Effect of Data Compression on Pattern Matching in Historical Data

Singhal, Anupam; Seborg, Dale E.

doi:10.1021/ie049707a

Cited by 20 publications

(18 citation statements)

References 28 publications

(53 reference statements)

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“…However, compression techniques of the time-series can significantly decrease the amount of the processed data preserving the desirable accuracy. 20 Therefore the modeling of the steam consumption can be carried out dynamically which enriches the applicability of the models in the field of steam consumption optimization. Furthermore, by utilizing this kind of data, a variety of models in the form of empirical parametric equations were developed to describe the losses due to radiation and the internal losses in the heating/ cooling system due to inefficient operation.…”

Section: Introductionmentioning

confidence: 99%

Bottom-up Modeling of the Steam Consumption in Multipurpose Chemical Batch Plants Focusing on Identification of the Optimization Potential

Szijjartô

Papadokonstantakis

Fischer

et al. 2008

Ind. Eng. Chem. Res.

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A detailed approach for modeling the steam consumption in a multipurpose chemical batch plant was developed, tested, and used for analysis of the energy-efficiency. The main advantage of the approach presented in this paper compared to available modeling approaches is the ability to describe the transient steam consumption. Thus, the new approach can be used for the dynamic optimization of batch operations with respect to the energy efficiency. The bottom-up method was implemented by modeling particular unit operations (UOs) in a case study plant, and validation was accomplished with direct measurements on both UO and building level. The principle of the bottom-up model is a detailed energy balance of each particular UO for which process parameter measurements are necessary as input data. These were extracted from the measurements archive of the case study plant for a period of two months. Process data from almost 1000 sensors installed in ca. 100 UOs were acquired, transformed into a time-series with common time basis, and used as an input data for the model. Special attention was paid to model the losses of the UOs because in earlier studies it was found that these are significant. Loss models were developed in the form of empirical parametric equations considering the losses due to radiation and the internal losses in the heating/cooling system due to inefficient operation. The parameters of the loss models were fitted, based on the developed methodology, to steam measurements of 4 UOs and consequently integrated into the overall bottom-up model for modeling other UOs as well. The energy usage efficiency of the UOs was inferred and the optimization spots were identified. The results in the case study plant have indicated that the energy savings potential for particular UOs with low steam-usage efficiency can be easily identified and serve as a good hint for the overall plant energy auditing and steam consumption optimization.

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Section: Introductionmentioning

confidence: 99%

Bottom-up Modeling of the Steam Consumption in Multipurpose Chemical Batch Plants Focusing on Identification of the Optimization Potential

Szijjartô

Papadokonstantakis

Fischer

et al. 2008

Ind. Eng. Chem. Res.

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“…The effectiveness of the proposed RCVA-FOP monitoring method for time-varying processes is verified with a simulated continuous stirred tank reactor (CSTR) system. − The diagram of the simulated CSTR system is shown in Figure . Under the assumption of a first order irreversible reaction, the reactant A and inlet flow of solvent produce a single component B, which is an outlet stream.…”

Section: Application To Continuous Stirred Tank Reactor Systemmentioning

confidence: 94%

Recursive Fault Detection and Identification for Time-Varying Processes

Shang

Liu²,

Zhang³

2016

Ind. Eng. Chem. Res.

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Canonical variate analysis (CVA) has been extensively applied in monitoring of different industrial processes. However, conventional CVA is unable to handle the characteristics of time-varying processes. It tends to interpret the natural changes of the process as faults, which would cause high false alarm rates. To solve this problem, a recursive canonical variate analysis based on the first order perturbation theory (RCVA-FOP) is proposed to detect faults in time-varying processes. Without recalling past training data, the covariance of past observation vectors is updated by the exponential weighted moving average (EWMA) method. Moreover, the first order perturbation theory is introduced to realize the recursive singular value decomposition (SVD) of the Hankel matrix, which can reduce computational time significantly compared with the conventional SVD. To identify the real reason for a fault, an EWMA contribution plot based on CVA is also proposed to enhance the fault identification rate. The proposed method is verified with simulations of the continuous stirred tank reactor. Simulation results indicate that the RCVA-FOP method not only can effectively adapt to the natural changes of time-varying processes but also can detect and identify three types of faults, which include sensor precision degradation, heat exchanger fouling fault, and sensor bias.

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“…The proposed LNSA process monitoring method is tested with a simulated continuous stirred tank reactor (CSTR) system [22,25,26], as shown in Fig. 3.…”

Section: Case Studiesmentioning

confidence: 99%