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Fit for Purpose - Layers of Analytics using the PI System - Part 2

Blog Post created by gopal on Jan 22, 2020

Oil refinery process unit operation – Alkylation feed dryer (Exercise 1)

This exercise uses an oil refinery Alkylation feed dryer process to illustrate the layers of analytics - descriptive, diagnostic, predictive and prescriptive.

First, the descriptive and diagnostic portions are reviewed below.

 

FeedDryers_Physical.png

 

The process consists of twin dryers – Dryer A and Dryer B - each with stacked beds of desiccant and molecular sieve to remove moisture from a hydrocarbon feed.  The dryers are cycled back and forth i.e. when one is removing moisture from the feed, the other is in a regeneration mode where the bed is heated to dry out the moisture from a previous run.

 

The modelling objective is to create a temperature profile representing proper regeneration of the dryer bed.  This profile is analyzed via AF Analytics and then a golden profile is extracted via R/MATLAB and subsequently operationalized again using AF Analytics, PI Notifications and PI Vision. 

 

The data used for this Exercise comes from an actual oil refinery and covers a year's (2017) data  at six-minute intervals.

 

PI Vision displays below show the Dryers in Process (green) and Regeneration (red) states.

FeedDryerOperatingStates.png

 

FeedDryerOperatingStates2.png

The descriptive analytics consists of calculations using sensor data for temperatures, flows, valve positions etc. to identify the dryer status i.e. Operations vs. Regeneration.

FeedDryers_AFModel.png

 

FeedDryers_AFAnalysis.png

The process piping configuration (via valve open/close) and the measurement instruments generating the sensor data are such that you have to perform several calculations similar to those shown above to prepare the data for subsequent steps i.e. diagnostic, predictive and prescriptive.

 

Also, event frames are constructed to track the start and end of each regeneration cycle for Dryers A and B.

FeedDryers_EFPIVision.png

 

More calculations with the flow sensor data is done for Dryer processing age defined as:

Lifetime volume of feed dried by a bed (bbls)
Molecular sieve load in dryer (lbs)

 

Since the feed flow rate varies, additional analysis is done to calculate the volume (bbls) of feed processed before each regeneration cycle.

 

FeedDryers_PIVisionFlowBuckets.png

 

FeedDryers_PIVisionProcessingAgeCurve.png

 

Event frames with the requisite data for additional diagnostics is exported using PI Integrator for Business Analytics.

FeedDryers_EF_PSE.png

 

 

Fit for Purpose - Layers of Analytics using the PI System

 

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