Process Modeling
Maximize efficiency and production using process simulation
Turnkey models provide in-depth intelligence for improved analysis, troubleshooting, and operation
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.
Improve unconventional well performance
IPCOS engineers developed a model of a horizontal fractured well that was used to diagnose the cause of poor performance and recommend improvements to increase oil production.
Analysis of Unconventional Multi-Stage Fractured Horizontal Well
Process model predicts safety margins
Inferential model predicts sulfur dew point using process parameters such as temperature and pressure in order to maintain operation within limits and prevent sulfur condensation in Claus reactors.
Sulfur Dew Point Inferentials
Simulation models plant layout changes
Dynamic simulation of high-pressure steam network provides process behavior insights and predicts impacts of changes to network topology, allowing a smoother transition and improved controls design.
Improving Base Layer Control Design Using Dynamic Simulation
End-to-end value
>5%
improved production target compliance
50%
reduction on simulation efforts, due to structured modeling work
20%
faster plant commissioning time as a result of simulation-tested control strategies
10-40%
More profit due to real time process simulation modeling
Other services
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.
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