Multi-Modular Integral Pressurized Water Reactor Control and Operational Reconfiguration for a Flow Control Loop

This dissertation focused on the IRIS design since this will likely be one of the designs of choice for future deployment in the U.S and developing countries. With a net 335 MWe output IRIS novel design falls in the “medium” size category and it is a potential candidate for the so called modular reactors, which may be appropriate for base load electricity generation, especially in regions with smaller electricity grids, but especially well suited for more specialized non-electrical energy applications such as district heating and process steam for desalination. The first objective of this dissertation is to evaluate and quantify the performance of a Nuclear Power Plant (NPP) comprised of two IRIS reactor modules operating simultaneously with a common steam header, which in turn is connected to a single turbine, resulting in a steam-mixing control problem with respect to “load-following” scenarios, such as varying load during the day or reduced consumption during the weekend. To solve this problem a single-module IRIS SIMULINK model previously developed by another researcher is modified to include a second module and was used to quantify the responses from both modules. In order to develop research related to instrumentation and control, and equipment and sensor monitoring, the second objective is to build a two-tank multivariate loop in the Nuclear Engineering Department at the University of Tennessee. This loop provides the framework necessary to investigate and test control strategies and fault detection in sensors, equipment and actuators. The third objective is to experimentally develop and demonstrate a fault-tolerant control strategy using this loop. Using six correlated variables in a single-tank configuration, five inferential models and one Auto-Associative Kernel Regression (AAKR) model were developed to detect faults in process sensors. Once detected the faulty measurements were successfully substituted with prediction values, which would provide the necessary flexibility and time to find the source of discrepancy and resolve it, such as in an operating power plant. Finally, using the same empirical models, an actuator failure was simulated and once detected the control was automatically transferred and reconfigured from one tank to another, providing survivability to the system

Study of power plant, carbon capture and transport network through dynamic modelling and simulation

The unfavourable role of CO₂ in stimulating climate change has generated concerns as CO₂ levels in the atmosphere continue to increase. As a result, it has been recommended that coal-fired power plants which are major CO₂ emitters should be operated with a carbon capture and storage (CCS) system to reduce CO₂ emission levels from the plant. Studies on CCS chain have been limited except a few high profile projects. Majority of previous studies focused on individual components of the CCS chain which are insufficient to understand how the components of the CCS chain interact dynamically during operation. In this thesis, model-based study of the CCS chain including coal-fired subcritical power plant, post-combustion CO₂ capture (PCC) and pipeline transport components is presented. The component models of the CCS chain are dynamic and were derived from first principles. A separate model involving only the drum-boiler of a typical coal-fired subcritical power plant was also developed using neural networks.The power plant model was validated at steady state conditions for different load levels (70-100%). Analysis with the power plant model show that load change by ramping cause less disturbance than step changes. Rate-based PCC model obtained from Lawal et al. (2010) was used in this thesis. The PCC model was subsequently simplified to reduce the CPU time requirement. The CPU time was reduced by about 60% after simplification and the predictions compared to the detailed model had less than 5% relative difference. The results show that the numerous non-linear algebraic equations and external property calls in the detailed model are the reason for the high CPU time requirement of the detailed PCC model. The pipeline model is distributed and includes elevation profile and heat transfer with the environment. The pipeline model was used to assess the planned Yorkshire and Humber CO₂ pipeline network.Analysis with the CCS chain model indicates that actual changes in CO₂ flowrate entering the pipeline transport system in response to small load changes (about 10%) is very small (<5%). It is therefore concluded that small changes in load will have minimal impact on the transport component of the CCS chain when the capture plant is PCC

Dynamic Modeling, Sensor Placement Design, and Fault Diagnosis of Nuclear Desalination Systems

