Intelligent Loop Tuning and Process Optimization of Engineered Settings – A Case study to optimize the MINOX Deoxygenation process system on EGINA FPSO.

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Intelligent Loop Tuning and Process Optimization of Engineered Settings – A Case study to optimize the MINOX Deoxygenation process system on EGINA FPSO.

BACKGROUND:

The proportional-Integral-Derivative (PID) algorithm is widely adopted for closed-loop process control although the best tuning means for a PID loop to achieve optimal performance is still widely opened research and development. Most especially in industrial processes subjected to variations in process dynamic behaviors and parameters perturbations, consequently contributing to process instability.

An improved control loop tuning method such as intelligent loop tuning that can identify the process dynamics within a short period to tune the PID parameters and adapt to the varying process dynamics would help achieve a more stable process system. A typical example of such an unstable process system is the case study of the MINOX Deoxygenation system on EGINA FPSO (where I currently work). The Minox Deoxygenation System is included as a part of the water injection system. The purpose of the unit is to reduce the oxygen content in the outlet water to 20 to 25 ppb or less. The process system package is based on treating seawater with a circulating inert gas, normally nitrogen.

AIMS:

 

Recently, we have experienced several downtimes from the MINOX package leading to shutdowns of the overall water injection systems due to varying causes but not limited to poor process PID control tunings, hence, the need to investigate and research on improved methods for loop tuning to achieve stable and optimized process systems-on the MINOX deoxygenation process package.

OBJECTIVES:

  1. Carry out critical analyses of literature reviews on related theories on Intelligent Loop Tuning for Process Optimization.
  2. Data collections of existing Engineer’ settings (provided by the vendor) on the package UCP vis-à-vis the current controller’s performance.
  3. Analyze any observed deviations and evaluate them accordingly.
  4. Characterize the systems to examine the process response due to changes in process inputs through simulations.
  5. Use real-time experimentations to show the flexibility of the controllers in handling process dynamics and different characteristics.
  6. Record all observations
  7. Highlight necessary recommendations for the systems.