Computational Research Challenges and Opportunities for the Optimization of Fossil Energy Power Generation System

Emerging fossil energy power generation systems must operate with unprecedented efficiency and near-zero emissions, while optimizing profitably amid cost fluctuations for raw materials, finished products, and energy. To help address these challenges, the fossil energy industry will have to rely increasingly on the use advanced computational tools for modeling and simulating complex process systems. In this paper, we present the computational research challenges and opportunities for the optimization of fossil energy power generation systems across the plant lifecycle from process synthesis and design to plant operations. We also look beyond the plant gates to discuss research challenges and opportunities for enterprise-wide optimization, including planning, scheduling, and supply chain technologies

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

Developing a Smart Proxy for Fluidized Bed Using Machine Learning

Using fossil fuel which has been grown dramatically during the recent century, causes an increase in greenhouse gas emission. The global warming issue pushes the engineers toward the cleaner type of energy like Hydrogen. Coal gasification is one of the cheapest methods to obtain Hydrogen. Coal gasification is a special case of more general problem called fluidized bed. In order to design and optimize a gasification process, a deep understanding of multiphase flow in a gasifier is needed. MFiX is a commercial multi-phase flow simulator which has been used to simulate the gas and solid transport and reaction in the gasifier using Computational Fluid Dynamics (CFD). Although simulating multiphase flow using commercial CFD software has a lot of flexibilities, it is really time-consuming and some other way could be implemented to reduce the run time. The effort of this project is to develop an alternate method to perform the same analysis but with much lower computational cost. A data-driven approach is used to build a smart proxy by employing the knowledge of Artificial Intelligence (AI) and Data Mining (DM).;In this project, a smart proxy will be developed to study and analyze the fluidized bed problem. This smart proxy is then will be used as a replicate of the CFD solver, with a good accuracy and faster speed. This proxy needs an incredible less amount of time in comparison to the CFD solver with a reasonable error (less than 10%). MATLAB neural network toolbox is used for training.;The goal of this project is to prove the concept of using AI&DM; for computational fluid dynamics especially predicting multiphase flow. Multiphase flow has a wide range of application in petroleum industry such as multi-phase flow in the wellbore, surface lines, and hydraulic fracturing such as proppant transport in the hydraulic fracture. This project opens a new way to accelerate the fluid dynamics analysis and reduce its costs

Mathematical Modeling and Simulation of a One-Dimensional Transient Entrained-flow GEE/Texaco Coal Gasifier

Numerous gasifier models of varying complexity have been developed to study the various aspects of gasifier performance. These range from simple one-dimensional (1D) models to rigorous higher order 3D models based on computational fluid dynamics (CFD). Even though high-fidelity CFD models can accurately predict many key aspects of gasifier performance, they are computationally expensive and typically take hours to days to execute even on high-performance computers. Therefore, faster 1D partial differential equation (PDE)-based models are required for use in dynamic simulation studies, control system analysis, and training applications.;In the current study, a 1D transient model of a single-stage downward-firing entrained flow General Electric Energy (GEE)/Texaco-type gasifier has been developed. The model comprises mass, momentum and energy balances for the gas and solid phases. A detailed energy balance across the wall of the gasifier has been incorporated in the model to calculate the wall temperature profile along the gasifier length. This balance considers a detailed radiative transfer model with variable view factors between the various surfaces of the gasifier and with the solid particles. The model considers the initial gasification processes of water evaporation and coal devolatilization. In addition, the key heterogeneous and homogeneous chemical reactions have been modeled. The resulting time-dependent PDE model is solved using the method of lines in Aspen Custom ModelerRTM, whereby the PDEs are discretized in the spatial domain and the resulting differential algebraic equations (DAEs) are then integrated over time using a variable step integrator.;Results from the steady-state model and parametric studies have been presented. These results include the gas, solid, and wall temperature profiles, concentrations profiles of the solid and gas species, effects of the oxygen-to-coal ratio and water-to-coal ratio on temperature, conversion, cold gas efficiency, and species compositions. In addition, the dynamic response of the gasifier to the disturbances commonly encountered in real-life is presented. These disturbances include ramp and step changes in input variables such as coal flow rate, oxygen-to-coal ratio, and water-to-coal ratio among others. The results from the steady-state and dynamic models compare very well with the data from pilot plants, operating plants, and previous studies

Modeling and Simulation of Components in an Integrated Gasification Combined Cycle Plant for Developing Sensor Networks to Detect Faults

