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Multivariable control of a debutanizer column using equation basedartificial neural network model inverse control strategies

Nasser Mohamed Ramli a,b, Mohd Azlan Hussain b,c,n, Badrul Mohamed Jan b

a Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysiab Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysiac UMPDEC, University of Malaya, Malaysia

a r t i c l e i n f o

Article history:Received 2 September 2015Received in revised form29 January 2016Accepted 14 February 2016

Communicated by J. Zhang

network techniques have been increasingly used for a wide variety of applications where statistical

Keywords:Debutanizer columnNeural networkEquation basedMultivariable control

x.doi.org/10.1016/j.neucom.2016.02.02612/& 2016 Elsevier B.V. All rights reserved.

esponding author at: Department of Chemring, University of Malaya, 50603 Kuala Lump0 3 79675214; fax: þ60 3 79675319.ail addresses: nasser_mr@siswa.um.edu.my (Nzlan@um.edu.my (M.A. Hussain), badrules@u

e cite this article as: N.M. Ramli, eork model inverse control strategies

a b s t r a c t

The debutanizer column is an important unit operation in petroleum refining industries as it is the maincolumn to produce liquefied petroleum gas as its top product and light naphtha as its bottom product.This system is difficult to handle from a control standpoint due to its nonlinear behavior, multivariableinteraction and existence of numerous constraints on both its manipulated and state variable. Neural

methods have been traditionally employed. In this work we propose to use an equation based MIMO(Multi Input Multi Output) neural network based multivariable control strategy to control the top andbottom temperatures of the column simultaneously, while manipulating the reflux and reboiler flowrates respectively. This equation based neural network model represented by a multivariable equation,instead of the normal black box structure, has the advantage of being robust in nature while being easierto interpret in terms of its input output variables. It is implemented for set point changes and disturbancechanges and the results show that the neural network based model method in the direct inverse andinternal model approach performs better than the conventional PID method in both cases.

& 2016 Elsevier B.V. All rights reserved.

1. Introduction

Debutanizer column operation is based on a multi-component,multivariable control strategy which is highly non-linear in natureinvolving nonlinear dynamics. The column is widely used in processplants and it constitutes a very difficult control problem. The productcomposition can normally be controlled by two variables, the pro-duct split and reflux ratio [1] as the two manipulated variables.However controlling these top and bottom compositions require anumber of complex instrumentation which are due to the interac-tions of the these composition loops which face dynamic stabilityproblems. Its control requires an on-line measurement performancevariable directly related to composition, which is normally tem-perature. Although, temperature-composition relationship is a func-tion of column pressure control, controlling the top and bottomtemperatures of the column seems to give tight control on productcomposition despite wide variations in other factors such as theinternal reflux ratio [1].

ical Engineering, Faculty ofur, Malaysia.

.M. Ramli),m.edu.my (B.M. Jan).

t al., Multivariable control, Neurocomputing (2016), h

However, application of composition control at both ends of adebutanizer column has shown very little success [1]. The diffi-culty arises since the two individual control loops tend to interactwhere the top loop controls the heavy key in the overhead streamwhile the bottom loop controls the light key in the bottom stream.Slight disturbances in the system can cause the light key con-centration in the bottom stream to increase while the lower loopmay change the concentration through addition of heat in thereboiler system.

At the same time, laboratory measurement procedures forcomposition measurement are also slow, tedious and time con-suming. Therefore, inferential model using linear regressionusually encounters co-linearity problem, which adversely affectslong-term prediction performance since the outputs of the debu-tanizer column usually depend on the feed composition whichalso cannot be determined online.

To circumvent some of these problems, the use of softwarebased sensors and controllers incorporating neural networkmodels is proposed in this work. This neural network based sys-tem is developed to simultaneously control the top and bottomtemperature while at the same time regulating the compositionsin a multi-input multi output approach. Since in the real industrylarge historical data are available, the use of neural network is alsoappropriate and economical as compared to hardware based

of a debutanizer column using equation based artificial neuralttp://dx.doi.org/10.1016/j.neucom.2016.02.026i

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instruments. It will also help improve product quality monitoringof the system by predicting the top and bottom compositions andtemperatures simultaneously with high accuracy [2]. The softwarebased are also cheap compared to hardware sensors and can beeasily integrated on existing hardware controllers in the industry.

Previously some work utilizing neural network as a controllerhave been done which include robust stability analysis with har-monic balance for a multivariable non-linear plant using theneural network controller under generic Lur'e configuration [3].The neural network controller was applied to describe the sinu-soidal input while the linearized model has been derived torepresent the nonlinear plant dynamics. The work was applied to amultivariable binary distillation column under feedback neuro-control and it illustrates the use of a robustness approach to pre-dict the presence of limit cycles subject to restriction of thedescribing function. In another work, the use of adaptive neuralnetwork for composition prediction which was used to controlboth the composition and inventory for a continuous ethanol-water pilot plant based distillation column has also been proposed[4]. A principal component analysis based algorithm was appliedto select the input vectors for the soft sensor. The proposed controlscheme offers high speed of change which is due to the set pointchanges with stationary error for composition and inventorycontrol.

A multi-loop nonlinear model control strategy has also beenproposed for a distillation column using an ARX-NN (Auto-regressive Neural Network) cascaded structure incorporated intothe PLS (Partial Least Square) inner model [5]. An optimizationprocedure is provided to identify the set parameter of the ARX-NNPLS in order to minimize the plant model mismatch. The approachwas used to demonstrate the control effectiveness for setpointtracking and disturbance rejection. Neural network has also beenapplied to handle the nonlinear dynamics of a hydrolyzer [6]. Amathematical model was used to simulate the dynamic responseof the temperatures when the controller was applied to the sys-tem. Two control strategies implemented include the directinverse control and internal model control and evaluated for set-point tracking and disturbances studies. The IMC (Internal ModelControl) was found to perform best for temperature control duringsetpoint and disturbance tests and found to be more stable thanthe conventional controllers.

In a novel implementation of a neural network inverse modelbased control method on an experimental system, a partiallysimulated reactor was used to test the neural network basedalgorithms [7]. The implementation involves the control of thereactor temperature in the face of set point changes and loaddisturbances which gave acceptable results as compared to theconventional controller. Neural networks for gain predictionwithin a nonlinear and multivariable system with constraints havealso been developed [8]. This strategy was implemented on a lab-scale, non ideal system for a methanol–water distillation columnusing servo, regulatory and constrained control. The experimentalresults applied a Generic Model Controller using the neural net-work as the steady-state model inverse that was developed earlier.A comparative study of these neural network model-based con-trollers with other advanced controllers such as the dynamicmatrix control showed better performance of the proposedcontroller.

