Reduce operator errors and optimize plant performance
The need for good static and dynamic process simulation models is growing as chemical processing plants increase in complexity and automation. These models can support operator training, concept design for controls, root cause analyses, and recommendations for hardware modification.
Implement new control designs on a first-time-right basis
Reduce or eliminate downtime for trial and error at plant startup or after process changes by testing new control systems and procedures on a simulation before putting into service at the plant.
Safely train new operators on simulated plant control systems
Models form the core of Operator Training Simulator (OTS) systems, which help operators to understand plant processes. Reduce the number of trips due to operator error and train new operators.
Understand process behavior to improve plant performance
A well-tuned process simulation can be used to gather data and perform investigations that bring to light root causes of unexpected or poor performance, leading to novel solutions.
improved production target compliance
reduction on simulation efforts, due to structured modeling work
faster plant commissioning time as a result of simulation-tested control strategies
More profit due to real time process simulation modeling
Integrated Asset Modeling
Cost-effective integration of GAP, MBAL and PROSPER to maximize production across multiple assets.
Machine Learning & Data Science
Data-driven models identify hidden patterns that can lead to increased quality and predictability.