Publications
212 results found
Zagorowska MA, Haugen T, Skourup C, et al., 2024, Load-sharing with degradation management in a compressor station, IEEE Transactions on Automation Science and Engineering, Vol: 21, Pages: 921-938, ISSN: 1545-5955
Management of compressor degradation is often considered from the perspective of maintenance of the compressor, but most frameworks for the operation of compressors do not take degradation into account. This paper proposes a method for operation of compressors that takes into account the current level of degradation in order to manage further degradation. The algorithm can be used in maintenance planning frameworks, in particular if the timings of maintenance activities are fixed. The algorithm can extend the lifetime of a compressor by mitigating its degradation, or, conversely, can intensify the degradation to reach the maximum level in time for planned maintenance. The performance of the algorithm has been demonstrated in a case study for five compressors. A comparison with equal load approach shows that the new algorithm improves the operation of the system by managing the degradation of selected compressors. Explicit management of degradation allows an extension of the lifetime of selected compressors before maintenance must be performed. Conversely, by ensuring that the desired level of degradation is attained before pre-planned maintenance actions, it contributes to increased efficacy of maintenance actions. Note to Practitioners —The paper presents a new framework for load-sharing in a compressor station with compressors subject to degradation. The main innovation of the framework is the use of relationships between custom degradation indicators to manage degradation of the compressors. The results in the paper prove that it is possible to manage degradation in an industrial setting by adjusting the load of each compressor. From a practical perspective, the framework allows more degraded compressors to follow the less degraded compressors (called leaders). The simplicity of the proposed framework enables an intuitive choice of leaders, in particular in compressor stations with more than two compressors. Focused directly on the load sharing, the framewo
Pantelides CC, Pereira FE, Stanger PJ, et al., 2024, Process operations: from models and data to digital applications, Computers and Chemical Engineering, Vol: 180, ISSN: 0098-1354
Digital Applications are complex software systems for decision support in process operations and for process control. Each such application involves one or more computational modules being executed in an arbitrary real-time schedule, and communicating with each other and the external environment within which they are deployed. Each module may involve a mathematical computation based on a process model derived from first-principles or via machine learning applied to plant data; alternatively, it may have a purely statistical basis derived directly from plant data. There has been much progress in the use of such digital applications in industrial practice. However, achieving true scalability and sustainability in this direction will require general platforms that will allow the essentially code-free development of new applications and their large-scale deployments. We describe one such, recently developed, platform. We also consider the potential role of digital applications in the context of major trends in process operations, such as autonomous plant operation and process plant modularization.
Lucke M, Chioua M, Thornhill N, 2022, From oscillatory to non-oscillatory disturbances: a comparative review of root cause analysis methods, Journal of Process Control, Vol: 113, Pages: 42-67, ISSN: 0959-1524
This paper reviews existing data-driven methods according to three criteria: the properties of the method, the impact of the systems through which the disturbance propagates on the results of the method, and the types of disturbances covered by the method. The comparative review is used to derive guidelines for Root Cause Analysis (RCA) of oscillatory and non-oscillatory disturbances in industrial processes. In particular, it suggests an approach for RCA of transient disturbances based on methods for detection of transient disturbances. The comparative review is targeting primarily engineers working on Fault Detection and Diagnosis (FDD). RCA methods provide tools to speed up the work of the process engineer troubleshooting an abnormal situation, and the review should help in selecting appropriate methods. It is intended as a tool to increase the awareness and the understanding of data-driven RCA methods and to promote their use. The focus on transient disturbances answers a need for methods to automate or facilitate RCA of transient disturbances in industrial processes or industrial machinery that was observed by the authors during discussions with service engineers.
