Bayesian method for multirate data synthesis and model calibration

Shao, Xinguang; Huang, Biao; Lee, Jong Min; Xu, Fangwei; Espejo, Aris

doi:10.1002/aic.12358

Cited by 25 publications

(18 citation statements)

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“…Significant contributions have addressed the development of state space models at the faster sampling rate [28], and parameter and state estimation [29]. Multi-rate data has also been used to calibrate models by applying Bayesian methods [30]. These techniques have been mostly applied on petroleum, chemical and biological systems, in which the measurements with lower sampling rate derive from quality variables which require laboratory analysis.…”

Section: Multi-rate Systemsmentioning

confidence: 99%

Multivariate Detection of Transient Disturbances for Uni- and Multirate Systems

Cecı́lio

Ottewill

Fretheim

et al. 2015

IEEE Trans. Contr. Syst. Technol.

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This paper presents a method to detect transient disturbances in a multivariate context, and an extension of that method to handle multi-rate systems. Both methods are based on a time series analysis technique known as nearest neighbors, and on multivariate statistics implemented as a singular value decomposition. The motivation for these developments is that there is an increasing industrial requirement for the analysis of data sets comprising measurements from industrial processes together with their associated electrical and mechanical equipment. These systems are increasingly affected by transient disturbances, and their measurements are commonly sampled at different rates. The paper demonstrates superior results with the multivariate method in comparison to the univariate approach, and with the multi-rate method in comparison to a uni-rate method, for which the fast-sampled measurements had to be downsampled. The method is demonstrated on experimental and industrial case studies.

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Section: Multi-rate Systemsmentioning

confidence: 99%

Multivariate Detection of Transient Disturbances for Uni- and Multirate Systems

Cecı́lio

Ottewill

Fretheim

et al. 2015

IEEE Trans. Contr. Syst. Technol.

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“…A Bayesian method is then utilised to synthesise all the related information from multiple observation sources (e.g. physical sensors and laboratory data) to provide more reliable and more accurate real‐time information about the query quality variable . As illustrated in Figure , the developed soft sensor produces accurate predictions that track the water content variations as measured by the laboratory analysis.…”

Section: Soft Sensing In Oil Sands Extraction Processesmentioning

confidence: 99%

Soft sensor solutions for control of oil sands processes

et al. 2013

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Oil sands development is both a costly and technically complex business with potential concerns in land use, water consumption and greenhouse gas emissions. Therefore, it is of practical interest to further investigate novel techniques to improve profitability while diligently maintaining environmental compliance. Our approach for finding solutions to achieve this objective is to develop innovative strategies for advanced monitoring, optimisation and control of plant operations. Development of reliable process models is a key requirement for investigating the behaviour of complex systems. Such descriptive models can help to improve analysis, simulation, optimisation, design, control and operation of process systems at both micro and macro levels. This paper presents a summary of some of the successful applications focussed on development and implementation of inferential process models, also known as soft sensors for oil sands processes.Keywords: soft sensor, oil sands, bitumen extraction INTRODUCTIONT he very nature of oil sands extraction processes is to separate highly viscous bitumen from other components, which are mainly water and solids (e.g. clays and sand). This task is performed in multiple steps that involve: breaking the feed received from the mine into smaller chunks; screening out oversized materials; preparing slurry through addition of hot process water and separating bitumen, solids and water through a series of gravity and/or centrifugal separation units and usage of process aids. Despite the apparent simplicity of the overall process, different factors render the operation of the process quite complex. Some of these factors are: variation of the feed quality and rate; mechanical and electrical constraints related to equipment size; existence of multiple operating conditions; restrictions of downstream processes; and availability of process utilities such as hot process water or steam.Oil sands development is both a costly and technically complex business with potential concerns raising due to land use, water consumption and greenhouse gas emissions. Therefore, real-time process monitoring and control techniques are needed in many areas in order to ensure a smooth operation, achieve high throughput, stay close to the optimal boundaries of the process operating envelope, and minimise environmental footprints. An essential prerequisite for a successful process monitoring and control scheme is continuous collection of reliable and accurate data reflecting the equipment and plant operating status. However, extraction operations are naturally subject to harsh operating conditions with climate (ambient temperature and rain) playing a secondary role as an enhancement agent. Such conditions would affect reliability, accuracy and efficiency (i.e. service factor) of the instrumentation system. Moreover, measurements of quality variables (e.g. stream composition) are normally obtained through on-line quality analysers or off-line laboratory analysis with time delays of a few hours. Discontinuity and signif...

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“…Up-sampling methods and down-sampling methods are two typical way to build multi-rate models (Lu, N. et al, 2004). Shao, X. et al (2011) proposed a Bayesian method for soft sensor model calibration. They proposed a soft sensor model using Bayesian method for the un-sampled data prediction as well as the model calibration.…”

Section: Introductionmentioning

confidence: 99%

Multirate Partial Least Squares for Process Monitoring

Cong

2015

IFAC-PapersOnLine

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In most chemical processes, variables are sampled at different rates which brings great challenges to traditional process monitoring methods that are built upon single sampling rate. In this paper, a multi-rate partial least squares algorithm is proposed. Compared to the traditional PLS method, the proposed algorithm takes use of the incomplete data samples through a modification of both of the covariance matrix of the input dataset and the covariance matrix between the input and output datasets. Iteration is used in the model training step to avoid the calculation of same parameters which requires complete training datasets. Then the fault detection and online prediction strategy is proposed based on this algorithm. A case study on TE process shows that the proposed method had an enhanced performance on both monitoring and online prediction, compared to the traditional PLS method.

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Bayesian method for multirate data synthesis and model calibration

Cited by 25 publications

References 50 publications

Multivariate Detection of Transient Disturbances for Uni- and Multirate Systems

Multivariate Detection of Transient Disturbances for Uni- and Multirate Systems

Soft sensor solutions for control of oil sands processes

Multirate Partial Least Squares for Process Monitoring

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