Operating Conditions and Calculated Parameters
1. Introduction
Esterification is a widely employed reaction in organic process industry. Organic esters are most frequently used as plasticizers, solvents, perfumery, as flavor chemicals and also as precursors in pharmaceutical products. One of the important ester is Citronellyl laurate, a versatile component in flavors and fragrances, which are widely used in the food, beverage, cosmetic and pharmaceutical industries. In industry, the most common ester productions are carried out in batch reactors because this type of reactor is quite flexible and can be adapted to accommodate small production volumes (BarbosaPóvoa, 2007). The mode of operation for a batch esterification reactor is similar to other batch reactor processes where there is no inflow or outflow of reactants or products while the reaction is being carried out. In the batch esterification system, there are various parameters affecting the ester rate of reaction such as different catalysts, solvents, speed of agitation, catalyst loading, temperature, mole ratio, molecular sieve and water activity (Yadav and Lathi, 2005). Control of this reactor is very important in achieving high yields, rates and to reduce side products. Due to its simple structure and easy implementation, 95% of control loops in chemical industries are still using linear controllers such as the conventional Proportional, Integral & Derivative (PID) controllers. However, linear controllers yield satisfactory performance only if the process is operated close to a nominal steadystate or if the process is fairly linear (Liu & Macchietto, 1995). Conversely, batch processes are characterized by limited reaction duration and by nonstationary operating conditions, then nonlinearities may have an important impact on the control problem (Hua et al., 2004). Moreover, the control system must cope with the process variables, as well as facing changing operation conditions, in the presence of unmeasured disturbances. Due to these difficulties, studies of advanced control strategy have received great interests during the past decade. Among the advanced control strategies available, the Model Predictive Control (MPC) has proved to be a good control for batch reactor processes (Foss et al., 1995; Dowd et al., 2001; Costa
NMPC is conceptually similar to its linear counterpart, except that nonlinear dynamic models are used for process prediction and optimization. Even though NMPC has been successfully implemented in a number of applications (Braun et al., 2002; M’sahli et al., 2002; Ozkan et al., 2006; Nagy et al., 2007; Shafiee et al., 2008; Deshpande et al., 2009), there is no common or standard controller for all processes. In other words, NMPC is a unique controller which is meant only for the particular process under consideration. Among the major issues in NMPC development are firstly, the development of a suitable model that can represent the real process and secondly, the choice of the best optimization technique. Recently a number of modeling techniques have gained prominence. In most systems, linear models such as partial least squares (PLS), Auto Regressive with Exogenous inputs (ARX) and Auto Regressive Moving Average with Exogenous inputs (ARMAX) only perform well over a small region of operations. For these reasons, a lot of attention has been directed at identifying nonlinear models such as neural networks, Volterra, Hammerstein, Wiener and NARX model. Among of these models, the NARX model can be considered as an outstanding choice to represent the batch esterification process since it is easier to check the model parameters using the rank of information matrix, covariance matrices or evaluating the model prediction error using a given final prediction error criterion. The NARX model provides a powerful representation for time series analysis, modeling and prediction due to its strength in accommodating the dynamic, complex and nonlinear nature of real time series applications (Harris & Yu, 2007; Mu et al., 2005). Therefore, in this work, a NARX model has been developed and embedded in the NMPC with suitable and efficient optimization algorithm and thus currently, this model is known as NARXMPC.
Citronellyl laurate is synthesized from DLcitronellol and Lauric acid using immobilized
2. Batch esterification reactor
The synthesis of Citronellyl laurate involved an exothermic process where Citronellol reacted with Lauric acid to produce Citronellyl Laurate and water.
The esterification process took place in a batch reactor where the immobilized lipase catalyst was mixed freely in the reactor. A layout of the batch esterification reactor with associated heating and cooling configurations is shown in Fig.2.
Typical operating conditions were 310K and 1 bar. The reactor temperature was controlled by manipulating the water flowrate within the jacket. The reactor’s temperature should not exceed the maximal temperature of 320K, due to the temperature sensitivity of the catalysts (Yadav & Lathi, 2004; Serri et. al., 2006; Zulkeflee & Aziz, 2007). The reactor’s temperature control can be achieved by treating the limitation of the jacket’s flowrate, Fj, which can be viewed as a state of the process and as the constraint control problem. The control strategy proposed in this paper was designed to meet the specifications of the laboratory scale batch reactor at the Control Laboratory of School of Chemical Engineering, University Sains Malaysia, which has a maximum of 0.2 L/min limitation on the jacket’s flowrate. Therefore, the constraint of the jacket’s flowrate will be denoted as F_{jmax}= 0.2 L/min.
The fundamental equations of the mass and energy balances of the process are needed to generate data for empirical model identification. The equations are valid for all
where
The reactor can be described by the following thermal balances (Aziz et al., 2000):
where
Eq. 1  Eq. 10 were simulated using a 4^{th}/5^{th} order of the Runge Kutta method in MATLAB® environment. The model of the batch esterification process was derived under the assumption that the process is perfectly mixed where the concentrations of
Parameters  Units  Values  Parameters  Units  Values 

