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

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, model predictive control (MPC) strategies are designed and implemented for improved power plant cycling. The MPC strategies addressed correspond to a dynamic matrix control (DMC)-based linear MPC, a classical sequential quadratic programming (SQP)-based nonlinear MPC, a direct transcription-based nonlinear MPC and a proposed modified SQP-based nonlinear MPC. The proposed modified SQP algorithm is based on the backtracking line search framework, which employs a group of relaxed step acceptance conditions for faster convergence. The numerical results for motivating examples, which are selected from literature problem sets, served as proof of concept to verify that the proposed modified SQP has the potential for implementation on high-dimensional systems. To illustrate the tracking performance and computational efficiency of the developed MPC strategies, three processes of different dimensionalities are addressed. The first process is an integrated gasification combined cycling power plant with a water-gas shift membrane reactor (IGCC-MR), which is represented by a first-principles and simplified systems-level nonlinear model in MATLAB. For this application, a setpoint tracking scenario simulating a step increase in power demand, a disturbance rejection scenario simulating a coal feed quality change, and a trajectory tracking scenario simulating a wind power penetration into the power grid are presented. The second application is an aqueous monoethanolamine (MEA)-based carbon capture process as part of a supercritical pulverized coal-fired (SCPC) power plant, whose model is built in Aspen Plus Dynamics. For this system, disturbance rejection scenarios considering a ramp decrease in the flue gas flow rate as well as wind power penetration, and a scenario considering a combination of disturbance rejection and setpoint tracking are addressed. The third process is the entire SCPC power plant with MEA-based carbon capture (SCPC-MEA), which simulation is also built in Aspen Plus Dynamics. Trajectory tracking and disturbance rejection scenarios associated with wind and solar power penetrations are presented for this process. The MPC implementations on the three processes for the different scenarios addressed are successful. The closed-loop results show that the proposed modified SQP-based nonlinear MPC enhances the tracking performance by up to 96% when compared to the DMC-based linear MPC in terms of integral squared error results. The novel approach also improves the MPC computational efficiency by 20% when compared to classical SQP-based and direct transcription-based nonlinear MPCs

Modelling and Optimization of a Pilot-Scale Entrained Flow gasifier using Artificial Neural Networks

In this research, the construction and validation of both ANN and RNN models was presented to accurately and efficiently predict both steady state and dynamic performance of a pilot-scale gasifier unit. The corresponding ANN and RNN models’ performance were validated using data generated from a gasifier’s ROM. After validation of ANN and RNN models, optimization studies on the steady state and transient performance of the gasifier were performed under different scenarios. In the optimization studies at steady state, results show that increasing the peak temperature limitation of the gasifier can promote a high maximum carbon conversion. In the dynamic optimization studies, the results show that increasing the peak temperature limitation of the gasifier can lead to higher CO compositions at the outlet of the gasifier. These optimization studies further showcase the benefit of the ANN and RNN models, which were able to obtain relatively accurate predictions for the gasifier similar to the results generated by ROM at a much lower computational cost

Development of Biomimetic-Based Controller Design Methods for Advanced Energy Systems

A biologically inspired optimal control strategy, denoted as BIO-CS, is proposed for advanced energy systems applications. This strategy combines the ant\u27s rule of pursuit idea with multi-agent and optimal control concepts. The BIO-CS algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower agents\u27 local interactions. The developed BIO-CS is integrated with an Artificial Neural Network (ANN)-based adaptive component for further improvement of the overall framework. In particular, the ANN component captures the mismatch between the controller and the plant models by using a single-hidden-layer technique with online learning capabilities to augment the baseline BIO-CS control laws. The resulting approach is a unique combination of biomimetic control and data-driven methods that provides optimal solutions for dynamic systems.;The applicability of the proposed framework is illustrated via an Integrated Gasification Combined Cycle (IGCC) process with carbon capture as an advanced energy system example. Specifically, a multivariable control structure associated with a subsystem of the IGCC plant simulation in DYNSIMRTM software platform is addressed. The proposed control laws are derived in MATLAB RTM environment, while the plant models are built in DYNSIM RTM, and a previously developed MATLABRTM-DYNSIM RTM link is employed for implementation purposes. The proposed integrated approach improves the overall performance of the process up to 85% in terms of reducing the output tracking error when compared to stand-alone BIO-CS and Proportional-Integral (PI) controller implementations, resulting in faster setpoint tracking.;Other applications of BIO-CS addressed include: i) a nonlinear fermentation process to produce ethanol; and ii) a transfer function model derived from the cyber-physical fuel cell-gas turbine hybrid power system that is part of the Hybrid Performance (HYPER) project at the National Energy Technology Laboratory (NETL). Other theoretical developments in this work correspond to the integration of the BIO-CS approach with Multi-Agent Optimization (MAO) techniques and casting BIO-CS as a Model Predictive Controller (MPC). These developments are demonstrated by revisiting the fermentation process example. The proposed biologically-inspired approaches provide a promising alternative for advanced control of energy systems of the future

