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Smart Simulations for Smarter Operations

Data-Driven Modeling Solutions​

Imagine Simulation Technologies offers a comprehensive suite of data-driven model building solutions powered by cutting-edge Machine Learning algorithms. These solutions address critical operational challenges such as Equipment Condition Monitoring, Predictive Maintenance, Process Performance Optimization, Abnormal Situation Management, and Sensor Reliability Analysis. By leveraging advanced analytics, they enable plants to achieve economic optimality with minimum downtime and maximum efficiency.

Technology

  • Robust, high-fidelity data-driven models developed using historical and real-time data

  • State-of-the-art machine learning and advanced multivariate statistical techniques

  • Integration of process knowledge from domain experts with data-based approaches

  • Smart data filtering, noise elimination, and data fusion (process, laboratory, and analyzer data)

  • Simulation-based model validation to ensure reliability and accuracy

  • Advanced constrained optimization techniques for operational improvements

  • Online monitoring using intuitive KPIs to track model performance

  • Real-time data integration with dynamic model adaptation to reflect current process conditions

Capabilities

  • Continuous condition monitoring of process equipment and units for preventive maintenance planning

  • Early fault detection, diagnosis, and root cause analysis to identify equipment degradation

  • Predictive and prescriptive analytics for maintenance scheduling and corrective actions

  • Process optimization and center-lining to maintain tighter operating ranges and improve product quality

  • Digital twin enablement for simulation, parameter tuning, and control strategy validation prior to implementation

  • Sensor validation and reliability analysis to improve measurement accuracy

  • Abnormal situation detection and advisory support to prevent process upsets and off-spec production

Benefits

  • Reduced unplanned downtime through early detection of faults and predictive maintenance

  • Improved equipment reliability and extended asset life

  • Enhanced process efficiency and throughput with optimized operating conditions

  • Consistent product quality with minimized variability and off-spec production

  • Reduced reliance on trial-and-error approaches through simulation and digital twin capabilities

  • Improved decision-making with real-time insights and actionable recommendations

  • Cost-effective implementation leveraging existing plant data infrastructure