Estimation of distillation compositions from multiple temperature measurements using partial-least-squares regression

Mejdell, Thor; Skogestad, Sigurd

doi:10.1021/ie00060a007

Cited by 185 publications

(89 citation statements)

References 8 publications

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“…The delay caused by the measurement and analysis of compositions will negatively affect the effectiveness and robustness of control [6]. An indirect method is proposed to monitor the products compositions of the column by using tray temperature inside the column, albeit this feature is an unreliable indicator of product compositions [7], [8]. Moreover, other considerations like consistent maintenance, regular calibrations, and high-cost equipment make composition analysers an ineffective solution for precise online measurements.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Intelligent Approach for Predicting Product Compositions of a Distillation Column

Al-Dunainawi¹,

Abbod²

2016

ijarai

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Abstract-Compositions measurement is a vitally critical issue for the modelling and control of distillation process. The product compositions of distillation columns are traditionally measured using indirect techniques via inferring tray compositions from its temperature or by using an online analyser. These techniques were reported as inefficient and relatively slow methods. In this paper, an alternative procedure is presented to predict the compositions of a binary distillation column. Particle swarm optimisation based artificial neural network PSO-ANN is trained by different algorithms and tested by new unseen data to check the generality of the proposed method. Particle swarm optimisation is utilised, here, to choose the optimal topology of the network. The simulation results have indicated a reasonable accuracy of prediction with a minimal error between the predicted and simulated data of the column.

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Section: Introductionmentioning

confidence: 99%

Hybrid Intelligent Approach for Predicting Product Compositions of a Distillation Column

Al-Dunainawi¹,

Abbod²

2016

ijarai

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“…In the 1990s, composition estimators using PLS were proposed (Mejdell and Skogestad, 1991;Kresta et al, 1994). In their work, inferential models of product compositions were built using steady-state data.…”

Section: In the Pls Eramentioning

confidence: 99%

Virtual Sensing Technology in Process Industries: Trends and Challenges Revealed by Recent Industrial Applications

Kano

Fujiwara

2013

J. Chem. Eng. Japan / JCEJ

169

110

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Virtual sensing technology is crucial for high product quality and productivity in any industry. This review aims to clarify the trend of research and application of virtual sensing technology in process industries. After a brief survey, practical issues are clari ed by introducing recent questionnaire survey results: 1) changes in process characteristics and operating conditions, 2) individual di erence of equipment, and 3) reliability of soft-sensors. Since input variable selection is crucial for high estimation performance, conventional methods and new group-wise variable selection methods are introduced, and the usefulness of the group-wise variable selection methods is demonstrated through industrial case studies. Just-in-time (JIT) modeling is dealt with as a promising virtual sensing technology that can cope with changes in process characteristics as well as nonlinearity. Recent developments leading to successful industrial applications are introduced: correlation-based JIT (CoJIT) modeling and locally weighted regression (LWR), especially LW-PLS, with modi ed similarity measures. Manufacturing processes in di erent industries are quite di erent in appearance, but they have very similar problems from the viewpoint of quality issue. There remain practical issues requiring further research e orts to realize high-performance, maintenance-free virtual sensing technology.

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“…As mentioned earlier compositions can be measured by use of a gas chromatograph, but it will add a measurement delay of 10-20 minutes (Mejdell & Skogestad, 1991). It is possible to estimate the compositions by an estimation algorithm.…”

Section: Control Of Product Compositionsmentioning

confidence: 99%

“…The measurement that is often available on a distillation column is temperature measurements. If all temperature measurements are collected in a vector θ it is possible to obtain a linear estimator that gives estimates of the compositions (Mejdell & Skogestad, 1991):…”

Section: Control Of Product Compositionsmentioning

confidence: 99%

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Model Predictive Control of a Kaibel Distillation Column

Kvernland

Halvorsen

Skogestad

2010

IFAC Proceedings Volumes

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A Kaibel distillation column separates a feed into four products with significant lower energy consumption than a conventional sequence of binary columns. Optimal operation and control of such systems is an important task in order to obtain the potential energy savings.A laboratory column has been built at NTNU, Department of Chemical Engineering. At the time of the diploma work the laboratory column has unfortunately not been available for MPC experiments.In practical operation a control structure based on temperature measurements is chosen for the given case. This structure gives a four-by-four multivariable system. The candidate shall base his work on a model developed by Jens Strandberg. The motivation of using a Kaibel distillation column is primarily its energy saving potential. There is no reason for using such an energy saving column if the product purities are below some acceptable values. These purities can be kept above these acceptable values by sufficient control of the column.The simulation model of the Kaibel column was extended to include an efficiency parameter which describes an insufficient vapor mixing effect that occurs in distillation columns. This insufficient mixing leads to increased impurity flows in open-loop, but this is counteracted by increased reflux flow in closed-loop. The column can obtain sufficient purities for increasing insufficient mixing until the reflux flow reaches its maximum value.A single layer MPC and a supervisory MPC approach have been described, implemented and tested on the simulation model of the column. These MPCs show improved dynamic responses compared to the existing decentralized control approach. These MPCs have also been compared in a sensitivity analysis part. The sensitivity analysis shows a clear improvement in the robustness properties for a supervisory MPC compared to decentralized control in terms of input uncertainty. The supervisory MPC has also shown to be more robust than the single layer MPC.A brief qualitative discussion regarding alternative MPC approaches has been done, where inferential control is suggested as an alternative MPC approach for the Kaibel column.An MPC implementation has been done for use at the existing laboratory column at Department of Chemical Engineering. Also here, based on implementation issues, the supervisory MPC is preferable compared to a single layer MPC. A parameter adjusted version of the developed simulation model is recommended as the MPC's internal prediction model.

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Estimation of distillation compositions from multiple temperature measurements using partial-least-squares regression

Cited by 185 publications

References 8 publications

Hybrid Intelligent Approach for Predicting Product Compositions of a Distillation Column

Hybrid Intelligent Approach for Predicting Product Compositions of a Distillation Column

Virtual Sensing Technology in Process Industries: Trends and Challenges Revealed by Recent Industrial Applications

Model Predictive Control of a Kaibel Distillation Column

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