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DION (GE Water & Process Technologies)

This is a complex question. There are many different chemical families that can be employed as either wax crystal modifiers or flow improvers. Flow improvers can be dragging reducers or pour point depressants. I recommend empirically measuring its impact. For instance, if the concern is the additives’ impact on desalter operations, then the additive can be easily tested in a laboratory desalter simulator. The emulsification tendency of the crude can be measured with and without the additive. All specialty chemical suppliers should have desalter simulators, most of which are portable. The testing can be conducted in the refinery’s laboratory, if desired.

In general, the most common types of pour point depressants will be complementary or synergistic to demulsification. There are too many different chemical families that can be employed to make a general statement about whether wax crystal modifiers are synergistic or antagonistic to demulsification. It can be empirically determined. Drag reducers are very high molecular weight molecules. They are sensitive to shear and break into smaller molecules when they pass through pumps. The larger molecules that are not sheared should act similar to an asphaltene. In one refinery, we saw a situation where a drag reducer was overfed. The excess drag reducer precipitated in their raw crude tanks. When the tank was opened for corrosion inspection, they found a five- or six-inch thick deposit of an elastic, sticky material that had to be cut with shovels to get it out of the tank.

DION (GE Water & Process Technologies)

Wax crystal modifiers are typically amine oxides, amides or sulfonates. They are utilized to keep paraffins dispersed and reduce wax deposition. Flow improvers generally fall into two categories; pour point depressants and drag reducers. They are typically stearates, vinyl acetate copolymers, polyacrylates or polymeric alpha olefins. Pour point depressants typically promote the formation of compact, three dimensional crystals as the crude oil cools. The compact crystals decrease the pour point of the oil and improve low temperature flow properties. Flow improver chemicals are typically extremely large molecular weight materials. Like asphaltenes, they can precipitate to an oil/water interface and stabilize emulsions. There have been no noticeable desalter performance issues at dose rates of approximately 5 ppm. When dose rates are roughly 15 ppm to 25 ppm, desalter upsets may occur. In at least one refinery, a six inch layer of sticky elastic, caulk-like material has been observed on the bottom of the tanks and believed to be due to flow improver chemicals. In general, pour point depressants can improve emulsion resolution, while on the other hand paraffin inhibitors may have a deleterious effect. The exact effect would require either more specific information on the chemistry or empirical testing results.

BASHAM (Marathon Petroleum Corporation)

We have run crudes with flow improver at Marathon Petroleum Company’s Catlettsburg refinery for four to five years now with no known issues on our crude units.

DENNIS HAYNES (Nalco Energy Services)

In the past, there were some types of these chemistries in oil production that were reported to have problems on oil-water separation in desalting at the refinery; however, in recent history, improvements seem to have been made, and the products we have been asked to review for other companies have not shown adverse impacts regarding emulsification. If there is an indication that a new product is being used, evaluation on downstream impacts is recommended.

SAM LORDO (Nalco Energy Services) There have been a few reports of impacts on the amps/volts from crudes treated with pour point depressants. The impact was minor but noticeable in that additional power-draw was observable.

BHARGAVA (KBC Advanced Technologies, Inc.) 

Some of the biggest challenges we have seen with “Big Data” revolve around security. Within the KBC software suite, we focus on security as our major differentiator. Given the connectors we have in The Cloud, we can now download the data from the refinery in a secure fashion and put it into one location; so, when people retrieve the data, they are all looking at one consistent set. That way, Yield Accounting is not looking at different numbers for closures from their perspective alone. The process engineer has his/her own mass balance that creates a lot of issues around the refinery. 

The other big issue here is accessibility of the data: who inputs the data and who has the access to change it. All those issues must be addressed before you start using The Cloud to improve your data that is already on The Cloud. Because we are able to use this data and have access to lots of capacity, we are now able to micromanage, if you please, in terms of identifying and doing performance audits. We are even using this approach for shift performance information to see how one shift is doing against another. We can now get most of the information from The Cloud because we are able to store more data. This easy access to information has allowed simulation to move to a different level. We have been able to use the gobs of data we have from the DCS (distributed control system) to develop models that are more accurate. We can calibrate these models. In fact, in some of the real-time applications we have used, we have been able to calibrate the models on the hour. These models also allow us to set operational targets more accurately. That is about as far as we have gone with “Big Data”. So, it is all about finding your best possible operation and what you have done across the shift on a 24-hour average.  

