This book provides a complete and comprehensive reference/guide to Pyomo (Python Optimization Modeling Objects) for both beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. The text illustrates the breadth of the modeling and analysis capabilities that are supported by the software and support of complex real-world applications. Pyomo is an open source software package for formulating and solving large-scale optimization and operations research problems. The text begins with a tutorial on simple linear and integer programming models. A detailed reference of Pyomo's modeling components is illustrated with extensive examples, including a discussion of how to load data from data sources like spreadsheets and databases. Chapters describing advanced modeling capabilities for nonlinear and stochastic optimization are also included. The Pyomo software provides familiar modeling features within Python, a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions. The software supports a different modeling approach than commercial AML (Algebraic Modeling Languages) tools, and is designed for flexibility, extensibility, portability, and maintainability but also maintains the central ideas in modern AMLs.

https://calhoun.nps.edu/bitstream/10945/37919/1/Dimitrov-PyomoReview.pdf

Pyomo - Optimization Modeling in Python

The development of an artificial pancreas is placed in the context of the history of the field of feedback control systems, beginning with the water clock of ancient Greece, and including a discussion of current efforts in the control of complex systems. The first generation of artificial pancreas devices included two manipulated variables (insulin and glucose infusion) and nonlinear functions of the error (difference between desired and measured glucose concentration) to minimize hyperglycemia while avoiding hypoglycemia. Dynamic lags between insulin infusion and glucose measurement were relatively small for these intravenous-based systems. Advances in continuous glucose sensing, fast-acting insulin analogs, and a mature insulin pump market bring us close to commercial realization of a closed-loop artificial pancreas. Model predictive control is discussed in-depth as an approach that is well suited for a closed-loop artificial pancreas. A major challenge that remains is handling an unknown glucose distur...

A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas.

A first contaminant selected from oxygen and carbon monoxide is removed from impure liquid carbon dioxide using a mass transfer separation column system which is reboiled by indirect heat exchange against crude carbon dioxide fluid, the impure liquid carbon dioxide having a greater concentration of carbon dioxide than the crude carbon dioxide fluid. The invention has particular application in the recovery of carbon dioxide from flue gas generated in an oxyfuel combustion process or waste gas from a hydrogen PSA process. Advantages include reducing the level of the first contaminant to not more than 1000 ppm.

Purification of carbon dioxide

Progress in dynamic simulation of thermal power plants

Process systems engineering: From Solvay to modern bio- and nanotechnology.: A history of development, successes and prospects for the future

Master process control hands on, through practical examples and MATLAB(R) simulationsThis is the first complete introduction to process control that fully integrates software tools-enabling professionals and students to master critical techniques hands on, through computer simulations based on the popular MATLAB environment. Process Control: Modeling, Design, and Simulation teaches the field's most important techniques, behaviors, and control problems through practical examples, supplemented by extensive exercises-with detailed derivations, relevant software files, and additional techniques available on a companion Web site. Coverage includes: Fundamentals of process control and instrumentation, including objectives, variables, and block diagrams Methodologies for developing dynamic models of chemical processes Dynamic behavior of linear systems: state space models, transfer function-based models, and more Feedback control; proportional, integral, and derivative (PID) controllers; and closed-loop stability analysis Frequency response analysis techniques for evaluating the robustness of control systems Improving control loop performance: internal model control (IMC), automatic tuning, gain scheduling, and enhancements to improve disturbance rejection Split-range, selective, and override strategies for switching among inputs or outputs Control loop interactions and multivariable controllers An introduction to model predictive control (MPC)Bequette walks step by step through the development of control instrumentation diagrams for an entire chemical process, reviewing common control strategies for individual unit operations, then discussing strategies for integrated systems. The book also includes 16 learning modules demonstrating how to use MATLAB and SIMULINK to solve several key control problems, ranging from robustness analyses to biochemical reactors, biomedical problems to multivariable control.

B. Bequette

Papers

Process Control: Modeling, Design and Simulation