Xuan IY, Skourup C, Jensen JB, et al., 2022, Flexible operation of a mixed fluid cascade LNG plant for electrical power management, Energy, Vol: 250, ISSN: 0360-5442
The paper discusses operation and control of a process for the liquefaction of natural gas in which the refrigeration compressors are driven by electric motors. The aim is to enable the plant to accommodate contingencies in the availability of electrical power and to continue running when there is a shortage of electrical power, avoiding the significant economic impact of a shutdown. The article provides a detailed first principles analysis of the relationships between the electrical power consumption of the process, the production rate of the liquefied natural gas, its exit temperature, and its purity. By doing this, it is possible to ascertain settings for operating the process at various levels of power consumption. The results show that the process can operate with reductions of electrical power of 30 percent or more. Hence, power shortages could be managed by operating the process flexibly to make best use of the available remaining power, rather than by shutting down. The paper also discusses how such a system could be implemented industrially and identifies aspects that require further study.
Cong T, Tan R, Ottewill JR, et al., 2021, Anomaly detection and mode identification in multimode processes using the field Kalman filter, IEEE Transactions on Control Systems Technology, Vol: 29, Pages: 2192-2205, ISSN: 1063-6536
A process plant can have multiple modes of operation due to varying demand, availability of resources or the fundamental design of a process. Each of these modes is considered as normal operation. Anomalies in the process are characterised as deviations away from normal operation. Such anomalies can be indicative of developing faults which, if left unresolved, can lead to failures and unplanned downtime. The Field Kalman Filter (FKF) is a model-based approach, which is adopted in this paper for monitoring a multimode process. Previously, the FKF has been applied in process monitoring to differentiate normal operation from known faulty modes of operation. This paper extends the FKF so that it may detect occurrences of anomalies and differentiate them from the various normal modes of operation. A method is proposed for offline training an FKF monitoring model and on-line monitoring. The off-line part comprises training an FKF model based on Multivariate Autoregressive State-Space (MARSS) models fitted to historical process data. A monitoring indicator is also introduced. On-line monitoring, on the basis of the FKF for anomaly detection and mode identification, is demonstrated using a simulated multimode process. The performance of the proposed method is also demonstrated using data obtained from a pilot scale multiphase flow facility. The results show that the method can be applied successfully for anomaly detection and mode identification.
Zagorowska M, Thornhill NF, 2021, Optimal operation of compressors in terms of power and efficiency, 29th Mediterranean Conference on Control and Automation (MED), Publisher: IEEE, Pages: 526-531, ISSN: 2325-369X
Zagorowska M, Skourup C, Thornhill NF, 2020, Influence of compressor degradation on optimal operation of a compressor station, Computers and Chemical Engineering, Vol: 143, Pages: 107104-107104, ISSN: 0098-1354
Normal practice in a compressor station with compressors in parallel is to allocate the mass flows equally. However, this strategy is not optimal if the compressors are not identical. A common reason why compressors become non-identical is because their performance degrades over time. Degradation increases the power necessary to run the compressor station and changes the optimal allocation of mass flows.This paper presents a framework for optimal operation in a compressor station with degrading compressors. The optimisation framework proposed in this work explicitly includes a model of degradation in the optimisation problem and analyses how the optimal load-sharing changes when the compressors are degrading.The optimisation framework was applied in an industrial case study of a compressor station in which three parallel compressors are subject to degradation. The case study confirms that it is possible to minimise the extra power consumption due to degradation by adjusting the operating conditions of the compressor station. The analysis also gives insights into the impact of degradation on the optimal solution when compressors work at their limits.
Tan R, Ottewill JR, Thornhill NF, 2020, Monitoring statistics and tuning of kernel principal component analysis with radial basis function kernels, IEEE Access, Vol: 8, Pages: 198328-198342, ISSN: 2169-3536
Kernel Principal Component Analysis (KPCA) using Radial Basis Function (RBF) kernels can capture data nonlinearity by projecting the original variable space to a high-dimensional kernel feature space and obtaining the kernel principal components. This article examines the tuning of the kernel width when using RBF kernels in KPCA, showing that inappropriate kernel widths result in RBF-KPCA being unable to capture nonlinearity present in data. The paper also considers the choice of monitoring statistics when RBF-KPCA is applied to anomaly detection. Linear PCA requires two monitoring statistics. The Hotelling’s T2 monitoring statistic detects when a sample exceeds the healthy operating range, while the Squared Prediction Error (SPE) monitoring statistic detects the case when the sample does not follow the model of the training data. The analysis in this article shows that SPE for RBF-KPCA can detect both cases. Moreover, unlike the case of linear PCA, the T2 monitoring statistic for RBF-KPCA is non-monotonic with respect to the magnitude of the anomaly, making it not optimal as a monitoring statistic. The paper presents examples to illustrate these points. The paper also provides a detailed mathematical analysis which explains the observations from a theoretical perspective. Tuning strategies are proposed for setting the kernel width and the detection threshold of the monitoring statistic. The performance of optimally tuned RBF-KPCA for anomaly detection is demonstrated via numerical simulation and a benchmark dataset from an industrial-scale facility.
