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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.

The descriptive and diagnostic portions are first reviewed below.




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 spans 2017 at six-minute intervals.


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




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.




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.



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.






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




Fit for Purpose - Layers of Analytics using the PI System

The following is from the lab notes for the hands-on lab "Fit for Purpose - Layers of Analytics using the PI System: AF, MATLAB, and Machine Learning" at PI World 2018, San Francisco, CA

Part 1 Introduction

Part 2 Alky feed dryer – process analytics - descriptive and diagnostic (Exercise 1)

Part 3 Alky feed dryer – process analytics - diagnostic/predictive/prescriptive (Exercise 1)

Part 4 Motor/Pump – maintenance analytics – usage based, condition based and predictive (Exercise 2)


Layers of analytics can be viewed through many lenses.  Frequently, it refers to the levels of complexity and the kinds of computations required to transform “raw data” to “actionable information/insight.”  It is often categorized into:

  • descriptive analytics - what happened
  • diagnostic analytics - why did it happen
  • predictive analytics - what can/will happen
  • prescriptive analytics - what should I do, i.e. prescribing a course of action based on an understanding of historical data (what happened and why) and future events (what might happen)

The purpose of the analytics i.e. whether it is for descriptive or diagnostic or predictive or prescriptive will influence the “raw data” calculations and transforms.  The following graph shows “value vs. difficulty” as you traverse the layers.



Layers of analytics can also be viewed through a “scope of a business initiative” lens – for example, in asset maintenance and reliability, the layers are:

  • UbM – Usage-based Maintenance  -  AF
  • CbM – Condition-based Maintenance -  AF
  • PdM – Predictive Maintenance - AF plus third party libraries




Layers of analytics can also be categorized by where the analytics is done, such as:

  • Edge analytics
  • Server based analytics
  • Cloud-based analytics


Analytics at the edge include those done immediately with the collected data.  It lessens network load by reducing the amount of data forwarded to a server - for example, Fast-Fourier Transform (FFT) on vibration time wave-forms to extract frequency spectrums. Or, when an action is to be immediately taken based on the collected data without waiting for a round-trip to a remote analytics server.


In the hands-on portion of this Lab:

  • Exercise 1 uses an oil refinery process unit operation (Alky feed dryer) to walk-through the layers i.e. descriptive, diagnostic, predictive and prescriptive
  • Exercise 2 uses a maintenance/reliability scenario (pump/motor assembly) to illustrate the layers i.e. UbM, CbM, and PdM


Items not included in the detailed hands-on portion will be covered as discussion topics during the Lab.


Continue reading:

Part 2 Alky feed dryer – process analytics - descriptive and diagnostic (Exercise 1)

On Dec 4th, 2019, we had a one-day "Data & Analytics to Support Knowledge Management in Life Sciences" event at the MIT Samberg Center in Cambridge, MA.  


The presentations were not recorded, but the links to the slides are below.

if you have questions, please ask in the Comments section below. 







This Lab was part of the TechCon Hands-on Labs during PI Users Conference in 2017 in San Francisco.  The Lab was also offered in Sao Paulo, Brazil during LATAM Regional 2017 and at UC EMEA London. The Lab manual is attached; the manual is intended for an instructor led interactive workshop – so you may find the written content short on some of the explanations.


You can access the VM used for the lab via Home Page - OSIsoft Learning and, look under the Virtual Learning Environment.

Below is an extract from the Intro section of the Lab manual:


At TechCon 2016, we reviewed an end-to-end use case for developing a machine learning (multivariate PCA - principal component analysis) model to predict equipment failure. This lab builds on those concepts but we now use data from a process unit operation and apply data science and machine learning methods for diagnostics.


Troubleshooting faulty processes and equipments – also known as FDD (fault detection and diagnostics) or anomaly detection is a challenge.  This hands-on-lab provides an end-to-end walk-through for applying data driven techniques - specifically machine learning - for such tasks.


