Papers by Christian Rosen
Young Researchers 2004, 2004
Water Science Technology, 2008
The potential for qualitative representation of trends in the context of process diagnosis and co... more 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.
Industrial Engineering Chemistry Research, 2010
Wastewater treatment systems have, over the past decades, been subjects for optimization and cont... more Wastewater treatment systems have, over the past decades, been subjects for optimization and control research. One of the most intricate problems faced is that direct measurements of the variables of interest are seldom available. A large part of research has therefore been aimed at the extraction of suitable information from indirect measurements such as dissolved oxygen, pH, and oxidation reduction potential (ORP). Even if relatively complex tools, such as neural networks and fuzzy logic, have been used to conceive control laws, advantage is seldom taken of such tools with respect to development of the actual control algorithm. In this paper, a simple yet effective tool is presented that allows the detection of a desired process state by means of the Hotelling's T 2 statistic. The detection tool is generic in nature and is thereby applicable to any process where a certain desired state is to be detected by means of measured variables reflecting the targeted state. Its advantages over formerly proposed control strategies are discussed, and the precautions that were taken to render its application robust are presented. It is shown by means of a laboratory-scale sequencing batch reactor (SBR) setup for nutrient removal from wastewater that the proposed controller allows one to detect the targeted endogenous state and that its application leads to effective optimization of the overall system performance. More specifically, the length of the optimized phase is reduced by 41% of its original default length and a reduction of 5% is estimated for the expected energy consumption by the aeration system. In addition, effluent concentrations of total nitrogen and nitrate nitrogen are estimated to be lower by 30 and 25%, respectively. This is attributed to the gained length of the anoxic phase subsequent to the aerobic phase.
The current work of developing a control simulation benchmark goes back to the work carried out w... more The current work of developing a control simulation benchmark goes back to the work carried out within the COST Action 682 ("Integrated Wastewater Management") and the IAWQ Task Group on Respirometry-Based control of the Activated Sludge Process in the late 90s. The work was continued in COST Action 624 until 2003624 until (Copp 2002. In 2005, the IWA Task Group on Benchmarking of Control Strategies for Wastewater Treatment Plants (BSM TG) was initiated and is now responsible for the continued development of the benchmark work.
Water Science Technology, 2008
Proceedings of the Weftec 2003, 2003
Plant-wide wastewater treatment modeling that combines the modeling of liquid and solids streams ... more Plant-wide wastewater treatment modeling that combines the modeling of liquid and solids streams into one overall model is becoming increasingly popular. The idea of whole-plant modeling is complicated by the fact that the models used for each unit process have been for the most part developed in isolation, typically without consideration of the whole treatment process or other unit process models. Hence, each unit process model often uses a different set of state variables. This is especially true if the models were developed to describe fundamentally different biological processes like aerobic and anaerobic treatment. To overcome these differences and facilitate plant-wide modeling, model interfaces are used as a means to convert state variables from one model into state variables of a different model. Recently, there has been significant interest in incorporating a new anaerobic digestion unit process model -ADM1 (Batstone et al., 2002) into the COST and IWA simulation benchmarks Copp 2001; to create a whole-plant simulation benchmark. To do this, an interface is needed to convert the activated sludge state variables to the anaerobic digestion state variables and visa versa. Standardised model interfaces are an essential component for allowing objective comparisons of simulation results within the wastewater treatment modeling community. This paper describes such a state variable interface. c xc li xc ch li,xc li
Water Science and Technology, Feb 1, 2007
In this paper, two approaches to data mining of time series have been tested and compared. Both m... more In this paper, two approaches to data mining of time series have been tested and compared. Both methods are based on the wavelet decomposition of data series and allow the localization of important characteristics of a time series in both the time and frequency domain. The first method is a common method based on the analysis of wavelet power spectra. The second approach is new to the applied field of urban water networks and provides a qualitative description of the data series based on the cubic spline wavelet decomposition of the data. It is shown that wavelet power spectra indicate important and basic characteristics of the data but fail to provide detailed information of the underlying phenomena. In contrast, the second method allows the extraction of more and more detailed information that is important in a context of process monitoring and diagnosis.
