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Conference paperXuan YY, Pretlove J, Thornhill N, 2018,
Process industries are becoming increasingly reliant on electrical power for reasons of efficiency and sustainability. A large industrial site typically has its own power management system to distribute electricity to the process and to manage electrical contingencies such as partial loss of supply. Recent work has illustrated more flexible alternatives to load shedding whereby an industrial process plant can continue to operate at a lower level making use of available electrical power. This paper presents a way for achieving such flexibility in a Liquefied Natural Gas (LNG) plant. It analyzes the consequences for production of varying the consumed power, and assesses the maximum flexibility within the feasible operating envelope of the process. The study has been conducted by modeling and simulation of an LNG plant using the Linde process with three refrigeration cycles. The results also show the relationships between electrical power consumption and production in terms of production rate and product characteristics. They also show that the vapour-liquid equilibrium plays a crucial role in establishing the operating points and setting the boundaries in which the process has to work. Thus, through the assessment and simulation of an LNG plant, this work demonstrates that flexible operation has benefits over alternatives. It achieves more operating points and therefore adds more flexibility.
Conference paperZagorowska M, Thornhill NF, Skourup C, 2018,
© 2018 ASME. The aim of this study is to apply a Chebyshev polynomial approximation of the compressor map for dynamic modelling and control of centrifugal compressors. The results are compared to those from an approximation based on the third order polynomials and a compressor map derived from first principles. In the analysis of centrifugal compressors, a combination of dynamic conservation laws and static compressor map provides an insight into the surge phenomenon, whose avoidance remains one of the objectives of compressor control. The compressor maps based on the physical laws provide accurate results, but require a detailed knowledge about the properties of the system, such as the geometry of the compressor and gas quality. Third order polynomials are usually used as an approximation for the compressor map, providing simplified models at the expense of accuracy. Chebyshev polynomial approximation provides a trade-off between the accuracy of physical modelling with the ease of use provided by third order polynomial approximation.
Journal articleSpuntrup FS, Londono JG, Skourup C, et al., 2018,
Physical assets of the process industries include compressors, pumps, heat exchangers, batch reactors and many more. A large company that operates over many sites typically manages such assets in a coordinated way as an asset fleet. Strategic planning of maintenance and scheduling requires information about reliability, availability and maintainability of the assets in an asset fleet.The work presented in this paper assesses the reliability of centrifugal compressors based on the data collected in OREDA (Offshore and onshore REliability DAta project). The fault tree (a top-down approach to illustrate all subsystems in a system) has been modeled by focusing on the six main subsystems of the compressor (power transmission, compressor, control and monitoring, lubrication system, shaft seal system, and miscellaneous). All the maintainable items described in ISO 14224 are considered. Based on the failure rates collected in OREDA, the most prevalent failures have been identified via a Pareto analysis. The article gives recommendations which subsystems should be prioritized for maintenance and which types of faults are likely to occur. The main contribution of this paper is an industry-based statistical analysis of the failure mechanisms in centrifugal compressor systems. It is expected to improve the reliability of centrifugal compressor systems and can be implemented in industrial settings with a similar documentation system like OREDA.
Book chapterBorghesan F, Chioua M, Thornhill NF, 2018,
This paper focuses on the prediction of persistent disturbances based on their past measurements using two versions of the k-nearest neighbours method: an unweighted and a weighted version. Results of tests on data from a refinery show that the two methods can predict the future trend of a disturbance. They also show that the weighted version is more robust against the choice of the number of nearest neighbours used. The method opens up the possibility of model-free feedforward control without the constraint of causality based on the whole history of a measurement.
Conference paperZagorowska M, Thornhill NF, 2017,
Compressor maps are one of the main elements describing the behaviour of centrifugal compressors. Although the compressor map is often provided by the manufacturer, there may be changes during the lifetime of the compressor due to refurbishments or wear. Since the compressor maps are often used in real-time optimization problems, there is a need for simple approximation methods. This paper focuses on approximation of physical models using Chebyshev polynomials instead of third order polynomials which are unable to capture some aspects of the compressor behaviour. Chebyshev polynomials capture the characteristics better than third order polynomials. They provide a flexible tool for compressor map approximation and analysis.
Journal articleYang Y, Velayudhan A, Thornhill NF, et al., 2017,
Multi-criteria manufacturability indices for ranking high-concentration monoclonal antibody formulations, Biotechnology and Bioengineering, Vol: 114, Pages: 2043-2056, ISSN: 1097-0290
The need for high-concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high-concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high-concentration mAb formulation conditions in terms of their ease of manufacture. This is illustrated by analysing published high-throughput biophysical screening data that explores the influence of different formulation conditions (pH, ions and excipients) on the solution viscosity and product thermostability. A decision tree classification method, CART (Classification and Regression Tree) is used to identify the critical formulation conditions that influence the viscosity and thermostability. In this work, three different multi-criteria data analysis frameworks were investigated to derive manufacturability indices from analysis of the stress maps and the process conditions experienced in the final UF/DF step. Polynomial regression techniques were used to transform the experimental data into a set of stress maps that show viscosity and thermostability as functions of the formulation conditions. A mathematical filtrate flux model was used to capture the time profiles of protein concentration and flux decay behaviour during UF/DF. Multi-criteria decision-making analysis was used to identify the optimal formulation conditions that minimize the potential for both viscosity and aggregation issues during UF/DF.