The learning objectives of this lab include:

  • Extracting data from the PI System using PI Integrator
  • Using the PI System data with R, data cleansing, feature selection, model development for a multivariate process using PCA (principal component), etc.
  • Using the PCA model with Shiny to create an interactive display for visualizing and exploring faults vs. normal operation; also using SVM (support vector machine) for classification and prediction of Air Handler (AHU) fault/no-fault state
  • Using Azure ML with PI System data for machine learning
  • Deploying the machine learning model for continuous execution with real-time data
  • Understanding the end-to-end data science process – data retrieval, data cleansing, shaping and preparation with meta-data context, feature selection via domain specific guidelines, applying machine learning methods, visualizing the results and operationalizing the findings


The application of data science and machine learning methods are well known in several fields – image and speech recognition, fraud detection, search, shopping recommendations, and others.  In manufacturing, including manufacturing operations management, and particularly in plant-floor operations with time-series sensor data, select data science/machine learning methods are highly effective.


Principal Component Analysis (PCA) is one such well-known and established machine learning technique for gaining insights from multivariate process operations data. PCA has several use cases – exploratory analysis, feature reconstruction, outlier detection, and others. And, other derived algorithms such as PLS (projection to latent structures), O-PLS (orthogonal …), PLS-DA (… discriminant analysis) etc. are widely used in the industry.

In a multivariate process, several parameters - sometimes just a handful but often dozens of parameters - vary simultaneously, resulting in multi-dimensional datasets that are difficult to visualize and analyze. Examples of multivariate processes are:

  • Brewery - Beer fermentation
  • Oil Refinery – Distillation column
  • Facilities – Heating, Ventilation and Air-Conditioning (HVAC) - Air Handler Unit


...In this lab, we use the Air Handler Unit (AHU) to illustrate an approach for analyzing such multivariate processes.  A typical HVAC system with AHU is shown below.



Figure HVAC system with Air Handling Unit (AHU)


Sensor data available from the AHU, as part of the BMS (building management system) are:

  • Outside air temperature
  • Relative Humidity
  • Mixed air temperature
  • Supply air temperature
  • Damper position
  • Chilled water flow
  • Supply air flow
  • Supply air fan VFD (variable frequency drive) power

During the course of a day, the AHU operating conditions change continuously as the outside air temperature rises and falls, along with changing humidity and wind conditions, changing thermostat set-points, building occupancy level, and others. The BMS control system adjusts the supply air flow rate, chilled water flow rate, damper position etc. to provide the necessary heating or cooling to the rooms to ensure tenant comfort.


However, fault conditions such as incorrect/drifting sensor measurements (temperature, flow, pressure …), dampers stuck at open/closed/partial-open position, stuck chilled water valve, and others, can waste energy, or lead to tenant complaints from other malfunctions causing rooms to get too hot or too cold.

For troubleshooting and diagnostics, HVAC engineers need tools to answer questions such as:

  • How can I use data to detect faulty AHU operations i.e. air damper stuck open at 100% open on a hot day in mid-July?
  • What’s the AHU “state” during 100 ºF + days? In 2016? In 2015? And, in 2014 before we installed the Economizer?
  • What are the AHU outlier/extreme operating states?
  • How did it get to the extreme state; what were the immediate prior operating states for that day?
  • What’s the AHU state at supply fan flow limit constraint? When did it happen?

For Part 1, see  PI-System-and-ISO-50001

For Part 2, see  The-7-steps-in-US-DoE-eGuide-for-ISO-50001-to-implement-an-energy-management-system-how-can-pi-system-products-meet-some-of-these-requirements



The PI System meets several of the EnMS requirements via a suite of integrated modules that are categorized under: Collect, Historize, Find, Analyze, Deliver, and Visualize.

  • Collect includes PI Interfaces that collect data from a multitude of data sources.
  • Historize includes PI Server that works with the PI Interfaces to provide the real-time data collection, archiving, and distribution services. The PI Server brings all the relevant data from disparate sources, such as enterprise systems, databases, and operational data sources into a single system, secures it so appropriate access is given to individuals based on their roles, and delivers it to users and applications at all levels of the enterprise.
  • Find includes PI Asset Framework (PI AF) and PI Event Frames (PI EF). PI AF provides for a configurable meta-data layer that can host multiple data models - a process flow, i.e. connectivity model, an equipment component model, a transaction object model, and others.  PI EF provides the infrastructure and a configurable model for storing data by manufacturing events.
  • PI AF and PI EF work together to quickly find the appropriate data in the correct context and inter-relate data and events to profile your energy situation.
  • Analyze PI Analytics provide real-time analytics and enables you to analyze and aggregate real-time and historical data and events into user-defined actionable information or key performance indicators (KPIs).
  • Deliver PI Data Access and PI Notifications provide the functionality to deliver data when, where and how it is needed. PI Data Access supports a number of industry standards such as Web Services (SOAP), OLEDB, ODBC, JDBC, OPC, and others. XML based i/o is supported and with appropriate XSL transform files, it can be used for XML messaging based data exchange
  • Visualize includes PI Coresight, PI WebParts, PI DataLink, PI ProcessBook and others to meet the functional requirements with regard to energy analysis, and energy tracking/reporting.

