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UTILIZATION OF NEURAL NETWORKS AS SOFT SENSORS TO MONITOR EMULSION POLYMERIZATION REACTIONS (AVERAGE PARTICLE DIAMETER AND CONVERSION) P. H. H. ARAÚJO, C. SAYER, J. C. DE LA CAL#, J. M. ASUA#, E. L. LIMA, and J. C. PINTO* Programa de Engenharia Química / COPPE - Universidade Federal do Rio de Janeiro Cidade Universitária - CP: 68502 - CEP 21945-970 Rio de Janeiro - RJ - Brazil Keywords: neural network, soft sensors, emulsion polymerization, particle size, conversion. Abstract To properly control emulsion polymerization reactions in a closed loop procedure, it is necessary to monitor important process variables, as conversion and average particle diameter. Conversion can be measured or inferred on-line by densimetry, gas chromatography, or calorimetry, but many industrial plants do not have the necessary equipment. Monitoring particle size on-line during emulsion polymerization reactions is still much more complex. Up to date, there are very few equipment available to measure this variable on-line, but only for lab scale application. Due to monitoring difficulties and the requirement of quite complex mathematical models presenting high computational costs that are inadequate for on-line simulation and control, artificial neural networks are winning more importance in the polymer reaction engineering area. Experimental data of vinyl acetate and Veova10 emulsion copolymerization reactions with 55% solids content carried out in a continuous loop reactor were used to train and to validate these networks. In order to improve robustness stacked neural networks were used. The networks were trained with experimental data of several reactions capturing a large range of operational conditions and were validated with experimental data of other reactions presenting a quite complex dynamic behavior as strong oscillations in average particle diameter and conversion. The results obtained by the neural networks demonstrated a good agreement with experimental data.

1. Introduction Emulsion polymerization is a process normally used to obtain polymers by free radical polymerizations in dispersed media of great industrial interest. One of the main advantages of this process, due to system compartimentalization, is the possibility to obtain simultaneously polymers of high molecular weight at high polymerization rates. The low viscosity of the latex allows higher heat dispersion and easier reaction temperature control. Using water as dispersion media, latex presents advantages, at environment and economic points of view, over polymers that use expensive and contaminant organic solvents. At the other side, the main disadvantages of this polymerization technique comes from its multiphase characteristic and the complexity of its mechanisms, that could be translated in higher difficulties in modeling, monitoring, and controlling product characteristics. To properly control the reactions in a closed loop procedure, it is necessary to monitor (on-line measurements) very important process variables, as monomer conversion and average particle diameter. The conversion can be measured or inferred on-line by densimetry, gas chromatography, or calorimetry, but many industrial plants do not have the necessary equipment to monitor the reactions on-line. Monitoring particle size on-line during emulsion polymerization reactions is still much more complex. Up to date, there are very few equipment available to measure this variable on-line, but only for lab scale application. There are some attempts to measure

particle size on-line with exclusion size chromatography and with light-scattering but these methods still present many problems as the development of an equipment for the automatic dilution of the sample (Dimitratos et al., 1994; Kammona et al., 1999; Liotta et al., 1998). Therefore, the possibility of inferring this variable by a neural network is extremely relevant. To study the viability of the utilization of neural networks as soft sensors to monitor these variables during emulsion polymerization reactions, neural networks with the following inputs were elaborated: reaction temperature; jacket temperature; holdup fraction; total initiator fraction (reacted and not reacted); emulsifier fraction. It is important to note that all these inputs are either easy to measure (reaction and jacket temperature) or are previously known (holdup fraction, total initiator fraction; emulsifier fraction). The neural network outputs were the following: conversion and average particle diameter. Experimental data of vinyl acetate and Veova10 emulsion copolymerization reactions with 55% solids content carried out in a continuous loop reactor were used to train and to validate these neural networks. The networks were trained with experimental data of several reactions capturing a large range of operation conditions and were validated with experimental data of other reactions presenting a quite complex dynamic behavior as strong oscillations in average particle diameter and conversion. The results obtained by the neural networks demonstrated a good agreement with experimental data.

