MULTIVARIATE LINEAR REGRESSION MODEL FOR SIMULTANEOUS ESTIMATION OF DEBUTANISER

PRODUCTS COMPOSITION

Obekpa, R.G

roseline261@gmail.com 08131807581

and *Alabi, S.B*sundayalabi@uniuyo.edu.ng 08063043106

Department of Chemical and Petroleum Engineering Faculty of Engineering,

University of Uyo, Uyo, Akwa Ibom State, Nigeria NSE Annual Conference: SUNSHINE 2015

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INTRODUCTIONDebutaniser products composition and specificationTop (Butane) Bottom (Pentane plus)

2.5% mole propane (max) 1.5% mole butane (max)

95% mole butane (min)

3.0% mole pentane (max)

In order to maintain these compositions at their optimal values, it is necessary to measure them with high accuracy and fast response

Available hardware measurement techniques Offline sample analysis in the laboratory Online product quality analyzers

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Unfortunately, these instruments are plagued with measurement delay and as such, hinder effective feedback control of the column

Challenges with available modelsInability to predict all the required compositions of both the top and bottom product

Project ObjectiveDevelopment of a linear regression model for the purposes of online prediction of any debutaniser top and bottom products composition.

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MATERIALS

Software Packages

MINITAB for design of experiment

MS EXCEL SPREADSHEET for data analysis

HYSYS for modelling and simulation

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METHODS Variable selection

Definition of operating conditions

Design of experiments

Data acquisition/generation

Model development and performance evaluation

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RESULTS AND DISCUSSION

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−0.00000003 − 0.00000005 … − 0.00000319−0.0000272 − 0.00002842 … − 0.00000459 0.0000275 0.000002844 … 0.000004580 0.00000055 0.00032265 … 0.000007370 0.00000754 − 0.00034750 … 0.000000370 ے�������������������ۑ��������������ۑ��������������ۑ��������������ې�����

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Where

A = Feed flow rate (kg/hr)

B = Feed temperature (oC)

C = Feed pressure (kPa)

D = Reflux flow rate (kg/hr)

E = Bottom flow rate (kg/hr)

F = Top pressure (kPa)

G = Total number of trays

H = Feed tray

I = Bottom temperature (oC)

J = Top temperature (oC)

Performance Indices of the Developed Model

Predicted Outputs R2 Percentage Mean Relative Error (%)

Total top propane 0.969 0.73

Total top butane 0.963 0.84

Total top pentane 0.963 9.66

Total bottom butane 0.416 2554.3

Total bottom pentane 0.960 3.33

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Interpolative and Extrapolative Performance Indices of the developed Model

Predicted Outputs Percentage Mean Relative Error (%)

  Interpolation Extrapolation

Total top propane 1.718 1.801

Total top butane 0.627 1.326

Total top pentane 5.755 8.150

Total bottom butane 6139.8 673.8

Total bottom pentane 3.580 14.43NSE Annual Conference: SUNSHINE 2015 8

Graphical representation of interpolation ability of the developed model

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DiscussionThe regression equations with high R2 and low PMRE

showed that the observed outcomes are well replicated by the model, thus indicating high accuracy.

The low R2 value of 0.416 and high PMRE of 2554.3 for bottom total butane equation indicates that the prediction accuracy level is low.

Both evaluation techniques (R2 and PMRE) point out the fact that the regression equations are highly accurate with the exception of the bottom total butane.

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CONCLUSIONS• The equations for top total propane, top total butane, top total

pentane and bottom total pentane have high accuracies and generalisation abilities.

• The proposed model can be used outside the range of data used for model development, as the resulting extrapolation errors are deemed reasonable for practical applications.

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RECOMMENDATIONThe performance of the linear regression equation for the total

bottom butane is very poor. Hence, in future work(s), linear equation of a higher order can be considered.

Alternatively, a nonlinear model like artificial neural network which has been noted for its ability to model nonlinear systems with high accuracy can be considered.

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Questions??