Process Control and Systems Commons^™

Augmentation And Hybridization Of Conventional Forms Of Process Control With Advanced Control Methods, Daniel Paul Beahr

Graduate Theses, Dissertations, and Problem Reports (ETD)

Within the last decade in the field of process control, there has developed a distinct gap between the technological advancements of today and the well-established theory that has preceded it. This is most evident where the novel developments and structures often outpace the necessary guarantees and strong foundation required to ensure the possibility of widespread adoption. Therefore, the goals of this work are to broadly integrate those novel advanced control methods into the field of process control in such a way to increase the applicability and encourage implementation.

The first portion of this work seeks to further explore the nature …

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 …

Nonlinear Dynamic Analysis And Control Of Chemical Processes Using Dynamic Operability, San Quan Dinh

Graduate Theses, Dissertations, and Problem Reports (ETD)

Nonlinear dynamic analysis serves an increasingly important role in process systems engineering research. Understanding the nonlinear dynamics from the mathematical model of a process helps to find the boundaries of all achievable process conditions and identify the system instabilities. The information on such boundaries is beneficial for optimizing the design and formulating a control structure. However, a systematic approach to analyzing nonlinear dynamics of chemical processes considering such boundaries in a quantifiable and adaptable way is yet to exist in the literature. The primary aim of this work is to formulate theoretical concepts for dynamic operability, as well as develop …

Actuator Cyberattack Handling Using Lyapunov-Based Economic Model Predictive Control, Keshav Kasturi Rangan, Henrique Oyama, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Cybersecurity has gained increasing interest as a consequence of the potential impacts of cyberattacks on profits and safety. While attacks can affect various components of a plant, prior work from our group has focused on the impact of cyberattacks on control components such as process sensors and actuators and the development of detection strategies for cybersecurity derived from control theory. In this work, we provide greater focus on actuator attacks; specifically, we extend a detection and control strategy previously applied for sensor attacks and based on an optimization-based control technique called Lyapunov-based economic model predictive control (LEMPC) to detect attacks …

Challenges And Opportunities For Next-Generation Manufacturing In Space, Kip Nieman, A. F. Leonard, Katie Tyrell, Dominic Messina, Rebecca Lopez, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

With commercial space travel now a reality, the idea that people might spend time on other planets in the future seems to have greater potential. To make this possible, however, there needs to be flexible means for manufacturing in space to enable tooling or resources to be created when needed to handle unexpected situations. Next-generation manufacturing paradigms offer significant potential for the kind of flexibility that might be needed; however, they can result in increases in computation time compared to traditional control methods that could make many of the computing resources already available on earth attractive for use. Furthermore, resilience …

Reinforcement Learning For Process Control: Applications To Energy Systems, Elijah Ballard Hedrick

Graduate Theses, Dissertations, and Problem Reports (ETD)

Reinforcement learning (RL) is a machine learning method that has recently seen significant research activity owing to its successes in the areas of robotics and gameplaying (Silver et al., 2017). However, significant challenges exist in the extension of these control methods to process control problems, where state and input signals are nearly always continuous and more stringent performance guarantees are required. The goal of this work is to explore ways that modern RL algorithms can be adapted to handle process control problems; avenues for this work include using RL with existing controllers such as model predictive control (MPC) and adapting …

Anomaly-Handling In Lyapunov-Based Economic Model Predictive Control Via Empirical Models, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

A question that faces data-driven autonomous systems is verification that they will perform in a safe manner despite changes in the environment on which they act over time or incomplete knowledge of the system model. This work analyzes closed-loop stability of nonlinear systems under Lyapunov-based economic model predictive control (LEMPC) with data-driven models in the case where it is desirable to have the ability to detect when the data-driven model is or becomes insufficiently accurate for maintaining the closed-loop state in an expected region of state-space. Implications of the results for false sensor measurement cyberattacks seeking to impact the fidelity …

Integrated Cyberattack Detection And Resilient Control Strategies Using Lyapunov-Based Economic Model Predictive Control, Henrique Oyama, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

The use of an integrated system framework, characterized by numerous cyber/physical components (sensor measurements, signals to actuators) connected through wired/wireless networks, has not only increased the ability to control industrial systems, but also the vulnerabilities to cyberattacks. State measurement cyberattacks could pose threats to process control systems since feedback control may be lost if the attack policy is not thwarted. Motivated by this, we propose three detection concepts based on Lyapunov‐based economic model predictive control (LEMPC) for nonlinear systems. The first approach utilizes randomized modifications to an LEMPC formulation online to potentially detect cyberattacks. The second method detects attacks when …

Interactions Between Control And Process Design Under Economic Model Predictive Control, Henrique Oyama, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

conomic model predictive control (EMPC) is a model-based control scheme that integrates process control and economic optimization, which can potentially allow for time-varying operating policies to maximize economic performance. The manner in which an EMPC operates a process to optimize economics depends on the process dynamics, which are fixed by the process design. This raises the question of how process and EMPC designs interact. Works which have addressed process and control design interactions for steady-state operation have sought to simultaneously develop process designs and control law parameters to find the most profitable way to operate a process that is able …

On Accounting For Equipment-Control Interactions In Economic Model Predictive Control Via Process State Constraints, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Traditionally, chemical processes have been operated at steady-state; however, recent work on economic model predictive control (EMPC) has indicated that some processes may be operated in a more economically-optimal fashion under a time-varying operating policy. It is unclear how time-varying operating policies may impact process equipment, which must be investigated for safety and profit reasons. It has traditionally been considered that constraints on process states can be added to EMPC design to prevent the controller from computing control actions which create problematic operating conditions for process equipment. However, no rigorous investigation has yet been performed to analyze whether, when a …

Design And Implementation Of Model Predictive Control Strategies For Improved Power Plant Cycling, Xin He

Graduate Theses, Dissertations, and Problem Reports (ETD)

Design and Implementation of Model Predictive Control Strategies for Improved Power Plant Cycling

Xin He

With the increasing focus on renewable energy sources, traditional power plants such as coal-fired power plants will have to cycle their load to accommodate the penetration of renewables into the power grid. Significant overshooting and oscillatory performance may occur during cycling operations if classical feedback control strategies are employed for plantwide control. To minimize the impact when power plants are operating away from their designed conditions, model-based optimal control strategies would need to be developed for improved power plant performance during cycling.

In this thesis, …

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 …

The Use Of A Cell Filter For State Estimation In Closed-Loop Nmpc Of Low Dimensional Systems, Sridhar Ungarala, Keyu Li

Chemical & Biomedical Engineering Faculty Publications

Combining variants of the Kalman filter and moving horizon estimation (MHE) with nonlinear MPC has been studied before. The MHE is appealing due to its ability to impose constraints and demonstrated superiority over extended Kalman filter. However, nonlinear MPC based on MHE requires solutions to two back to back nonlinear programs. In this paper we propose to use the cell filter (CF) to provide state feedback to the MPC regulator. The cell filter is a piecewise constant approximation of the conditional probability density of the states, whose temporal evolution is modeled by an aggregate Markov chain. Since the CF …