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한국과학기술원 화학공학과

Acetylene Converter Modeling via Neural Network

Eunjung Joo, Sanggyu Lee  and Sunwon Park

Dept. of Chem. Eng., KAIST

Introduction

Acetylene is a by-product of the cracking of ethane up to vacuum gas oil in the furnace area. As the cracking temperature of the furnaces increases, the acetylene content increases. The reverse is also true. □Tailend□ clean-up is the purification procedure where the acetylene is selectively hydrogenated in the concentrated mono-olefin streams after the major fractionation steps have been completed(de-ethanizer overhead) and just prior to the final separation into the product olefin. Operating parameters such as temperature, [eqn: H_{2}/C_{2}H_{2}] molar ratio and CO level in the feed, along with inlet [eqn: H_{2}/C_{2}H_{2}] concentration and reactor space velocity are all critical to converter performance. The effect of each one of these variables on performance is discussed as well as their interrelationships.

Acetylene Converter Process Overview

In the naphtha cracking process, acetylene is formed as a byproduct, which is a harmful contaminant in polymer grade ethylene. So the removal of acetylene is a key step in the manufacture of ethylene. Although the purity of product would be improved by this reaction, the ethylene gain would not be improved as expected because to get and maintain the optimal reactor condition is not easy at all. So for the purpose of improving the operability and the ethylene yield, the model based optimization would be needed. Because a higher concentration of acetylene would affect the subsequent process signigicantly, the concentration of acetylene should be lowered by about 2-3ppm in the product stream. To remove acetylene down to the required level most effectively, selective hydrogenation over palladium catalysts in a multi-bed adiabatic reactor is used in most commercial plants. According to the CO content in feed stream, the number of bed is determined. If the inlet CO content is lower than 0.7 mol%, 1 bed reactor is sufficient, if that is in the range from 0.7 to 1.7 mol%, 2 bed reactor is suggested, and if greater than 1.7 mol%, then 3 bed is better to operate safely.

The acetylene converter unit can be located either before the cold-train or after the cold-train in the ethylene purification section. When the acetylene converter is located before cold-train where hydrogen is removed from the cracked gas, it is called □front-end configuration□. In early days, the front-end configuration prevailed, but nowadays the tail-end configuration is more popular as the catalyst have been greatly advanced and  additionally the target purity of ethylene have been increased considerably. So the tail-end configuration is considered in this work.

The detail schematic diagram of the reactor is shown in fig.1.

그림  ACU schematic diagram.

Selective Hydrogenation

In the acetylene converter, there are two main competing hydrogenation reactions: one is acetylene to ethylene, the other is ethylene to ethane. The ethylene forming reaction is the desired reaction and the ethane forming reaction is highly undesired side reaction. They are both exothermic reactions:

[eqn: C_{2}H_{2}+H_{2} &-> C_{2}H_{4} ~~~&Delta H_{298K}=-172 MJ/kmol # C_{2} H_{4} + H_{2} &->C_{2} H_{6} ~~~~~&Delta H_{298K} = -137 MJ/kmol]

These reactions are highly affected by some operating conditions, for example,temperature, [eqn: H_{2} / C_{2}H_{2}] ratio, and CO content. To obtain maximum ethylene gain, these operating variables should be adjusted pertinently.

In addition to these, there are other side reactions forming c4-cut or green oil and hydrocarbon cracking reactions at higher temperature.

[eqn: C_{2}H_{2}+nC_{2}H_{2}+H_{2} -> C_{2n+2}H_{2n+4}]

But the effect is not great compared to that of the hydrogenation reactions, so those effects will not be considered.

Neural Network Modeling

When the system under consideration is very difficult to identify or complex to model mathematically, the Neural Network modeling is very attractive if the input/output data can be easily obtained. Figure 2 shows the basic structure of the artificial neural network based on a multilayer feedforward architecture. The input layer simply transmits the input variables to the next layer without any processing. The hidden or output layers receive the weighted sum of all inputs in the previous layer. In the final layer, the difference between the target value and the calculated output, or the error signal is propagated back through the hidden and input layers modifying each weight proportionally to the degree in which it contributed to the error. Once trained and tested, the knowledge  is stored in the network through the weight distribution, which represents the relationship between inputs and outputs.

그림  Multilayer feedforward  neural network.

In this work, the available data sets were divided into the training set for building the neural network model and testing set for the testing its performance.

Results and Discussion

The selection of the appropriate set of input/ourput variables is very important for modeling a process via neural network. In ACU the catalyst deactivation is most difficult to describe because of its time varying nature.

The catalyst activity can describe the dynamics of the catalyst pertinently but it cannot be obtained directly from the process, so for the purpose of future on-line adaptation, the bed temperature difference was used. As shown the reactor in Fig. 3, the trend of the activity change is very similar to that of △T of bed. So the △T of each bed is selected as an input variable. And other variables such as feedrate, hydrogen to hydrocarbon ratio, CO moderator content, and feed inlet temperature were also selected as input variables of the model of this unit. Also the selectivity that is, the mole ratio between the produced [eqn: C_2 H_4] over the reacted [eqn: C_2 H_2].

To determine the neural network structure, the effect of the number of hidden nodes was evaluated and the optimal number of hidden nodes were determined to be 3 or 5 as shown in Fig. 4. The learning rate [eqn: eta] = 0.06 and the momentum [eqn: alpha] = 0.09 were chosen. After building the NN model, the recall results were simulated. The model showed sufficiently good performance and it can be concluded that the model has the potential to be used as an optimization tool.

Conclusion

For the purpose of improving the productivity of the acetylene converter in the naphtha cracking unit, a neural Network model was developed. It is much easier to develop the neural network model than the first principle model. This model can be applied to the optimization of the acetylene converter.

그림  The number of hidden nodes vs. the prediction error.

그림  The similarity between activity and △T.

그림  The results of recall error.

Acknowledgment

Financial support from the Korea Science and Engineering Foundation through

the Automation Research Center at Postech is gratefully acknowledged. This

research was also supported by Korea Research Foundation.