* Author to whom correspondence should be addressed. # Institute for Polymer Materials, Facultad de Ciencias Químicas, Univ. of the Basque Country, 20080 San Sebastián, Spain 525

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2. Average Particle Size Experimental measurements of the average particle size are usually made with light-scattering techniques in which the samples need to be very dilute. Up to this moment this sample preparation step still prevents the usage of these techniques for on-line particle size measurements. Therefore this variable was monitored by means of on-line inference. Using a mathematical model to infer particle size evolution is not a simple task especially in continuos reactors. Under certain specific conditions, emulsion polymerization in continuous loop reactors presents an oscillatory behavior of the particle number due to intermittent nucleation. This often results in quite complex multimodal particle size distributions (PSD) as observed by Araújo et al., 1999. This phenomenon resembles that found in emulsion polymerizations carried out in CSTRs (Kiparissides et al., 1980, Schork and Ray, 1987 and Ohmura et al., 1998). The amplitude of the oscillations diminishes along reaction time until a “pseudo” steady state is often reached. Modeling this kind of oscillatory behavior is still a quite challenging aspect of emulsion polymerization. Mathematical models that do not include the computation of the whole PSD and therefore do not require very long computational times have shown to be inadequate to describe this oscillatory behavior of the particle number (Araújo et al., 1998). On the other hand, models that include the computation of the PSD (Rawlings and Ray, 1988, Coen et al., 1998 and Araújo et al., 2000) require prohibitively long computational times, what makes them absolutely inadequate for on-line inference and control. 3. Neural Networks Due to monitoring difficulties and the requirement of quite complex mathematical models presenting high computational costs that are inadequate for on-line simulation and control, artificial neural networks are winning more importance in the polymer reaction engineering area. The multi-layer feed-forward neural network, that is used in this work, is the most common network structure (Chan and Nascimento, 1994, Tsen et al., 1996, Ni and Hunkeler, 1997, Sayer, et al., 1997, Nascimento and Giudici, 1998, Nascimento et al., 1999), performing a non-linear transformation of the input data to approximate the output data. The network input layer is defined by the network input data, the network output layer is defined by the network output data, and the topology of feed-forward network is defined by the hidden network layer(s) (number of hidden layers and number of neurons in this hidden layers). To train the neural networks presented in this work the commonly used back propagation algorithm was applied. In order to improve the robustness stacked neural networks (aggregation of several neural networks, Fig.1) were used in this work. This approach was recently applied to polymerization reactions by Zhang et al., 1997, 1998 and 1999. In this stacked neural

network procedure, several neural networks with different structures (different number of hidden layers and different number of neurons in the hidden layers) were trained with several different training data sets. The global outputs of the stacked neural network were given by weighted combinations of the individual networks. In this work tests were made using equal weights and also using weights computed as a function of the individual network training errors (computed as mean square errors).

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Fig. 1. Stacked neural network representation. In order to enhance the ability to model dynamic polymerization systems (Meert and Catfolis, 1994) and to allow the implementation of predictive control strategies (Vega et al., 1997) used recurrent neural networks, in which the outputs of the nodes of the network were fed back into the network itself. In this work tests were made with dynamic neural networks using delayed experimental conversion measurements as network inputs. 4. Experimental Procedure Emulsion polymerization in continuous loop reactors presents several advantages when compared to CSTRs, such as higher operational flexibility, reduced losses during the start-up and shut-down due to the smaller volume, and easier temperature control, allowing the achievement of higher conversions in shorter residence times. The main commercial polymers produced in loop reactors are vinyl acetate homopolymers and copolymers for paint and adhesive industries. Despite of the advantages presented by loop reactors and their industrial applications, only few articles have been published about this subject (Geddes, 1983, Abad et al., 1994 and 1995a,b, Araújo et al., 1999). Figure 2 presents a schematic presentation of the continuous loop reactor used in this experimental procedure. product

reactant pump reactant

Fig. 2. Continuous loop reactor.

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Startup procedure in all reactions were performed filling the reactor with water phase and heating until reaction temperature. In all reactions there was used a recycle ratio of 55. According to Abad et al. (1995b), this value of recycle ratio guarantees that the behavior of flux inside this loop reactor is very similar to a perfectly backmixed CSTR, that is the same condition of an industrial continuous loop reactor. The reactants were fed in three different streams: 1) an aqueous solution of emulsifiers, protective colloid and Na2S2O5; 2) the organic phase, constituted of the mixture of monomers Vinyl Acetate, Veova10 and Butyl Acrylate; and 3) an aqueous solution of the initiator (K2S2O8). Table 1 shows the composition of each feed stream, proportions presented are related to reaction RL2 (see Table 2). Feed streams 1 and 2 are pre-emulsified together before entering the reactor. Table 1: Formulation of reaction RL2, based on 100 parts in weight of VA and Veova10.

