Intelligent severity control stabilizes ethylene crackers

The solution was provided by the East China University of Science and Technology, which has successfully completed regular control for cracking furnaces and key columns in five ethylene plants owned by Sinopec.¹ This project started in March 2007 and completed in December 2008. It covered all 11 liquid furnaces dealing with four kinds of feedstocks.

Based on the successful application in Sinopec Yangzi Petrochemical Co., implementation of the solutions in Sinopec's Qilu and Shanghai ethylene plants is now under way.

Process

SYPC has increased its ethylene capacity from the original design in 1987 to about 800,000 tpy as a result of continuous revamping.

There are 14 cracking furnaces. Seven of them are Lummus SRT furnaces, 4 are Sinopec-Lummus furnaces, and 3 are modified KTI GK furnaces. Eleven of them deal with liquid feedstock, including light naphtha and heavy naphtha, hydrotreated vacuum gas oil, and atmospheric gas oil. Three of them handle gas feedstock, such as ethane and LPG.

Real time feed compositional data are unavailable except for the density.

The Honeywell TPS DCS system provides all the typical operating data of the furnace and transfer line exchanger, including feed rate, steam flow rate, coil inlet/outlet temperature (CIT/COT), and coil outlet pressure in the furnace, and the temperature and pressure at the TLE outlet.

The cracked-gas components including H₂, CH₄, C₂H₄, C₂H₆, C₃H₆, and C₃H₈ are measured by six on line analyzers, each shared by two liquid furnaces. For one furnace, the time delay of the cracked-gas component value is around 15 min.

Regular controls like the COT control, COT balancing control, and throughput control are in good service. More details on the process and control are available in a previous article (OGJ, Jan. 3, 2011, p. 96).

Objective

Cracking-severity control has been installed in one furnace of SYPC but deteriorated into disuse partially due to the high maintenance cost of analyzer and frequent feed composition changes. The project failure in one furnace made the managers reluctant to install the severity-control project for the second time. Then all the effluent analyzers became less important and received little attention for a long time.

The severity varied with the feed composition changes under COT control. The inferred PER followed well with the analyzed PER (Fig. 2).

Although COT has been used to express severity for decades since the plant was installed, it cannot completely replace cracking severity. As the feed composition changes or gradual coking proceeds, cracking-severity changes even if the COT is a controlled invariant (Fig. 2). On the other hand, unlike the effluent analyzer, it is difficult to detect coil outlet thermometer problems because of the drift or mounting deficiency.

After discussions and evaluations of potential benefits and challenges, plant management decided to install severity control.

The primary objective was to stabilize the effluent composition from a given feedstock without adversely affecting the run length. The effluent composition is determined exclusively by severity and selectivity. Since the steam-to-hydrocarbon ratio is normally fixed in plant operation, which means the selectivity is almost fixed, the effluent composition can only be affected by the severity. By reducing the standard deviation of the cracking severity within a defined limit, management expected changes to the downstream recovery section to be minimized.

Another objective was to make full use of the on line analyzer by integrating it into severity control. The improvement in analyzer performance, in turn, would benefit not only this quality control project, but also the severity optimization project in the future.

Solution

The feed composition, operating conditions, and effluent composition of the cracking furnace are the three key factors for cracking-severity control. The first two were used to generate a predicted cracking-severity value, and the last one served as the necessary feedback information of the real cracking-severity value.^1-2

Fig. 1 shows an overview of control strategies implemented on the ethylene crackers. It is a typical cascade control scheme. The primary control variable is the severity and the secondary control variable is the COT, which is also the manipulated variable for severity control.

The process data, including feed density, operating data of the cracking furnace, and TLE, first go through the data verification module to check validity. Then a specific inferential model is selected based on feed type, and it generates the predicted severity value.

Meanwhile, the effluent analyzer goes through a rule-based fault diagnosis expert system. The model is biased by the analyzer measurement only if there is no fault sign for the effluent analyzer. Then the biased result is filtered to serve as the process value of the severity proportional integral and derivative (PID) controller.

The COT SP (set point) processor constrained the movement of COT SP in a range specified by the process engineer. This COT SP range is specified based on the weekly or daily measured tube metal temperature (TMT), the TLE outlet temperature, and the desired run length. By restricting the COT SP limit, the severity control will not adversely affect the furnace run length.

