Dynamic Modelling and Control of MEA

Greenhouse gas (GHG) emission control has been extensively studied over the past decade. One GHG mitigation alternative is post-combustion carbon dioxide (CO2) capture using chemical absorption, which is a promising alternative due to its proven technology and the relative ease to install on existing coal-fired power plants. Nevertheless, the implementation of commercial-scale CO2 capture plants faces several challenges, such as high energy consumption, commercial availability, and geological CO2 storage. Therefore, there is a great incentive to develop studies that provide insights needed to design and dynamically operate industrial-scale CO2 capture plants for coal-fired power plants. This work presents a mechanistic dynamic model of a pilot plant of a post-combustion CO2 capture plant using the monoethanolamine (MEA) absorption processes. This model was implemented in gPROMS. The process insights gained from the sensitivity analysis, on six manipulated variables and six potential controlled variables, was used to determine promising control schemes for this pilot plant. This study then proposed three decentralized control structures. The first control scheme was designed based on the traditional-RGA (Relative Gain Array) analysis, whereas the other two control schemes were designed using heuristics. The performance evaluation of those control structures were conducted under eight scenarios, e.g. changes in flue gas composition, set point tracking, valve stiction, reboiler heat duty constraint, and flue gas flow rate. Under the condition where the reboiler temperature is to be controlled, a control scheme obtained from the heuristic showed faster response to achieve the process control objectives (90% CO2 capture rate and 95 mol% CO2 purity in the CO2 product stream) than the RGA-based control scheme. Furthermore, this study describes a step-by-step method to scale-up an MEA absorption plant for CO2 capture from a 750 MW supercritical coal-fired power plants. This industrial-scale CO2 capture plant consists of three absorbers (11.8 m diameter, 34 m bed height) and two strippers (10.4 m diameter, 16 m bed height) to achieve 87% CO2 captured rate and 95% CO2 purity in the CO2 product stream. It was calculated that the reboiler heat duty of 4.1GJ is required to remove 1 tonne of CO2 at the base case condition (20 kmol/s of flue gas flow rate with 16.3 mol% of CO2). The mechanistic model of an industrial-scale CO2 capture plant including a proposed control structure was evaluated using different scenarios. The performance evaluation result revealed that this plant can accommodate a maximum flue gas flow rate of +22% from the nominal condition due to absorbers’ flooding constraints. Moreover, it is able to handle different disturbances and offers prompt responses (After a plant is disturbed by an external perturbation, control variables in that plant are able to return to their set points in timely fashion using the adjustment of manipulated variables.) without significant oscillating signal or offset. In addition, this study highlights that the poor wetting in the strippers can be avoided by the implementation of a process scheduling, which has not been presented in any publications. Based on the above, the mechanistic models of CO2 absorption plants and proposed control structures provide insights regarding dynamic behaviour and controllability of these plants. In addition, the industrial-scale CO2 capture plant model can be used for future studies, i.e. integration of power plant and CO2 capture plant, feasibility of plant operation, and controllability improvement

Robust Observability, Control, & Economics of Complex Cyber-Physical Networks

This dissertation deals with various aspects of cyber-physical system. As an example of cyber physical systems, we take transportation networks and solve various problems, namely: 1) Network Observability Problem, 2) Network Control Problem, and 3) Network Economics Problem. We have divided the dissertation into three parts which solve these three problems separately. First part of the dissertation presents a novel approach for studying the observability problem on a general network topology of a traffic network. We develop a new framework which investigates observability in terms of flow information on arcs and the routing information. Second part of the dissertation presents a feedback control design for a coordinated ramp metering problem for two consecutive on-ramps. We design a traffic allocation scheme for ramps based on Godunov’s numerical method and using distributed model. Third part of the dissertation presents a novel approach to model Vehicle Miles Traveled (VMT) dynamics and establish a methodology for designing an optimal VMT tax rate. An Optimal control problem is formulated by designing a cost function which aims to maximize the generated revenue while keeping the tax rate as low as possible. Using optimal control theory, a solution is provided to this problem. To the best knowledge of authors all the three problems have not been solved using the methods proposed in this dissertation, and hence they are a novel contribution to the field

Modeling and supervisory control design for a combined cycle power plant

The traditional control strategy based on PID controllers may be unsatisfactory when dealing with processes with large time delay and constraints. This paper presents a supervisory model based constrained predictive controller (MPC) for a combined cycle power plant (CCPP). First, a non-linear dynamic model of CCPP using the laws of physics was proposed. Then, the supervisory control using the linear constrained MPC method was designed to tune the performance of the PID controllers by including output constraints and manipulating the set points. This scheme showed excellent tracking and disturbance rejection results and improved performance compared with a stand-alone PID controller’s scheme