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

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

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

Simplification of detailed rate-based model of post-combustion CO₂ capture for full chain CCS integration studies

As post-combustion CO₂ capture (PCC) technology nears commercialisation, it has become necessary for the full carbon capture and storage (CCS) chain to be studied for better understanding of its dynamic characteristics. Model-based approach is one option for economically and safely reaching this objective. However, there is need to ensure that such models are reasonably simple to avoid the requirement for high computational time when carrying out such study. In this paper, a simplification approach for a detailed rate-based model of post-combustion CO₂ capture with solvents (rate-based mass transfer and reactions assumed to be at equilibrium) is presented. The simplified model can be used in model-based control and/or full chain CCS simulation studies. With this approach, we demonstrated significant reduction in CPU time (up to 60%) with reasonable model accuracy retained in comparison with the detailed model

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

Process analysis of intensified absorber for post-combustion CO₂ capture through modelling and simulation

Process intensification (PI) has the potential to significantly reduce capital and operating costs in postcombustion CO₂ capture using monoethanolamine (MEA) solvent for power plants. The intensified absorber using rotating packed bed (RPB) was modelled based on Aspen Plus® rate-based model, but some build-in correlations in Aspen Plus® rate-based model were replaced with new correlations suitable for RPB. These correlations reflect centrifugal acceleration which is present in RPB. The new correlations were implemented in visual FORTRAN as sub-routines and were dynamically linked to Aspen Plus® rate based model. The model for intensified absorber was validated using experimental data and showed good agreement. Process analysis carried out indicates: (a) CO₂ capture level increases with rotating speed. (b) Higher lean MEA inlet temperature leads to higher CO₂ capture level. (c) Increase in lean MEA concentration results in increase in CO₂ capture level. (d) Temperature bulge is not present in intensified absorber. Compared with conventional absorber using packed columns, the insights obtained from this study are (1) intensified absorber using RPB improves mass transfer significantly. (2) Higher flue gas temperature or lean MEA temperature will not be detrimental to the reactive separation as such cooling duty for flue gas can be saved. (3) Inter-cooling cost will not be incurred since there is no temperature bulge. A detail comparison between conventional absorber and intensified absorber using RPB was carried out and absorber volume reduction factor of 12 times was found. These insights can be useful for design and operation of intensified absorber for CO₂ capture

Study of the impacts of supplements on the specific methane production during anaerobic digestion of the West African Gamba and Guinea Grass

Nutrient supplementation could improve the biomethane production of different biomass feedstocks during anaerobic digestion. In this study, the impact of nutrient supplementation on the anaerobic digestion of the West African Gamba and Guinea Grass for biomethane production is presented. This was undertaken in 6 separate continuous stirred tank reactors for a hydraulic residence time of 25 days under supplementation regime with trace elements (TE), cocoa pod (CP) ash-extract, and commercial cellulase from Aspergillus niger (CCA) or Trichoderma reesei ATCC 26921 (CCT). The results showed that TE inhibits the specific methane production (SMP) with about 5% lower SMP than the control. In contrast, the other supplements namely CP, CP + CCA, CCA, and CCT + TE showed about 13, 28, 18, and 12% higher SMP than the control respectively. This study is the first demonstration of the impacts of different supplements on SMP during the anaerobic digestion of the West African Gamba and Guinea grass

Simulation-based techno-economic evaluation for optimal design of CO₂ transport pipeline network

For large volumes of carbon dioxide (CO₂) onshore and offshore transportation, pipeline is considered the preferred method. This paper presents a study of the pipeline network planned in the Humber region of the UK. Steady state process simulation models of the CO₂ transport pipeline network were developed using Aspen HYSYS®. The simulation models were integrated with Aspen Process Economic Analyser® (APEA). In this study, techno-economic evaluations for different options were conducted for the CO₂ compression train and the trunk pipelines respectively. The evaluation results were compared with other published cost models. Optimal options of compression train and trunk pipelines were applied to form an optimal case. The overall cost of CO₂ transport pipeline network was analyzed and compared between the base case and the optimal case. The results show the optimal case has an annual saving of 22.7 M€. For the optimal case, levelized energy and utilities cost is 7.62 €/t-CO₂, levelized capital cost of trunk pipeline is about 8.11 €/t-CO₂ and levelized capital cost of collecting system is 2.62 €/t- CO₂. The overall levelized cost of the optimal case was also compared to the result of another project to gain more insights for CO₂ pipeline network design

Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control

Post-combustion carbon capture (PCC) with chemical absorption has strong interactions with coal-fired power plant (CFPP). It is necessary to investigate dynamic characteristics of the integrated CFPP-PCC system to gain knowledge for flexible operation. It has been demonstrated that the integrated system exhibits large time inertial and this will incur additional challenge for controller design. Conventional PID controller cannot effectively control CFPP-PCC process. To overcome these barriers, this paper presents an improved neural network inverse control (NNIC) which can quickly operate the integrated system and handle with large time constant. Neural network (NN) is used to approximate inverse dynamic relationships of integrated CFPP-PCC system. The NN inverse model uses setpoints as model inputs and gets predictions of manipulated variables. The predicted manipulated variables are then introduced as feed-forward signals. In order to eliminate steady-state bias and to operate the integrated CFPP-PCC under different working conditions, improvements have been achieved with the addition of PID compensator. The improved NNIC is evaluated in a large-scale supercritical CFPP-PCC plant which is implemented in gCCS toolkit. Case studies are carried out considering variations in power setpoint and capture level setpoint. Simulation results reveal that proposed NNIC can track setpoints quickly and exhibit satisfactory control performances