Understanding Exchanger Practically for Improving Performance

Exchanger is one of the key asset in any operating unit and important aspect to ensure overall process performance improvement. Optimization of exchanger operation is critical and is feasible through application of right predictive and prescriptive analytics techniques.

A good monitoring process continuously tracks an equipment closely to identify the deviation in the operating parameters and understand issues before it start impacting performance. Nevertheless with multiple process parameter changes it’s not easy, hence use of advanced analytical tools in combination with machine learning techniques and AI is valuable and effective to capture the deviations early and identify it correctly much better than traditional manual efforts. Pro-active technical approach always helps in anticipating the problem early before it can reach alarming levels and generally provides time for correction.

This paper will discuss techniques to identify deviation in equipment performance easily, accurately and get insights quickly. Additionally, the paper will describe how predictive and prescriptive analytics can help determine the impact of deviation, need for corrective action and assist in reaching challenging solutions.

Exchanger performance troubleshooting can be a tedious process as multiple operating and design factors affects the actual performance. At certain times it would require smart design improvements, but at times options are available to restore the desired performance as much as possible through intelligent process adjustments. Ensuring optimal exchanger operation will require complete understanding of the design, process complexities and overall functioning of the equipment.

This paper will cover some of Ingenero’s case studies to demonstrate on how Manufacturers were able to gain substantially enhancing their Overall Asset Effectiveness by improving the exchanger monitoring and performance.