The benefits of having more data, sifting through it, and identifying good stable operation data to develop simulation models are all being used to generate LP (linear program) vectors, which are then used by the planners to help with crude selection and then do a gross optimization. From a yield perspective, it has definitely helped a lot. From a safety reliability standpoint, we now have enhanced unit monitoring. The old method of unit monitoring was to gather a number of KPIs (key performance indicators), have an operating range, and then try to optimize the unit in the operating envelope. We have used simulation as a way to help with some calculated variables that affected reliability of the unit. We have expanded the windows from operating envelopes to an integrity operating window to allow people to have a much better idea of or prediction capabilities on equipment failure. 

Finally, having this data on The Cloud has allowed the SMEs (subject matter experts) from within the corporate level of the company to look at the same data set and help with the optimization of the unit from a remote location. You can also do this with third-party consulting companies to allow for another perspective with a fresh set of eyes to help make sure that the unit is running optimally on a daily basis. 

SANJAY BHARGAVA (KBC Advanced Technologies) 

With the advent of “Big Data” and cloud-based technologies, refiners are now able to harvest mega data in a process environment. Hundreds of thousands of values are being generated every few seconds to measure, monitor, control, and optimize plant operations. The major benefit of “Big Data” is to demystify the different plant data used by various parts of the organization by using with a common set of information that is utilized by all departments. In the modern refinery, the key is transparency in strategy, philosophy, and shared goals. The benefits are seen in reliability, safety, and operator consistency. 

Also, refiners using in-house or third-party simulation technologies are able to extract precise information from these “lakes” of data about the performance and compare it to the best possible performance of the plant. Some companies are already tapping into this pool of information – through the use of simulations – to provide a robust means for selecting a set point or target setting of the independent variables. 

The data is also being used in open-loop controls (and limited closed-loop controls) to help make advanced decisions to improve plant performance. The simulation models are being calibrated and updated to maintain prediction fidelity. The calibrated models are being used to generate LP vectors to keep the refiner’s planning models updated in order to mimic actual plant operation which will optimize feed and operating conditions, such as riser outlet temperature, feed preheat temperature, and main fractionator cutpoints in FCCs. 

Advanced or enhanced unit monitoring applications are being used by refiners to convert raw data into key performance indicators. These applications are being linked to simulation technologies in real-time to generate calculated/derived key performance indicators focused on ensuring operation within integrity operating windows (IOWs) to help improve the plant reliability and safety and to maximize plant life by monitoring for corrosion, erosion, fouling, and accelerated mechanical equipment damage in order to allow for early action and prevention which will improve reliability and safety performance. These enhanced applications are also being used for predictive maintenance, as well as for monitoring the performance of operators between operating shifts and bringing shift performance closer to best possible operations, which will benefit all shift teams. These advanced unit monitoring applications, in addition to the cloud-based technologies with added security, are now being used by managers to view plant performance dashboards on their mobile devices. Finally, the data can also be used to perform remote monitoring by subject matter specialists – both within and outside of the organization – to optimize FCC operations. This monitoring can be now done due to enhanced security systems put into place to protect refinery data. 

KATHERINE JONES (Honeywell UOP) 

Honeywell recognizes the untapped potential of the industrial internet of things (IIoT) and sees these capabilities as the next revolution in industry: 

• IIoT enables users to leverage all data available including stranded data that, until now, has not readily used in business decisions. 

• Cloud-based solutions allows for a more collaborative environment to enable users to benefit from global expertise. 

• The use of big data and analytics, combined with the computing power of the Cloud, enables the investigation of large data sources and the discovery of new insights. 

Maintaining high onstream efficiency and reliability are critical parameters for maximizing plant profitability. To help our customers maximize the use of their assets, Honeywell UOP offers a suite of cloud-based services with the Honeywell Connected Plant. One of these services, Process Reliability Advisor, combines plant data with UOP Process models that are tuned to current operation, plus a fault model which are cause-and-effect relationships and troubleshooting guides specific for the process. The service utilizes a cloud-based platform that supports big data analytics and machine learning to drive continual innovation.  

