Data Based Model Building

Imagine Simulation Technologies offers a wide variety of Data-based Model Building Solutions. These solutions are based on the latest cutting-edge Machine Learning algorithms to address issues like Equipment Condition Monitoring, Predictive Maintenance, Process Performance Optimization, Abnormal Situation Management, Sensor Reliability Analysis, etc. These solutions are key to driving the plant operations towards economic optimality with minimum downtime and maximum efficiency. 

Condition Monitoring and Predictive Maintenance

Condition monitoring of process equipment and units for preventive maintenance scheduling and suggest corrective actions.


  • Robust data-based models

  • State-of-the-art machine learning algorithms

  • Real-time monitoring using intuitive KPIs to continuously track model performance

  • Smart data filtering and noise elimination

  • Model is always up to date and represents the current process condition

  • On-line integration to stream live data and prediction


  • Fault Detection & Diagnosis for early identification of equipment degradation and root cause analysis preventing failures leading to excessive downtime

  • Prescriptive Analytics for predictive maintenance and corrective action to prevent performance and efficiency degradation

Process Performance Optimization


  • Off-line model development using historical data

  • Simulation based model validation

  • Models integrated with process knowledge from domain experts

  • Advanced constrained optimization techniques

  • Continuous online performance monitoring

  • Model adaptation to automatically use real-time data and update the model dynamically


  • Process Optimization for centerlining, resulting in tighter range of operations and minimizing off-specification product or the extent of product quality deviations

  • Digital Twins using dynamic models to tune process parameters and control tuning before online configuration, eliminating time consuming trial and error approach

Process Analytics and Abnormal Situation Management

Process analytics to assess sensors reliability and to suggest corrective operations to prevent process upsets.


  • High fidelity data-based models

  • Data fusion to integrate process measurements, laboratory data, analyzer data, etc.

  • Advanced multivariate statistical methods


  • Sensor Validation using models to improve field sensor reliability

  • Prevent process upsets and off-spec production