The goal of this work is to help synthesize a sensor network to detect and diagnose faults and to monitor conditions of the key equipment items. The desired algorithm for sensor network design would provide information about the number, type and location of sensors that should be deployed for fault diagnosis and condition monitoring of a plant. In this work, the focus was on the integrated gasification combined cycle (IGCC) power plant where the faults at the equipment level and the plant level are considered separately. At the plant level, the objective is to observe whether a fault has occurred or not and identify the specific fault. For component-level faults, the objective is to obtain quantitative information about the extent of a particular fault. For the model-based sensor network design, high-fidelity process model of the IGCC plant is the key requirement.;For component level sensor placement, high-fidelity partial differential algebraic equation (PDAE)-based models are developed. Mechanistic models for faults are developed and included in the PDAE-based models. For system-level sensor placement, faults are simulated in the IGCC plant and the dynamic response of the process is captured. Both the steady-state and dynamic information are used to generate markers that are then utilized for sensor network design.;Whether faults in a particular equipment item should be considered at the unit level or system level depend on the criticality of the equipment item, its likelihood to failure, and the resolution desired for specific faults. In this work, the sour water gas shift reactor (SWGSR) and the gasifier are considered at the unit level. Fly ash may get deposited on the SWGSR catalyst and in the voids in the SWGSR resulting in decreased conversion of carbon monoxide. A MATLAB-based PDAE model of the SWGSR has been developed that considers key faults such as changes in the porosity, surface area, and catalyst activity. In a slagging gasifier, the molten slag that flows along the inner wall can penetrate into the refractory layer, and due to chemical corrosion and thermal and mechanical stress eventually result in thinning or spalling of the refractory. Extent of penetration of slag into the refractory wall and the spalling of the refractory are considered to be important variables for condition monitoring of the gasifier. In addition, as an increasing slag layer thickness can eventually lead to shutdown of the gasifier yet the slag layer thickness cannot be directly measured using the current measurement technology, slag layer thickness is also considered to be an important variable for condition monitoring. For capturing the slag formation, and detachment phenomena accurately, a novel hybrid shrinking core-shrinking particle (HSCSP) model is developed. For tracking the detached slag droplets and the char particles along the gasifier, a particle model is developed and integrated with the HSCSP model. A slag model is developed that captures the process of the detachment of the slag droplets from the char surface, transport of the droplets towards the wall, deposition of a fraction of the droplets on the wall and formation of a slag layer on the wall. Finally, a refractory degradation model is developed for calculating the penetration of the slag inside the wall and the size and time for a spall to occur due to the combined effects of volume change as a result of slag penetration as well as thermal and mechanical stresses.;System-level models are enhanced and faults are simulated spanning across various sections of the IGCC plant. For example, in the SELEXOL-based acid gas removal unit the available area in the trays of distillation columns may get reduced due to deposition of solids. This can result in loss of efficiency. Leakages in heat exchangers in this unit can result in the loss of expensive solvent or hazardous gases. In the combined cycle section, faults such as leakages and fouling in the heat exchangers, increased loss of heat through the combustor insulation that can result in loss of efficiency are simulated.;Sensor placement using a two-tier approach is also performed by developing a sensor network for a combined system that includes unit level as well as system level faults. A model of the gasification island is developed by integrating the SWGSR model developed in MATLAB with the model of the rest of the plant developed in Aspen Plus Dynamics. Since the two models are developed using different software platforms, an integration framework is developed that couples and synchronizes the two dynamic models. The sensor network obtained using the models developed in this work is found to be effective in observing and resolving faults both at the unit level as well as the plant level. (Abstract shortened by UMI.)

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

Chemical looping combustion for carbon capture

Among the well-known state-of-art technologies for CO2 capture, Chemical Looping Combustion (CLC) stands out for its potential to capture with high efficiency the CO2 from a fuel power plant for electricity generation. CLC involves combustion of carbonaceous fuel such as coal-derived syngas or natural gas via a red-ox chemical reaction with a solid oxygen carrier circulating between two fluidised beds, air and fuel reactor, working at different hydrodynamic regimes. Avoided NOx emissions, high CO2 capture efficiency, low CO2 capture energy penalties and high plant thermal efficiency are the key concepts making worthy the investigation of the CLC technology. The main issue about the CLC technology might concern the cost of the solid metal oxides and therefore the impact of the total solid inventory, solid make-up and lifetime of the solid particles on the cost of the electricity generated. A natural gas fired power plant embedding a CLC unit is presented in this work. Macro scale models of fluidised beds (i.e. derived applying macroscopic equations) are developed and implemented in Aspen Plus software. Kinetic and hydrodynamic phenomena, as well as different operating conditions, are taken into account to evaluate their effect on the total solid inventory required to get full fuel conversion. Furthermore, a 2D micro scale model of the fuel reactor (i.e. derived applying partial differential equations), making use of a CFD code, is also developed. The results, in terms of the effect of the different kinetic and hydrodynamic conditions on the outlet gas conversion, are compared with the results using the macro-scale model implemented in Aspen Plus. Based on the micro scale (CFD) outcomes, the macro scale model is enhanced to capture the main physics influencing the performance of the fuel reactor. Thus, the improved macro scale model is embedded into different power plant configurations and mass and energy balances are solved simultaneously. Thermal efficiency evaluations for the different plant arrangements are carried out. A detailed economic evaluation of the CLC power plant is undertaken by varying two relevant parameters: fuel price and lifetime of the solid particles. The effect of the aforementioned parameters on the Levelised Cost Of Electricity (LCOE) is investigated and the resulting outcomes are critically discussed

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