Value of monitoring exchanger networks

Heat exchanger fouling has a direct impact on profitability. Over time, fouling leads to higher energy consumption, higher

maintenance costs, reduced feed rates and shorter intervals between turnarounds. The rela-tionship between fouling and energy becomes more significant when you consider the link between additional fuel gas consumption, higher CO2 emissions and the detrimental impact on a refinery’s energy intensity index (EII). The envi-ronment and the total cost of operation (TCO) are negatively impacted.

Proven energy savings can be realised when the fouling of a crude unit preheat network exchanger can be effectively monitored. Monitoring will determine how fouling in a network changes with time. Crude units see the highest charge rates and the largest temperature increase of any refinery unit,1 so the benefits of a successful monitoring and fouling control programme can be significant.

This article will include a brief review of crude unit heat exchanger fouling mechanisms, how fouling affects energy management costs, and potential solutions.

Fouling mechanismsWhat is fouling? It is the formation of deposits in process equipment that impedes the transfer of heat and increases the resistance to fluid flow. Several physical, operational and chemical factors can combine to form these deposits. Most crude preheat deposits have low thermal conduc-tivity and reduce heat transfer. Fouling can have a substantial economic impact upon a refiner’s profitability when it causes throughput reduc-tions due to hydraulic limits or furnace tube

Laura Copeland Nalco Company

temperature limits. Fouling always leads to energy losses when fuel has to be increased to the furnace to make up for lower crude tempera-tures coming from the fouled crude preheat network.

What causes fouling?There are three main operational factors that lead to fouling: blending crudes, the velocity through the process and crude quality. Changes to any of these factors can lead to a change in fouling throughout the process.

When crudes are blended together, there is the potential for instability that can lead to fouling. If the crudes are processed individually, the foul-ing potential can be different than if two or more crudes are blended together. For example, a refiner could be processing a heavy, low API crude and have very little fouling, but when they blend a light, high API crude with it they see increased fouling in their crude preheat train. The introduction of another type of crude has caused instability in what is normally a stable crude. Preheat train monitoring can be used to support the refiner’s decision-making process when implementing a strategy to prevent fouling deposition due to the incompatibility of crudes.2 Figure 1 shows the results of testing done on the Nalco Fouling Potential Analyzer (FPA), where each crude individually has a lower fouling potential than when they are blended together. The FPA value is the inflection point of each trend, and a lower FPA value equals less stabil-ity. In this example, when crude A is blended with crude B, the stability decreases and, there-fore, there is a higher potential for fouling.

Another operational factor that can cause foul-

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A rigorous exchanger simulation model can be used to calculate the true cost of fouling in crude preheat networks

ing is a change in the velocity through the process. Initially, most heat exchangers are designed and sized to achieve a maximum heat transfer at design throughput conditions. Often these design conditions achieve very low fouling rates due to the high velocity and proper baffle design and spacing. As throughput changes or heat exchang-ers are added or redesigned, velocity changes and the rate of fouling can also change.

Particulates travelling along with the crude have a greater potential to fall out and cause fouling at lower velocities. A proper monitoring programme should take into account the veloci-ties of the various streams, how they are changing with time, and the impact on fouling and temperatures throughout the system. Just as many exchanger networks may encounter a “cleaning” effect from a sudden increase in throughput, they can also experience a “fouling

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event” with a sudden lowering of throughput. A well-monitored system can locate which exchangers tend to foul the most as a result of decreased throughput (velocity).

The third operational factor that can lead to fouling issues throughout the refinery is the quality of the incoming crude. Crude oil can contain asphaltenes and inorganic materials that can contribute to fouling in the system. Asphaltenes are the most common fouling material found in the hot preheat train. They are naturally stabilised by

resins that prevent them from agglomerating, but asphaltenes can easily agglomerate when destabilised and cause fouling. Another type of foulant is exist-ent debris such as sand or sediment carried in the crude oil that may be deposited when stressed by heat. The deposition of inorganic salts can result in fouling if the refinery has no desalting capability or if the desalters are not working prop-erly. Finally, one more potential type of fouling material is poly-

meric gums that can form if a reactive stream is added to the crude oil. Figure 2 shows an exam-ple plot of normalised furnace inlet temperature (NFIT). This shows how a change in incoming crude to a refinery could have a significant impact on fouling. More discussion of NFIT can be found in the next section of this article.

Cost of fouling + cost of fouling control = total cost of operation It is possible to operate a crude preheat to achieve the lowest TCO by calculating the total cost of fouling and the total cost of fouling control. The costs of fouling are all related to the extra fuel burned in the furnace due to the foul-ing layer inhibiting heat to be transferred to the crude. As the fouling increases in the different exchangers, the crude exiting each exchanger leaves at colder and colder temperatures. That

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Figure 2 Example trend of NFIT; circle indicates where a crude change took place that led to increased fouling

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lower temperature crude must be heated up to the fixed furnace exit temperature in order for the refiner to meet their target cut points. This additional fuel due to fouling is difficult to calcu-late without a proper heat exchanger simulator being run on a regular basis.