A neural network controller design based on the processinverse dynamic modeling was also applied for product compo-sition control of a distillation plant. The algorithm was applied toobtain the dynamic nonlinear relationship between productcomposition and reflux flow rate [9]. Neural networks model hasalso been used as the steady state inverse of a process which isthen coupled with a simple reference system synthesis to generatea multivariable controller [10]. The control strategy was applied

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for controlling a distillation column in the lab and for anindustrial-scale high-purity column. An efficient training algo-rithm based on a nonlinear least-squares technique was used totrain the networks. The neural network model based controllersshowed better performances for both setpoint and disturbancechanges over the conventional feedback controllers.

The various works presented so far on the use of neural net-work models and controllers involve the use of black box models.This is non-versatile and non-robust in nature as well as beingdifficult to see the correlations between the inputs and outputs tothe system, which are important factors for practitioners in manycases. In this work, which lies one of its main novelty and con-tribution, we have proposed using an equation based inverseneural network models in a MIMO system to control the top andbottom temperature of the debutanizer column simultaneouslyusing the DIC (Direct Inverse Control) and IMC approach. Neuralnetwork equation based models have also been used to estimatethe compositions in the column. The other contribution of thiswork is that it utilize a mixture of online close loop and open loopdata for data available online and simulation data for data whichare not available online, for training the neural network models.The simulation data was validated with the actual loop output toascertain its accuracy.

The paper is organized in several sections. Section 2 describesthe column and plant in detail while Section 3 outlines the hybridmodeling of the distillation column. Section 4 discusses themethodology for the hybrid model. Finally Section 5 covers theoverall analysis results using the hybrid model for compositionand temperature while section 6 covers the conclusion.

2. Plant and debutanizer column description

The plant under study in this paper is a crude oil processingunit to produce high value petroleum products for domestic andexport markets as seen in Fig. 1. The plant consists of a refineryprocess and involves condensate fractionation and reforming ofaromatics. The products are petroleum fractions, liquefied petro-leum gas, naphtha and low sulfur waxy residue while the feedstock of the refinery is crude oil. There are two main process unitsfor the refinery, which are the CDU (Crude Distillation Unit) andthe CRU (Catalytic Reforming Unit) while the Crude Oil Terminalprovides the crude oil feed stock. Heat exchangers are used topreheat the crude oil from 190 °C to 210 °C. The preheated streamis then further heated in a furnace with a temperature range ofabout 340 °C to 342 °C. The crude is then routed to the CDU. Thecrude oil is split into a number of fractions, which includes theheavy straight run naphtha as overhead vapor, untreated kerosene,straight run kerosene and straight run diesel. From the crudetower, there are three branches of cut streams, which are drawn toa stripper column that consists of naphtha stripper, kerosenestripper and diesel stripper.

The hydrogen from the reformer is mixed with the feed of theHSRN (Heavy Straight Run Naphtha) from the CDU and is thenheated up to the reaction temperature prior to being fed into apretreater catalytic reactor. The reactions consist of desulfurisationand denitrification, which protect the reformer catalyst from poi-soning. The product from the reactor is then sent to the pretreaterstripper. The feed to the reforming unit includes the bottom productof the stripper. The treated naphtha is heated to the reaction tem-perature and is then fed to the reforming reactors. Effluent from thereactor is cooled and collected in a reformer separator. One part ofthe gas is sent to an absorber while the other is recycled to thereactor feed stream. In the absorber, hydrogen gas is purged andrecycled to the pretreater heater. The raw naphtha feed consists ofhydrogen make-up gas while the liquid phase is drawn off and fed

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into a LPG (Liquefied Petroleum Gas) absorber. The liquid fraction ispumped into a stabilizer and the reformate is withdrawn from thestabilizer bottom and cooled before being sent for storage. Theoverhead vapor from the stabilizer are cooled, condensed andrecovered from the stabilizer reflux drum and part of the liquidstabilizer is sent as raw LPG to the recovery unit.In the current work, the major focus is on the debutanizer columnsince it produces the major product namely, the LPG.

Fig. 1. Flow chart for th

Fig. 2. Debutanizer colu

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The debutanizer column is located at the CDU section. The feedto the debutanizer column is the De-ethanizer's bottom product.High boiling point heavy components flow down in contact withvapor produced in the debutanizer reboiler while low boilingpoint components rise up the tower in contact with the internalreflux. In order to cool the overhead vapor, the debutanizer con-denser is used. Part of the collected condensed hydrocarbon isrouted to the top of the debutanizer as reflux. At the debutanizer

e refinery process.

mn configuration.

of a debutanizer column using equation based artificial neuralttp://dx.doi.org/10.1016/j.neucom.2016.02.026i

Table 1Column specification.

Number of tray of the column 35Feed tray – stage number 23Type of tray used ValveColumn diameter 1.3 mColumn height 23.95 mCondenser type PartialFeed mass flowrate 44106 kg h�1

Feed temperature 113 °CFeed pressure 823.8 kPaOverhead vapor mass flowrate 11286 kg h�1

Overhead liquid mass flowrate 5040 kg h�1

Condenser pressure 823.8 kPaReboiler pressure 853.2 kPa

Table 2Tag name description of the column

Tag Description Units

Temp 1 Debutanizer top temperature °CTemp 2 Debutanizer bottom temperature °CTemp 3 Debutanizer receiver bottom temperature °CTemp 4 Light Naphtha temperature after condenser E 1 °CTemp 5 Reboiler outlet temperature to column °CTemp 6 Debutanizer feed temperature °CLevel 1 Debutanizer level %Level 2 Debutanizer condenser level %Flow 1 Light Naphtha flow to storage m3/hrFlow 2 LPG flow to storage m3/hrFlow 3 Reflux flow rate m3/hrPressure 1 Debutanizer receiver overhead pressure kPa

N.M. Ramli et al. / Neurocomputing ∎ (∎∎∎∎) ∎∎∎–∎∎∎4

bottom section, the debutanizer reboiler is used to strip the lightcomponent. To control the bottom temperature of the column, adebutanizer reboiler control valve is manipulated. On the otherhand the reflux flow rate is manipulated to control the top tem-perature in the column. Hence the control of the debutanizercolumn is categorized as a MIMO control system. Fig. 2 shows thecolumn configuration for the debutanizer column. Table 1 outlinesthe column specifications and Table 2 describes the tag namesurrounding the column. A common scheme is to use reflux flowto control top product composition and the heat input is used tocontrol bottom product composition. However, changes in refluxalso affect bottom product composition and the top productstreams are also affected by changes in heat input and henceseveral loop interactions can occur in the debutanizer column dueto this process design.

