Aspen PIMS: Enhance Planning Results through DR to AO Migration

To provide an intensive problem-solving workshop designed for the intermediate Aspen PIMS user. Detailed emphasis is placed on reviewing Aspen PIMS modeling techniques and troubleshooting errors and warnings in Distributive Recursion (DR) and Advanced Optimization (AO) models.

Audience:

Participants who are familiar with Aspen PIMS and would like a more detailed understanding of the inner workings of the Aspen PIMS system and want advantages of the expanded capabilities of the Aspen PIMS Advanced Optimization software.

Training Details

  • Course Id:

    RPA231

  • Duration:

    5 day(s)

  • CEUs Awarded:

    3.5

  • Level:

    Intermediate

Benefits

  • Learn how to correctly use intermediate level PIMS tables
  • Develop more sophisticated Aspen PIMS models and understand how the matrix structures are generated
  • Develop troubleshooting skills to analyze and solve model problems
  • Learn how to migrate model from PIMS DR to PIMS AO 
  • Gain familiarity with the XNLP and XSLP technologies
  • Use Advanced Optimization Analysis tools to evaluate various scenarios and deviations from optimal solution
  • Execute different Global Optimization procedures
  • Add and edit non-linear formulas to model

Approach

  • This course is oriented toward the practical aspects of refinery operations and planning.
  • Challenging class problems have been designed to illustrate advanced modeling techniques which are used to solve real planning problems. Students learn how Aspen PIMS table entries impact the model at the matrix level.
  • This course also covers approaches to debugging and troubleshooting an Aspen PIMS model. One of the workshop sessions leads the student through analytical steps required to debug a model and migrating to PIMS AO.
  • Class lectures describe the differences between standard Aspen PIMS using Distributed Recursion and Aspen PIMS Advanced Optimization using the XNLP framework, and focus on the use of the new functionality.
  • The features of Aspen PIMS Advanced Optimization are thoroughly described in lectures and demonstrations.
  • Class problems are designed to familiarize the student with the features of Aspen PIMS Advanced Optimization. Time is allotted to allow the student to explore these features.

Pre-requisites

  • Must have attended RPA101 Aspen PIMS: Introduction to Refinery Planning
  • Attendee should have a good understanding of refinery operations and of the basics needed to operate Aspen PIMS

Subsequent Courses

This course serves as a prerequisite for more advanced courses.
  • RPA301 Aspen PIMS: Advanced Refinery Planning Models

Agenda

  • Distributive Recursion: Understanding rules for 999 resolution, error distribution coefficients and error vectors built by Aspen PIMS; non-convergence problems and solutions
  • Matrix Structure: A study of the Aspen PIMS matrix to understand rules for column name, row name and coefficient construction
  • Miscellaneous Tables: Introduce some of the tables used for additional modeling and constraining capabilities
  • Complex LP Structure: Explains several techniques for implementing additional and more sophisticated LP structures; use of tables ROWS, BOUNDS and PROCLIM along with E, L and G rows; create specification rows in submodel tables, etc.
  • Blending: Introduce non-linear blending tables and advanced blending structure
  • Economics: Review of relation between Marginal Cost and Revenue Curves, Marginal Values, Incremental Values, Break Even

Introduction to PIMS Advanced Optimization (AO)
  • Migrating model from DR to AO
  • Comparing Global Optimization technique in DR vs. AO
  • Identify solution analysis methods available in Distributive Recursion and Advanced Optimization and apply correct solution analysis technique to corresponding type of problem
  • Use Parallel Processor architecture for running multiple cases and Global Optimization Multistart
  • Identify main sources of nonlinearities in models and add non-linear formula to the model
  • Workshop: Provide the student with hands-on experience in solving problems such as troubleshoot a model by reviewing warning and errors, exploring AO functionality in detailed, adding non-linearity into the model

Register for a Class

Date Class Type Location Price Language
Date(s): 08/18/2025 - 08/22/2025 Type: Public Classroom Location:
Seoul , South Korea 		Price: (KRW) 1925000.00 Language: English Register
Date(s): 09/1/2025 - 09/5/2025 Type: Public Virtual Location: Virtual-EMEA Price: (USD) 5000.00 Language: English Register
Date(s): 09/1/2025 - 09/5/2025 Type: Public Classroom Location: C2, Reading International Business Park, Basingstoke Road
Reading , United Kingdom RG2 6DT 		Price: (USD) 5000.00 Language: English Register
Date(s): 10/6/2025 - 10/10/2025 Type: Public Classroom Location: C2, Reading International Business Park, Basingstoke Road
Reading , United Kingdom RG2 6DT 		Price: (USD) 5000.00 Language: English Register
Date(s): 12/1/2025 - 12/5/2025 Type: Public Classroom Location: C2, Reading International Business Park, Basingstoke Road
Reading , United Kingdom RG2 6DT 		Price: (USD) 5000.00 Language: English Register
Date(s): 07/21/2025 - 07/25/2025 Type: Public Virtual Location: Virtual-India Price: (INR) 105000.00 Language: English Register
Date(s): 10/20/2025 - 10/24/2025 Type: Public Virtual Location: Virtual-Americas Price: (USD) 5000.00 Language: English Register
Date(s): 07/14/2025 - 07/18/2025 Type: Public Classroom Location: 2500 Citywest Blvd, Suite 1600
Houston , Texas USA 77042 		Price: (USD) 5000.00 Language: English Register
Date(s): 10/20/2025 - 10/24/2025 Type: Public Classroom Location: 2500 Citywest Blvd, Suite 1600
Houston , Texas USA 77042 		Price: (USD) 5000.00 Language: English Register
Date(s): 02/2/2026 - 02/6/2026 Type: Public Virtual Location: Virtual-EMEA Price: (USD) 5000.00 Language: English Register

Aspen Technology, Inc. awards Continuing Education Units (CEUs) for training classes conducted by our organization. One CEU is granted for every 10 hours of class participation.