KBC (A Yokogawa Company) has announced the launch of Petro- SIM® v7.6, the latest release of its flagship digital twin process simulation platform for the upstream and downstream oil and gas sectors, including the refining, petrochemical, polymer, and sustainable aviation fuel (SAF) industries.
Building on its digital twin foundation, Petro-SIM v7.6 delivers enhancements across both first-principles and hybrid modeling to deliver advanced optimization with the integration of artificial intelligence and machine learning (AI/ML). According to KBC, it empowers engineers, operators, and business leaders to accelerate digitalization, energy transition and decarbonization. “This momentum drives faster progress toward profitability, operational performance, and sustainability goals,” KBC wrote in a news release.
“Any miscalculation in product quality or properties directly impacts production planning, energy use, and profitability,” said Rodolfo Tellez-Schmill, Product Champion for the Petro-SIM process twin. “In a volatile market, process optimization is no longer optional.” Petro-SIM v7.6 addresses this challenge by combining high-fidelity modeling with improved workflows that allow operators to explore new, low-carbon pathways.
These improvements help operators reduce energy waste and production losses, which are key contributors to approximately 5.1 gigatonnes of CO₂-equivalent emissions generated by oil and gas operations each year, as reported in the 2023 IEA World Energy Outlook Report. By simulating and optimizing performance across traditional and emerging processes, Petro-SIM supports more efficient and lower-emission operations in crude refining, renewable fuel production, and plastic waste-to-polymer circular processes.
Key features and benefits include:
FTR-SIM (Fischer-Tropsch reactor model) is a robust kinetic-based reactor model that simulates Fischer-Tropsch synthesis for SAF production. It enables users to optimize operations and model the integration with downstream hydroprocessing for a complete digital twin of sustainable fuel production.
New NOMAD solver for optimization solves difficult blackbox optimization problems to optimize energy efficiency while streamlining production planning and reducing emissions.
AI/ML integration and digitalization combines AI/ML models with first-principals physics-based models to deliver smarter predictions, improved optimizations, and enhanced decision-making within scheduling and planning.
Advanced polymer modeling powered by Predici predicts polymer properties, optimizes grade transitions, and monitors catalyst impacts to support dynamic production digital twins that minimize downtime and off-spec material.
Renewables and hydroprocessing enhancements improve kinetic modeling for renewable feedstocks. Enhancements in hydrotreating, isomerization, and hydrocracking simulations include upgrades to liquid recycling and external gas quenching processes that optimize reactor performance while reducing solving times by up to 30%.
FCC-SIM improvements for bio-oil co-processing enhance oxygen balance modeling and improve assessment of inert loads in FCC overheads during co- processing of bio-oils to help control emissions and optimize product quality.