Any detected abnormal situations, either in the process data or the analyzer measurements, will be presented to the operators through the user interface embedded in the operator console. Furthermore, the possible analyzer fault reason is provided, which aims to reduce the restoration time as much as possible.

The effective severity control depends on four elements: basic control, severity inferential model, on line analytical system, and its fault-diagnosis expert system.

After years of endeavor by technical personnel of the ECUST and the ethylene plant, the basic control has been working smoothly. Hence, the effective severity control depends mainly on the inferential model and the analytical system.

Inferential models

The inferential models were designed to provide real-time and steady prediction for the severity. Since the on line gas chromatographs are normally shared by two furnaces and the analytical system is subjected to frequent faults, the inferential model would enable the severity control to hold the severity during analyzer sample intervals and fault recovery periods. Besides, the steady estimation by the inferential model can cross check the analyzer results.

A total of 23 inferential models were developed for all 11 liquid furnaces, each of which can deal with one to three kinds of feedstock. One model deals with one feedstock of one furnace.

The inferential models were developed based on the well established neural network tool. The performance of the NN model depends on the process data collection and processing, in-depth process knowledge, and skilled manipulation of NN. Fig. 3 shows four major stages to develop the NN-based inferential model: data collection, data cleaning, model establishment, and model evaluation.

Data collection for each furnace began from the project's preliminary design phase. Normally, each model requires more than 4 months of historical data at sampling frequency of 1 min. The collected data were checked by both project engineers and plant engineers, and those data points during nonrepresentative operation or instrument failure were marked.

In the data cleaning stage, the process data and analyzer samples at different times were synchronized first. Since the dead time was not always the same for every analyzer, the synchronization was done manually. Then the marked data and other bad data can be removed instead of before synchronization, so as not to deteriorate the time sequence. The gross error and random error were detected and removed by statistical and filtering methods.

The inputs for the inferential model include the COT, feed flow rate, steam-hydrocarbon ratio, and feed density, etc. They were chosen based on variable availability and reliability. Although the COP has great impact on the effluent composition and severity, it was not chosen as the input because the measurement are unreliable, possibly resulting from low instrument accuracy and the high temperature environment at the measuring point.

The model output can be propylene-ethylene ratio (PER) and methane-propylene ratio (MPR). Plant personnel always like the PER because it contains the ethylene component that is their major concern. Plant data trending, however, showed that PER was insensitive to the input variables for heavy feedstock, while MPR showed reasonable sensitivity. Therefore, PER was chosen as the severity for LNAP and HNAP and MPR for HVGO and AGO.

The weights and hidden nodes number were determined by in-house software. Evaluating the model performance considered both the maximum error and mean square error. Testing the model considered tracking directionality more important than accuracy.

Once a model with satisfactory performance was developed, the model information was added into the DCS program with control language for on line tracking analysis.

If the on line prediction performance was acceptable, then the model can be configured to the model bias module. The model was biased by analyzer results, the reliability of which depends on the analytical system maintenance and fault diagnosis system.

Analytical maintenance

The analytical system consists of sampling, pretreatment, and GC analysis. Sampling and pretreatment provide representative and clean lighter hydrocarbons from the hot cracked gas by removing the water, solids, and high boiling compounds. With the help of the carrier gases, N2 and H₂, the GC analyzed components from hydrogen to propane.

Any problem in the design, installation, equipment, and maintenance can lead to analysis error. In most cases, the first two processes yield more problems than GC. The liquid carryover may lead to GC column contamination, and plugging may result in no sample gas for GC.

At the earlier stage of this project, SYPC set up a special maintenance group to smooth away key problems and optimize the process and parameters in the analytical system, especially for the samplers and pretreatment. Following are the key improvements:

• Rectification of faulty equipment and improper installation.

• Optimization of the sample flow and temperature controller's set point in the sampler.

• Preliminary pretreatment added to remove the oil and water carried with samples.

• Heat tracing added for the pretreatment before injection to GC.

• Rules made for preventive maintenance of the analytical system.

• DCS data points established for corresponding compositions.