Water Science and Technology, 1998
The development in sensor technology has made many wastewater treatment systems data rich but not... more The development in sensor technology has made many wastewater treatment systems data rich but not necessarily information rich. To extract the adequate information from several sensors is not trivial, and it is not sufficient to consider only the time series. Different tools for detecting unusual on-line measurement data and deviating process behaviour are discussed. In this paper various dimension reduction as well as advanced filtering methods arc considered in order to extract adequate information for fault detection and diagnosis. Both the operator and the process engineer can take advantage of such methods for proper monitoring of the plant, in particular extreme events and their causes.
Water Science Technology, May 1, 2008
The potential for qualitative representation of trends in the context of process diagnosis and co... more 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.
Water Science & Technology
In this paper a methodology for integrated multivariate monitoring and control of biological wast... more 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 o-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.
The IWA Anaerobic Digestion Model No.1 (ADM1) was presented in 2002 and is expected to represent ... more The IWA Anaerobic Digestion Model No.1 (ADM1) was presented in 2002 and is expected to represent the state-of-the-art model within this field in the future. Due to its complexity and stiffness the implementation of the model is not a simple task and several computational aspects need to be considered, in particular if the ADM1 is to be included in dynamic simulations of plantwide or even integrated systems. In this paper, the experiences gained from a Matlab/Simulink implementation of ADM1 into the extended COST/IWA Benchmark Simulation Model (BSM2) are presented. Aspects related to ODE vs DAE implementations, system stiffness and varying time constants, model interfacing with the ASM family, mass balances, acid-base equilibrium and algebraic solvers for pH and other troublesome state variables, numerical solvers and simulation time are discussed. The main conclusion is that if implemented properly, the ADM1 will produce high-quality results also in dynamic plant-wide simulations including noise, discrete sub-systems, etc. without imposing any major restrictions due to extensive computational efforts.
Proceedings of the Water Environment Federation, 2003
Plant-wide wastewater treatment modeling that combines the modeling of liquid and solids streams ... more Plant-wide wastewater treatment modeling that combines the modeling of liquid and solids streams into one overall model is becoming increasingly popular. The idea of whole-plant modeling is complicated by the fact that the models used for each unit process have been for the most part developed in isolation, typically without consideration of the whole treatment process or other unit process models. Hence, each unit process model often uses a different set of state variables. This is especially true if the models were developed to describe fundamentally different biological processes like aerobic and anaerobic treatment. To overcome these differences and facilitate plant-wide modeling, model interfaces are used as a means to convert state variables from one model into state variables of a different model. Recently, there has been significant interest in incorporating a new anaerobic digestion unit process model -ADM1 (Batstone et al., 2002) into the COST and IWA simulation benchmarks Copp 2001; to create a whole-plant simulation benchmark. To do this, an interface is needed to convert the activated sludge state variables to the anaerobic digestion state variables and visa versa. Standardised model interfaces are an essential component for allowing objective comparisons of simulation results within the wastewater treatment modeling community. This paper describes such a state variable interface. c xc li xc ch li,xc li
Despite of promising results in research, advanced control strategies fail to gain trust in waste... more Despite of promising results in research, advanced control strategies fail to gain trust in wastewater treatment practice. Due to the sensitivity of the biological processes to disturbances, operators are often unable to find the causes of faults due to the lack of effective real-time on-line monitoring. Strategies for on-line monitoring are therefore essential to enhance biological process control. Therefore, a suitable multivariate soft-sensor is desired for fault detection and control for a pilot-scale sequencing batch reactor (SBR) system to allow effluent quality to be estimated well before off-line analysis is finished. For this purpose, several multivariate methods are available, including (linear) partial least squares (PLS), Neural Net PLS (NNPLS) and Kernel PLS (KPLS). While non-linear extensions of PLS such as NNPLS require fitting of non-linear functions, KPLS does not. KPLS is based on a non-linear transformation of the process data, followed by the fitting of a linear PLS model between the transformed inputs and outputs. PLS, NNPLS and KPLS were compared regarding their ability to predict effluent quality data and their computational requirements. While (linear) PLS and NNPLS lead to acceptable prediction, KPLS results in poor model performance. Moreover, the computational requirement of KPLS were large compared to PLS and NNPLS. When comparing PLS and NNPLS to each other, it was found that NNPLS leads to the best possible prediction given the experimental data set, while the extra computational requirements are minimal.
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Papers by Christian Rosen