Mass flow rate (g/min)

   72.0 1.50 2.00 0.10 0.105  81.22

      107.96

Stream 3 Wt. %o    9.81     0.30

0.06 0.003

10.808

The following operational variables were varied throughout the experiments:

error in x

Vinyl acetate Veova10 Butyl acrylate Water Alipal (CO 436) Arkopal (N-230) Hydroxiethyl cellulose Na2S2O5 K2S2O8

Stream 2 Wt. %o 75.0 25.0 1.0

5. Results and Discussion Feedforward neural networks used to compose the stacked neural network presented 5 inputs, 1 or 2 intermediate layers with neurons varying between 2 and 15 in each layer and 2 outputs. The inputs were: reaction temperature (T); jacket temperature (TJ); holdup fraction; total initiator fraction (reacted and not reacted); emulsifier fraction. And the outputs were: conversion and average particle diameter. The largest stacked neural network tested was composed of 14 individual networks. Table 2 shows operational conditions of the reactions that were used to train the neural network.

error in Dp

Stream 1 Wt. %o

5010) acts on the heating resistance and on the valve that adjusts the cold water flow rate to control the jacket temperature. During the reactions samples were taken periodically for off-line analysis. Monomer conversion was measured by gravimetry and average particle diameter was measured by quasi-elastic light scattering (COULTER N4 PLUS) and by disk centrifuge (BI – DCP PARTICLE SIZER, BTC). This second equipment allows the measurement of the whole particle size distribution, but this measurement is also rather time consuming. Depending on the operational conditions, reactions presented an oscillatory behavior of the particle size number due to intermittent nucleation.

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Average reaction temperature; Initiator concentration, keeping constant the molar relation between the redox initiation system (K2S2O8 / Na2S2O5 = 2 / 1); Total stabilizer concentration, keeping constant the mass ratio between ionic emulsifier / non-ionic emulsifier / protective colloid (1.5 / 2 / 0.1). Variables like solid content and mass ratio between monomers (vinyl acetate / Veova10 / butyl acrylate 75 / 25 / 1) were not varied. To control reactor temperature a cascade controller was applied using the following measured temperatures: average reactor temperature (T), composed of 7 independent measurements throughout the loop reactor and temperature of the water circulating through the reactor jacket (TJ). The main controller stores the desired reactor temperature (T.d) and, as a function of the average reactor temperature and of the control algorithm (PI or PID, refer to Table 2), the new value of the desired jacket temperature (TJd) is sent to the secondary controller. This secondary controller (commercial PID, CONATEC

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Fig. 3. Training errors of average particle diameter (Dp) and conversion (x). ( ) individual networks; ( ) stacked networks with weights computed as functions of training errors. Results of two reactions (RL9 and RL5, presented in Table 3) were used to assess the efficiency of this stacked neural network. It is important to mention that although these two reactions were carried out in the same range of operational conditions as the training set reactions, they presented different kinds of behavior. In reaction RL5 used for validation, jacket temperature (TJ) was fixed and reactor temperature (T) was allowed to oscillate. This procedure was not used in any reaction used to train the neural network, in all

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training reactions the desired reactor temperature was fixed (T.d).

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5.1 Comparison between individual and stacked neural networks Figures 3 and 4 present, respectively, training and validation errors of individual and stacked neural networks.

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It might be observed in Fig. 3 and 4 that the best individual network according to the training errors does not always present the smallest validation errors. Using stacked neural networks this effect is pondered leading to more robust results. When stacked networks outputs are computed with differentiated weights (calculated as functions of training errors) results are slightly better than when simple equal weights are used.

0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0

1 2 3 4 5 6 7 8 9 1011121314 of neural networks numbernumber of neural networks

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Fig. 4. Validation errors of average particle diameter (Dp) and conversion (x). ( ) individual networks; ( ) stacked networks with weights computed as functions of training errors.

Table 2: Reactions used to train the neural networks. Reaction RL2 RL4 RL7 RL8 (a)

Residence time (min) 13.3 13.3 13.3 13.3

Recycle ratio Temp.(ºC) 55 55 55 55

60 50 60 60

Emulsifier (a) (Wt. %) 3.6 3.6 3.6 / 7.2 3.6

K2S2O8 (a) (Wt. %) 0.30 0.30 0.30 0.18

Mass ratio VA/Veova10 75 / 25 75 / 25 75 / 25 75 / 25

Control

Emulsifier (a) (Wt. %) 3.6 3.6

K2S2O8 (a) (Wt. %) 0.30 0.30 / 0.18

Mass ratio VA/Veova10 75 / 25 75 / 25

Control

PI PID PID PI

in relation to monomers.

Table 3: Reactions used to test the neural networks. Reaction RL5 RL9 (a)

Residence time (min) 13.3 13.3

0.03 0.02 0.01 1 2 3 4 5 6 7 8 9 1011121314 number of neural networks

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Fig. 4. Validation errors of average particle diameter (Dp) and conversion (x). ( ) individual networks; ( ) stacked networks with weights computed as functions of training errors.