L mol/s  18.20871 

J/mol K  75.40 
AAl  L mol/s  24.04675 

L  1.5 
Ai  L mol/s  0.319947 

L  0.8 
EAc  J mol/K  105.405 

J/m^{3}  11.648 
EAl  J mol/K  66.093 

kJ  16.73 
Ei  J mol/K  249.944 

  1 
Tji  K  294 

  1 
CpAc  J/mol K  420.53 

J/s m^{2} K  2.857 
CpAl  J/mol K  235.27 

m^{2}  0.077 
CpEs  J/mol K  617.79 

J/mol K  8.314 
3. NARX model
The Nonlinear Autoregressive with Exogenous inputs (NARX) model is characterized by the nonlinear relations between the past inputs, past outputs and the predicted process output and can be delineated by the high order difference equation, as follows:
where
where
where
The Eq. 13 can alternatively be expressed as:
and can be simplified as:
where
Finally, the solution of the above identification problem is represented by
The procedures fora NARX model identification is shown in Fig.3. This model identification process includes:
Identification pretesting: This study is very important in order to choose the important controlled, manipulated and disturbance variables. A preliminary study of the response plots can also gives an idea of the response time and the process gain.
Selection of input signal: The study of input range has to be done, to calculate the maximal possible values of all the input signals so that both inputs and outputs will be within the desired operating conditions range. The selection of input signal would allow theincorporationof additional objectives and constraints, i.e. minimum or maximum input event separations which are desirable for the input signals and the resulting process behavior.
Selection of model order: The important step in estimating NARX models is to choose the model order. The model performance was evaluated by the Means Squared Error (MSE) and Sum Squared Error (SSE).
Model validation: Finally, the model was validated with two sets of validation data which were unseen independent data sets that are not used in NARX model parameter estimation.
The details of the identification of NARX model for the batch esterification can be found at Zulkeflee & Aziz (2008).
4. MPC algorithm
The conceptual structure of MPC is depicted in Fig. 4. The conception of MPC is to obtain the current control action by solving, at each sampling instant, a ﬁnite horizon openloop optimal control problem, using the current state of the plant as the initial state. The desired objective function is minimized within the optimization method and related to an error function based on the differences between the desired and actual output responses. The first optimal input was actually applied to the plant at time t and the remaining optimal inputs were discarded. Meanwhile, at time
A formulation of the MPC online optimization can be as follows:
Where
The above online optimization problem could also include certain constraints. There can be bounds on the input and output variables:
It is clear that the above problem formulation necessitates the prediction of future outputs
In this NARX model, for k step ahead:
The error
The prediction of future outputs:
Substitution of Eq. 35 and Eq. 36 into Eq 31 yields:
Where
The above optimization problem is a nonlinear programming (NLP) which can be solved at each time
In this work, the optimization problem was solved using constrained nonlinear optimization programming (
5. Results
5.1. NARX model identification
The input and output data for the identification of a NARX model have been generated from the validated first principle model. The input and output data used for nonlinear identification are shown in Fig. 6.The minimummaximum range input (0 to 0.2 L/min) under the amplitude constraint was selected in order to achieve the most accurate parameter to determine the ratio of the output parameter. For training data, the inputs signal for jacket flowrate was chosen as multilevel signal. Different orders of NARX models which was a mapping of past inputs (
5.2. NARXMPC
The identified NARX model of the process has been implemented in the MPC algorithm. Agachi et al., (2007) proposed some criteria to select the significant tuning parameters (prediction horizon, P; control horizon, M; penalty weight matrices
Model  Training  Validation1  Validation2  
( 
mse  sse  mse  sse  mse  sse 
0,1  0.0205  6.1654  0.0285  8.5909  0.0254  7.6357 
1,1  0.0202  6.0663  0.0307  9.2556  0.0251  7.5405 
2,1  0.0194  5.8419  0.0392  11.8036  0.0266  8.0157 
1,2  0.0025  0.7512  0.0034  1.0114  0.0059  1.7780 
2,2  0.0026  0.7759  0.0029  0.8639  0.0038  1.1566 
3,2  0.0024  0.7289  0.0035  1.0625  0.0097  2.9141 
2,3  0.0024  0.7143  0.0033  0.9930  0.0064  1.9212 
Tuning Parameter  SSE  Tuning Parameter  SSE 
M=2  424.04 