Fault diagnosis of sensors, devices, and equipment is an important topic in the nuclear industry for effective and continuous operation of nuclear power plants. All the fault diagnostic approaches depend critically on the sensors that measure important process variables. Whenever a process encounters a fault, the effect of the fault is propagated to some or all the process variables. The ability of the sensor network to detect and isolate failure modes and anomalous conditions is crucial for the effectiveness of a fault detection and isolation (FDI) system. However, the emphasis of most fault diagnostic approaches found in the literature is primarily on the procedures for performing FDI using a given set of sensors. Little attention has been given to actual sensor allocation for achieving the efficient FDI performance. This dissertation presents a graph-based approach that serves as a solution for the optimization of sensor placement to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. This would potentially facilitate an automated sensor allocation procedure. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data, and to fit a hyper-plane to the data. The fault directions for different fault scenarios are obtained from the prediction errors, and fault isolation is then accomplished using new projections on these fault directions. The effectiveness of the use of an optimal sensor set versus a reduced set for fault detection and isolation is demonstrated using this technique. Among a variety of desalination technologies, the multi-stage flash (MSF) processes contribute substantially to the desalinating capacity in the world. In this dissertation, both steady-state and dynamic simulation models of a MSF desalination plant are developed. The dynamic MSF model is coupled with a previously developed International Reactor Innovative and Secure (IRIS) model in the SIMULINK environment. The developed sensor placement design and fault diagnostic methods are illustrated with application to the coupled nuclear desalination system. The results demonstrate the effectiveness of the newly developed integrated approach to performance monitoring and fault diagnosis with optimized sensor placement for large industrial systems

Hybrid nuclear-solar power

Nuclear and solar power, in the form of concentrated solar power (CSP), play a significant role in achieving the ambitious global targets of reducing greenhouse emissions and guaranteeing security of energy supply. However, both power generation technologies still require further development to realise their full potential, especially in terms of attaining economic load following operations and higher thermal efficiencies. Therefore, the aim of this research is to investigate and thermo-economically evaluate the available options of upgrading the flexibility and enhancing the thermal efficiency of nuclear and solar power generation technologies (i.e., through the integration with thermal energy storage (TES) and by hybridising both power generation technologies) while providing reasonable economic returns. The thesis starts with describing the development and validation of several thermodynamic and economic computational models and the formulation of the whole-energy system model. The formulated models are utilised to perform several thermo-economic studies in the field of flexible nuclear and solar power, and to quantify the economic benefits that could result from enhancing the flexibility of nuclear power plants from the whole-energy system perspective. The studies conducted in this research are: (i) a thermo-economic assessment of extending the conventional TES system in direct steam generation (DSG) CSP plants; (ii) a thermo-economic evaluation of upgrading the flexibility of nuclear power plants by the integration with TES and secondary power generation systems; (iii) an investigation of the role of added flexibility in future low-carbon electricity systems; and (iv) a design and operation analysis of a hybrid nuclear-solar power plant. The most common TES option in DSC CSP plants is steam accumulation. This conventional option is constrained by temperature and pressure limits, leading to lower efficiency operations during TES discharging mode. Therefore, the option of integrating steam accumulators with sensible-heat storage in concrete to provide higher-temperature superheated steam is thermo-economically investigated in this research, taking an operational DSG CSP plant as a case study. The results show that the integrated concrete-steam TES (extended) option delivers 58% more electricity with a 13% enhancement in thermal efficiency during TES discharging mode, compared to the conventional steam accumulation (existing) configuration. With an estimated additional investment of $4.2M, the projected levelised cost of electricity (LCOE) and the net present value (NPV) for the considered DSG CSP plant with the extended TES option are respectively 6The option of upgrading the flexibility of nuclear power plants through the integration with TES and secondary power generation systems is investigated for two conventional nuclear reactors, a 670-MWel advanced gas-cooled reactor (AGR) and a 1610-MWel European pressurised reactor (EPR). In both investigated case studies, the reactors are assumed to continuously operate at full rated thermal power, while load following operations are conducted through the integrated TES tanks and secondary power generators. Based on the designed TES and secondary power generation systems, the AGR-based configuration can modulate the power output between 406 MWel and 822 MWel, while the EPR-based configuration can operate flexibly between 806 MWel and 2130 MWel. The economic analysis results demonstrate that the economics of added flexibility are highly dependent on: (i) the size of the TES and the secondary power generation systems; (ii) the number of TES charge/discharge cycles per day; and (iii) the ratio and difference between off-peak and peak electricity prices. Replacing conventional EPR-based nuclear power plants with added flexibility ones is found to generate whole-system cost savings between$30.4M/yr and $111M/yr. At an estimated cost of added flexibility of$53.4M/yr, the proposed flexibility upgrades appear to be economically justified with net system economic benefits ranging from $5.0M/yr and$39.5M/yr for the examined low-carbon scenarios, provided that the number of flexible nuclear plants in the system is small. The concept of hybridising a small modular reactor (SMR) with a solar-tower CSP integrated with two-tank molten salt TES system, with the aim of achieving economically enhanced load following operations and higher thermal efficiency levels, is also thermo-economically investigated in this research. The integration of both technologies is achieved by adding a solar-powered superheater and a reheater to a standalone SMR. The obtained results demonstrate that hybridising nuclear and solar can offer a great amount of flexibility (i.e., between 50% and 100% of nominal load of 131 MWel) with the SMR continuously operated at full rated thermal power output. Furthermore, the designed hybrid power plant is able to operate at higher temperatures due to the addition of the solar superheater, resulting in a 15% increase of thermal efficiency compared to nuclear-only power plant. Moreover, the calculated specific investment cost and the LCOE of the designed hybrid power plant are respectively 5410 $/kWel and 77$/MWhel, which are 2% and 4% lower than those calculated for the nuclear-only power plant.Open Acces