The present practice for controlling these loops involve conven-tional controllers controlling the top and bottom temperaturesseparately with different loops. These are unstable at times due tothe nonlinearity and interaction inherently present in the column,which also involves tuning problems for the controller. The mainobjective of this paper is to develop an advanced equation basedneural network control strategies to control the top and bottomtemperatures while estimating the top and bottom compositionssimultaneously for the debutanizer using a mixture of industrial andsimulation data. The proposed approach using neural network con-trollers and MIMO based controllers for controlling both loopssimultaneously, inherently takes into account the interactions withinthese loops. The proposed method of using equation based NNcontrollers and estimates add further advantage due to the fact thatthe relationship between the input and output variables can berepresented by equations which can be modified and adapted easilywithout tedious retraining of the networks, hence is more robust andversatile than the black box NN controllers.

Please cite this article as: N.M. Ramli, et al., Multivariable controlnetwork model inverse control strategies, Neurocomputing (2016), h

3. Methodology

3.1. Data generation

Although most online open loop response from the plant sur-rounding the column is available, some of the variables in the openloop are not available. In this work, dynamic simulation of the debu-tanizer column is performed using the plant process simulator HYSYSto obtain the unavailable data sets from the plant. Unavailable vari-ables include Temp 5 (Reboiler outlet temperature), Pressure 1(Debutanizer receiver overhead pressure) and composition at bothends of the column. The simulated close loop response of the com-position of n-butane at the top and bottom of the column is alsoestablished to compare them with the online close loop data. Thesteady state for the column needs to be developed in HYSYS beforetransition of the steady-state to the dynamic state. Steady statesimulations can be cast easily into dynamic simulations by specifyingadditional engineering details, including pressure/flow relationshipsand equipment dimensions. The necessary information such as feedconditions, feed compositions, reflux ratio, condenser pressure andreboiler pressure have to be provided to the selected unit operation inthe simulation. The simulation data is performed using similar steptests as in the plant to obtain the fluctuation of the process variablesunder open loop response, where the manipulated variables are thereboiler and reflux flow rates.

Comparison between the close loop responses in simulation tothe actual plant data is performed to evaluate the deviationbetween the simulated and actual composition of n-butane, toascertain that the simulation data available closely resemble theactual online industrial data. Fig. 3 shows the representation of thecomposition for the bottom of n-butane as an example. The cal-culated RMSE (Root Mean Square Error) calculated for the topcomposition is 0.0251 and the bottom composition is 0.008184respectively. This indicates that there is a small deviation betweenthe online and simulation data which hence can be used indeveloping the neural network models

The data used for the process are obtained online from industry istaken for 540 minutes with 1-minute sampling interval, whichamounts to a total data of 5410 as seen in Fig. 4. It could seen thatthere is some noise in the actual data collected as seen in Fig. 4.Section 4.1 describes in detail how the data are generated. The avail-able data from the plant are large and need to be screened by per-forming PCA (Principal Component Analysis) and PLS to determine thesignificant variables affecting the top composition, bottom composi-tion and the top and the bottom temperatures of the column whichdetermine the inputs for the neural network model. From these ana-lysis, the main inputs to the forward neural network model tem-perature are obtained i.e Overhead pressure flow rate, the Feed flow,Temp 2 (Bottom temperature), and Temp 1 (Top temperature), refluxflow rate and the reboiler flow rate. These variables are the necessaryinputs to the neural network model where 2 of these i.e Temp 2 andTemp 1 are the measured controlled variables and reflux flow rate andreboiler flow rate are the manipulated variables. Details of theseanalysis can be seen in [11].

3.2. Application of artificial neural network

ANN (Artificial Neural Network) is a popular and reliable tool whendealing with problems involving prediction of variables in engineeringproblems at the present age. Details of the ANN and its applicationscan be found elsewhere [12–17]. In essence, the main advantage ofANN is in its ability to approximate an arbitrary function mechanismthat learns from observed data. However, it is not a straightforwardstep to apply neural network for control. A relatively good under-standing of the underlying theory is essential. The first criterion is themodel selection which depends on the representation of data and its

of a debutanizer column using equation based artificial neuralttp://dx.doi.org/10.1016/j.neucom.2016.02.026i

0.000.010.020.030.040.05

0.060.070.080.090.10

0 2000 4000 6000 8000 10000 12000

simulation online

Bottom composition n-butane

Time (min)

Fig. 3. Bottom composition of n-butane simulation versus online.

Step test Temp 1

19

19.520

20.521

21.522

22.5

1 51 101 151 201 251 301Time (min)

55

56

57

58

59

60

61

Tem

pera

ture

(0C

)

Reflux.flowrate Temp 1

Step test Temp 2

140

141

142

143

144

145

1 51 101 151 201 251 301 351 401 451 501Time (min)

Reb

oile

r flo

wra

te

(m3 /h

r)

132134136138140142144146

Tem

pera

ture

(0 C)

Reboiler.Flow Temp 2

Ref

lux

flow

rate

(m

3 /hr)

Fig. 4. Changes in top and bottom temperature with manipulated variable.

N.M. Ramli et al. / Neurocomputing ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 5

application. Selecting and tuning an algorithm for training on anunseen data requires a significant number of experiments. The othercriterion involves the robustness of the selected model. If the model,cost function and learning algorithm are selected appropriately, theresulting ANN can be robust but otherwise not. Neural network hasbeen extensively used for a number of chemical engineering applica-tions involving prediction, control and nonlinear process identification.A review of various applications utilizing neural network for controlboth in simulation and online implementation for chemical processescan be seen elsewhere [12].