Zagorowska M, Wu O, Ottewill JR, et al., 2020, A survey of models of degradation for control applications, Annual Reviews in Control, Vol: 50, Pages: 150-173, ISSN: 1367-5788
The analysis of equipment degradation has traditionally developed in two main directions. One approach, of great interest for control system design, has been to consider that degradation causes fundamental changes to the behaviour of a system. Another approach, used in optimal maintenance planning and production scheduling, considers degradation as a separate process that affects performance but does not necessarily change the behaviour. This article provides a review of mathematical models of degradation that will facilitate the integration of degradation modelling into control and optimisation schemes. To this end, a new unified classification is proposed. It takes into account the influence of degradation on the behaviour of the system, as well as the factors influencing degradation. Understanding these mutual influences will enable improved optimization, design and operation of control systems. The flexibility of the proposed classification is demonstrated in an industrial application to a multi-product batch scheduling process.
Tan R, Ottewill JR, Thornhill NF, 2020, Non-stationary discrete convolution kernel for multimodal process monitoring, IEEE Transactions on Neural Networks and Learning Systems, Vol: 31, Pages: 3670-3681, ISSN: 1045-9227
Data-driven process monitoring has benefited from the development and application of kernel transformations, especially when various types of nonlinearity exist in the data. However, when dealing with the multimodality behavior which is frequently observed in process operations, the most widely used Radial Basis Function kernel has limitations in describing process data collected from multiple normal operating modes. In this paper, we highlight this limitation via a synthesized example. In order to account for the multimodality behavior and improve fault detection performance accordingly, we propose a novel Non-stationary Discrete Convolution kernel, which derives from the convolution kernel structure, as an alternative to the RBF kernel. By assuming the training samples to be the support of the discrete convolution, this new kernel can properly address these training samples from different operating modes with diverse properties, and therefore can improve the data description and fault detection performance. Its performance is compared with RBF kernels under a standard kernel PCA framework and with other methods proposed for multimode process monitoring via numerical examples. Moreover, a benchmark data set collected from a pilot-scale multiphase flow facility is used to demonstrate the advantages of the new kernel when applied to an experimental data set.
Xuan IY, Pretlove J, Haugen T, et al., 2020, Determining the minimum energy requirement of an LNG process: New insights into the impact of the vapour liquid equilibrium, Energy, Vol: 203, ISSN: 0360-5442
The paper provides a detailed first principles analysis of a process of liquefaction of natural gas in which the refrigeration compressors are driven by electric motors. The aim is to determine and understand the impact of the operation of the refrigeration cycles on the power consumption of the process. This study gives a detailed insight into the relationship between the discharge pressure of the compressors and the thermodynamic performance of the refrigeration cycles. By doing this, the findings of this research highlight the importance of the vapour liquid equilibrium. The results show that, for a given production rate, there is a minimum in the power consumption when the discharge pressures are close to the refrigerant saturation points. The paper provides a discussion and analysis of the advantages and disadvantages of operating at such points.
