A methodology for implementing a water balance of ESKOM power stations using the online condition monitoring software EtaPRO

Eskom produces approximately 90% of the electricity used in South Africa of which approximately 90.8% is from fossil fuel power plants. The process of electricity generation requires a significant quantity of raw water; therefore, Eskom is considered a strategic water user in South Africa. Water management is a growing focus area due to the increase in water usage and requires continuous improvement. Water management has been identified as an area lagging behind on the advanced analytics initiatives in Eskom. Excel based tools were used for the development of water balance models and water performance calculations in Eskom. This was attributed to the user-friendly functionality and availability to all users. However, the Excel tool posed challenges in allowing for standardisation and validation of calculations, tracking of model changes, continuous trending and storage of data as well as structured graphical user interfaces for screens and dashboard developments. There was therefore a need to develop a methodology on how to structure a water balance model for coal-fired power plants with standard calculation templates that allowed for customisation by each power plant within Eskom. It was required that the water balance model be implemented on a performance and monitoring tool allowing for comparison of power plant targets to actual online data in real time, enhancing the monitoring capabilities. It should have the ability to generate real time water performance data creating an opportunity for improved water management across the generation fleet. The approach adopted in this dissertation was to learn from existing Eskom Excel water balance tools and develop a standard mathematical model in the form of EtaPRO calculation templates. These templates are be structured such that they function as process components to develop water balances at power plants. The mathematical verification of the Excel calculations were to be conducted using Mathcad. The access to real time data, performance monitoring capabilities and availability at all Eskom Power plants, led to the selection of EtaPRO as the modelling platform. The research conducted led to the development of a methodology for setting up a water balance model for a wet-cooled coal-fired power plant. Calculation templates developed into EtaPRO were validated against the Mathcad mathematical model. The results included a well-documented mathematical model of a water balance in Mathcad and the development of 19 calculation templates that perform the function of standard process components. In addition to calculation templates, multiple Non Volatile (NV) records were created to allow the power plants to capture and track permanent data inputs. NV records also allow for creation of case studies, improving the process monitoring capabilities. A water balance model for a selected power plant was simulated in EtaPRO using the developed calculation templates and user defined formulae. Test screens and dashboards were created to illustrate how the calculation templates and water balance framework would be used to develop a typical water balance model and monitoring system. In conclusion, it is possible to develop process models within the EtaPRO software from well-defined mathematical models to address the performance monitoring concerns on water systems within Eskom

Artificial intelligence model of fuel blendings as a step toward the zero emissions optimization of a 660 MWe supercritical power plant performance

Accurately predicting fuel blends' lower heating values (LHV) is crucial for optimizing a power plant. In this paper, we employ multiple artificial intelligence (AI) and machine learning-based models for predicting the LHV of various fuel blends. Coal of two different ranks and two types of biomass is used in this study. One was the South African imported bituminous coal, and the other was lignite thar coal extracted from the Thar Coal Block-2 mine by Sind Engro Coal Mining Company, Pakistan. Two types of biomass, that is, sugarcane bagasse and rice husk, were obtained locally from a sugar mill and rice mill located in the vicinity of Sahiwal, Punjab. Bituminous coal mixture with other coal types and both types of biomass are used with 10%, 20%, 30%, 40%, and 50% weight fractions, respectively. The calculation and model development procedure resulted in 91 different AI-based models. The best is the Ridge Regressor, a high-level end-to-end approach for fitting the model. The model can predict the LHV of the bituminous coal with lignite and biomass under a vast share of fuel blends

Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NOx, and SO2

We present a modelling framework for fossil fuel CO2 emissions in an urban environment, which allows constraints from emission inventories to be combined with atmospheric observations of CO2 and its co-emitted species CO, NOx , and SO2. Rather than a static assignment of average emission rates to each unit area of the urban domain, the fossil fuel emissions we use are dynamic: they vary in time and space in relation to data that describe or approximate the activity within a sector, such as traffic density, power demand, 2m temperature (as proxy for heating demand), and sunlight and wind speed (as proxies for renewable energy supply). Through inverse modelling, we optimize the relationships between these activity data and the resulting emissions of all species within the dynamic fossil fuel emission model, based on atmospheric mole fraction observations. The advantage of this novel approach is that the optimized parameters (emission factors and emission ratios, N D 44) in this dynamic emission model (a) vary much less over space and time, (b) allow for a physical interpretation of mean and uncertainty, and (c) have better defined uncertainties and covariance structure. This makes them more suited to extrapolate, optimize, and interpret than the gridded emissions themselves. The merits of this approach are investigated using a pseudo-observation-based ensemble Kalman filter inversion set-up for the Dutch Rijnmond area at 1km-1km resolution. We find that the fossil fuel emission model approximates the gridded emissions well (annual mean differences < 2 %, hourly temporal r2 D 0:21-0.95), while reported errors in the underlying parameters allow a full covariance structure to be created readily. Propagating this error structure into atmospheric mole fractions shows a strong dominance of a few large sectors and a few dominant uncertainties, most notably the emission ratios of the various gases considered. If the prior emission ratios are either sufficiently well-known or well constrained from a dense observation network, we find that including observations of co-emitted species improves our ability to estimate emissions per sector relative to using CO2 mole fractions only. Nevertheless, the total CO2 emissions can be well constrained with CO2 as the only tracer in the inversion. Because some sectors are sampled only sparsely over a day, we find that propagating solutions from day-to-day leads to largest uncertainty reduction and smallest CO2 residuals over the 14 consecutive days considered. Although we can technically estimate the temporal distribution of some emission categories like shipping separate from their total magnitude, the controlling parameters are difficult to distinguish. Overall, we conclude that our new system looks promising for application in verification studies, provided that reliable urban atmospheric transport fields and reasonable a priori emission ratios for CO2 and its co-emitted species can be produced

Energy Transition and Climate Change in Decision-making Processes

There is a growing concern about the climate; numerous voices stress that, in order to overcome the climate crisis, the transition to a low-carbon society is the most reasonable path to follow. In this type of society, individuals would be characterized by making mindful efforts to drastically decrease carbon and greenhouse gas emissions, and promote benign energy sources. In order to facilitate this transition, a social perspective in addition to technological, political and economic aspects must be integrated into the relevant decision-making processes. This is necessary because the public can strongly affect actions aimed at driving profound changes in traditional energy systems. To contribute to the effort of promoting energy transition, the Editors of this book invited scholars and practitioners conducting research in the areas of climate change and the energy transition to submit their work. This book includes studies that establish a valuable source of information which can be used to enhance decision-making processes which, in turn, can turn the energy transition into reality. Hopefully, efforts such as this collection of knowledge can help economies make a step towards a secure and sustainable energy future in which renewables will have replaced the centuries-long human dependence on fossil fuels

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Modeling and Control of Post-Combustion CO2 Capture Process Integrated with a 550MWe Supercritical Coal-fired Power Plant