Today FANN (Feed Forward Neural Network) architecture is themost studied and used neural network architecture. It models a global

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approximation of a multi-input multi-output function in a similarmanner as fitting a low order polynomial through a set of data points.A rich collection of different network and learning algorithms areavailable in the literature [18,19] but the network is selected as thebasic building block to be used in this study. The mathematical for-mula describing the networks takes the following form:

y¼ FiXnk

j ¼ 1

Wi;j:f jXnϕl ¼ 1

wj;lϕlþwj:0

!þWi:0

24

35 ð1Þ

where φ is the external input, nφ is the number of input in an inputlayer, nk is the number of hidden neurons in a hidden layer, W and w

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Fig. 5. Control loop of neural network based Direct Inverse Model Control (DIC) strategy.

N.M. Ramli et al. / Neurocomputing ∎ (∎∎∎∎) ∎∎∎–∎∎∎6

are the weights, f and F are activation functions for hidden layer andoutput layer respectively.

In order to model the dynamics of a system, either ELMAN(Recurrent Neural Network) or neural network with ARX are used.However, in this work, Neural network with NARX (Non-linearAutoregressive Network with exogenous inputs) structure isapplied to model the dynamic system based on time-series datasince it gives better result than the ELMAN type network as will beseen later in Section 4.1.1 The equations describing the NARXstructure can be expressed as follows:

Y ¼ f Y1;Y2; ::::::::Yn;U1;U2; :::::Umð Þ ð2Þwhere:

Y ¼ y1ðkþ1Þy2ðkþ1Þ� �TY1 ¼ y1ðkÞ; y1ðk�1Þ; ::::::y1ðk�ny1Þ

� �:::::::::::::

Yn ¼ ynðkÞ; ynðk�1Þ:::::::; ynðk�nynÞ� �

U1 ¼ u1ðkÞ;u1ðk�1Þ; ::::::u1ðk�nu1Þ� �

Um ¼ umðkÞ;umðk�1Þ; :::::;umðk�numÞ� �

and m is number of input variables, n is number of output variablesand ny and nu are the history length for output variables and inputvariables, respectively. The procedure for obtaining the past valuesdone by first setting all ny and nu to be 1 and calculating the RMSEvalues, after which, we gradually increased until some maximumvalue of k, normally 5. The combination ny and nuwhich gives the leastRMSE values is chosen as the best values for the delayed inputs, whichcorresponds to one delayed time in this study. This approach followsthat of the previous work in [20,21]. These past values is veryimportant in term of the performance of the model, too many pastvalues may increase the complexity of the system or for less pastvalues, the model may not be able to capture the dynamics of theprocess.

However, all applications before have utilized neural networkas a black box model, which has its own disadvantages and lim-itations especially in respect to the robustness problems. In thiswork, we have shown that by proper choice of the activationfunction, the neural network can be represented by an algebraicequation. The general equation for the output from the neural

Please cite this article as: N.M. Ramli, et al., Multivariable controlnetwork model inverse control strategies, Neurocomputing (2016), h

network can be given as (for a 2 layer network)

y¼ f 2 LW2;1f 1 IW1;1pþb1� �

þb2� �

ð3Þ

IW1;1 ¼weight at layer 1

LW2;1 ¼weight at layer 2 hidden layerð Þp¼ inputs to the neural network

y¼ outputs f rom the neural network

f ¼ activation f unction at layer i

b1 ¼ bias value at layer 1

b2 ¼ bias value at layer 2

The f 1 and f 2 are simplified by multiplying the matrix inputlayer and the biases value with the matrix hidden layer, If wechoose the activation function to be linear, we can simplify theequation to be in the form of;

y¼y1y2

" #¼ LW2;1 IW1;1pþb1

h iþb2

h ið4Þ

where the matrix definition LW2,1, IW1,1, b1 and b2are given as;

IW1;1 ¼weight at layer 1 input layerð Þb1 ¼ bias value at layer 1

LW2;1 ¼weight at layer 2 hidden layerð Þb2 ¼ bias value at layer 2

These representations can then be utilized in this work as anestimator to ascertain the top and bottom compositions andmultivariable controllers to control the top and bottom tempera-tures simultaneously as will be shown in the next sections.

3.3. Neural network based control strategies

Two types of Neural Network based control strategies areimplemented in the inverse model based control schemes for thedebutanizer column under study in this paper i.e. the DIC (DirectInverse Control) and the IMC (Internal Model Control) methods asdescribed next.

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Fig. 6. Control loop of neural network based Internal Model Control (IMC) strategy.

Table 3Neural network architecture for n-butane composition prediction.

Parameters Description

Network NARX series parallel network (newnarxsp)Training function Levenberg MarquardtPerformance function MSEEpochs 1000Goal 1e-6Number of layers 2Layer 1: number of neuron 10Transfer function PURELINLayer 2: number of neuron 2Transfer function PURELIN

Table 4Neural network architecture for temperature prediction.

Parameters Description

Network NARX series parallel network (newnarxsp)Training function Levenberg MarquardtPerformance function MSEEpochs 1000Goal 1e-6Number of layers 2Layer 1: number of neuron 12Transfer function PURELINLayer 2: number of neuron 2Transfer function PURELIN

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3.3.1. Direct inverse control methodThis strategy consists of a plant which is placed in a series with the

neural network inverse models that act as the controller. In thisscheme, the outputs predict the desired current system input, whilethe desired set-point acts as the desired output which is fed to thenetwork together with the past plant inputs. The structure of the DICis in multi-input multi output form and appropriate control para-meters for the desired target will be predicted based on the inputs bythe neural network model acting as the controller as shown in Fig. 5.The variables to be controlled are the top and bottom temperatureswhile the manipulated variables are the reflux and the reboiler flowrates for this case.

3.3.2. Internal model control methodNeural network based IMC method incorporates both the inverse

and the forward models in the control scheme. The dynamic of theprocess is modeled by the forward model, which is placed in parallelwith the system to cater for mismatches of the model with the plantduring implementation [22]. The inverse neural networkmodel acts asthe controller as in the DIC strategy. In this scheme, the error betweenthe neural network forward model and the plant output is subtractedfrom the set- point before being fed into the inverse model, as seen in

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Fig. 6. With a mismatch detection feature, the internal model basedcontroller can be used to drive the controlled parameter to the desiredset-point evenwhen noise and disturbances are present in the system.The error produced by the process can be minimized and compen-sated by the error produced by the neural network forward processmodel [22]. In most cases, the IMC performs better when disturbancesare present which is the reason for implementing both strategies inthis case study.

