Optimizing Refinery Catalytic Reforming Units with the Use of Simple Robust On-line Analyzer Technology

This article uses the example of the catalytic reforming unit generally found in a refinery in order to illustrate the options presently available for using simple, robust on-line analyzers to deliver useful timely and available process stream quality data in advanced process control.

Refinery

Refinery

Measurement Made Easy

This article first considers the background: why optimization of refinery process units is so necessary and so common, and what analytical tools exist to help. The main problem in refining is that, although crude oil refining is a continuous and high-volume process with extremely vital raw-material and energy costs, it is not steady-state.

Crude oil feedstocks differ continuously in quality, cost and availability – while at the same time refinery products and their markets are extremely dynamic in terms of demand, pricing and specifications. This leads to the use of relatively complex whole-refinery linear programming (LP) models in order to manage these changes. Underneath these models, individual process unit Advanced Process Control (APC) packages need to keep the units on target (even though these targets will change) and under control.

The Refinery Naphtha Complex with CCR and Integrated Petrochemical Units

Considering one specific area within the refinery — the naphtha complex, or naphtha conversion area — it is possible to see the interaction between many different process units and streams. Key to these process units and streams is the Catalytic Reformer (CCR) unit. This unit is responsible for taking low-value heavy naphtha from the CDU and converts it, after hydro-treating, into a higher-value high-aromatics, high-octane feedstock.

Note: In this article, CCR is used as a generic in order to indicate a catalytic reforming unit. The arguments presented apply mainly to continuous catalytic regeneration reformers, however, they can also be applied to fixed bed units. The questions are – what alternatives might there be for naphtha processing or sources of CCR naphtha feeds, and what alternative uses exist for the different unit products?

Cost, capacity, quality, value & demand all change continuously.

Cost, capacity, quality, value & demand all change continuously.

The above diagram attempts to present a simplified and idealized view of these scenarios. For instance, the reformate product from the CCR is often directed to the gasoline blending pool as a useful high-octane blend component, but the high octane value of reformate derives from high aromatics (BTX) content. This has alternative uses and, based on the price breaks between blended gasoline product and the aromatics unit, diversion as an aromatics unit feed might be determined. Similarly, the straight-run naphtha from the CDU, generally hydro-treated as CCR feed, might be better used as raw material for the naphtha steam cracker olefins unit, again based on the relative instantaneous profitability of aromatics, gasoline and olefin products.

Optimizing Refinery Catalytic Reforming Units

Measurement at some level is essential to process optimization. Measurement yields information which permits the possibility of control. What form this measurement takes is a somewhat more open question, and one subject to considerable debate between those (chiefly engineers) who like statistics and dislike analyzers, and those (chiefly chemists) who do not trust anything which is not considered to be a directly traceable analytical result.

This leads to different approaches to APC:

APC based on inferential models

  • Use of a number of basic mass-flow, temperature and pressure transmitters
  • Needs laboratory test data in order to calibrate and maintain the inferential quality estimator
  • Needs chemical engineering model of unit

APC based on physical analyzers

  • Use of a number of single-property physical analyzers for direct measurement
  • Needs extensive maintenance, training, calibration and spares stockholding

APC based on advanced analyzers

  • Use of a smaller number of multi-stream multi-property analyzers
  • Generally offers significant improvement in precision, speed and reliability
  • Needs calibration or calibration model development

APC based on actual process stream quality measurements from real analyzers is considered to be superficially attractive but fraught with risk.

Historically this approach was hindered by:

  • High capital cost
  • High life-cycle costs, limited reliability
  • Complex operational requirements (validation, calibration)
  • Large infrastructural requirements for installation

Technical advances have led to:

  • More robust, simpler, lower cost analyzers
  • Extensive range of available technologies
  • Significantly reduced operational and installation demands

The article considers two examples of modern, robust analyzer technologies that have enabled more reliable and easier implementation of APC strategies based on real-time process analytical measurement. Long maintenance intervals, low lifecycle costs Fourier-Transform Near IR (FT-NIR) analyzers have offered one route in order to deal with part of the problem. Chosen wisely, they provide space technology levels of reliability and uptime (quite literally since the technology is normally used in climate sensing satellites).

On-line FT-NIR analyzers presently have an established track record in reliable hydrocarbon stream property measurement (in this case RON and BTX in reformate product and PINA in heavy naphtha feed). The second technology refers to a solid-state electrochemical sensor-based method for observing the hydrogen recycle/net gas stream also critical in CCR operation.

ABB Process FT-NIR analyzer TALYS ASP400-Ex.

ABB Process FT-NIR analyzer TALYS ASP400-Ex.

ABB Process Hydrogen Analyzer HP30.

ABB Process Hydrogen Analyzer HP30.

Typical UOP CCR platforming process unit.

Typical UOP CCR platforming process unit.

Thus, the Catalytic Reforming Unit, whether a CCR, as shown here, or a fixed-bed type, takes a heavy naphtha feed and, by catalytic conversion at reasonably high temperatures but fairly low operating pressure, transforms the naphthenes and paraffins to mainly aromatics.

The resulting product is an aromatics-rich reformate stream, and a hydrogen net gas is produced within the unit and recycled partially.