Journal articleCai L, Thornhill NF, Kuenzel S, et al., 2017,
Efficient disturbance detection is important for power system security and stability. In this paper, a new detection method is proposed based on a time series analysis technique known as k nearest neighbor (kNN) analysis. Advantages of this method are that it can deal with the electrical measurements with oscillatory trends and can be implemented in real time. The method consists of two stages which are the off-line modelling and the on-line detection. The off-line stage calculates a sequence of anomaly index values using kNN on the historical ambient data and then determines the detection threshold. Afterwards, the on-line stage calculates the anomaly index value of presently measured data by readopting kNN and compares it with the established threshold for detecting disturbances. To meet the real-time requirement, strategies for recursively calculating the distance metrics of kNN and for rapidly picking out the kth smallest metric are built. Case studies conducted on simulation data from the reduced equivalent model of Great Britain power system and measurements from an actual power system in Europe demonstrate the effectiveness of the proposed method.
Journal articleCai L, Thornhill NF, Pal BC, 2017,
Multivariate detection of power system disturbances based on fourth order moment and singular value decomposition, IEEE Transactions on Power Systems, Vol: 32, Pages: 4289-4297, ISSN: 1558-0679
This paper presents a new method to detect power system disturbances in a multivariate context, which is based on Fourth Order Moment (FOM) and multivariate analysisimplemented as Singular Value Decomposition (SVD). The motivation for this development is that power systems are increasingly affected by various disturbances and there is a requirement for the analysis of measurements to detect these disturbances. The application results on the measurements of an actual power system in Europe illustrate that the proposed multivariate detection method achieves enhanced detection reliability and sensitivity.
Conference paperXenos, Kahrs O, Leira FM, et al., 2017,
Challenges of the application of data-driven models for the real-time optimization of an industrial air separation plant, 2016 European Control Conference (ECC), Publisher: IEEE Conference Publications, Pages: 1025-1030
The optimization of the operation of chemical plants may require the development of mathematical models of the process units of a plant. These mathematical models can be either first-principles or data-driven models. The former type of modeling may be complex for the use in optimization and especially for online applications such as real time optimization. Available measured process data can be used to develop the latter type of modeling. Although data-driven models offer several benefits for online applications, there are some very significant challenges related to their development in a practical industrial implementation. This paper discusses the important aspects of the building of data-driven models and demonstrates the effects of these types of models on the optimization results. The current work demonstrates the application of a real time optimization framework applied to an industrial air compressor station of an air separation plant when the models are based on operating data.
Journal articleXenos DP, Mohd Noor I, Matloubi M, et al., 2016,
Demand-side management and optimal operation of industrial electricity consumers: An example of an energy-intensive chemical plant, Applied Energy, Vol: 182, Pages: 418-433, ISSN: 0306-2619
Concerns about the reliability of electricity supplies have motivated researches to investigate the possibility of electrical consumers to take a more active role in the operations of the power system. The work in this paper looks into the potential of an industrial chemical plant to provide support to the electricity grid by means of demand-side response (DR) programs. To do so, this paper proposes a method to assess the flexibility of the plant to provide electrical power reserves while ensuring that the production demand is satisfied, as well as an economic analysis of the plant operations incorporating DR programs to quantify the incentives the plant should receive in order to participate in these programs. Therefore, the current study presents a novel optimization framework which integrates production scheduling with DR programs, with the aim to determine optimal decisions for the operating conditions within the plant while safely providing services to the electricity grid.
Journal articleCecılio IM, Ottewill JR, Fretheim H, et al., 2016,
Transient disturbances in process measurements compromise the accuracy of somemethods for plant-wide oscillation analysis. This paper presents a method to removesuch transients while maintaining the dynamic features of the original measurement.The method is based on a nearest neighbors imputation technique. Itreplaces the removed transient with an estimate which is based on the time seriesof the whole measurement. The method is demonstrated on experimental andindustrial case studies. The results demonstrate the efficacy of the method andrecommended parameters. Furthermore, inconsistency indices are proposed whichfacilitate the automation of the method.
Journal articleXenos DP, Lunde E, Thornhill NF, 2016,
Optimal Operation and Maintenance of Gas Compressor Stations: An Integrated Framework Applied to a Large-Scale Industrial Case, Journal of Engineering for Gas Turbines and Power-Transactions of the Asme, Vol: 138, ISSN: 1528-8919
This paper presents a framework which integrates maintenance and optimal operation of multiple compressors. The outcome of this framework is a multiperiod plan which provides the schedule of the operation of compressors: the schedule gives the best decisions to be taken, for example, when to carry out maintenance, which compressors to use online and how much to load them. These decisions result in the minimization of the total operational costs of the compressors while at the same time the demand of the plant is met. The suggested framework is applied to an industrial gas compressor station which encompasses large multistage centrifugal compressors operating in parallel. The optimization model of the framework consists of three main parts: the models of compressor maps, the operational aspects of compressors, and a maintenance model. The results illustrate the optimal schedule for 90 days and an example of the optimal distribution of the load of the compressors for 5 days. Finally, the results show the economical benefits from the integration of maintenance and optimization.