Gasification for Practical Applications

Although there were many books and papers that deal with gasification, there has been only a few practical book explaining the technology in actual application and the market situation in reality. Gasification is a key technology in converting coal, biomass, and wastes to useful high-value products. Until renewable energy can provide affordable energy hopefully by the year 2030, gasification can bridge the transition period by providing the clean liquid fuels, gas, and chemicals from the low grade feedstock. Gasification still needs many upgrades and technology breakthroughs. It remains in the niche market, not fully competitive in the major market of electricity generation, chemicals, and liquid fuels that are supplied from relatively cheap fossil fuels. The book provides the practical information for researchers and graduate students who want to review the current situation, to upgrade, and to bring in a new idea to the conventional gasification technologies

A Virtual Engineering Framework for Simulating Advanced Power System

In this report is described the work effort performed to provide NETL with VE-Suite based Virtual Engineering software and enhanced equipment models to support NETL's Advanced Process Engineering Co-simulation (APECS) framework for advanced power generation systems. Enhancements to the software framework facilitated an important link between APECS and the virtual engineering capabilities provided by VE-Suite (e.g., equipment and process visualization, information assimilation). Model enhancements focused on improving predictions for the performance of entrained flow coal gasifiers and important auxiliary equipment (e.g., Air Separation Units) used in coal gasification systems. In addition, a Reduced Order Model generation tool and software to provide a coupling between APECS/AspenPlus and the GE GateCycle simulation system were developed. CAPE-Open model interfaces were employed where needed. The improved simulation capability is demonstrated on selected test problems. As part of the project an Advisory Panel was formed to provide guidance on the issues on which to focus the work effort. The Advisory Panel included experts from industry and academics in gasification, CO2 capture issues, process simulation and representatives from technology developers and the electric utility industry. To optimize the benefit to NETL, REI coordinated its efforts with NETL and NETL funded projects at Iowa State University, Carnegie Mellon University and ANSYS/Fluent, Inc. The improved simulation capabilities incorporated into APECS will enable researchers and engineers to better understand the interactions of different equipment components, identify weaknesses and processes needing improvement and thereby allow more efficient, less expensive plants to be developed and brought on-line faster and in a more cost-effective manner. These enhancements to APECS represent an important step toward having a fully integrated environment for performing plant simulation and engineering. Furthermore, with little effort the modeling capabilities described in this report can be extended to support other DOE programs, such as ultra super critical boiler development, oxy-combustion boiler development or modifications to existing plants to include CO2 capture and sequestration

Polymer Reactor Modeling, Design and Monitoring

Polymers range from synthetic plastics, such as polyacrylates, to natural biopolymers, such as proteins and DNA. The large molecular mass of polymers and our ability to manipulate their compositions and molecular structures have allowed for producing synthetic polymers with attractive properties. new polymers with remarkable characteristics are synthesized. Because of the huge production volume of commodity polymers, a little improvement in the operation of commodity-polymer processes can lead to significant economic gains. On the other hand, a little improvement in the quality of specialty polymers can lead to substantial increase in economic profits

