Advanced And Novel Modeling Techniques For Simulation, Optimization And Monitoring Chemical Engineering Tasks With Refinery And Petrochemical Unit Applications

CATEGORY: PROCESS CONTROL
THESIS
Advanced And Novel Modeling Techniques For Simulation, Optimization And Monitoring Chemical Engineering Tasks With Refinery And Petrochemical Unit Applications
Gregory Robertson
A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Department of Chemical Engineering (2014)
Abstract
Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making.
Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models.
Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed.
Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit.
Thesis Focus
In an effort to automate and improve decision-making, chemical engineers create tools to aid their goals of predictability, profitability, and safety. Process control engineers specifically perform predicting, optimizing, and process monitoring tasks. Complex mathematical models are solved through a computationally expensive iterative procedure. With advancements of computational ability, modeling software’s ability to mimic industrial problems has improved significantly. This has allowed engineers to push the boundaries of the size, complexity and detail of the engineering problems they model to make process decisions with a vastly improved level of accuracy.
The purpose of this research is to study and apply advanced and novel mathematical modeling techniques to aid in solving chemical engineering problems. A large amount of the creativity of this work has been not only in the designing methodology, but also in overcoming practical implementation difficulties.
Free Full Text Source: http://etd.lsu.edu/docs/available/etd-01222014-142700/