• Real-Time Optimization of Factory Operations

Improve Processes in Real-Time by Integration 
of Production Scheduling with Automation Logic

 
As the chemical industry moves towards product diversification and customization, chemical manufacturing, as well as discrete parts manufacturing, is performed in multi-product facilities which are characterized by the production of a suite of products using shared resources according to a demand profile. The performance of these facilities is highly dependent on the quality of production planning and scheduling that directs their overall operation and on the fidelity by which these plans are carried out in the manufacturing process. Human intervention is often necessary for monitoring the process to respond to circumstances that would require reworking plans and schedules to keep them feasible.

This course will present a new methodology for addressing the integration of production planning and scheduling with the discrete logic of the process automation system, thereby closing a capability gap in achieving real-time optimization of factory operations. This new methodology, termed Manufacturing Execution Optimization (MEO), is the result of a collaborative effort involving The Dow Chemical Company, the University of Michigan, the University of Wisconsin, Siemens Corporation, and Kent Displays Inc., under funding from the Digital Manufacturing and Design Innovation Institute (DMDII).
 

Program Agenda

 
Day 1: Overview, Integration of Scheduling & Automation Logic, Simulation

Morning: (3 hours)

  • Course description; presentation of test problem: Lafortune
  • Primer on chemical production scheduling and factory operations: Maravelias and Wassick
  • Simulation of factory operations using SIMIT: Nandola

Afternoon: (3 hours)

  • Real-time optimization of schedules in factory operations: Maravelias
  • Automation logic and its integration with real-time scheduling: Rawlings and Lafortune
Day 2: Implementation of Real-Time Optimization

Morning: (3 hours)

  • Demonstration of integrated approach on case study using software tools: Team
  • Implementation of dynamic real-time optimization of full-scale factory operations: Dow’s experience: Lin and Wassick

Afternoon: (up to 2 hours; end by 3pm)

  • Discussion, more Q&A, wrap up: Team

Learning Objectives

The integrated MEO methodology for real-time optimization that will be taught is composed of:

  • a scheduling optimization model enhanced to consider automation logic
  • a delay monitoring module that monitors the feasibility or lack thereof of the current schedule under the constraints of the automation logic and triggers, as necessary, schedule re-optimization in real time

The course will present the various steps of the integrated MEO methodology, along with demonstration of software tools that implement its key elements. In addition, the course will present a detailed simulation environment for chemical processes in plant operations, employing the tool SIMIT of Siemens Corp., that mimics both process dynamics and automation logic and can be used for high-fidelity analysis of system performance. To make the course as self-contained as possible, some fundamentals on chemical production scheduling and on automation logic in process control systems will also be introduced.

 

Instructors

 
 
Stéphane Lafortune
Stéphane Lafortune
Professor, Electrical Engineering and Computer Science
 
Bao Lin
Bao Lin
Lead Process Automation Manager, Dow Chemical Company
 
Christos Maravelias
Christos Maravelias
Vilas Distinguished Achievement Professor, University of Wisconsin - Madison
 
Nareshkumar Nandola
Nareshkumar Nandola
Research Scientist, Siemens Corp.
 
Blake Rawlings
Blake Rawlings
Postdoctoral Research Fellow, College of Engineering
 
John Wassick
John Wassick
Research Fellow, Dow Chemical Company