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The College of Engineering: Integrative Systems + Design

Real-Time Optimization of Factory Operations

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Description

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.

Who should attend

This course is aimed at professional engineers and researchers in the chemical processing industry and in discrete manufacturing who are faced with real-time optimization challenges as a consequence of process variability and a wide range of disturbances.

Experts

Stéphane Lafortune was born in Montréal, Québec, Canada. He received the B.Eng degree from École Polytechnique de Montréal in 1980, the M.Eng degree from McGill University in 1982, and the Ph.D degree from the University of California at Berkeley in 1986, all in electrical engineering. Since Sept...
The goal of our research is to develop theory, models and algorithms for the solution of fundamental and practically important problems in the area of Process Systems Engineering (PSE). Our current projects include: (a) Production planning and scheduling; (b) supply chain optimization; (c) chemi...
Data based Time-series Modeling, Advanced Process Control (Model Predictive Control) and Mixed Integer (Linear and Nonlinear) Optimization Expert with 10+ years’ of post-PhD experience in process control, mixed-integer optimization and model identification (Time-series modeling). Practical exper...
Experience Postdoctoral Research Fellow Company Name University of Michigan Graduate Intern Company Name The Dow Chemical Company Dates EmployedJun 2014 – Aug 2014 Employment Duration 3 mos Application of hybrid/discrete systems research to process control logic. Education Carnegie Mel...
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