3.3.3. Neural networks modelsBefore applying the inverse model neural network control strate-

gies for the debutanizer column, it is crucial to discuss the develop-ment and configuration of the forward and inverse neural networkmodels which is fundamental in these model based control strategies.The details of the neural networkmodel used are given in Tables 3 and4. The LM (Levenberg–Marquardt) algorithm method is used to trainthe neural network in the NARX structure designed for second ordertraining. The inverse neural network model that has been developedto control the top and bottom temperatures, there are 12 inputs (pinputs as given in next section) and 2 outputs, which are the refluxand reboiler flow rates. These outputs are the manipulated variablesused to control top and bottom temperatures respectively. For thecomposition prediction, the forward neural network model that has

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Fig. 7. Forward and inverse model to control temperature.

Manipulated variable reboiler and reflux

141

141.5

142

142.5

143

143.5

144

144.5

145

0 50 100 150 200 250 300Time (min)

MV

(m

3 /hr)

20

20.5

21

21.5

22

22.5

MV

(m

3 /hr)

Reboiler Reflux

Fig. 8. Partition of data sets for manipulated variable.

N.M. Ramli et al. / Neurocomputing ∎ (∎∎∎∎) ∎∎∎–∎∎∎8

been developed to estimate the top and bottom composition, contains10 inputs and 2 outputs which are the top and bottom compositionrespectively.

3.3.3.1. Forward models. The procedure of training a neural networkto predict the outputs by giving the required inputs is called forwardmodeling and the model obtained from this method is referred to asthe forward models. The most straightforward and popular approach isto augment the network inputs data signals in real number form, fromthe model or system being identified [23]. Other fundamental statevariables can also be fed into the network and considered as part of theinputs. In this method, the network is fed with the present inputs, pastinputs as well as the past outputs to predict the necessary outputs. Theneural network is placed in parallel with the model or system. Theerror between the network output and system output, which is the

y¼T1

T2

" #¼ �0:16�0:14 0:04�0:002�0:094�0:95 1:03�0:61�0:71 0:81 0:16�0:049

0:42 0:07 0:04 0:20�0:30�0:19 0:12�0:28 0:35�0:29�0:48 0:168

� �pþ �0:28

�0:22

� �ð5Þ

prediction error is used as the training signal for the neural network.In this work, the equation based models are used to replace the blackbox neural network modeling and the forward model is used both inthe IMC strategy and as the neural network estimator to predict the topand bottom composition. The matrix of the weights and biases values

Please cite this article as: N.M. Ramli, et al., Multivariable controlnetwork model inverse control strategies, Neurocomputing (2016), h

are extracted from the network developed in the neural networktraining and these weights are used to obtain the neural networkmodel as given by the general form of Eq. (4).

The forward model for temperature is then given as;

T1

T2

" #¼ LW2;1 IW1;1pþb1

h iþb2

h i

In this case, p is the inputs to the neural network temperaturegiven by the vector

mv1ðkÞ mv1ðk�1Þ mv2ðkÞ mv2ðk� �1Þ mv3ðkÞ mv3ðk�1Þ f ðkÞf ðk�1Þ TtopðkÞ Ttopðk�1Þ TbotðkÞ Tbotðk�1Þ�TAfter pruning the neural network structure (simplifying the

weights and biases values) the equation above can be furthersimplified to give the equation below;

where T1, T2 are the output for the top and bottom temperature ofthe column.

This forward model for temperature is used in the IMC approachdescribed in Section 3.3.2. If the activation function used is logsiginstead of linear in the output layer, the equation obtained from this is

of a debutanizer column using equation based artificial neuralttp://dx.doi.org/10.1016/j.neucom.2016.02.026i

N.M. Ramli et al. / Neurocomputing ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 9

given in Eq. (A1), Appendix A. If the activation function used is logsiginstead of linear in the hidden layer, the equation obtained is given inEq. (A2), Appendix A. But if the activation function for both layers,using logsig transfer function the equation, it will be more complexand meaningless to be used in the model control strategy. Howeverour initial study shows that the linear activation function gives betterresults than logsig transfer function [11].

The forward model for composition is given by;

y1y2

" #¼ LW2;1 IW1;1pþb1

h iþb2

h i

where in this case, p is the inputs to the neural network compo-sition given by the vector

mv2ðkÞ mv2ðk�1Þ mv3ðkÞ mv3ðk�1Þ f ðkÞ f ðk�1Þ�ptopðkÞ ptopðk�1Þ pbotðkÞ pbotðk�1Þ

iT

After pruning the neural network structure (simplifyingthe weights and biases values) the equation above can further besimplified to give the composition equation below;

Actual and simulated plot compo

0.025

0.03

0.035

0.04

0.045

0.05

0.055

0.06

0.065

0.07

0.075

0 10 20

Time (min)

Bot

tom

com

posi

tion

(mol

e fra

ctio

n):V

alid

atio

n

Fig. 9. Actual and simulated n-butane bottom compo

Top te

0

10

20

30

40

50

60

70

80

90

100

0 50 100 15

Tim

Tem

pera

ture

(C

)

pid

Fig. 10. Set point changes

y1y2

" #¼ �0:26 0:15 0:37 0:23 0:38 0:40�0:50 0:97 0:12�0:31

�0:09 0:006 0:31�0:10 0:02 0:02�0:42�0:12 0:36�0:085

�

Please cite this article as: N.M. Ramli, et al., Multivariable controlnetwork model inverse control strategies, Neurocomputing (2016), h

where y1 and y2 are the output for the top and bottom composi-tion predictions.

3.3.3.2. Inverse models. Inverse models are basically the neural netstructure representing the inverse of the system dynamics at thecompletion of training. The method for obtaining the inverse models isachieved by switching the inputs with the required outputs. Thepurpose of the switching is to enable the predictions of the manipu-lated variables for control in the MIMO fashion. In this case themanipulated variables that is the reboiler and reflux flow rates areswitched with the future predictions of top and bottom temperatureswhich are basically the set points of the top and bottom temperatures.The sequence of the inputs of the network needs to be maintained asin the forward models. The training procedure in this case is calledinversed modeling and y(kþ1) corresponds to the required referencesignal or set-point. The final network representation of the inverse isgiven in the general form;

uðkÞ ¼ f �1½ypðkþ1Þ; ypðkÞ; ypðk�1Þ;uðkÞ;uðk�1Þ� ð7Þ

where, f�1 represents the inverse map of the forward model

stion n-butane: Validation

30 40 50

Actual

NARX

ELMAN

sition validation for NARX and ELMAN network.

mperature

0 200 250 300

e (min)

imc dic

for top temperature.

