process analysis of a pilot-scale SBR removing nitrogen and phosphorus. The first step of this method is to build a multi-way PCA (MPCA) model using the historical process data. In the second step, the principal scores and the Q-statistics resulting from the MPCA model are fed to the LAMDA clustering algorithm. This procedure is iterated twice. The first iteration provides an efficient and effective discrimination between normal and abnormal operational conditions. The second iteration of the procedure allowed a clear-cut discrimination of applied operational changes in the SBR history. Important to add is that this procedure helped identifying some changes in the process behaviour, which would not have been possible, had we only relied on visually inspecting this online data set of the SBR (which is traditionally the case in practice). Hence the PCA based clustering methodology is a promising tool to efficiently interpret and analyse the SBR process behaviour using large historical online data sets.

Kalman-based strategies for Fault Detection and Identification (FDI): Extensions and critical evaluation for a buffer tank system

This paper is concerned with the application of Kalman filter based methods for Fault Detection and Identification (FDI). The original Kalman based method, formulated for bias faults only, is extended for three more fault types, namely the actuator or sensor being stuck, sticky or drifting. To benchmark the proposed method, a nonlinear buffer tank system is simulated as well as its linearized version. This method based on the Kalman filter delivers good results for the linear version of the system and much worse for the nonlinear version, as expected. To alleviate this problem, the Extended Kalman Filter (EKF) is investigated as a better alternative to the Kalman filter. Next to the evaluation of detection and diagnosis performance for several faults, the effect of dynamics on fault identification and diagnosis as well as the effect of including the time of fault occurrence as a parameter in the diagnosis task are investigated.

Data reconciliation for wastewater treatment plant simulation studies-planning for high-quality data and typical sources of errors.

Model results are only as good as the data fed as input or used for calibration. Data reconciliation for wastewater treatment modeling is a demanding task, and standardized approaches are lacking. This paper suggests a procedure to obtain high-quality data sets for model-based studies. The proposed approach starts with the collection of existing historical data, followed by the planning of additional measurements for reliability checks, a data reconciliation step, and it ends with an intensive measuring campaign. With the suggested method, it should be possible to detect, isolate, and finally identify systematic measurement errors leading to verified and qualitative data sets. To allow mass balances to be calculated or other reliability checks to be applied, few additional measurements must be introduced in addition to routine measurements. The intensive measurement campaign should be started only after all mass balances applied to the historical data are closed or the faults have been detected, isolated, and identified. In addition to the procedure itself, an overview of typical sources of errors is given.

Generalized Shape Constrained Spline Fitting for Qualitative Analysis of Trends

Abstract In this work, we present a generalized method for analysis of data series based on shape constraint spline fitting which constitutes the first step toward a statistically optimal method for qualitative analysis of trends. The presented method is based on a branch-and-bound (B&B) algorithm which is applied for globally optimal fitting of a spline function subject to shape constraints. More specifically, the B&B algorithm searches for optimal argument values in which the sign of the fitted function and/or one or more of its derivatives change. We derive upper and lower bounding procedures for the B&B algorithm to efficiently converge to the global optimum. These bounds are based on existing solutions for shape constraint spline estimation via Second Order Cone Programs (SOCPs). The presented method is demonstrated with three different examples which are indicative of both the strengths and weaknesses of this method.

Performance evaluation of fault detection methods for wastewater treatment processes

Several methods to detect faults have been developed in various fields, mainly in chemical and process engineering. However, minimal practical guidelines exist for their selection and application. This work presents an index that allows for evaluating monitoring and diagnosis performance of fault detection methods, which takes into account several characteristics, such as false alarms, false acceptance, and undesirable switching from correct detection to non‐detection during a fault event. The usefulness of the index to process engineering is demonstrated first by application to a simple example. Then, it is used to compare five univariate fault detection methods (Shewhart, EWMA, and residuals of EWMA) applied to the simulated results of the Benchmark Simulation Model No. 1 long‐term (BSM1_LT). The BSM1_LT, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor and actuator faults and process disturbances in a wastewater treatment plant. The results from the method comparison using BSM1_LT show better performance to detect a sensor measurement shift for adaptive methods (residuals of EWMA) and when monitoring the actuator signals in a control loop (e.g., airflow). Overall, the proposed index is able to screen fault detection methods. Biotechnol. Bioeng. 2011;108: 333–344.