Zagorowska M, Schulze Spüntrup F, Ditlefsen A-M, et al., 2020, Adaptive detection and prediction of performance degradation in off-shore turbomachinery, Applied Energy, Vol: 268, Pages: 114934-114934, ISSN: 0306-2619
Performance-based maintenance of machinery relies on detection and prediction of performance degradation. Degradation indicators calculated from process measurements need to be approximated with degradation models that smooth the variations in the measurements and give predictions of future values of the indicator. Existing models for performance degradation assume that the performance monotonically decreases with time. In consequence, the models yield suboptimal performance in performance-based maintenance as they do not take into account that performance degradation can reverse itself. For instance, deposits on the blades of a turbomachine can be self-cleaning in some conditions. In this study, a data-driven algorithm is proposed that detects if the performance degradation indicator is increasing or decreasing and adapts the model accordingly. A moving window approach is combined with adaptive regression analysis of operating data to predict the expected value of the performance degradation indicator and to quantify the uncertainty of predictions. The algorithm is tested on industrial performance degradation data from two independent offshore applications, and compared with four other approaches. The parameters of the algorithm are discussed and recommendations on the optimal choices are made. The algorithm proved to be portable and the results are promising for improving performance-based maintenance.
Tan R, Cong T, Ottewill JR, et al., 2020, An on-line framework for monitoring nonlinear processes with multiple operating modes, Journal of Process Control, Vol: 89, Pages: 119-130, ISSN: 0959-1524
A multivariate statistical process monitoring scheme should be able to describe multimodal data. Multimodality typically arises in process data due to varying production regimes. Moreover, multimodality may influence how easy it is for process operators to interpret the monitoring results. To address these challenges, this paper proposes an on-line monitoring framework for anomaly detection where an anomaly may either indicate a fault occurring and developing in the process or the process moving to a new operating mode. The framework incorporates the Dirichlet process, which is an unsupervised clustering method, and kernel principal component analysis with a new kernel specialized for multimode data. A monitoring model is trained using the data obtained from several healthy operating modes. When on-line, if a new healthy operating mode is confirmed by an operator, the monitoring model is updated using data collected in the new mode. Implementation issues of this framework, including the parameter tuning for the kernel and the selection of anomaly indicators, are also discussed. A bivariate numerical simulation is used to demonstrate the performance of anomaly detection of the monitoring model. The ability of this framework in model updating and anomaly detection in new operating modes is shown on data from an industrial-scale process using the PRONTO benchmark dataset. The examples will also demonstrate the industrial applicability of the proposed framework.
Lucke M, Chioua M, Grimholt C, et al., 2020, Integration of alarm design in fault detection and diagnosis through alarm-range normalization, Control Engineering Practice, Vol: 98, Pages: 1-12, ISSN: 0967-0661
Alarm systems designed according to engineering and safety considerations provide the primary source of information for operators when it comes to abnormal situations. Still, alarm systems have rarely been exploited for fault detection and diagnosis. Recent work has demonstrated the benefits of alarm logs for fault detection and diagnosis. However, alarm settings conceived during the alarm design stage can also be integrated into fault detection and diagnosis methods. This paper suggests the use of those alarm settings in the preprocessing of the process measurements, proposing a normalization based on the alarm thresholds of each process variable. Normalization is needed to render process measurements dimensionless for multivariate analysis. While common normalization approaches such as standardization depend on the historical process measurements available, the proposed alarm-range normalization is based on acceptable variations of the process measurements. An industrial case study of an offshore oil gas separation plant is used to demonstrate that the alarm-range normalization improves the robustness of popular methods for fault detection, fault isolation, and fault identification.
Lucke M, Stief A, Chioua M, et al., 2020, Fault detection and identification combining process measurements and statistical alarms, Control Engineering Practice, Vol: 94, Pages: 1-12, ISSN: 0967-0661
Classification-based methods for fault detection and identification can be difficult to implement in industrial systems where process measurements are subject to noise and to variability from one fault occurrence to another. This paper uses statistical alarms generated from process measurements to improve the robustness of the fault detection and identification on an industrial process. Two levels of alarms are defined according to the position of the alarm threshold: level-1 alarms (low severity threshold) and level-2 alarms (high severity threshold). Relevant variables are selected using the minimal-Redundancy-Maximal-Relevance criterion of level-2 alarms to only retain variables with large variations relative to the level of noise. The classification-based fault detection and identification fuses the results of a discrete Bayesian classifier on level-1 alarms and of a continuous Bayesian classifier on process measurements. The discrete classifier offers a practical way to deal with noise during the development of the fault, and the continuous classifier ensures a correct classification during later stages of the fault. The method is demonstrated on a multiphase flow facility.
