Virtual Test Beds For Image-Based Control Simulations Using Blender, Akkarakaran Francis Leonard, Govanni Gjonaj, Minhazur Rahman, Helen E. Durand

Chemical Engineering and Materials Science Faculty Research Publications

Process systems engineering research often utilizes virtual testbeds consisting of physics- based process models. As machine learning and image processing become more relevant sensing frameworks for control, it becomes important to address how process systems engineers can research the development of control and analysis frameworks that utilize images of physical processes. One method for achieving this is to develop experimental systems; another is to use software that integrates the visualization of systems, as well as modeling of the physics, such as three-dimensional graphics software. The prior work in our group analyzed image-based control for the small-scale example of level in …

Triggered Online System Re-Identification Applied To Model Predictive Control Using Gaussian Processes, Daniel Augusto Kestering

Graduate Theses, Dissertations, and Problem Reports (ETD)

Safety, product quality, enhanced performance, and increased profit all depend on the control of chemical and energy processes. However, operational issues can lead to control challenges, especially when processes are subject to disturbances during their operation. Process control methods work best when processes operate close to their designed operat- ing conditions, but lack of performance or other issues may occur when the process is far from such conditions. To overcome these challenges, in this dissertation, online model re- identification is proposed for Model Predictive Control (MPC). This involves reassessing the predictive model of an advanced controller, namely MPC, when re-identification …

Development Of Dynamic Mass-Energy-Thermodynamics Constrained Hybrid Neural Network Models For Process Systems Applications, Angan Mukherjee

Graduate Theses, Dissertations, and Problem Reports (ETD)

First-principles models can provide very good predictions even for cases when there are no data at all, or data are limited in certain range of operating conditions, or for cases where data collection is infeasible. However, the development of accurate first-principles models for complex nonlinear dynamic systems can be time consuming, computationally expensive, and may be infeasible for certain systems due to lack of sufficient knowledge (information). It is also challenging to adapt first-principles models for time-varying systems. Furthermore, it can be difficult, if not impossible, to develop accurate models for some complex phenomena that are poorly understood. On the …

Schaeffler Icvd Coating Machine, Louis Mcgrath

Williams Honors College, Honors Research Projects

Chemical Vapor Deposition (CVD) is mutual technology used to deposit thin film through a gaseous phase by vaporizing the solid materials. This conventional process usually requires high thermal stability of the materials, which is not applicable for most of the polymeric materials. Therefore, a novel process, initiated Chemical Vapor Deposition (iCVD) is developed by introducing the gaseous monument and initiator to form the thin film in-situ. By adjusting the free-radical polymerization in the vapor phase, a variety of thin and uniform polymer films can be achieved. Depending on the chemistry, iCVD has many categories. This report explains the design …

Modeling, Control, And Fault Detection Of Energy Systems Under Limited High-Confidence Data Scenarios, Selorme K. Agbleze

Graduate Theses, Dissertations, and Problem Reports (ETD)

Abstract

Modeling, Control, and Fault Detection of Energy Systems under Limited High-Confidence Data Scenarios

Selorme K. Agbleze

Utilizing process measurements for fault detection is an established approach for processes with adequate datasets. For systems with limited high-confidence data representing fault cases and some amount of low-confidence data, few quantitative hybrid techniques exist for performing fault detection. In real systems, it is time-consuming, expensive, and sometimes not productive to generate enough high-confidence data with fault characteristics of a specific process. The problem of limited high-confidence data scenarios may also arise due to process novelty, the need for new operating conditions, or …

Strategies For Process Systems Mapping And Control Based On Operability Analysis, Victor Manuel Cunha Alves

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation aims to develop strategies for process systems engineering (PSE) mapping using models, emerging tools and algorithms motivated by process operability analysis research. Such strategies will be employed to ensure simultaneous design and control of large-scale industrial systems. The emerging tools and techniques in this research include supervised machine learning-based (ML-based) operability mapping, automatic differentiation (AD) for implicit mapping, and the development of a systematic mapping approach for control structure selection using operability analysis. Thus far, the developed operability algorithms either recur to nonlinear programming (NLP) solutions which are computationally expensive or to linearizing the underlying modeling task at …

State Estimation And Economic Analysis For Electrochemical Sensor-Based Corrosion Monitoring, Chandra Sekhar Somayajula

Graduate Theses, Dissertations, and Problem Reports (ETD)

Coal is currently the third-largest source of electricity production in the U.S. However, renewables are projected to be the primary source of electricity production in the future. Though coal-fired power plants (CFPPs) are rapidly retiring in the U.S., many CFPPs worldwide are expected to stay operational for a while due to factors like easy and cheap access to comparatively inexpensive coal. Therefore, non-renewable sources like coal are still expected to be prevalent and be used to ensure the stability of the electric grid in the foreseeable future. The existing coal-fired power plants generally designed to operate at base-loaded conditions are …

Application Of Predictive Control Strategies For Water-Gas Shift Membrane Reactors Using Data-Driven Models, Bernardo De Oliveira Vecchio

Graduate Theses, Dissertations, and Problem Reports (ETD)

As demand for more efficient processes keeps rising and more restrictive environmental legislation requires higher levels of both yield and purity, the process industry has now, more than ever, been looking for new ways to achieve these goals. One approach has been the implementation of Process Intensification (PI). PI aims to combine multiple unit operations into a single one, promoting efficiency gains on multiple levels. This combination, however, leads to complex process dynamics, and controlling such processes, particularly when setup as multi-input-multi-output (MIMO) systems, presents a great challenge due to highly interactive complex process dynamics and the imposition of many …

Development Of Dynamic Modeling And Estimation Techniques For Condition Monitoring Of Advanced Energy Systems, Vivek Saini

Graduate Theses, Dissertations, and Problem Reports (ETD)

The integration of renewable energy sources into the electric grid requires the current fleet of fossil-fired power plants to operate more flexibly, leading to frequent load changes that stress critical high temperature boiler components that were primarily designed for base load operations. The more complex operating scenarios can modify component damage, compromising reliability and increasing operational costs. To ensure efficient plant operations, adaptive condition monitoring tools generalizable for different plant configurations are essential for recognizing the impacts of load-following, improving safety, and preventing outages. This dissertation work is primarily focused on the development of advanced modeling and estimation techniques frameworks …