Resilient control system execution agent (ReCoSEA)

In an increasingly connected world, critical infrastructure systems suffer from two types of vulnerability. The first is the traditionally recognized problem of monitoring the systems for faults and failures, recognizing and analyzing data, and responding with real understanding to the problems of the system. Increasingly complex systems create the opportunity for single points of failure to cascade when inaccurate assessment of system health increases response time or leads to faulty analysis of the problems involved. A second problem involves vulnerability to cyber intrusion, in which malignant actors can mask system degradation or present false data about system status. A resilient system will protect stability, efficiency, and security. To ensure these three states, the system must react to changing conditions within the system with coordination: no one component of the system can be allowed to react to problems without real consideration of the effects of that action on other components within the system. Systems with multi-agent design typically have three layers of action, a management layer, a coordination layer, and an execution layer. A resilient multi-agent system will emphasize functions of the execution layer, which has the responsibility of initiating actions, monitoring, analyzing, and controlling its own processes, while feeding information back to the higher levels of management and coordination. The design concept of a resilient control system execution agent (ReCoSEA) grows out of these underpinnings, and through the use of computational intelligence techniques, this paper suggests an associated design methodology.

Membrane bioreactor fouling behaviour assessment through principal component analysis and fuzzy clustering

Adequate membrane bioreactor operation requires frequent evaluation of the membrane state. A data-driven approach based on principal component analysis (PCA) and fuzzy clustering extracting the necessary monitoring information solely out of transmembrane pressure data was investigated for this purpose. Out of three tested PCA techniques the two functional methods proved useful to cope with noise and outliers as opposed to the common standard PCA, while all of them presented similar capabilities for revealing data trends and patterns. The expert functional PCA approach enabled linking the two major trends in the data to reversible fouling and irreversible fouling. The B-splines approach provided a more objective way for functional representation of the data set but its complexity did not appear justified by better results. The fuzzy clustering algorithm, applied after PCA, was successful in recognizing the data trends and placing the cluster centres in meaningful positions, as such supporting data analysis. However, the algorithm did not allow a correct classification of all data. Factor analysis was used instead, exploiting the linearity of the observed two dimensional trends, to completely split the reversible and irreversible fouling effects and classify the data in a more pragmatic approach. Overall, the tested techniques appeared useful and can serve as the basis for automatic membrane fouling monitoring and control.

Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques

This paper investigates fault diagnosis in batch processes and presents a comparative study of feature extraction and classification techniques applied to a specific biotechnological case study: the fermentation process model by Birol et al. (Comput Chem Eng 26:1553–1565, 2002), which is a benchmark for advanced batch processes monitoring, diagnosis and control. Fault diagnosis is achieved using four approaches on four different process scenarios based on the different levels of noise so as to evaluate their effects on the performance. Each approach combines a feature extraction method, either multi-way principal component analysis (MPCA) or multi-way independent component analysis (MICA), with a classification method, either artificial neural network (ANN) or support vector machines (SVM). The performance obtained by the different approaches is assessed and discussed for a set of simulated faults under different scenarios. One of the faults (a loss in mixing power) could not be detected due to the minimal effect of mixing on the simulated data. The remaining faults could be easily diagnosed and the subsequent discussion provides practical insight into the selection and use of the available techniques to specific applications. Irrespective of the classification algorithm, MPCA renders better results than MICA, hence the diagnosis performance proves to be more sensitive to the selection of the feature extraction technique.

Qualitative representation of trends: an alternative approach to process diagnosis and control

The potential for qualitative representation of trends in the context of process diagnosis and control is evaluated in this paper. The technique for qualitative description of the data series is relatively new to the field of process monitoring and diagnosis and is based on the cubic spline wavelet decomposition of the data. It is shown that the assessed qualitative description of trends can be coupled easily with existing process knowledge and does not demand the user to understand the underlying technique in detail, in contrast to, for instance, multivariate techniques in Statistical Process Control. The assessed links can be integrated straightforwardly into the framework of supervisory control systems by means of look-up tables, expert systems or case-based reasoning frameworks. This in turn allows the design of a supervisory control system leading to fully automated control actions. The technique is illustrated by an application to a pilot-scale SBR.