�pþ �0:28

�0:21

� �ð6Þ

of a debutanizer column using equation based artificial neuralttp://dx.doi.org/10.1016/j.neucom.2016.02.026i

Bottom temperature

0

20

40

60

80

100

120

140

160

180

200

0 50 100 150 200 250 300

Time (min)

Tem

pera

ture

(C

)

pid imc dic

Fig. 11. Set point changes for bottom temperature.

Table 5PID tuning.

Parameter Kc Ti Td

Top temperature 0.71 1.41 20Bottom temperature 1.76 3.25 15Top composition 13.73 3.26 10Bottom composition 8.74 3.26 5

Table 6Controller performance during set point changes.

IMC eq DIC eq PID

IAE top 830.76 912.78 1219.70IAE bottom 3809 4289 4666

ISE top 2.10Eþ04 2.23Eþ04 2.69Eþ04ISE bottom 1.21Eþ05 2.67Eþ05 3.06Eþ05

ITAE top 4.25Eþ04 4.48Eþ04 1.44Eþ05ITAE bottom 1.92Eþ05 2.16Eþ05 4.45Eþ05

N.M. Ramli et al. / Neurocomputing ∎ (∎∎∎∎) ∎∎∎–∎∎∎10

The prediction of the control output i.e. mv2(k) and mv3(k) isperformed for a one-step ahead prediction, in conformity withthat of the forward model and also the one-step ahead controlaction is applied in the control strategies since this is an inversemodel based approach which need a one step immediate controlaction to give good results. The one step ahead is due to using theinverse model control strongly which need immediate action. Ourprevious work also shows that the network with multiple outputsprediction are not as stable as single output prediction which isvery critical for control implementation studies [23,24].

The training and validation data set generated for the networksare similar to that used for forward modeling but with differentinput and output configurations.

The inverse neural network model to predict the reboiler andreflux flow rates is given as;

mv2ðkÞmv3ðkÞ

" #¼ LW2;1 IW1;1pþb1

h iþb2

h i

In this case, p is the inputs to the neural network inversetemperature given by the vector

mv1ðkÞ mv1ðk�1Þ mv2ðk�1Þ mv3ðk�1Þ f ðkÞ f ðk�1Þ Ttopðkþ1Þ TtopðkÞTtopðk�1Þ Tbotðkþ1Þ TbotðkÞTbotðk�1Þ

" #T

After pruning the neural network structure (simplifying theweights and biases values) the equation above can further besimplified to give the equation below as;

mv2ðkÞmv3ðkÞ

" #

¼ �0:16 0:14 0:039�0:004�0:09�0:95 1:03�0:61�0:72 0:81 0:17�0:050:42 0:077 0:039 0:20�0:30�0:19 0:13�0:27 0:34�0:28�0:47 0:16

� �p

Please cite this article as: N.M. Ramli, et al., Multivariable controlnetwork model inverse control strategies, Neurocomputing (2016), h

þ �0:79�0:008

� �ð8Þ

where mv2(k) and mv3(k) are the manipulated reflux and reboilerflow rates respectively and p is the inputs to the neural networkinverse model.

The equation is implemented in SIMULINK (MATLAB) by havingthe system with more than one control loop in a MIMO strategy.Fig. 7 shows the forward and inverse models used in the multi-variable control strategies in this work.

4. Results and discussion

4.1. Step test for reboiler flow rate and reflux flow rate to generateinput–output data

In order to generate the input–output data for the neural net-work training, various step changes are applied to the input data toobtain the corresponding outputs. The inputs for the system in this

case are the reboiler flow rates and reflux flow rates while theoutputs are the top and bottom temperatures respectively. Thestep test for these inputs, which are the manipulated variables, isgenerated by using a multi amplitude, multi step rectangular pulseas seen in Fig. 4. This figure shows the step tests for the reboilerflow rate and the reflux flow rate data sets. The step test isimportant to see the effect and the fluctuations of the various

of a debutanizer column using equation based artificial neuralttp://dx.doi.org/10.1016/j.neucom.2016.02.026i

Manipulated variable top temperature

20

20.5

21

21.5

22

22.5

23

0 50 100 150 200 250 300 350

Time (min)

MV

(m3/

hr)

0

5

10

15

20

25

30

35

40

MV

(m3/

hr)

reflux imc reflux pid

Fig. 12. Manipulated variable top temperature.

Manipulated variable bottom temperature

140

140.5

141

141.5

142

142.5

143

143.5

144

0 50 100 150 200 250 300 350

Time (min)

MV

(m3/

hr)

0

10

20

30

40

50

60

MV

(m3/

hr)

reboiler imc reboiler pid

Fig. 13. Manipulated variable bottom temperature.

N.M. Ramli et al. / Neurocomputing ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 11

important process variables when performing changes to thereboiler flow rate and as can be seen in this case the fluctuations ofthe temperatures of the top and bottom of the column changesdynamically as the reboiler flow rate changes. The step test for thereflux flow rate is also conducted in the same way. However onlythe step test results for the reboiler flow rate are shown in thispaper for brevity. The data generated are partitioned according tothe training; validation and testing data sets as shown in Fig. 8.

4.1.1. Comparison between NARX and ELMAN networkAs mention before, the type of dynamic network used for

training, validation and testing in this work is the NARX inputswith series-parallel architecture. However to compare the NARXwith the ELMAN network and justify the use of the NARX model,we have simulated the output for both results for the bottomcomposition of n-butane as seen in Fig. 9. The same results areobtained for the temperature which is not given here for brevity.From these results it can be concluded that the NARX networkgives better prediction than the ELMAN network, hence justifyingits use for our control purposes.

4.2. Neural network control implementation

In order to develop and analyze the controller performance forthe debutanizer column, two typical test criteria are implementedwhich are the set point changes and disturbance changes for thecolumn. The set point changes have been performed to check theperformance of the neural network control under differentrequired conditions and the disturbances are introduced by

Please cite this article as: N.M. Ramli, et al., Multivariable controlnetwork model inverse control strategies, Neurocomputing (2016), h

changing the column feed temperature. As mentioned before, thecontrol is done in MIMO fashion where the top and bottom tem-peratures are controlled simultaneously by manipulating thereboiler and reflux flow rates. The top and bottom compositionsare monitored by the use of the forward neural network model asthe estimators to ensure that the compositions regulate closely totheir set point values. There are 3 types of control strategiesimplemented for the control strategies, which are the IMC, DIC andthe conventional PID controller for comparison purposes.

