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

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

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

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.

Pressure transient analysis of multi-stage fractured horizontal wells in unconventional reservoirs

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.


IPCOS Case Study

Analysis of Unconventional Multi-Stage Fractured Horizontal Well

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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.


IPCOS Case Study

Sulfur Dew Point Inferentials

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Improve Base-layer control: simulation models plant layout changes

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.


IPCOS Case Study

Improving Base Layer Control Design Using Dynamic Simulation

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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: Minimize Manual Data Processing

Integrated Asset Modeling

Cost-effective integration of GAP, MBAL and PROSPER to maximize production across multiple assets.

Machine Learning & Data Science

Machine Learning & Data Science

Data-driven models identify hidden patterns that can lead to increased quality and predictability.

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