5.2 Simulation results using stacked neural networks Figures 5 and 6 compare experimental and stacked neural network average particle diameter (Dp) and conversion (x) results of reaction RL9. It might be observed that the stacked network was able to represent quite properly the reactor start-up including the initial particle nucleation. The stacked neural network also kept track of conversion results throughout the whole reaction and the mean values around which experimental Dp data oscillated. But the network was not able to represent properly oscillations observed in experimental Dp due to particle renucleations. In reaction RL9, as well as in all training reactions, the desired reactor temperature was fixed (T.d) and the jacket temperature (TJ) was allowed to oscillate. Therefore reactor temperature (T), which is one of the networks inputs, does not contain much information about the reaction heat history and, consequently, about reaction kinetics.

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variable on-line. In this case two previous values of conversion were used, x(t-1) and x(t-2), with, respectively, around 10 and 20 minutes of delay.

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Fig. 5. Comparison between experimental and stacked neural network average particle diameter evolution during reaction RL9. ( ) neural network; ( ) experimental.

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Fig. 7. Comparison between experimental and stacked neural network average particle diameter evolution during reaction RL5. ( ) neural network; ( ) experimental.

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Fig. 6. Comparison between experimental and stacked neural network conversion evolution during reaction RL9. ( ) neural network; ( ) experimental.

5.3 Simulation results using individual dynamic neural networks Considering that on-line conversion measurements presenting delays of 10 minutes are often available (densimetry, gas chromatography or calorimetry), a dynamic neural network was also tested to predict this

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Fig. 8. Comparison between experimental and stacked neural network conversion evolution during reaction RL5. ( ) neural network; ( ) experimental. 1 0.8

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Figures 7 and 8 compare experimental and stacked neural network average particle diameter (Dp) and conversion (x) results of reaction RL5. It might be observed that in this reaction the stacked network was able to represent quite properly the reactor start-up including the initial particle nucleation as well as the pseudo-steady state change around 20 residence times. In this reaction the stacked neural network kept track rather properly of oscillations observed in experimental Dp and x due to particle renucleations. In reaction RL5, opposite to reaction RL9 and the other reactions used to train the networks, jacket temperature (TJ) was fixed and reactor temperature (T) was allowed to oscillate. Therefore reactor temperature (T), which is one of the networks inputs, contains more information than in the other reactions allowing the neural network to track oscillations observed in conversion and average particle diameter.

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Fig. 9. Comparison between experimental and dynamic neural network conversion evolution during reaction RL5. ( ) neural network; ( ) experimental. Figure 9 compares experimental and dynamic neural network conversion (x) results of reaction RL5. It might be observed that this dynamic neural network was able to represent very well experimental results. Unfortunately using dynamic neural networks considering only previous values of conversion did

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present no enhancement in average particle diameter (Dp) predictions. And, as on-line Dp measurements are not available in industry even with rather long time delays the construction of dynamic networks considering experimental previous values of Dp would be rather unrealistic and, therefore, even useless. A way of surrounding this situation is the construction of recursive neural networks that use network predictions of Dp as delayed inlets. But, for the tested examples that present quite complex dynamics, this approach presented rather poor results. 6. Conclusions Neural network models were built in order to be used as soft sensors to monitor conversion (x) and average particle diameter (Dp) evolution during emulsion polymerization reactions. Results of two reactions presenting quite complex dynamic behaviors (RL9 and RL5) were used to assess the efficiency of the neural networks. It is important to mention that although these two reactions were carried out in the same range of operational conditions as the training set reactions, they presented different kinds of behavior. In reaction RL5 used in for validation, jacket temperature (TJ) was fixed and reactor temperature (T) was allowed to oscillate. This procedure was not used in any reaction used to train the neural network. In all training reactions and also in validation reaction RL9 the desired reactor temperature was fixed (T.d). It was observed that the best individual network according to the training errors does not always present the smallest validation errors. Using stacked neural networks this effect is pondered leading to more robust results. The stacked network was able to represent quite properly the reactor start-up including the initial particle nucleation. The stacked neural network also kept track of conversion results throughout the whole reaction and the mean values around which experimental Dp data oscillated. Depending on operational conditions (RL5) the network was also able to represent oscillations observed in experimental Dp and x due to particle renucleations. Considering that on-line conversion measurements presenting delays of 10 minutes are often available (densimetry, gas chromatography or calorimetry), a dynamic neural network was also tested to predict this variable on-line. It was observed that the dynamic neural network was able to represent very well experimental conversion results. Unfortunately this approach may not be applied for the prevision of Dp as, up to this moment, this variable may not be measured on-line in the industrial environment. A way of surrounding this situation is the construction of recursive neural networks that use network predictions of Dp as delayed inlets. But, for the tested examples with quite complex dynamics, this approach presented rather poor results.

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Received September 2, 2001. Accepted for publication June 22, 2001. Recommended by A. Bandoni.

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