4.2.1. Set point changesFigs. 10 and 11 show the fluctuations for the top and bottom

temperatures which are due to the set point changes. First the toptemperature is increased from 30 °C to 58 °C while the bottomtemperature is increased from 40 °C to 137 °C. The starting pointfor the top temperature is 30 °C and for bottom temperature is40 °C where these starting point temperatures are based on thenominal temperatures used in the industry. The one step predic-tion is used since this is an inverse model control based strategythat demands fast control action, without the need for multiplepredictions as in the optmization method for the top and bottomtemperatures. It can be seen that the IMC and DIC show similartrend with small error, no overshoot and fast settling times to theset point of about 200 min. The IMC and DIC method also give lessfluctuations for step up tracking of the set point. The fluctuationsduring step up for the conventional PID controller give unac-ceptable results because it exhibits very large overshoot with smalldecay ratio while the settling time for PID is also larger comparedto the IMC and DIC methods. The PID controller also gives offset

of a debutanizer column using equation based artificial neuralttp://dx.doi.org/10.1016/j.neucom.2016.02.026i

Fig. 14. Control of top temperature with disturbances in feed temperature.

Bottom temperature disturbances

120

125

130

135

140

145

150

0 50 100 150 200 250 300 350

Time (min)

Tem

pera

ture

(0 C

)

pid imc dic

Fig. 15. Control of bottom temperature with disturbances in feed temperature.

Table 7Controller performance during disturbance changes.

IMC eq DIC eq PID

IAE top 817.21 836.95 1736.30IAE bottom 2811.80 2876.00 7891.20

ISE top 6.02Eþ03 6.63Eþ03 3.37Eþ04ISE bottom 1.14Eþ05 1.23Eþ05 1.75Eþ06

ITAE top 7.78Eþ04 7.90Eþ04 1.78Eþ05ITAE bottom 1.28Eþ05 1.30Eþ05 4.64Eþ05

N.M. Ramli et al. / Neurocomputing ∎ (∎∎∎∎) ∎∎∎–∎∎∎12

for the set point changes applied and Table 5 shows the parameterof the PID controller which is obtained from the normal ZieglerNichols method with fine tuning [11]. Table 6 summarizes theperformance of these controllers to control the top and bottomtemperatures. In terms of the results, the IMC controller gives thebest performance as the IAE, ISE values and ITAE values are thesmallest compared to the other controllers. Figs. 12 and 13 alsoshow the fluctuation of the manipulated variable to control the topand bottom temperatures for the neural network and PID con-trollers respectively . The neural network based controller is ableto predict the manipulated variable for reboiler and reflux more

Please cite this article as: N.M. Ramli, et al., Multivariable control of a debutanizer column using equation based artificial neuralnetwork model inverse control strategies, Neurocomputing (2016), http://dx.doi.org/10.1016/j.neucom.2016.02.026i

Manipulated variable top temperature disturbances

20

20.5

21

21.5

22

22.5

23

0 50 100 150 200 250 300 350

Time (min)

MV

(m3/

hr)

0

10

20

30

40

50

60

70

MV

(m3/

hr)

reflux imc reflux pid

Fig. 16. Manipulated variable top temperature disturbances.

Manipulated variable bottom temperature disturbances

140

140.5

141

141.5

142

142.5

143

143.5

144

0 50 100 150 200 250 300 350Time (min)

MV

(m3/

hr)

0

50

100

150

200

250

300

MV

(m3/

hr)

reboiler imc reboiler pid

Fig. 17. Manipulated variable bottom temperature disturbances.

N.M. Ramli et al. / Neurocomputing ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 13

accurately compared to the PID controller and hence the perfor-mance of these neural network based controllers showbetter performance than the conventional PID method. The fluc-tuations of these manipulated variables for the reboiler and refluxis very important to see how the final control element reacts tothe changes in controller actions and hence on the long termeffect and performance of the control valves. The MV for the IMC isa bit static due to the way it was trained before, step changepattern with constraint maximum and minimum values while thePID had no training and is flexible in its valve movement.

4.2.2. Disturbances testsFigs. 14 and 15 show the fluctuations for the top and bottom

temperatures as a result of the disturbances test. The disturbancesintroduced to the debutanizer column is the feed temperature with amagnitude increase of 10%, which is introduced after 140 min and fixat that value throughout the process onwards. The major controlobjective is to keep the top and bottom temperatures as close aspossible to the set points in spite of fluctuations in the feed tem-peratures which affect the top temperature significantly as can be seenin Fig. 14. The disturbance of the system is chosen as the feed tem-perature because one of the inputs to the neural networkmodel is alsothe feed temperature and hence any change in the magnitude of thefeed temperature will definitely affect the system behavior. Similartrends were observed for the DIC and IMC methods for the top andbottom temperatures as a result of these disturbances. The neural

Please cite this article as: N.M. Ramli, et al., Multivariable controlnetwork model inverse control strategies, Neurocomputing (2016), h

network control performs well compared to the PID controller withsmall overshoot, fast settling time and small error. The PID controllergives unacceptable results as they generate high overshoot, offsets andlarge error. Table 7 shows the performances of these controllers tocontrol the top and bottom temperatures. The results indicate thatIMC based method gives slightly better performance than the DIC andits values for the IAE, ISE and ITAE are the smallest compared to othercontrollers and can be seen by the enlarge partition in Fig. 14. Figs. 16and 17 show the fluctuations of the manipulated variables for thedifferent type of controllers respectively. The neural network is able topredict the manipulated variable for reboiler and reflux accurately ascompared to the PID controller hence giving better performance fromthis approach.

4.2.3. Neural network estimatorThe neural network estimator used in the IMC and DIC method

is to monitor and estimate the top and bottom compositions.Figs. 18 and 19 show the fluctuations for the top and bottomcompositions when there are due to set point changes. For the topcomposition for the neural network controller using the IMC andDIC methods, it can be concluded that the IMC approach showsoptimum result compared to DIC. This is due to, fast settling timeto the required set point for the composition. However both IMCand DIC methods are superior compared to the conventional PIDcontroller which gives large overshoot, large error and longersettling time. For the bottom composition fluctuations, the IMC

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pid imc dic

Top composition

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Com

posi

tion

(mol

e fra

ctio

n)

pid imc dic

0 50 100 150

Time (min)

200 250 300

Fig. 18. Neural network estimator for the top composition with set point changes.