What choices and issues exist for the operation of this unit? As earlier indicated, the product of the CCR unit is considered to be more than a potential blend-stock for gasoline blending. This is the standard key product, but with different markets, and more complex refineries with extensive heavy oil up-conversion, what were earlier seen as CCR byproducts now become vital and hypothetically attractive economic choices.

Reforming transforms heavy naphtha into:

  • High-octane feedstock for gasoline blending
  • High-aromatics (BTX) feed for petrochemicals
  • High-purity hydrogen suitable for use as hydrocracker make-up gas

CCR unit operation provides a surprisingly huge number of degrees of freedom including severity vs. pressure vs. selectivity, which can all be traded off to:

  • Run for maximum Net Gas
  • Run for maximum catalyst life-time
  • Run for minimum energy usage
  • Run for maximum octane barrels
  • Run for maximum BTX yield

The key operating parameters for the unit will be pressure, severity, catalyst bed temperatures and profiles, which are interlinked and simultaneously affect octane number, aromatics content, yield and BTX spread along with net hydrogen make.

Example operating parameter trade-offs in CCR operation.

Example operating parameter trade-offs in CCR operation.

Calibration dataset, 1st derivative and PLS regression plot for RON.

Calibration dataset, 1st derivative and PLS regression plot for RON.

On-line FT-NIR vs Lab Test Method RON

on-line FT-NIR RON data vs lab test samples

Example validation plot of on-line FT-NIR RON data (Series 1) vs lab test samples (Series 2).

The most standard measurement is octane number monitoring (usually RON) of the reformate product stream as an sign of reactor severity, to which measurement one can easily integrate chemical compositional parameters such as total aromatics %, or discrete components such as toluene %, benzene % and xylenes %.

For illustration, ABB shows a typical RON and aromatics modeling data set, and also the resulting RON calibration model.

Note that the model accuracy (vs lab test) at about 0.2 RON @ 1 sigma is better than the ASTM standard technique reproducibility (R) because of good site laboratory precision. Thus, the on-line FTIR does a better job than an online CFR engine, which would in any case be considerably more expensive on the whole.

This is the main advantage of advanced optical or solid-state devices for process stream quality analysis: better, faster and cheaper data.

The second stream analysis, which may be measured using the same FTIR unit as the one used for the reformate product, is the heavy naphtha feed. Here, the target properties, which greatly impact the CCR unit yield and selectivity, are distillation and PINA.

PLS regression calibration plots for PIONA in Naphtha Feed

PLS regression calibration plots for PIONA in Naphtha Feed.

Naphtha quality differences can arise from variable CDU feedstocks and operation, but also from substitute naphtha feed sources. Where CCR units are operated to have excess catalyst regeneration capacity, then sub-optimum heavy naphtha feeds (for example – from the FCC unit) can be run or mixed with conventional straight-run naphtha, resulting in a lot more dynamic unit envelope.

For the final measurement in this set of real-time on-line process analyzes for unit enhancement, ABB looks at the net gas/ hydrogen recycle stream. Here, the main parameter is just H2 mol %, but it must be measured in the context of a changing background of mixed light hydrocarbons content. Obviously, the net gas recycle stream is not pure hydrogen. It is blended with other light gases recovered in the separator/ recovery stages

This is a noteworthy challenge for conventional technologies like thermal conductivity detection (TCD) that can only handle a limited number of interfering components (not more than two). The solid-state sensor is specific in response to hydrogen and is also protected against possible contaminants such as H2S, and CO by a diffusion membrane, therefore allowing quick hydrogen transport but blocking larger contaminant species.

Optimizing Refinery Catalytic Reforming Units

Summary

In this article, ABB has reviewed the use of robust and simple yet advanced process analyzer technologies, particularly FT-NIR and solid-state sensor-based hydrogen detection, to the most crucial process unit streams in the catalytic reforming unit. ABB has seen that the octane, aromatics, PINA and hydrogen measurements can be done using these comparatively direct analytical techniques, and that this data is reported in nearly real-time (one minute stream cycle time), allowing close integration with unit advanced process control. This allows improved management of unit operational parameters, with a view to improving the production of high-quality reformate and net gas/ hydrogen, with yields and composition better aligned with total refinery and product market demands.

References

1. Mohaddecy, S.R.S; Sadighi, S.; Bahmani, M.; “Optimization of Catalyst Distribution in the Catalytic Naphtha Reformer of Tehran Refinery”, Petroleum & Coal 50 (2), 60–68, 2008

2. Taskar, M.U.; “Modelling and Optimization of a Catalytic Naphtha Reformer” PhD Dissertation, Texas Tech University, USA, May 1996

3. Kavousi, K.; Mokhtarian, N.; “Simulation of the CCR Unit of Isfahan Refinery with PETROSIM Software”, International Science & Investigation Journal 4 (3), 14–33, 2015

4. Poparad, A.; Ellis, B.; Glover, B.; Metro, S.; “Reforming Solutions for Improved Profits in an Up-Down World”, UOP LLC, a Honeywell Company, Des Plaines, Illinois, USA, 2011

5. Robert A. Meyers, Handbook of Petroleum Refining Processes, Third Edition, 2004

6. Chapter 4.1 – UOP Platforming Process

This information has been sourced, reviewed and adapted from materials provided by ABB Analytical Measurements.

For more information on this source, please visit ABB Analytical Measurements.

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