Zhou B, Chioua M, Bauer M, et al., 2019, Improving root cause analysis by detecting and removing transient changes in oscillatory time series with application to a 1,3-butadiene process, Industrial && Engineering Chemistry Research, Vol: 58, Pages: 11234-11250, ISSN: 0888-5885
Oscillations occurring in industrial process plants often reflect the presence of severe disturbances affecting process operations. Accurate detection and root-cause analysis of oscillations is of great interest for the economic viability of the process operation. Standard oscillation detection and root cause analysis methods require a large enough number of data samples. Unrelated transient changes superimposed on the oscillation pattern reduce the number of useful data samples. The present paper proposes simple heuristic methods to effectively detect and remove two types of transient changes from oscillatory signals, namely step changes and spikes. The proposed methods are used to pre-process oscillatory time series. The accuracy gained when using auto-correlation function method for oscillation detection and transfer entropy method for oscillation propagation is experimentally evaluated. The methods are carried out on a 1.3-Butadiene production process where several measurements showed an established oscillation occurring after a production level change.
Bauer M, Auret L, Bacci di Capaci R, et al., 2019, Industrial PID control loop data repository and comparison of fault detection methods, Industrial & Engineering Chemistry Research, Vol: 58, Pages: 11430-11439, ISSN: 0888-5885
This paper presents the control loop data of industrial controllers that are recently made available online. All data is confirmed and some of it has been published previously to develop fault detection and diagnosis methods. Methods to detect faults that occur during the operation of an industrial process are important and have attracted attention previously but are not always widely used in industry. One of the reasons is that any method needs to be robust and fully automated. The purpose of the data repository is to present data to test methods so that false positives and negatives are reduced to an insignificant number. Three previously published methods – oscillation detection based on the autocorrelation function, the idle index and a method for quantization detection – together with a simple, novel saturation detection method and one new detection methods are applied to all industrial data. The results are discussed and ways to improve the robustness and automation potential of these methods.
Tan R, Cong T, Thornhill NF, et al., 2019, Statistical monitoring of processes with multiple operating modes, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), Publisher: IFAC Secretariat, Pages: 635-642, ISSN: 1474-6670
Varying production regimes and loading conditions on equipment often result in multiple operating modes in process operations. The data recorded from such processes will typically be multimodal in nature leading to challenges in applying standard data-driven process monitoring approaches. Moreover, even if a monitoring approach is able to account for the variability present in a training set comprised of historical process data, in order to be robust and reliable the method will need to account for any new operating modes which might emerge during production. Therefore, it is desirable to have a monitoring algorithm that can both handle data multimodality in off-line training and, when implemented on-line, can actively update in order to incorporate new operating modes. This paper proposes a monitoring framework which combines an unsupervised clustering approach with a kernel-based Multivariate Statistical Process Monitoring (MSPM) algorithm. A monitoring model is trained off-line and is subsequently used to detect anomalies on-line. An anomaly might be indicative of either a developing fault or a change in the process to a new operating mode. In the latter case, the monitoring model can be updated to account for the new mode whilst still being able to detect faults under this framework. The advantages of the off-line training procedure relative to a standard kernel-based method are demonstrated via a numerical simulation. Additionally, the monitoring performance in the presence of faults and the capability of updating the model in the presence of new operating modes is demonstrated using a benchmark data set from an experimental pilot plant.
Zagorowska M, Ditlefsen A-M, Thornhill NF, et al., 2019, Turbomachinery degradation monitoring using adaptive trend analysis, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), Publisher: International Federation of Automatic Control (IFAC), Pages: 679-684, ISSN: 1474-6670
Performance deterioration in turbomachinery is an unwanted phenomenon that changes the behaviour of the system. It can be described by a degradation indicator based on deviations from expected values of process variables. Existing models assume that the degradation is strictly increasing with fixed convexity and that there are no additional changes during the considered operating period. This work proposes the use of an exponential trend approximation with shape adaptation and apply it in a moving window framework. The suggested method of adjustment makes it possible for the model to follow the evolution of the indicator over time. The approximation method is then applied for monitoring purposes, to predict future degradation. The influence of the tuning parameters on the accuracy of the algorithm is investigated and recommendations for the values are derived. Finally directions for further work are proposed.
