Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Entire DC Network
Large-Scale Non-Linear Dynamic Optimization For Combining Applications Of Optimal Scheduling And Control, Logan Daniel Beal
Large-Scale Non-Linear Dynamic Optimization For Combining Applications Of Optimal Scheduling And Control, Logan Daniel Beal
Theses and Dissertations
Optimization has enabled automated applications in chemical manufacturing such as advanced control and scheduling. These applications have demonstrated enormous benefit over the last few decades and continue to be researched and refined. However, these applications have been developed separately with uncoordinated objectives. This dissertation investigates the unification of scheduling and control optimization schemes. The current practice is compared to early-concept, light integrations, and deeper integrations. This quantitative comparison of economic impacts encourages further investigation and tighter integration. A novel approach combines scheduling and control into a single application that can be used online. This approach implements the discrete-time paradigm from …
State Measurement Spoofing Prevention Through Model Predictive Control Design, Helen Durand
State Measurement Spoofing Prevention Through Model Predictive Control Design, Helen Durand
Chemical Engineering and Materials Science Faculty Research Publications
Security of chemical process control systems against cyberattacks is critical due to the potential for injuries and loss of life when chemical process systems fail. A potential means by which process control systems may be attacked is through the manipulation of the measurements received by the controller. One approach for addressing this is to design controllers that make manipulating the measurements received by the controller in any meaningful fashion very difficult, making the controllers a less attractive target for a cyberattack of this type. In this work, we develop a model predictive control (MPC) implementation strategy that incorporates Lyapunov-based stability …
Gekko Optimization Suite, Logan Beal, Daniel Hill, Ronald Abraham Martin, John Hedengren
Gekko Optimization Suite, Logan Beal, Daniel Hill, Ronald Abraham Martin, John Hedengren
Faculty Publications
This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and …
Smart Technologies For Oil Production With Rod Pumping, Brigham Wheeler Hansen
Smart Technologies For Oil Production With Rod Pumping, Brigham Wheeler Hansen
Theses and Dissertations
This work enables accelerated fluid recovery in oil and gas reservoirs by automatically controlling fluid height and bottomhole pressure in wells. Several literature studies show significant increase in recovered oil by determining a target bottomhole pressure but rarely consider how to control to that value. This work enables those benefits by maintaining bottomhole pressure or fluid height. Moving Horizon Estimation (MHE) determines uncertain well parameters using only common surface measurements. A Model Predictive Controller (MPC) adjusts the stroking speed of a sucker rod pump to maintain fluid height. Pump boundary conditions are simulated with Mathematical Programs with Complementarity Constraints (MPCCs) …
Integrated Scheduling And Control In Discrete-Time With Dynamic Parameters And Constraints, Logan Beal, Damon Petersen, David R. Grimsman, Sean Warnick, John Hedengren
Integrated Scheduling And Control In Discrete-Time With Dynamic Parameters And Constraints, Logan Beal, Damon Petersen, David R. Grimsman, Sean Warnick, John Hedengren
Faculty Publications
Integrated scheduling and control (SC) seeks to unify the objectives of the various layers of optimization in manufacturing. This work investigates combining scheduling and control using a nonlinear discrete-time formulation, utilizing the full nonlinear process model throughout the entire horizon. This discrete-time form lends itself to optimization with time-dependent constraints and costs. An approach to combined SC is presented, along with sample pseudo-binary variable functions to ease the computational burden of this approach. An initialization strategy using feedback linearization, nonlinear model predictive control, and continuous-time scheduling optimization is presented. The formulation is applied with a generic continuous stirred tank reactor …