Refiners will also change the pumparound rates to manipulate the cuts in the atmospheric tower for maximum profitability. This will also add or delete heat from the preheat, but this is not due to fouling. A proper monitoring programme will be able to distinguish the difference between temperature losses due to operational changes from temperature losses due to fouling. This can be achieved by calculat-ing a NFIT using a base set of operating conditions. The NFIT will be equal to the actual furnace inlet temperature (FIT) as long as the operating conditions remain the same. When pumparound flow rates or temperatures change, the heat load to the preheat will change and affect the FIT, causing the FIT and the calcu-lated NFIT to be different. The difference will be the result due solely to operating changes between the base case and the current case.

Figure 3 is an example of the differences that can be seen between FIT and NFIT. The NFIT will show the temperature decline due to fouling, while the FIT will show the temperature decline due to both fouling and operational changes. The NFIT trend is useful to show the impact of changing any variable that has an effect on the fouling rate.

The decline in temperature due to fouling can be converted into lost BTUs (energy) that must be made up in the furnace by burning extra fuel. Incorporating furnace efficiency and cost of fuel, the NFIT reflects a cost of fouling.

An antifoulant programme could be added to improve (reduce) the cost of fouling. The cost of the antifoulant would not be a cost of fouling, but it should be considered as cost of fouling control. The only costs that should be considered as fouling costs are those costs that occur due to fouling, such as increased furnace fuel spend, lost

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throughput margin or even environmental penal-ties from firing the furnace harder. The costs of fouling control are the total spend the refiner makes to clean or keep an exchanger network clean. This would include the maintenance clean-ing costs, extra fuel to the furnace (if an exchanger is taken off-line to clean), lost throughput margin (if rates are reduced to clean), antifoulant chemical (if used), cleaning chemical (if used) and any other cost the refiner absorbs when taking action to reverse or control the existing fouling in the exchangers.

The most common method of fouling control is to take exchangers off-line and mechanically clean them by hydroblasting or lancing the inside and outside of the exchanger tube bundle. Whatever the cleaning process, the refiner should add all the costs associated with the cleaning to deter-mine the optimum time to clean. Calculating the cleaning cost is relatively easy, but knowing when to clean is the hard part.

SolutionsIn order to calculate the true cost of fouling, a proper monitoring programme is critical. The total spend on fouling and fouling control discussed in this article is the total cost of opera-tion for the crude preheat. The optimum TCO is the lowest combined cost of fouling and spend on fouling control.3 Each exchanger’s contribu-tion to the furnace inlet temperature is maximised by cleaning exchangers at the

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Figure 3 Example showing the difference between NFIT and FIT that can be seen due to operational changes

optimum cleaning cycle frequency, and good exchanger network simulators will calculate the optimum time between cleanings. The optimum TCO is totally dependent on the fouling rate. If the unit starts to process a crude that fouls at a faster rate, there will be exchangers in the network that will need to be cleaned more often and, therefore, the optimum TCO will increase. The same holds true if the unit starts to process a crude that is less fouling in nature — the opti-mum TCO will decrease. By trending the optimum TCO for each data set the refiner can see how much is being spent and so manage cleaning to achieve the lowest possible spend.

This includes all costs: cleaning, fouling, chem-ical, lost production, and so on. What it does not include is a discount the refiner receives for processing opportunity crudes. The refiner will now be able to see the added cost of processing opportunity crude(s) and include these econom-ics into future buying decisions.

Antifoulants can also decrease the fouling rate, but they add to the cost of fouling control. The addition of antifoulants has to reduce the fouling rate enough to lower the overall TCO of the preheat to justify the added cost. By using a proper simulation model on a regular basis, the refiner can evaluate the benefits of using an anti-foulant (or not) and will always be able to schedule the right exchanger for cleaning in time to keep the preheat operating at the lowest possi-ble total cost.

ConclusionCrude preheat networks can be managed to achieve the lowest possible total cost of opera-tion. It requires a rigorous exchanger simulation model that can normalise input data to calculate the true cost of fouling. The same model should be used to calculate the optimum cleaning cycle frequency for each exchanger in order to deter-mine the true cost of fouling control. In this way, antifoulant chemistries can be evaluated based

on the impact on the total cost of operation. Do:

• Use a rigorous exchanger simulation model• Normalise the furnace inlet temperature• Base cleaning decisions on the maximum impact on the furnace inlet temperature when the TCO calculation shows it is time to clean• Evaluate discounted crude purchases based on impact to TCO• Evaluate antifoulant chemistry based on impact to TCO.

Do not:• Make cleaning decisions based on individual exchanger data (no way to achieve lowest TCO)• Normalise the data based on crude flow only — can cause you to miss >75% of the operating changes that affect the furnace inlet temperature.

References1 Worrel E, Galitsky C, Energy Efficiency Improvement and Cost Saving Opportunities for Petroleum Refineries, an ENERGY STAR Guide for Energy and Plant Managers, 2005.2 Wiehe I A, Kennedy R J, The Oil Compatibility Model and Crude Oil Incompatibility, Energy & Fuels, 14, 56-59, 2000.3 Mason B, McAteer G, Nalco Company, Energy Services Division (USA), Crude Preheat Energy Management Leads to Sustainable Energy Savings, Hydrocarbon Processing, 105-110, Sept 2008.

Laura C Copeland is Global Industry Development Manager with Nalco in Sugar Land, Texas. She holds a BS in chemical engineering from The University of Iowa and a MBA from Northwestern University. Email: lccopeland@nalco.com

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