Journal articleBudinis S, Thornhill NF, 2016,
This paper presents computer-based design and analysis of control systems for centrifugal compressors when the operating fluid is supercritical CO2.It reports a non-linear dynamic model including a main forward compression line and two different configurations for the recycle antisurge line. Disturbance scenarios are proposed for testing the configurations and performance indicators are suggested to evaluate control performance and power consumption of the compression system.The paper demonstrates that compared to the hot recycle, the process configuration including a cold gas recycle has better overall stability, but higher power consumption and lower values for the control performance indicators. Based on the previous considerations, the paper gives suggestions regarding the choice of the recycle configuration. Moreover it compares subcritical and supercritical compression during surge prevention and highlights the importance of the selection of the gas recycle configuration when full recycle is needed.
Journal articleBauer M, Horch A, Xie L, et al., 2016,
Journal articleCecilio IM, Ottewill JR, Thornhill NF, 2016,
Determining the propagation path of a disturbance in multi-rate process and electromechanical systems, Control Engineering Practice, Vol: 49, Pages: 187-193, ISSN: 1873-6939
This paper proposes a multi-rate method to identify the propagation path of a persistent disturbance in an enlarged system envelope which includes the process plant and its electromechanical equipment. The need to integrate process and equipment diagnosis has been highlighted by industrial commentators. However, process and electromechanical measurements often have different sampling rates. The multi-rate method proposed extends a state-of-the-art propagation path method so that it combines fast-sampled electromechanical measurements and slow-sampled process measurements. The method is based on non-linear mutual prediction, which yields the directionality in the relationship between two time series. The method was demonstrated and validated, giving the expected outcome in an experimental case study, in which the root cause and propagation path of the disturbance were known.
Journal articleErsdal AM, Imsland L, Uhlen K, et al., 2015,
Nonlinear model predictive control (NMPC) is investigated for load frequency control (LFC) of an interconnected power system which is exposed to increasing wind power penetration. The robustified NMPC (RNMPC) proposed here uses knowledge of the estimated worst-case deviation in wind-power production to make the NMPC more robust. The NMPC is based on a simplified system model that is updated using state- and parameter estimation by Kalman filters, and it takes into account limitations on among others tie-line power flow. Tests on a proxy of the Nordic power system show that the RNMPC is able to fulfill system constraints under worst-case deviations in wind-power production, where the nominal NMPC is not.
Journal articleCicciotti M, Xenos DP, Bouaswaig AEF, et al., 2015,
Journal articleChioua M, Bauer M, Chen S-L, et al., 2015,
Plant-wide root cause identification using plant key performance indicators (KPIs) with application to a paper machine, Control Engineering Practice, Vol: 49, Pages: 149-158, ISSN: 1873-6939
Previously, plant-wide disturbance analysis has looked into the propagation of faults through an industrial production process by investigating process measurements. However, the extent of the analysis has mostly been limited to a section of a plant. In this work, we propose a top-down approach which investigates measurements of the complete plant and identifies a section where the disturbance originates. Root cause analysis is carried out thereafter to pinpoint the faulty asset. The proposed approach has three novel elements: Using key performance indicators (KPI) as reference and starting point of the analysis, restricting measurements to a measurement type (e.g. flow) thus focusing on a section and applying the novel method of contribution plots of spectral PCA T2 statistic to find the contribution of each measurement towards the disturbance observed in the KPI. The approach is described and carried out on a paper machine where a quality KPI showed an established oscillation.
Journal articleBarocio E, Pal BC, Thornhill NF, et al., 2015,
Conference paperCecilio IM, Ottewill JR, Thornhill NF, 2015,
Adapting nearest neighbors-based monitoring methods to irregularly sampled measurements, Annual Conference of the Prognostics and Health Management Society 2015
Prognostics and Health Management Society. All rights reserved.Irregularly spaced measurements are a common quality problem in real data and preclude the use of several feature extraction methods, which were developed for measurements with constant sampling intervals. Feature extraction methods based on nearest neighbors of embedded vectors are an example of such methods. This paper proposes the use of a timebased construction of embedded vectors and a weighted similarity metric within nearest neighbor-based methods in order to extend their applicability to irregularly sampled measurements. The proposed idea is demonstrated within a method of univariate detection of transient or spiky disturbances. The result obtained with an irregularly sampled measurement is benchmarked by the original regularly sampled measurement. Although the method was originally implemented for off-line analysis, the paper also discusses modifications to enable its on-line implementation.
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