Browsing by Author "Haffejee, Rashid Ahmed"

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    Open Access
    A biomass-fueled combined steam and sCO2 heat and power cycle for Southern African conditions
    (2025) Haffejee, Rashid Ahmed; Collier-Reed, Brandon
    Biomass is a renewable, cost-efficient, carbon-neutral fuel obtained from agricultural waste streams or energy crops that can be combusted in a furnace to co-generate electricity and heat. Integrating a supplementary high efficiency cycle, such as the supercritical-CO2 (sCO2) Brayton cycle, with an existing industrial Rankine cycle and a biomass fired boiler may be an economical option to increase overall thermal efficiency and net generation. However, the integration of sCO2 heaters within the biomass boiler presents challenges related to operating philosophies and component specifications. The focus of this research was to investigate the integration of a sCO2 Brayton cycle with a combined heat and power steam cycle with a modular biomass boiler firing typical Southern African bagasse fuel. A quasi-steady state 1D thermofluid network-based process model of the sCO2, steam and flue gas cycles was developed for nominal and partial load analysis. It accounts for the detailed component characteristics for the Rankine and Brayton cycles, as well as the biomass boiler, together with the complex interactions between all of the components in the different cycles. To facilitate the analysis of these intricate systems, a sophisticated simulation code was developed to allow for necessary customization and enforcement of required boundary conditions and control parameters. The network model solves the mass, energy, momentum, and species balance equations for the various fluid streams, accounting for radiative and convective heat transfer phenomena in the boiler. Due to the novelty of the proposed integrated cycle, high-fidelity 3D CFD modelling was then also used to validate the heat uptakes for the sCO2 heaters in the biomass boiler. Two configurations with the sCO2 heater/s situated within the flue gas flow path were investigated, namely a single convective-dominant heater, and a dual heater configuration with a radiative and a convective heater. At nominal load, the network model results show the required rate of overfiring for the sCO2 configurations, with a 15.3% increase in fuel flow rate resulting in an additional 21.2% in net power output. The impact of the sCO2 heaters situated in the gas flow path was quantified, with reduced heat uptakes for downstream steam heat exchangers offset by increased furnace waterwall heat transfer. At partial loads, between 100%-60%, inventory control proves to be the better performing control strategy for load following, maintaining high thermal efficiency across partial loads. Notably, at 60% load, the sCO2 compressor inlet conditions are near the pseudo-critical point, which requires careful management of inventory control. The boiler CFD modelling highlighted lower heat uptakes for sCO2 heaters compared to the 1D model, exacerbated at lower loads, particularly for the dual heater configuration. The 1D model was consequently calibrated based on these results. The single sCO2 heater configuration is recommended as the preferred configuration to minimise adverse impacts on the Rankine cycle superheaters. Further iterations between the 1D process model and CFD model are recommended.
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    Open Access
    Development of a process modelling methodology and condition monitoring platform for air-cooled condensers
    (2021) Haffejee, Rashid Ahmed; Laubscher, Ryno
    Air-cooled condensers (ACCs) are a type of dry-cooling technology that has seen an increase in implementation globally, particularly in the power generation industry, due to its low water consumption. Unfortunately, ACC performance is susceptible to changing ambient conditions, such as dry bulb temperatures, wind direction, and wind speeds. This can result in performance reduction under adverse ambient conditions, which leads to increased turbine back pressures and in turn, a decrease in generated electricity. Therefore, this creates a demand to monitor and predict ACC performance under changing ambient conditions. This study focuses on modelling a utility-scale ACC system at steady-state conditions applying a 1-D network modelling approach and using a component-level discretization approach. This approach allowed for each cell to be modelled individually, accounting for steam duct supply behaviour, and for off-design conditions to be investigated. The developed methodology was based on existing empirical correlations for condenser cells and adapted to model double-row dephlegmators. A utility-scale 64-cell ACC system based in South Africa was selected for this study. The thermofluid network model was validated using site data with agreement in results within 1%; however, due to a lack of site data, the model was not validated for off-design conditions. The thermofluid network model was also compared to the existing lumped approach and differences were observed due to the steam ducting distribution. The effect of increasing ambient air temperature from 25 35  −  C C was investigated, with a heat rejection rate decrease of 10.9 MW and a backpressure increase of 7.79 kPa across the temperature range. Condensers' heat rejection rate decreased with higher air temperatures, while dephlegmators' heat rejection rate increased due to the increased outlet vapour pressure and flow rates from condensers. Off-design conditions were simulated, including hot air recirculation and wind effects. For wind effects, the developed model predicted a decrease in heat rejection rate of 1.7 MW for higher wind speeds, while the lumped approach predicted an increase of 4.9 . MW For practicality, a data-driven surrogate model was developed through machine learning techniques using data generated by the thermofluid network model. The surrogate model predicted systemlevel ACC performance indicators such as turbine backpressure and total heat rejection rate. Multilayer perceptron neural networks were developed in the form of a regression network and binary classifier network. For the test sets, the regression network had an average relative error of 0.3%, while the binary classifier had a 99.85% classification accuracy. The surrogate model was validated to site data over a 3 week operating period, with 93.5% of backpressure predictions within 6% of site data backpressures. The surrogate model was deployed through a web-application prototype which included a forecasting tool to predict ACC performance based on a weather forecast.