Lucke M, Mei X, Stief A, et al., 2019, Variable selection for fault detection and identification based on mutual information of alarm series, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), Publisher: International Federation of Automatic Control (IFAC), Pages: 673-678, ISSN: 1474-6670
Reducing the dimensionality of a fault detection and identification problem is often a necessity, and variable selection is a practical way to do it. Methods based on mutual information have been successful in that regard, but their applicability to industrial processes is limited by characteristics of the process variables such as their variability across fault occurrences. The paper introduces a new estimation strategy of mutual information criteria using alarm series to improve the robustness of the variable selection. The minimal-redundancy-maximal-relevance criterion on alarm series is suggested as new reference criterion, and the results are validated on a multiphase flow facility.
Lucke M, Chioua M, Grimholt C, et al., 2019, Advances in alarm data analysis with a practical application to online alarm flood classification, Journal of Process Control, Vol: 79, Pages: 56-71, ISSN: 0959-1524
During an alarm flood, the alarm rate is greater than the operator can effectively manage. Many alarm data analysis methods have been proposed in the literature to mitigate the impact of alarm floods. This paper gives a review of the state of the art in alarm data analysis and aims at structuring the field. A distinction between sequence mining methods that apply to alarm sequences and time series analysis methods that apply to alarm series is suggested. The review highlights that online applications to help the operators during alarm flood episodes have been only treated as sequence mining problem in the literature to date. To address this gap, the paper also presents a binary series approach to classify ongoing alarm floods based on a set of historical alarm floods. The motivation for a binary series approach is demonstrated through an industrial case study of a gas-oil separation plant, and the performance of the presented method is compared with the performance of an established sequence alignment method.
Stief A, Tan R, Cao Y, et al., 2019, A heterogeneous benchmark dataset for data analytics: multiphase flow facility case study, Journal of Process Control, Vol: 79, Pages: 41-55, ISSN: 0959-1524
Improvements in sensing, connectivity and computing technologies mean that industrial processes now generate data from a variety of disparate sources. Data may take a number of forms, from time-domain signals, sampled at various rates using a variety of sensors, to alarm and event logs. Novel techniques need to be developed to tackle the challenges of heterogeneous data. Testing such algorithms requires benchmark datasets that allow direct comparison of the performance of the methods. This work presents the PRONTO heterogeneous benchmark dataset. Experiments were conducted on a multiphase flow facility under various operational conditions with and without induced faults. Data were collected from heterogeneous sources, including process measurements, alarm records, high frequency ultrasonic flow and pressure measurements. The presented dataset is suitable for developing and validating algorithms for fault detection and diagnosis and data fusion concepts. Three algorithms are tested using the dataset, illustrating the applicability of the dataset.
Borghesan F, Chioua M, Thornhill NF, 2019, Forecasting of process disturbances using k-nearest neighbours, with an application in process control, Computers and Chemical Engineering, Vol: 128, Pages: 188-200, ISSN: 1873-4375
This paper examines the prediction of disturbances based on their past measurements using k-nearest neighbours. The aim is to provide a prediction of a measured disturbance to a controller, in order to improve the feed-forward action. This prediction method works in an unsupervised way, it is robust against changes of the characteristics of the disturbance, and its functioning is simple and transparent. The method is tested on data from industrial process plants and compared with predictions from an autoregressive model. A qualitative as well as a quantitative method for analysing the predictability of the time series is provided. As an example, the method is implemented in an MPC framework to control a simple benchmark model.
Cai L, Thornhill NF, Kuenzel S, et al., 2019, A test model of a power grid with battery energy storage and wide-area monitoring, IEEE Transactions on Power Systems, Vol: 34, Pages: 380-390, ISSN: 0885-8950
This paper presents a test model for investigating how to coordinate a power grid and Energy Storage Systems (ESSs) by Wide-Area Monitoring (WAM). It consists of three parts: (1) a model of a power grid containing different types of generators, loads and transmission network; (2) a model of lithium-ion battery ESSs; (3) a model of multivariate statistical analysis based WAM built to capture the grid information for guiding the operation of ESSs. Simulation studies using a reduced equivalent model specifically built for a UK power grid enhanced with lithium-ion battery ESSs and WAM illustrate the way in which WAM can coordinate a power grid and ESSs, and also demonstrate the benefit of ESSs on a power grid.