410.13 
M=3  511.35 

404.94 
M =4  505.26 

386.45 
M=5  509.95 

439.37 
with P= 7; wk= 1; rk= 1  with P= 11; M= 2; 

P =7  424.04 

439.23 
P =10  405.31 

386.45 
P =11  404.94 

407.18 
P =12 
406.06 

410.02 
with M= 2; wk= 1; rk= 1  with P =11; M=2; 
The responses obtained from the NARXMPC and the IMCPID controllers with parameter tuning,
With respect to the conversion of ester, the implementation of the NARXMPC controller led to a higher conversion of Citronellyl laurate (95% conversion) as compared to the IMCPID, with 90% at time=150min (see Fig. 9).Hence, it has been proven that the NARXMPC is far better thanthe IMCPID control scheme.
With a view to setpoint changing (see Fig. 10), the responses of the NARXMPC and IMCPID for setpoint change have been varied from 310K to 315K at t=25min. The NARXMPC was found to drive the output response faster than the IMCPID controller with settling time, t= 45min and had shown no overshoot response with SSE value = 352.17.On the other hand, the limitation of input constraints for IMCPID was evidenced in the poor output response with some overshoot and longer settling time, t= 60min (SSE=391.78). These results showed that NARXMPC response controller had managed to cope with the setpoint change better than the IMCPID controllers.
Fig. 11 shows the NARXMPC and the IMCPID responses for 10% load change (jacket temperature) from the nominal value at t=25min. The NARXMPC was found to drive the output response faster than the IMCPID controller. As can be seen in the lower axes of Fig 9, the input variable response for the IMCPID had varied extremely as compared to the input variable from NARXMPC. From the results, it was concluded that the NARXMPC controller with SSE=10.80 was able to reject the effect of disturbance better than the IMCPID with SSE=32.94.
The performance of the NARXMPC and the IMCPID controllers was also evaluated under a robustness test associated with a model parameter mismatch condition. The tests were;
Test 1: A 30% increase for the heat of reaction, from 16.73 KJ to 21.75 KJ. It represented a change in the operating conditions that could be caused by a behavioral phase of the system.
Test 2: Reduction of heat transfer coefficient from 2.857 J/s m^{2} K to 2.143 J/s m^{2} K, which was a 25 % decrease. This test simulated a change in heat transfer that could be expected due to the fouling of the heat transfer surfaces.
Test 3: A 50% decrease of the inhibition activation energy, from 249.94 J mol/K to 124.97 J mol/K. This test representeda change in the rate of reaction that could be expected due to the deactivation of catalyst.
Test 4: Simultaneous changes in heat of reaction, heat transfer coefficient and inhibition activation energy based on previous tests. This test represented the realistic operation of an actual reactive batch reactor process which would involve more than one input variable changes at one time.
Fig.12 Fig.15 have shown the comparison of both IMCPID and NARXMPC control scheme’s response for the reactor temperature and their respective manipulated variable action for robustness test 1 to test 4 severally. As can be seen in Fig. 12 Fig. 15, in all tests, the time required for the IMCPID controllers to track the setpoint is greater compared to the NARXMPC controller. Nevertheless, NARXMPC still shows good profile of manipulated variable, maintaining its good performance. The SSE values for the entire robustness test are summarized in Table 4. These SSE values shows that both controllers manage to compensate with the robustness. However, the error values indicated that the NARXMPC still gives better performance compared to the both IMCPID controllers.
Controller  Test 1  Test 2  Test 3  Test 4 
NARXMPC  415.89  405.37  457.21  481.72 
IMCPID  546.64  521.47  547.13  593.46 
6. Conclusion
In this work, the NARXMPC controller for the Batch Citronellyl Laurate Esterification Reactor has been developed. The validated first principle model was used as a process model to generate data required for NARX model identification. The NARX model with
Acknowledgments
The authors wish to acknowledge the financial support by Universiti Sains Malaysia (USM), for the research funding through the Research University Grant scholarship and Ministry of Science, Technology and Innovation, Malaysia (MOSTI) for the scholarship for the first author.
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