PTQ Q4 2024 Issue

Axens maximisation of Aromatic Yield (MAY) V2.0 module

Limit

Lower Upper Unit

1.5

mol/mol

H/HC ratio

The Axens MAY module: Maximisation of Aromatic Yield uses a high-delity model to optimise the reactor WAIT, H/HC ratio and the catalyst circulation rate in order to miximise the aromatic yield for the given feed.

Catalyst circulation rate WAIT Pressure xed at lower bound

750 510 0.3

850 534 0.3

kg/h ˚C MPag

Optimiser Theory

The optimiser will only search within the dened upper and lower boundaries of the WAIT, H/HC ratio and the catalyst circulation rate. The optimiser is provided with a constraint on the spent catalyst coke content which must satisfy >3 wt% and <6 wt%.

Coke content Constraints Value Unit 3-6 wt%

Daily recommendation view (-24H)

Unit operating conditions could be improved

Recommended AVG process conditions (-24) to maximise Aromatic Production

H/HC Ratio Change [mol fr]

Catalyst Circulation Rate Change [kg/h]

WAIT Change [˚C]

3.4 0.6

Figure 3 Connect’In aromatic maximisation dashboard

These shift vectors provide important insights into reactor yield, catalyst coke yields, product properties, and utility consumption. The lab data collection provides updates on main product qualities of the LP model, which serves as a basis to opti- mise blending. An alert system is employed to notify plant personnel in charge in case of any abnormal or excessive deviation of the shift vector. Daily Excel sheets can also be automatically or manually generated and exported to pro- vide LP planning updates. The tool allows the operator to select a desired calibra- tion period, with model outputs defined by the client. The LP model drivers are based on our expertise, providing clients with much greater accuracy in their refinery LP models and continuous updates of catalyst run length predictions. Maximising paraxylene production The ParamaX Suite consists of several technologies for the cost-effective production of benzene, paraxylene (PX), and orthoxylene. Using ML and industrial reactor models, Connect’In continuously calculates the ideal operating con- ditions for PX production based on the latest data. With ever-changing feed characteristics, operating parameters can be fine-tuned to capture improvement opportunities. In the ParamaX complex, the reforming unit upstream is the main source of aromatics. The target is to maximise reforming catalyst usage within the boundaries of the pro- cess to improve the complex’s overall profitability. To reach this target, our reforming kinetics model is integrated into the Connect’In platform. This integration optimises the reactor’s inlet temperature profile to maxim - ise aromatics production while controlling coke formation. The objective is to prevent excessive coke production in the regenerator and maintain sufficient reaction heat for the furnace section duties. Figure 3 is an illustration of the interface. To verify the coke estimation from the first principle kinetic model, a ML model has also been developed. This

cross-check prevents misoperations in the regenerator sec- tion, which could lead to significant operational losses. Common needs for using the platform, such as with the interface shown in Figure 3, include process optimisation, predictive maintenance, real-time monitoring and control, simulation of different production scenarios, and testing the impact of changes in raw material prices, product demand or production capacity. Critical role of materials balancing Another way Connect’In improves process monitoring is through naphtha reforming. Ensuring an accurate calcula- tion of the material balance (MB) is crucial because it helps accurately account for unit yields, which are indicators of catalyst performance and operation. However, on an industrial scale unit, 100% MB closure is not always achieved, making it necessary to ‘close’ the MB to deliver a full accounting of unit yields. Excluding rigorous statistical approaches, typical meth- ods to close the MB involve assumptions about which flow meters are in error without a means to test and validate these assumptions. Connect’In employs MB closure based on carbon balance (CB) and hydrogen balance (HB) recon- ciliation for improved yield reliability. The platform performs these calculations automatically and displays them to cli- ents in real time. In one example, without using this specific mass balance closure method, customer data suggested an under-chlo- rinated catalyst, leading to a recommendation to increase chloride injection. However, after applying the carbon- hydrogen closure methodology, the team confirmed that the chloride content was correct and significantly reduced the standard deviations of the yields. This improved unit monitoring and enabled more accurate detection of any unit deactivation. Future prospects Continuous improvements to the Connect’In platform involve closely working with end users and technology

PTQ Q4 2024