Dynamic modelling, validation and analysis of coal-fired subcritical power plant

Coal-fired power plants are the main source of global electricity. As environmental regulations tighten, there is need to improve the design, operation and control of existing or new built coal-fired power plants. Modelling and simulation is identified as an economic, safe and reliable approach to reach this objective. In this study, a detailed dynamic model of a 500 MWe coal-fired subcritical power plant was developed using gPROMS based on first principles. Model validations were performed against actual plant measurements and the relative error was less than 5%. The model is able to predict plant performance reasonably from 70% load level to full load. Our analysis showed that implementing load changes through ramping introduces less process disturbances than step change. The model can be useful for providing operator training and for process troubleshooting among others

Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant

There is increasing need for tighter controls of coal-fired plants due to more stringent regulations and addition of more renewable sources in the electricity grid. Achieving this will require better process knowledge which can be facilitated through the use of plant models. Drum-boilers, a key component of coal-fired subcritical power plants, have complicated characteristics and require highly complex routines for the dynamic characteristics to be accurately modelled. Development of such routines is laborious and due to computational requirements they are often unfit for control purposes. On the other hand, simpler lumped and semi empirical models may not represent the process well. As a result, data-driven approach based on neural networks is chosen in this study. Models derived with this approach incorporate all the complex underlying physics and performs very well so long as it is used within the range of conditions on which it was developed. The model can be used for studying plant dynamics and design of controllers. Dynamic model of the drum-boiler was developed in this study using NARX neural networks. The model predictions showed good agreement with actual outputs of the drum-boiler (drum pressure and water level)

Case study on CO₂ transport pipeline network design for Humber region in the UK

Reliable, safe and economic CO₂ transport from CO₂ capture points to long term storage/enhanced oil recovery (EOR) sites is critical for commercial deployment of carbon capture and storage (CCS) technology. Pipeline transportation of CO₂ is considered most feasible. However, in CCS applications there is concern about associated impurities and huge volumes of high pressure CO₂ transported over distances likely to be densely populated areas. On this basis, there is limited experience for design and economic assessment of CO₂ pipeline. The Humber region in the UK is a likely site for building CO₂ pipelines in the future due to large CO₂ emissions in the region and its close access to depleted gas fields and saline aquifers beneath the North Sea. In this paper, various issues to be considered in CO₂ pipeline design for CCS applications are discussed. Also, different techno-economic correlations for CO₂ pipelines are assessed using the Humber region as case study. Levelized cost of CO₂ pipelines calculated for the region range from 0.14 to 0.75 GBP per tonne of CO₂. This is a preliminary study and is useful for obtaining quick techno-economic assessment of CO₂ pipelines

Modelling of a post-combustion CO₂ capture process using neural networks

This paper presents a study of modelling post-combustion CO₂ capture process using bootstrap aggregated neural networks. The neural network models predict CO₂ capture rate and CO₂ capture level using the following variables as model inputs: inlet flue gas flow rate, CO₂ concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. In order to enhance model accuracy and reliability, multiple feedforward neural network models are developed from bootstrap re-sampling replications of the original training data and are combined. Bootstrap aggregated model can offer more accurate predictions than a single neural network, as well as provide model prediction confidence bounds. Simulated CO₂ capture process operation data from gPROMS simulation are used to build and verify neural network models. Both neural network static and dynamic models are developed and they offer accurate predictions on unseen validation data. The developed neural network models can then be used in the optimisation of the CO₂ capture process

Study of absorber intercooling in solvent-based CO2 capture based on rotating packed bed technology

Application of process intensification (PI) technologies such as rotating packed beds (RPBs) to replace packed beds (PBs) in solvent-based CO2 capture could reduce plant footprint. Concentrated monoethanolamine (MEA) solvents are generally expected to be used in RPBs. Under this circumstance, expected temperature rise during CO2 absorption should be estimated to determine whether or not intercooling is necessary for RPBs. In this study, we demonstrated that intercooling is inevitable with RPBs using 40-70 wt% monoethanolamine (MEA) solvent through liquid phase energy balance for a hypothetical scenario. Our analysis showed that liquid phase temperature rise could be as high as 80°C in some cases and this will significantly reduce absorption rate without intercooling

Development of dense membranes for high-density hydrogen production from ammonia catalytic decomposition (cracking) for PEM fuel cells power in long-haul passenger aircraft transportation.

Aviation is a major greenhouse gas contributor responsible for around 3.2% of global CO2 emissions to the atmosphere. That corresponds to over than 1 billion metric tons of carbon (A metric ton is slightly smaller than the American imperial ton—but to be precise, it is 1,000 kilograms—however the two are comparable) being added to the atmosphere yearly. Therefore, the race to find alternatives to fossil fuels for planes is being intensified and in recent years, new and more highly efficient engines have contributed to reducing fuel consumption and harmful emissions. However, despite the impact of the COVID-19 pandemic, global passenger and cargo air traffic is projected to grow by 4% per year to 2040. Biofuels, hydrogen, and electricity are three ways in which the aviation industry can respond to rising emissions and sustainability. The aim is to develop a more compact design for hydrogen production from ammonia to offer a viable means for hydrogen air transportation and storage in the form of ammonia. More importantly, we are considering this approach to a be a viable solution for long-haul aviation powered by hydrogen. We will present results demonstrating our world-class expertise in membrane development (hydrogen-nitrogen separation)

Study of intercooling for rotating packed bed absorbers in intensified solvent-based CO2 capture process

Rotating packed beds (RPBs) are a compact and potentially more cost-effective alternative to packed beds for application in solvent-based carbon capture process. However, with concentrated monoethanolamine (MEA) (up to 70–80 wt%) as the solvent, there is a question as to whether intercooler is needed for the RPB absorbers and how to design and operate them. This study indicates that the liquid phase temperature could rise significantly and this makes it essential for RPB absorber to have intercoolers. This is further assessed using a validated RPB absorber model implemented in gPROMS ModelBuilder® by evaluating the impact of temperature on absorption performance. Different design options for RPB absorber intercoolers (stationary vs rotary) were introduced and their potential sizes and associated pressure drop were evaluated based on a large scale flue gas benchmark of a 250 MWe Natural Gas Combined Cycle Power Plant. This paper addresses a fundamental question about intercooling in RPB absorber and introduces strategies for the intercooler design

Non-linear system identification of solvent-based post-combustion CO2 capture process

Solvent-based post combustion capture (PCC) is a well-developed technology for CO2 capture from power plants and industry. A reliable model that captures the dynamics of the solvent-based capture process is essential to implement suitable control design. Typically, first principle models are used, however they usually require comprehensive knowledge and deep understanding of the process. System identification approach is adopted to obtain a model that accurately describes the dynamics between key variables in the process. The nonlinear auto-regressive with exogenous (NARX) inputs model is employed to represent the relationship between the input variables and output variables as two Multiple-Input Single-Output (MISO) sub-models. The forward regression with orthogonal least squares (FROLS) algorithm is implemented to select an accurate model structure that best describes the dynamics within the process. The prediction performance of the identified NARX models is promising and shows that the models capture the underlying dynamics of the CO2 capture process