As a follow up to the previous post, the items in red below indicate the steps from the US DoE eGuide where the PI System capabilities can be used.  For several items, one or more PI System products can completely meet a Step’s requirements. However, in other Steps, the requirements are outside the scope of the PI System and you have to use other tools.

Step 1 Getting Started

Step 2 Profile Your Energy Situation

Step 3 Develop Objectives, Targets and Action Plans

Step 4 Reality Check: Stop! Look! Can I Go?

Step 5 Manage Current State and Improvements

Step 6 Check the System

Step 7 Sustain And Improve The System

Do you have an active ISO 50001 or Energy Management project where you are using the PI System stack?  Please share your findings.

For Part 1 in this series, please see  PI-System-and-ISO-50001


For Part 3 in this series, please see  Using-the-OSIsoft-PI-System-in-ISO-50001-energy-management-system-enms-implementations



The US DoE eGuide for ISO 50001 helps you to implement an EnMS through a step-by-step process. It includes forms, checklists, templates, examples and guidance to assist you throughout the implementation. This eGuide outlines the following steps:

  • Step 1 Getting Started
  • Step 2 Profile Your Energy Situation
  • Step 3 Develop Objectives, Targets and Action Plans
  • Step 4 Reality Check: Stop! Look! Can I Go?
  • Step 5 Manage Current State and Improvements
  • Step 6 Check the System
  • Step 7 Sustain And Improve The System

For details, please see: https://save-energy-...Pages/home.aspx

As illustration, for Step 2.2.3 Formulate a process for acquiring and recording data the eGuide gives the following example:

It is easy to recognize how PI System products such as PI Interfaces and PI Manual Logger that are part of the PI System data collection stack can be used in this step.

Another illustration is for Step 2.3.5 Analyze and track significant energy uses and the eGuide gives the following example:





Here again, you see PI System products such as PI DataLink, PI ProcessBook, PI WebParts and others that are part of the PI System visualization can be used to the meet the requirements in this Step.

Overall, various PI System products can help you with ISO 50001 EnMS implementation. For several items, one or more PI System products can completely meet a Step’s requirements. And, in some cases it may be beyond the scope of PI System and you will have to either extend the PI System or supplement it with other outside tools and methods.

Using the OSIsoft PI System in ISO 50001 Energy Management System (EnMS) Implementations to see the next post in this series.


PI System and ISO 50001

Posted by gopal Oct 15, 2012

Briefly, the purpose of ISO 50001 is to enable organizations to establish the systems and processes necessary to improve energy performance, including energy efficiency and intensity. It specifies requirements for an energy management system (EnMS) to develop and implement an energy policy, establish objectives, targets, and action plans.
An energy management system addresses:

  • energy supply
  • measurement, documentation, and reporting of energy use
  • procurement and design practices for energy-using equipment, systems, and processes

ISO 50001 does not itself state specific performance criteria with respect to energy. The standard applies to all factors affecting energy use that can be monitored and influenced by the organization. It has been designed to be used independently but can be aligned or integrated with other management systems such as ISO 9000 (Quality Management) and ISO 14000 (Environmental Management).


It is based on the Plan-Do-Check-Act continual improvement framework and incorporates energy management into everyday organization practices. The basis of this approach is shown in Figure 1.


Figure 1 Energy Management System Model


The PI System provides the software to implement the data infrastructure for the EnMS data collection, performance analysis and tracking/reporting.


Please respond with your field experiences as you use the PI System stack for meeting ISO 50001 requirements.


If you are new to ISO 50001, please see this: ISO 50001 Energy Management Standard


For Part 2 in this series, please see The 7 steps in US DoE eGuide for ISO 50001 to implement an Energy Management System - How can PI System products meet some of these requirements?

For Part 3 in this series, please see  Using-the-OSIsoft-PI-System-in-ISO-50001-energy-management-system-enms-implementations



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