Process Reliability Advisor enables our customer to: 

• Detect performance gaps early and perform mitigation steps, 

• Understand the unit’s current performance and asset utilization based on constraints, and  

• Promote learning and knowledge retention, leveraging UOP insights and context. 

More information can be found at www.uop.com/cps including videos on how Process Reliability Advisor works. 

Dwight Agnello-Dean (BP)

We have not routinely attempted to identify the organic and inorganic iron components in our feeds. An internal expert suggested two approaches. First to determine the organic iron using ICP-OES, then wash the same sample with DI water, or slightly acidified water, and utilizing atomic absorption, determine the inorganic iron. The second approach is to determine total iron utilizing X-Ray, ICP or Atomic Absorption. Following this step remove the solids and water soluble, which would contain the inorganic iron, and rerun the hydrocarbon sample to determine the organic iron. The difference between total iron and organic iron is the inorganic iron.

As far as which form of iron affects catalyst performance, we follow the general consensus that organic iron is the issue and of primary interest. Over the last decade we have experienced catalyst performance issues that were correlated with elevated iron on the catalyst. During these events, ecat testing did not indicate an appropriate drop-in catalyst activity, which supports the questioner’s point that ecat program test methods are not adequate for this purpose. The symptoms we do look for are loss in bottoms upgrading (higher DCO yields), a drop in catalyst bulk density (ABD), a change in fluidization properties, potential circulation issues, and of course elevated iron on ecat. Across our units we don’t have a single critical iron level where we would expect problems because we consider this to be impacted by both the catalyst and the feedstock. Therefore, our current practices are to monitor our normal iron on ecat level and begin looking for other symptoms if we see the iron increase 0.3 wt.% over normal. For the units I am most closely associated with I expect to stay below 0.8 wt% iron. These units process very clean VGO feeds.

Ray Fletcher (Intercat)

Testing methods for feed stock analyses may be found in ASTM D7691 - 11 which include standard test methods for multi-element analysis of crude oils using inductively coupled plasma atomic emission spectrometry (ICP-AES). We believe that the high-performance liquid chromatography ICP-AES may be better at distinguishing organic and inorganic metal species.

It is widely accepted that organic iron in porphyries and naphthenates negatively affect the catalytic performance more than does inorganic iron.

The primary reason for the different performance effects between organic and inorganic iron is related to the size of these molecules. It is believed that small inorganic iron species are able to penetrate into the catalyst particle in most cases and disperse easily. On the other hand, large organic iron bearing molecules are typically sterically hindered and thus are unable to penetrate within the FCC catalyst particle. These molecules tend to deposit on the particle surface forming a barrier to diffusion. These iron rich layers are often several microns thick having a very rough nodular structure.

Our R&D team has found that the ECAT surface contaminated by organic iron contains a very dense amorphous iron rich layer which seals the macro, meso and micro-pores of catalyst. There is no penetration and reaction found at the interface between the iron layer and catalyst. The outer surface of an iron poisoned catalyst particle consists of a dense amorphous aluminosilicate phase with polycrystalline magnetite (Fe3O4) nanoparticles on top.

Most laboratory deactivation and testing methodologies used today are unable to accurately predict the effect of iron poisoning in a commercial unit. FCC catalyst suppliers have been active in developing special deactivation conditions to enable more accurate prediction capability with iron poisoning. None of these deactivation procedures have yet been able to accurately model an iron contaminated unit. The one test unit which may be capable of distinguishing iron effects is the circulating pilot plant. However, due to the sample sizes involved and the time & cost commitments few refiners have been willing to carry out extensive testing with this equipment.

The best method for monitoring iron poisoning is systematic and detailed unit monitoring. The focus is on "add-on iron". Most FCC catalysts contain 0.2-0.4 wt% iron depending upon the kaolin content and source. The add-on iron is the equilibrium iron less the fresh iron. Most catalyst systems are capable of handling +0.3 wt% add-on iron before negative effects are observed. Catalysts with alumina-based binding systems are usually capable of absorbing higher levels of iron at approximately 0.4-0.5 wt%.