This work focuses on the development of both steady-state and dynamic models for an monoethanolamine (MEA)-based CO2 capture process for a commercial-scale supercritical pulverized coal (PC) power plant, using Aspen PlusRTM and Aspen Plus DynamicsRTM. The dynamic model also facilitates the design of controllers for both traditional proportional-integral-derivative (PID) and advanced controllers, such as linear model predictive control (LMPC), nonlinear model predictive control (NMPC) and H? robust control.;A steady-state MEA-based CO2 capture process is developed in Aspen PlusRTM. The key process units, CO2 absorber and stripper columns, are simulated using the rate-based method. The steady-state simulation results are validated using experimental data from a CO2 capture pilot plant. The process parameters are optimized with the goal of minimizing the energy penalty. Subsequently, the optimized rate-based, steady-state model with appropriate modifications, such as the inclusion of the size and metal mass of the equipment, is exported into Aspen Plus DynamicsRTM to study transient characteristics and to design the control system. Since Aspen Plus DynamicsRTM does not support the rate-based model, modifications to the Murphree efficiencies in the columns and a rigorous pressure drop calculation method are implemented in the dynamic model to ensure consistency between the design and off-design results from the steady-state and dynamic models. The results from the steady-state model indicate that between three and six parallel trains of CO2 capture processes are required to capture 90% CO2 from a 550MWe supercritical PC plant depending on the maximum column diameter used and the approach to flooding at the design condition. However, in this work, only two parallel trains of CO2 capture process are modeled and integrated with a 550MWe post-combustion, supercritical PC plant in the dynamic simulation due to the high calculation expense of simulating more than two trains.;In the control studies, the performance of PID-based, LMPC-based, and NMPC-based approaches are evaluated for maintaining the overall CO2 capture rate and the CO2 stripper reboiler temperature at the desired level in the face of typical input and output disturbances in flue gas flow rate and composition as well as change in the power plant load and variable CO2 capture rate. Scenarios considered include cases using different efficiencies to mimic different conditions between parallel trains in real industrial processes. MPC-based approaches are found to provide superior performance compared to a PID-based one. Especially for parallel trains of CO2 capture processes, the advantage of MPC is observed as the overall extent of CO2 capture for the process is maintained by adjusting the extent of capture for each train based on the absorber efficiencies. The NMPC-based approach is preferred since the optimization problem that must be solved for model predictive control of CO2 capture process is highly nonlinear due to tight performance specifications, environmental and safety constraints, and inherent nonlinearity in the chemical process. In addition, model uncertainties are unavoidable in real industrial processes and can affect the plant performance. Therefore, a robust controller is designed for the CO2 capture process based on ?-synthesis with a DK-iteration algorithm. Effects of uncertainties due to measurement noise and model mismatches are evaluated for both the NMPC and robust controller. The simulation results show that the tradeoff between the fast tracking performance of the NMPC and the superior robust performance of the robust controller must be considered while designing the control system for the CO2 capture units. Different flooding control strategies for the situation when the flue gas flow rate increases are also covered in this work

Solid formation in piperazine rate-based simulation

AbstractPiperazine is a promising solvent for reducing CO2 emissions. It can be applied for the post-combustion capture process and it has limited degradation and fast kinetics. However, precipitation and slurry formation still represent a challenge for the PZ-CO2-H2O system from an operational point of view but also from a modeling perspective.The present work develops a rate-based model for CO2 absorption and desorption modeling for gas-liquid-solid systems and it is demonstrated for the piperazine CO2 capture process. This model is an extension of the DTU CAPCO2 model to precipitating systems. It uses the extended UNIQUAC thermodynamic model for phase equilibria and thermal properties estimation. The mass and heat transfer phenomena is implemented in a film model approach, based on second order reactions kinetics. The transfer fluxes are calculated using the concentration of the dissolved species since the piperazine is deactivated when present as solid. It is assumed that solid-gas reactions are slow compared to normal liquid side reactions.In the current work, the formation of solids is described in an equilibrium approach, assuming instantaneous formation of hydrates such as PZ•6H2O, PZ•½H2O, and anhydrous PZ.The simulation of a 100t/hr post-combustion capture plant outlines that 5% solid reduces the CO2 capture rate with 13%. Therefore, it demonstrates that an accurate description of the precipitation phenomenon is essential for realistic and accurate modeling

Remote sensing of fugitive methane emissions from oil and gas production in North American tight geologic formations

In the past decade, there has been a massive growth in the horizontal drilling and hydraulic fracturing of shale gas and tight oil reservoirs to exploit formerly inaccessible or unprofitable energy resources in rock formations with low permeability. In North America, these unconventional domestic sources of natural gas and oil provide an opportunity to achieve energy self-sufficiency and to reduce greenhouse gas emissions when displacing coal as a source of energy in power plants. However, fugitive methane emissions in the production process may counter the benefit over coal with respect to climate change and therefore need to be well quantified. Here we demonstrate that positive methane anomalies associated with the oil and gas industries can be detected from space and that corresponding regional emissions can be constrained using satellite observations. On the basis of a mass-balance approach, we estimate that methane emissions for two of the fastest growing production regions in the United States, the Bakken and Eagle Ford formations, have increased by 990 ± 650 ktCH4 yr−1 and 530 ± 330 ktCH4 yr−1 between the periods 2006–2008 and 2009–2011. Relative to the respective increases in oil and gas production, these emission estimates correspond to leakages of 10.1% ± 7.3% and 9.1% ± 6.2% in terms of energy content, calling immediate climate benefit into question and indicating that current inventories likely underestimate the fugitive emissions from Bakken and Eagle Ford