Low-head pumped hydro storage: A review of applicable technologies for design, grid integration, control and modelling

To counteract a potential reduction in grid stability caused by a rapidly growing share of intermittent renewable energy sources within our electrical grids, large scale deployment of energy storage will become indispensable. Pumped hydro storage is widely regarded as the most cost-effective option for this. However, its application is traditionally limited to certain topographic features. Expanding its operating range to lowhead scenarios could unlock the potential of widespread deployment in regions where so far it has not yet been feasible. This review aims at giving a multi-disciplinary insight on technologies that are applicable for low-head (2-30 m) pumped hydro storage, in terms of design, grid integration, control, and modelling. A general overview and the historical development of pumped hydro storage are presented and trends for further innovation and a shift towards application in low-head scenarios are identified. Key drivers for future deployment and the technological and economic challenges to do so are discussed. Based on these challenges, technologies in the field of pumped hydro storage are reviewed and specifically analysed regarding their fitness for low-head application. This is done for pump and turbine design and configuration, electric machines and control, as well as modelling. Further aspects regarding grid integration are discussed. Among conventional machines, it is found that, for high-flow low-head application, axial flow pump-turbines with variable speed drives are the most suitable. Machines such as Archimedes screws, counter-rotating and rotary positive displacement reversible pump-turbines have potential to emerge as innovative solutions. Coupled axial flux permanent magnet synchronous motor-generators are the most promising electric machines. To ensure grid stability, grid-forming control alongside bulk energy storage with capabilities of providing synthetic inertia next to other ancillary services are required

Dynamic surrogate modelling for multistep-ahead prediction of multivariate nonlinear chemical processes

This work proposes a methodology for multivariate dynamic modeling and multistep-ahead prediction of nonlinear systems using surrogate models for the application to nonlinear chemical processes. The methodology provides a systematic and robust procedure for the development of data-driven dynamic models capable of predicting the process outputs over long time horizons. It is based on using surrogate models to construct several nonlinear autoregressive exogenous models (NARX) with each one approximating the future behavior of one process output as a function of the current and previous process inputs and outputs. The developed dynamic models are employed in a recursive schema to predict the process future outputs over several time steps (multistep-ahead prediction). The methodology is able to manage two different scenarios: (1) one in which a set of input–output signals collected from the process is only available for training and (2) another in which a mathematical model of the process is available and can be used to generate specific datasets for training. With respect to the latter, the proposed methodology includes a specific procedure for the selection of training data in dynamic modeling based on design of computer experiment (DOCE) techniques. The proposed methodology is applied to case studies from the process industry presented in the literature. The results show very high prediction accuracies over long time horizons. Also, owing to the flexibility, robustness, and computational efficiency of surrogate modeling, the methodology allows dealing with a wide range of situations, which would be difficult to address using first-principles models.Peer ReviewedPostprint (author's final draft

Activities in nuclear engineering at M.I.T.

"List of theses (February 1986-June 1987)"--Pages [133]-[138]Progress report; August 198