This work laid a solid foundation for the severity-control project. Occasional analysis errors may still occur, however, and because they are part of the closed control loop, it is vital to ensure that only correct analysis results be used for model correction.

Analytical diagnosis

A rule-based analytical diagnosis expert system for each furnace effluent analysis was developed. The rules include operation and maintenance experience, characteristics of the analytical instrument, and pyrolysis mechanism.

Conducting spatial and temporal correlation analysis on the operating conditions and the compositional results collected identified the analytical fault and its possible reason. The basic principle was to ensure analysis reliability, even at the cost of ignoring a good analysis.

Once a fault is identified, a fault flag and warning will be generated, which disappear only when the fault has been repaired or operators have confirmed. The warnings and corresponding indicators displayed on the DCS screen will shorten the restoration time.

The fault flag will also change the model's correction logic. Normally the severity value is a combination of the inferred value and a moving-window error between inference and analysis. When the failure flag is triggered, the moving-window error will be replaced by a cumulated average error before the analytical fault. Therefore, the severity controller can still work properly during the analytical fault recovery period, earning more on line time.

Realization

Inferential models and analytical fault diagnosis for each furnace were embedded in the DCS with a control language. The control logics were realized by logic point configuration in the DCS. No additional hardware was needed.

This is the second of 5 pages of the panel operator's severity-control display (Fig. 4).

User interface for operators was created based on the Picture Editor in the DCS. Fig. 4 shows the second page of the panel operator's severity control 5-page display set. It consists of four parts: the preconditions for severity control application, key variable summary for monitoring, severity controller module, and on line analysis status.

The user interfaces were well received by operators for operational ease and security consideration. Once the constraints of COT SP have been confirmed and severity control enabled, the severity controller will be put on smoothly. If any abnormal situations occur, e.g., COT control failure or analytical fault, corresponding warnings will be provided.

Controller performance

The severity-control system has been in continuous use since the end of 2008. An investigation by Sinopec showed that the average service factor was nearly 95% in 2009.³

Many factors contributed to the controllers' longevity, including managers' commitment, maintainers' efforts in analytical system maintenance, operators' active participation and valuable suggestions, comprehensive analytical fault diagnosis, and well behaving basic APCs.

The standard deviation of the PER has been reduced by about 60%, based on a comparison of two 20-day run test of naphtha pyrolysis.

Adjusting COT during feed composition variation stabilized the severity (Fig. 5).

Fig. 5 shows the 96-hr performance of the PER after implementation. Much smoother severity can be observed in the face of feed change (compared with Fig. 2).

With use of the severity control, the NN-based inferential model can easily sense the feed composition change reflected by feed density. To stabilize the severity against the feed variation, the COT was forced to increase until it reached the up boundary constraint of the furnace's run length. The PER was almost constant when COT was adjustable within the boundaries and steadily went up a little after the COT reached its up boundary.

With more trust on the system by operators and much wider range of the COT, the application will offer more stabilized severity and gain more benefits.

Benefits

The estimated quantifiable benefit is $2.6 million/year and the payback period is less than 6 weeks. Weekly payback for the severity control was calculated as $ returned/week/$ invested.

Benefits are achieved by reduction in severity variance, increase in desirable products, and longevity of controllers.

We could not readily quantify a few additional benefits such as high-quality inferential control variables for severity (Fig. 2).

Another intangible benefit is that the consistent cracked product would stabilize the downstream separation section, allowing it to run closer to constraints. Moreover, it provides a foundation for the severity optimization project in future.

Acknowledgment

This work was supported by National Natural Science Funds for Distinguished Young Scholars, National High-Tech Research and Development Program of China, Shanghai Key Technologies R&D Program. OGJ

References

1. Qian, F., et al., "Intelligent severity control technique for cracking furnaces in ethylene plant," CN1686973, October 2005 (Chinese patent).

2. Wang, Z.L., et al., "Intelligent control technique for ethylene plant," Chemical Industry and Engineering Progress, December 2006, p. 1454 (in Chinese).

3. Ji, W.Y., Zhao, X.J., "Review of Sinopec's ethylene production in 2009," Ethylene Industry, April 2010, p. 1 (in Chinese).

The authors

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