Bottom composition

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

pid imc dic

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Com

posi

tion

(mol

e fra

ctio

n)

pid imc dic

0 50 100 150 200 250 300Time (min)

Fig. 19. Neural network estimator for the bottom composition with set point changes.

Top composition disturbances

0.13

0.132

0.134

0.136

0.138

0.14

0 50 100 150 200 250 300 350

Time (min)

Com

posi

tion

(mol

e fra

ctio

n)

pid imc dic

Fig. 20. Neural network estimate for top composition with disturbances.

N.M. Ramli et al. / Neurocomputing ∎ (∎∎∎∎) ∎∎∎–∎∎∎14

and DIC methods show similar trend as both methods show lessfluctuation compared to that using the PID controller.

Figs. 20 and 21 show the fluctuations for the top and bottomcompositions that are due to the disturbance changes. For the topcomposition for neural network controller for using IMC and DIC

Please cite this article as: N.M. Ramli, et al., Multivariable controlnetwork model inverse control strategies, Neurocomputing (2016), h

method, it can be concluded that the IMC trend shows similarresults to the DIC method but both IMC and DIC methods aresuperior in comparison to the conventional PID controller.The results for PID controller are unacceptable because of the largeovershoot, large error and longer time to settle. For the bottom

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Bottom composition disturbances

0.049

0.05

0.051

0.052

0.053

0.054

0.055

0 50 100 150 200 250 300 350

Tme (min)

Com

posi

tion

(mol

e fra

ctio

n)

pid imc dic

Fig. 21. Neural network estimate for bottom composition with disturbances.

N.M. Ramli et al. / Neurocomputing ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 15

composition fluctuations, the IMC and DIC methods show similartrend but less fluctuations compared to the PID controller.

5. Conclusions

Established control system design techniques rely on the avail-ability of non-linear system models. Multivariable and nonlinear sys-tems such as the debutanizer column must first be modeled using aset of differential equations to describe their behaviors to an assumedstructure of the process. However the resulting control strategy per-formance depends on the accuracy of the model for the debutanizercolumn. There is a high degree of uncertainty about its processbehavior. Hence in this work, with the large amount of online dataavailable, MIMO equation based neural network based controllerswith NARX structure is proposed and developed to control thedebutanizer column top and bottom temperature for set point anddisturbance changes. It is observed that the neural network equationbased method gives good performance and better than the PID con-troller for all the cases. Future work will include the neural networkmodels in an online optimization approach.

Acknowledgment

The authors would like to acknowledge PETRONAS for provid-ing the required data and information for the research. I would liketo acknowledge University Malaya for providing the grant for theresearch (PS107/2010B).

Appendix A

The general equation for the output from the neural networkcan be given as (for a 2 layer network)

y¼ f 2 LW2;1f 1 IW1;1pþb1� �

þb2� �

where f 2 is the activation function in the output layer and f 1is theactivation function in the hidden layer

If the f 2is given by the logsig transfer function, f 2 ¼ 11�expðnÞ, the

equation can be simplified in the form;

y¼T1

T2

" #

¼ 0:68�1:14�1:01�0:48�1:30�1:03 0:02�0:65�0:86 1:29 0:57�0:040:56�0:59 0:87 0:74 0:11 0:29 1:47 0:34 0:35�0:17 0:57�0:04

� �p

Please cite this article as: N.M. Ramli, et al., Multivariable controlnetwork model inverse control strategies, Neurocomputing (2016), h

þ 1:021:34

� �ðA1Þ

If the f 1is given by the logsig transfer function, f 1 ¼ 11�expðnÞ, the

equation can be simplified in the form;

y¼T1

T2

" #

¼ 6:12 0:31 7:47 6:48�0:05 6:43 5:53�0:45 7:46 5:75 5:81 0:1814:64�0:38 15:36 14:48�0:32 15:28 16:12 0:72 15:68 16:06 15:61 0:79

� �p

þ 7:2415:94

� �ðA2Þ

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Nasser Mohamed Ramli is a Ph.D. student in theChemical Engineering Department, Faculty of Engi-neering, University of Malaya. He obtained his bache-lor's degree in chemical engineering from Loughbor-ough University, United Kingdom and his master'sdegree from University of Queensland, Australia. Hisarea of research is in artificial intelligence, processmodeling and control.

Please cite this article as: N.M. Ramli, et al., Multivariable controlnetwork model inverse control strategies, Neurocomputing (2016), h

Prof Mohamed Azlan Hussain joined the Departmentof Chemical Engineering, University of Malaya in 1987as a lecturer and obtained his Ph.D. in Chemical Engi-neering from Imperial College, London in 1996. He is amember of the American Institute of Chemical Engi-neers and British Institute of Chemical Engineers. Atpresent he is holding the post of Professor in thedepartment of chemical Engineering. His main researchinterests are in modeling, process controls, nonlinearcontrol systems analysis and applications of artificialintelligence techniques in engineering systems. He haspublished more than 250 papers in book chapters,

journals and conferences within these areas at present.

He has also publish and edited a book on “Application of Neural Networks andother learning Technologies in Process Engineering” published by Imperial CollegePress in 2001.

Badrul Mohamed Jan, SPE is a researcher and aca-demic lecturer attached to the Department of ChemicalEngineering, University of Malaya, Malaysia. He holds aBS, MS and Ph.D. degrees in petroleum engineeringfrom New Mexico Institute of Mining and Technology.Jan's research areas and interest include the develop-ment of super lightweight completion fluid for under-balance perforation, ultra low interfacial tensionmicroemulsion for enhanced oil recovery, and conver-sion of palm oil mill effluent into super clean fuel fordiesel replacement. He has worked closely withindustry in oil and gas project such as 3M Asia Pacific

and BCI Chemical Corporation. He has also published

numerous technical conference and journal papers. Jan is the deputy director ofUniversity Malaya Center of Innovation & Commercialization. His responsibilitiesinclude providing an environment at the University of Malaya conducive toresearchers bringing their research outputs to a commercialization-ready level.

of a debutanizer column using equation based artificial neuralttp://dx.doi.org/10.1016/j.neucom.2016.02.026i