Reduced Order Modeling and Scale-up of an Entrained Flow Gasifier

Climate change has increased attention towards reduction of carbon dioxide (CO2) emissions and other heat-trapping gases to the atmosphere. This has affected the operation of process industries, particularly solid fuel power plants which are responsible for almost a third of the total CO2 emissions. Among the choices for power generation from solid fuels, gasification-based power plants have been accepted as one of the most efficient means of generating electricity from solid fuels, when a CO2 capture unit is considered in the plant’s layout. However, improvements in the cost and availability of gasifiers are still required to make this technology competitive with combustors. In recent years, a new class of compact gasifiers, also known as short-residence time gasifiers, has been proposed to reduce the cost of power generation. To speed up the development of this technology, insights regarding the operability, efficiency and feasibility of these gasifiers are required through a mathematical modelling analysis. This research aims to develop a computationally efficient dynamic reduced order model (ROM) that considers the essential features of a short-residence time gasifier. The ROM was initially validated for steady-state simulation of the pilot-scale gasifier by using data obtained from computational fluid dynamic (CFD) simulations and experimental tests. Although the framework of the ROM was fixed and developed based on CFD simulation generated at a base-case condition, the results showed reasonable agreement between the two models under different operating conditions and kinetic parameters. In addition, the ROM predicted the experimental observations for conversion in the range of 48-90%. The proposed ROM has shown to be computationally attractive as it reduces the computation time by two orders of magnitude when compared to CFD simulations. The attractive computational costs of the ROM has allowed the evaluation of the gasifier’s performance through sensitivity analysis, uncertainty quantification, parameter estimation, dynamic simulation and process scale-up. The results of a sensitivity analysis indicated that the recirculation ratio and oxygen flowrate have a greater effect on the process compared to model geometry and kinetic parameters. An uncertainty quantification was performed to investigate the variability in the ROM’s key outputs in the presence of uncertainty in parameters that affect the feedstock’s properties and the mixing/laminar flows within different zones of the reactor network. The study revealed significant variability in the conversion, peak temperature and steam percentage in the syngas; while the dry syngas composition does not seem to be significantly affected by the uncertainty of the considered parameters. Since the recirculation ratio is the most influential parameter in the ROM, and its true value is typically uncertain, a new semi-empirical correlation was proposed to estimate this parameter. The proposed correlation improved the well-known method of Thring and Newby for jet-flow recirculation by adding a term that takes into account the changes in the feed streams on the recirculation. This feature enhances the prediction capabilities of the reactor network, especially in dynamic simulations where the inlet flowrates may change over time, e.g., for load-following power plants. The dynamic simulation of the gasifier was then performed by implementing this correlation. Accordingly, the operability of the pilot-scale gasifier based on the responses the dry syngas composition, temperature distribution, cold gas efficiency, slag thickness and flowrate were studied under sinusoidal changes in the feed, load-following and co-firing scenarios. Furthermore, the ROM was scaled-up to perform the steady-state simulation of a 3,000 TPD commercial-scale short-residence time gasifier which uses a multi-element injector feed system with 36 nozzles. The performance of the gasifier was then examined under changes in the operating pressure, number of injectors and fuel distribution among injection tubes. The results provided valuable insights regarding the suitability of design parameters and the operational conditions which may damage the gasifier’s refractory and injectors. Based on the simulations performed through this research, a systematically developed ROM that captures the streamlines of the multiphase flow, can predict the behaviour of a gasifier for a wide range of operating conditions with reasonable accuracy. Moreover, a ROM can provide valuable insights on the following objects: 1) the suitability of the design parameters and model assumptions; 2) identifying the critical operating conditions or demand scenarios that may impose a safety hazard or operational constraints; and 3) the flexibility of the system under the changes in fuel, load and failure of mechanical equipment

Development and Application of Optimal Design Capability for Coal Gasification Systems

The basic objective of this research is to develop a model to simulate the performance and cost of oxyfuel combustion systems to capture CO{sub 2} at fossil-fuel based power plants. The research also aims at identifying the key parameters that define the performance and costs of these systems, and to characterize the uncertainties and variability associated with key parameters. The final objective is to integrate the oxyfuel model into the existing IECM-CS modeling framework so as to have an analytical tool to compare various carbon management options on a consistent basis