PTQ Q2 2025 Issue

Hybrid digital twin for DHDT unit performance monitoring

High-fidelity models with collation of ‘theory and plant data’ are essential to track hydrotreating unit performance

SK Shabina, Ranjith Kumar Bojja, Indranil Roy Choudhury and Sarvesh Kumar Research & Development Centre, Indian Oil Corporation limited

I n today’s pursuit of net-zero emissions , refineries aim to increase efficiency and flexibility for processing a crude mix along with optimum hydrogen (H2 ) consump- tion. Diesel hydrotreatment (DHDT) units are one such fundamental treating process in the refinery to produce marketable fuel from heavier and refractory feeds, such as straight-run gasoil (SRGO), straight-run vacuum diesel (SRVD), light cycle oil (LCO), and light coker gasoil (LCGO). Recent developments for wider options in coprocessing renewable feedstock to produce lower-carbon-intensive diesel while extending catalyst life present challenges for refineries to operate units optimally, meeting diesel product specifications. Digital tools and their solutions play a key role in optimisation. They proactively take actions such as operating units at optimum temperatures, extending cat - alyst life by reducing throughput, scaling back refractory feed streams, and scheduling procurement for the next cat - alyst charge. Such diligent solutions from digital tools are made possible by integrated models built on both: • Fundamental kinetics. • ‘High-quality process data’ of inferring units’ operation. Conventional refinery process kinetic models based on fundamental kinetics use mathematical equations to describe the process, hence relying less on data. This type of modelling approach is successful when there is a deep process understanding and feed/product stream character - isation. They are developed using programming platforms such as Fortran, C++, and MATLAB. However, these models cannot adequately represent the complexity involved in deactivation mechanisms and non- ideal phenomena. Hence it becomes difficult to use them for monitoring catalyst health throughout its lifecycle. On the other hand, only data-driven models based on ML algo - rithms use a huge reams of datasets to learn and identify the patterns and associations between process operations, stream properties, and yield variables, enabling them to make predictions or decisions. They are developed using programming platform such as MATLAB and Python. These models become helpful for describing complex phe - nomena where explicit mathematical equation formulations become difficult. Also, these models adapt to new scenarios whenever retrained with updated data. However, commer - cial process data availability with sufficient variability limits

this approach. Also, lab-scale data generation that includes variability requires cost, time, and resources. Thus, hybrid models – by combining fundamental principles and process data – power the strength of both kinetic and data-driven model approaches, improving the model’s robustness for continuous application. DHDT kinetic model Hydrotreating is a catalytic process typically operated with pressures ranging from 7 to 11 MPa for feeds with a boil - ing range of up to 400°C in the presence of hydrogen. The objectives of the process are: • Removing sulphur compounds, including refractory compounds like dibenzothiophene (hydrodesulphurisation [HDS]). Hybrid models – by combining fundamental principles and process data – power the strength of both kinetic and data-driven model approaches, improving the model’s robustness for continuous application • Removing nitrogen (N) compounds such as porphyrins and quinolines (hydrodenitrogenation [HDN]). • Saturating mono, di, and polyaromatics (hydrodearomati - sation [HDA]). • To improve the quality of fuel in terms of density, Cetane Index (CI), and T95. Slight thermal cracking also takes place in this operating regime. The main chemical reactions associated with the hydrotreating process can be seen in Figure 1 . IOCL proprietary DHDT model is developed based on rigorous structure-oriented kinetics of desulphurisation, dearomatisation, denitrogenation, olefin saturation, and cracking reactions. Desulphurisation kinetics is based on the detailed chemistry of different sulphur species analysed through GC-SCD ASTM D 5623 that are present in DHDT

PTQ Q2 2025