The actual amount of iron the equilibrium catalyst is capable of absorbing without negative impact is directly related to the concentration of all contaminant metals including: nickel, vanadium, sodium, calcium plus iron. Most catalyst systems begin to observe negative effects when the total metal levels exceed 13,000 to 15,000 ppm. There are a few FCC units which regularly defy this rule of thumb having iron levels exceeding one wt%. These units are believed to be exceptions rather than the rule.

Iron nodules have been observed on the surface of equilibrium catalysts which have undergone high levels of iron contamination. The net result of these nodules has been poor blockage with a corresponding loss in bottoms conversion plus an apparent drop in ABD due to altered packing efficiencies. Many times, in spite of a lower ABD the fluidization characteristics of the unit have deteriorated. Additionally, as stated in question #99, step change increases in iron have been observed to precipitate that changes in SOx emissions. These step change increases in emissions are easily controlled by SOx reducing additive.

It is recommended that the process engineer monitor closely the levels of add-on iron and total contaminant metals. Both of these variables may be plotted against bottoms conversion or conversion. The refiner may then draw their own conclusion regarding maximum allowable iron contamination. Three methodologies exist for combating iron excursions include: 1) flush catalyst, 2) higher fresh catalyst additions &/or 3) reformulation to a more iron tolerance catalyst system.

Finally, Intercat would like to propose an alternative explanation to the underlying mechanism leading to nodule formation in iron contaminated units. It has been observed that the composition of these nodules is not solely iron but is enriched with silica. Interestingly, one would expect that beneath the iron nodules there would be silica depleted regions within the catalyst particle. However, this is not the case. The composition of the catalyst directly beneath the iron nodule has equivalent silica concentrations as those regions not affected by these nodules. This leads us to hypothesize that iron acts as a nucleating site for silica/iron nodule formation. As stated earlier, these nodules have multiple negative impacts such as reduction in conversion, reduction in ABD, and occasionally, deterioration in fluidization characteristics. (Our thanks to Dr. Diddams for sharing this hypothesis with us.)

Ann Benoit (Grace Davison Refining Technologies)

Yaluris discussed that there are two types of iron. One type is particles of inorganic iron from hardware and is usually considered benign in FCC unit performance. The second type is organic based Fe potentially coming in with the feed and/or from hardware corrosion by naphthenic acids and other corrosive feed components. This type of iron can negatively impact unit performance. (1) ACE testing is a better technique to measure the conversion impact of organic based iron contaminated catalyst, compared to traditional fixed bed MAT testing. However, ACE testing should be used together with other tests to confirm iron poisoning.

Below are several methods that can be used to monitor catalyst performance under Fe contamination:

•Ecat analysis (Fe, Na, CaO, ABD, color)

•Scanning Electron Microscope imaging (SEM)

•Optical microscope

•Ecat diffusivity

A decline in unit performance such as, reduction in bottoms conversion, and/or poor catalyst circulation coupled with an increase in equilibrium catalyst Fe levels can indicate Fe poisoning. Yaluris discussed how pore closure and nodule formation can be potentially caused by iron contamination (1). Pore closure can negatively affect bottoms conversion in the unit. An excellent way to show the actual nodules is by Scanning Electron Microscope imaging (SEM). It is important to note that some nodules may not be a problem, but there is a problem when the nodules become obviously raised from the surface and all over the catalyst particle. A drop in ABD (apparent bulk density) on Ecat can indicate nodules formation due to iron poisoning as Ecat does not pack as densely. The ABD change, pore plugging and the potential for particles to stick together can negatively impact unit catalyst circulation.

Optical microscope is another method to indicate iron poisoning. Ecat samples with iron poisoning will show glassy reflections under an optical microscope with illumination. (1) The color of Ecat could potentially indicate high levels of Fe. Ecat samples can have a reddish brown tint when poisoned by iron.(1) In addition to Ecat Fe levels, Na and CaO should be monitored as well. Fe in the presence of Na and/or CaO can act as a fluxing agent which can aggravate the effects of Fe.(1)