KBC, a Yokogawa company, announced the Petro SIM 7.7, an advanced process simulation, optimization and digital twin platform for engineers and safety specialists in the refining, petrochemical, chemical, and process industries.  

The platform integrates AI/ML-enabled hybrid modeling with first-principles simulation within one platform, according to the company.  

By combining engineering physics with machine learning, it can help engineers make informed decisions while maintaining digital twins and operations across traditional and emerging energy systems.  

"Industrial AI delivers the greatest value when it's grounded in engineering physics," said Philippa Hayward, product manager for Petro-SIM. "Process engineers need an accessible way to develop and apply hybrid models within a trusted simulation environment, helping them solve increasingly complex problems without specialist data science expertise."  

Key capabilities, according to KBC, include:  

• Apply accessible AI/ML within engineering workflows through the embedded ML Utility Hub to generate synthetic data and develop, train, and deploy hybrid models within the Petro-SIM environment.  
• Improve complex refinery process operations through an expanded optimizer library and new optimization algorithms.  
• Model refinery units and integrated value chains with greater fidelity through an expanded suite of reactor capabilities.
• Support traditional and emerging energy technologies, including bio-oil processing, electrolysis, and new decarbonization correlations while providing the functionality to support pyrolysis and biomass gasification modeling.  

Petro-SIM 7.7 provides the engineering foundation for process digital twins and KBC Acuity Process Twin Pro, a digital twin application. Together, they enable more accurate monitoring and operational decision-making across refinery and petrochemical value chains, according to KBC. 

 "Industrial AI is only valuable when engineers can trust it," said Simon Rogers, chief technology officer at KBC. "That trust is built on transparency, engineering rigor, and results that can be validated. AI should strengthen engineering judgment, not replace it. Our vision is to embed intelligent technologies across our software portfolio, helping engineers solve increasingly complex challenges with confidence."