A Johanna Fernengel, Product Manager, Syngas and Fuels, Johanna.Fernengel@clariant.com, and Rainer Albert Rakoczy, Technology Advisor Fuel and Hydrocarbons, Syngas and Fuels, Rainer.Rakoczy@clariant.com , Clariant Catalysts Besides topping upgrading, the key to maximising fuel pro- duction is the right balance of hydrocracking (HC), delayed coking (DC), and catalytic cracking (FCC), as this gives the highest flexibility in utilising nearly any crude source, including renewable sources. In particular, utilisation of the light olefins from the FCC off-gas with alkylation and oligo - merisation with alternative concepts can give a higher flex - ibility, moving from sole gasoline focus towards distillates as potential diesel and jet blending components. A Ioan-Teodor Trotus, Team Leader Refining, Ioan- Teodor.Trotus@hte-company.de, hte GmbH Which reactor system will be the most effective depends on multiple factors, such as the feed or feed mix to be con- verted, the actual fuel to be produced – diesel, gasoline, or aviation fuel – and, of course, on which reactors are already operating in the refinery. For a refinery that aims to convert mainly crude oil with existing plants – be it hydrotreaters, hydrocrackers or FCC units – pilot plant tests will yield the most reliable results for choosing the right catalyst system. The right catalyst system must show a reasonable level of activity and stability to maximise the duration of an operating cycle. This can be determined in pilot plant testing either as the start-of-run activity or by performing accelerated deacti- vation studies to estimate the mid-run or end-of-run activity. At the same time, pilot plant testing will give information about the yields and properties of each fuel fraction, allow- ing one to feed a techno-economic model with actual plant data and make a like-for-like comparison of all the catalyst systems to be compared. For a refinery aiming to co-process or process renewable feedstocks in existing equipment, a pilot plant test is even more important because it also allows the operator to see a new application in action before testing in a production unit. The number of industrial references for the conversion of renewable fuels is still relatively low compared to the num- ber of references for the conversion of crude-derived feeds. Simply relying on models and paper studies is particularly risky in these cases, as such models still have relatively little data on which to build their estimates. In short, the most effective catalyst – be it for hydropro- cessing or FCC applications aimed at the production of fuels and the conversion of renewables – will most likely be the one that was determined by a pilot plant test. A Kurt du Mong, CEO, Zeopore Technologies A key value creator in the refinery, specifically to yield fuels, remains the hydrocracker. These units feature multicom- ponent catalysts involving NiW or NiMo hydrogenation components supported on acidic zeolite/alumina carriers. These types of catalysts have gone through generations of remarkable developments, particularly with respect to the optimisation of the zeolite component.
are the vast amount of data available and the high com- plexity of interconnected unit operations. Traditional first-principles models and tools for optimisation have dis - advantages in coping with these challenges as they usually require high effort and thus cost to develop, maintain, and adapt to changes in the operation. In contrast, AI systems with machine learning-based models at their core can be designed with reasonable effort for complex systems. They offer the opportunity to learn automatically from continuous data streams in the plant and adapt quickly to changing conditions. Thus, AI systems will see rapid adoption in the coming years to replace, complement or enhance existing optimi- sation approaches. Like the developments in autonomous driving, AI systems are expected first to be adopted as assisting systems to support and enable better human decision-making for plant-wide optimisation. Q Gasoline, diesel, and aviation fuel are still expected to dominate refinery markets to 2030; what reactor and catalyst systems will be the most effective in maximising fuel production? A Pierre-Yves le-Goff, Global Market Manager Reforming and Isomerisation, Pierre-Yves.LE-GOFF@axens.net, Laurent Watripont, Clean Fuels Technologies Director Expert, Laurent.WATRIPONT@axens.net, Christophe Pierre, Reforming Product Line Manager, Gasoline Product Line Technology and Technical Support Business Division, Christophe.PIERRE@axens.net, Matthew Hutchinson, Senior Technology Manager, Gasoline and Petrochemical Technologies, Technology Dept., Matthew. HUTCHINSON@axens.net , Axens For gasoline production, among the building blocks of the gasoline pool, we can mention isomerate and refor- mate. For reforming, maximisation of gasoline production is linked to a reduction of cracking while ensuring a stable operation. The addition of modifiers is one of the possibili - ties to reduce cracking; however, rigorous selection process is needed to ensure that stability and regenerability are not impacted. Axens, formerly Procatalyse, has been involved in such a field of expertise since the mid-1990s. From a process standpoint, reduction of the pressure will improve the fuel production. However, such a reduction needs to be compatible with unit constraints (for example, pressure drop). To mitigate these pressure drops, a pos- sibility is to move from a standard axial flow reactor to a radial flow reactor. Axens has already performed such modifications and has proprietary internals to improve gas distribution. On the isomerisation side, depending on the octane tar- get and feed composition, different schemes can be pro- posed. For example, if the feed is rich in C 6 paraffin, the deisohexaniser (DIH) column can be implemented to maxi- mise octane without selectivity debit. In addition, to reduce cracking, the use of high-activity catalyst is of paramount importance. Therefore, Axens process expertise with ATIS-2L catalyst provides the best combination for isom- erisation unit optimisation.
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PTQ Q1 2024
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