Stief A, Ottewill J, Thornhill NF, 2018, Digital innovation driven by university collaboration, Publisher: ABB
Zagorowska M, Thornhill N, Haugen T, et al., 2018, Load-sharing strategy taking account of compressor degradation, Conference on Control Technology and Applications (CCTA), Publisher: IEEE, Pages: 489-495
The objective of designing a control structure that takes the degradation of the system into account is to preserve its performance and mitigate further damage. This problem is often encountered in process industries, e.g. in gas processing plants, where the question arises how to distribute the control effort among multiple actuators based on their degradation. The main focus of this work is to investigate how to assign the loads in a two-compressor system taking the degradation, i.e. the loss of available performance, into consideration. Contrarily to other approaches, such as methods based on distance to surge or predictive control, the algorithm proposed in this work does not require a reconfiguration of the control structure, at the same time taking explicitly the degradation into account. The simulation results confirm that this approach mitigates further loss of performance, in particular for compressors, which have significantly different degradation rates.
Cai L, Thornhill NF, Kuenzel S, et al., 2018, Wide-area monitoring of power systems using principal component analysis and k-nearest neighbor analysis, IEEE Transactions on Power Systems, Vol: 33, Pages: 4913-4923, ISSN: 0885-8950
Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate analysis known as Principal Component Analysis (PCA) and time series analysis known as k-Nearest Neighbor (kNN) analysis. Advantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method.
Lucke M, Chioua M, Grimholt C, et al., 2018, On improving fault detection and diagnosis using alarm-range normalisation, IFAC-PapersOnLine, Vol: 51, Pages: 1227-1232, ISSN: 2405-8963
Alarm systems based on engineering and safety considerations are the prime source of information for operators when it comes to abnormal situations. Conversely, the presence of fault detection and diagnosis algorithms in process plants is still limited, in comparison with other process control technologies. This work presents a simple way to integrate the information contained in the alarm systems into the fault detection and diagnosis algorithm. A normalisation of the process measurements based on the alarm thresholds is proposed, improving the robustness of the algorithm with regard to the variability of the measurements across fault occurrences in industrial systems.
Borghesan F, Chioua M, Thornhill NF, 2018, An MPC with disturbance forecasting for the control of the level of a tank with limited buffer capacity, Mediterranean Conference on Control and Automation (MED), Publisher: IEEE, Pages: 727-734, ISSN: 2473-3504
The paper deals with the behavior of an MPC for the control of a level of a tank, whose inflow is subject to persistent plantwide disturbances. It is shown that the response of an industrial MPC can be aggressive and oscillatory in such situations. The result is that the disturbance propagates further. The reason is the assumption made by the industrial MPCs regarding the future evolution of the disturbance. To improve the response in presence of plantwide disturbances, an MPC with disturbance forecasting is proposed. Such MPC is able to handle tight constraints and still reduce the movement of the outflow of the tank, therefore reducing the disturbance propagation. To compare the MPC with prediction forecasting with other two strategies used in industrial practice to handle measured disturbances, the paper uses sinusoidal disturbances and real disturbances coming from a refinery.
Lucke M, Chioua M, Grimholt C, et al., 2018, Online alarm flood classification using alarm coactivations, IFAC-PapersOnLine, Vol: 51, Pages: 345-350, ISSN: 2405-8963
Alarms indicate abnormal operation of the process plants and alarm floods constitute specific abnormal episodes that cannot be handled safely by the operators. In that regard, online alarm flood classification based on a bank of past historical episodes provides support on how to handle ongoing alarm sequences. This paper introduces a new approach based on alarm coactivations that is appropriate for the analysis of ongoing sequences. The method shows improvements when compared to an established sequence alignment approach for abnormal episode analysis of a gas oil separation plant.
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