OMS-03 Principles and Applications of LP/NLP Programming in the Refining Industry

Advanced process control strategies invariably use linear and non-linear programming methods to solve complex and non-unique process models solutions. These techniques are employed in refinery planning, blend recipe formulations, optimum operating parameters for process units, etc

The information imparted during the seminar will introduce the techniques of linear programming and affirm the shared knowledge by hand-on lab exercise. Attendees can bring information about their own refinery and blending operations to solve them using actual commercial system demonstrated in the course. The seminar will cover all technical, operational, modeling and economical aspects of planning, optimization of daily refinery operations.

Fule Bledning Optimization System

PROGRAM OUTLINE

Each of modules below will discuss 4-5 topics each of 20-30 minutes duration.  The entire course will be covered in 800+ slides.

Module-1 Overview of Mathematical Programming
  • Modelling of Physical Processes
  • System of Equations- A treatise
  • Introduction to Optimization – Graphical Methodology
  • Linear programming Simplex Algorithm
Module-2 Analysis of Solutions and Integer/NLP Optimization Problems
  • Revised and Dual Problem Simplex Algorithm
  • Marginal values, Infeasibility
  • Use of Sensitivity Analysis to handle infeasibility
  • Integer Programming
  • Non-linear Optimization
Module-3 Case Studies – Linear Problem Optimization
  • Example-1 Diet Planning
  • Example-2 Traveling Salesman routes
  • Example-3 Crude Blending
  • Example-4 Small Refinery products optimization
  • Example-5 Crude Distillation Dual LP Problem
Module-4 Hand-on Lab Exercises
  • Formulation of a Optimization problem
  • Graphical Solution of LP problem
  • Excel GRG2 based Solution
  • Excel based VBA/Fortran LP Solution
Module-5 Introduction to Blending
  • Fuel Blending Operations in Refining
  • Blending Problem Definition
  • Linear Blend Models
  • Non-linear Blend Models
Module-6 Blend Problem Formulation
  • Methods to Handle Blend Non-linearity
  • Fuels Specifications
  • Control matrix of qualities
  • Blending LP formulation
Module-7 Blend Optimization
  • Blend Optimization Methods
  • How to handle Quality violations and giveaways
  • Data Reconciliation and Feedback
  • Levels of Blend optimization
Module-8 Fuel Blending Control System
  • An example of Integrated Fuel Blending System
  • Overview of Fuel Blending Economics
  • Installation and Testing of Offline Blend Optimizer
Module-9 Optimizer Overview
  • Blending Modeling Process
  • Required Data
  • Data Entry Screens
  • Optimization and Discussion of output
Module-10 Lab Exercise – Single Product, Multi-Periods Optimization
  • Single Product, Single Period Optimization
  • Single Product, Multi-Periods Optimization
  • Multi-products, Multi-Periods Optimization
  • Discussion of Results
Module-11 Strategies for Advanced Blend Control System
  • Online versus Offline Optimizers
  • Blending Status Survey Methodology
  • Blending Project Implementation Strategy
  • Why Blending Project Fail
Module-12 Wrap-up and Winding down
  • Demo of Commercial Blending Optimiztion Systems-I
  • Demo of Commercial Blending Optimiztion Systems-II
  • Discussion Forum
  • Feedback and Certificate Awards

Who Should Attend ?

  • Mandatory –  Blending Manager, IT Manager, Refinery Planner,  Refinery Scheduler, Process Engineer, Blending Engineer, Control System Engineer, IT/Engineers/Analyst
  • Recommended – Control System Manager,  Blending Operator
  • Optional – Refinery Manager,  OM&S Manager, Analyzer Engineer, Offsite Operator, Field Operator, Maintenance Engineer, Fuel Traders, Crude Traders

Summary of Course Evaluation

Each course is evaluated by the course attendees for the following 12 categories as in the figure shown below.

 

OMS-3 Evaluation Summary

Few Testimonials

I liked the lab exercise the best about this course as I got to use LP via Excel interface to formulate, solve and analyze a variety of LP problems. It started with mathematics of linear programming, explained the simplex method and graphical way to solve a LP problem. It demonstrated LP/NLP for refinery blending for a number of operational scenarios. It was well worth to attend.—Ales Ponert, Ceska Refinery

Refinery uses LP/NLP for their fuels blending to save money by optimizing the recipe. The course explained complex blending models, method to solve them, optimize the recipe and resolve the problems in case they run into infeasibility. Good course material and presentation. — Marek Betlejewski, Plock Refinery, Poland

We used offline blend optimizers via Excel, stand-alone VB module to run many LP problems such as diet planning, crude blending, small refinery production and fuel blending configurations and learned to create LP models and solve them. The instructor was very interactive to help us learn the concept, mathematics and technology of linear programming as used in a refinery. —Marcos Monguzzi, RefiningNZ

The course is well structured, from concept to real problem to economics and does so by case studies of real refinery LP problems. It discussed linear and non-linear models for fuels blending and showed various options to handle the non-linearity. Would recommend my colleagues to attend next scheduled course.–Rafael Leon, Reficar