Description of modules
In this section we provide the description of the lectures and seminars for each of the modules of the workshop:
Part 1. Optimization (Aug. 16, 17, 18)
Nonlinear and Dynamic Optimization. Instructor: Lorenz T. Biegler
This module considers optimization problems described by continuous variables and smooth objective and constraint functions, described by algebraic and/or differential equations. Emphasis is placed on core concepts and algorithms for nonlinear programming, both for unconstrained and constrained optimization, as well as extensions to deal with large-scale problems with algebraic and differential-algebraic equation models. Several chemical engineering examples illustrate these concepts.
Mixed-integer Optimization. Instructor: Ignacio E. Grossmann
This module will present an overview of mixed-integer linear and mixed-integer nonlinear programming for the discrete/continuous optimization of process systems. Emphasis will be placed on basic concepts of theory and methods, as well as on modeling through the use of propositional logic and disjunctive programming. Finally, recent trends in generalized disjunctive programming will also be presented.
Global Optimization and Optimization under Uncertainty. Instructor: Nikolaos V. Sahinidis
This module will present algorithms and applications of global optimization and optimization under uncertainty. Emphasis will be placed on deterministic algorithms including convexification, branch-and-bound, and techniques that integrate optimization and constraint propagation for the solution of discrete and continuous problems. Students will be introduced to the use of state-of-the-art global optimization software and the various models of uncertainty, including stochastic programming and probabilistic programming.
María Soledad Díaz:
Dynamic modeling and optimization of large-scale cryogenic separation processes.
Disjunctive Programming: algorithms, implementation and solution of linear and non-linear models.
Uncertainty analysis of process design and
Part 2. Process and Product Design (Aug. 19, 20)
Biological pathways analysis and engineering. Instructor: Costas Maranas
In this lecture, we will discuss how systems engineering and optimization techniques can be allied in the analysis and redesign of biological pathways. Specifically, we will focus on regulatory networks and metabolic network reconstructions. First, we will explore how LP and MILP frameworks enable us to probe the performance limits of metabolic networks in response to gene additions and/or deletions. We will also explore the use of bilevel optimization to suggest metabolic networks modifications that lead to targeted overproduction. We will finally highlight a framework for efficiently analyzing the topological properties of genome-scale stoichiometric models revealing partial, total or even directional couplings between different reactions under a variety of conditions.
Heat Integration. Instructor: Miguel J. Bagajewicz
This module will present heat integration techniques in process plants. The well-known pinch design methods and its variants, two-step and one-step superstructure approaches as well as newly developed transportation/transshipment models will be included. In a second part, energy integration in the total site, models for retrofit of existing units, including planning models considering budgeting and financial risk will be presented.
Mass Integration and Pollution Prevention. Instructor: Mahmoud M. El-Halwagi
This module will present fundamentals and tools of mass integration along with applications in the area of industrial pollution prevention. Graphical, algebraic, and optimization techniques will be presented. Emphasis will be given to the holistic understanding of generation, separation, and allocation of species and streams throughout the process. Future directions in the area of mass integration will also be discussed.
Water treatment networks.
Ana Maria Eliceche:
Synthesis of membrane processes for effluent treatment and metal recovery
Synthesis of crystallization-based separation systems.
Part 3. Process and Supply Chain Operations (Aug. 22, 23)
Batch Scheduling. Instructor: Jaime Cerdá
Batch processes often involve batch mixing and splitting together with recycle streams and batch size-dependent processing times. Until recently, few algorithmic approaches dealing with such a general batch scheduling problem had been published. This module will provide a general description of discrete and continuous-time mathematical formulations for the scheduling of general batch processes available in the literature from the past decade, as well as the most recent advances in this field. In addition, a unified approach for predictive & reactive scheduling of batch processes based on the notion of generalized predecessor will be presented.
Supply chain optimization. Instructor: Jose Pinto
This module focuses on optimization-based techniques for supply chain management. Topics include detailed coverage of decisions in the supply chain world, building blocks of a supply chain network, performance measures, and mainly optimization models and solution techniques for supply chain decision-making. Additional topics covered include applications in specific areas such as the chemical processing and pharmaceutical industries.
New product development. Instructor: Rex Reklaitis
The strategic and tactical decisions associated with the management of the new product development process constitute an important category of large scale supply chain decision problems. In this module the aspects of project portfolio selection and sequencing, resource assignment and test scheduling, and capacity allocation will be discussed. Models and solution approaches considered will include deterministic MILP, stochastic MILP, and a combination of Monte Carlo simulation with multilevel optimization subproblems which allow consideration of risk measures.
Operations management in the fruit industry.
Constraint programming techniques for batch scheduling.
Part 4. Process Dynamics and Control (Aug. 24, 25)
Advanced Process Dynamics and Control. Instructor: Oscar Crisalle.
This module will present an overview of advanced process dynamics and control theory. The scope includes modeling of dynamic systems using physical principles, linearization of dynamics, and analysis of linear state-space descriptions including stability, controllability and observability. Advanced design strategies, including pole-placement control, optimal control, predictive control, l-1 control, and H-infinity control will be presented.
Model Predictive Control. Instructor: Jay H. Lee
This module will present an overview of model predictive control (MPC) for continuous and batch systems. We will start with a brief history of MPC and what features make MPC a powerful tool for practical control applications in the process industries. We will then cover the standard formulation of Quadratic Dynamic Matrix Control. We then discuss some potential enhancements and extensions, including the state-space formulation. Finally, some case studies will be shown.
Process control Design. Instructor: Thomas E. Marlin
This module will present theory and practice of process control design. The course will provide a method for achieving the goals for the design (dynamic performance, robustness, integrity, and profitability) using a range of heuristics, short-cut metrics, and rigorous optimization methods. The importance of realistic scenarios, equipment models, and process structures will be demonstrated through case studies.
Dynamic process simulation