Browsing by Author "Mudau, Hope Thendo"

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    Extension and verification of the PWM_SA model to incorporate SANI-specific bioprocesses using experimental data from the Hong Kong SANI saline sewage treatment pilot plant.
    (2025) Mudau, Hope Thendo; Ikumi, David
    This study addresses water scarcity in South Africa's coastal regions, exacerbated by climatic, demographic, and pollution challenges. Cape Town's 2018 "Day Zero" crisis high lighted the need for innovative solutions. Adapting Hong Kong's SANI system, proven effective for saline wastewater treatment, offers a sustainable approach to conserve fresh water and enhance resilience. This study extends the Plant Wide Model for South Africa (PWM_SA) by integrating SANI-specific bioprocesses to evaluate its applicability in ad dressing these challenges. The study's primary objectives included adapting the PWM_SA model to incorporate sulfate reduction, autotrophic denitrification, and nitrification processes characteristic of the SANI system, focusing on achieving accurate mass balance across key chemical species and overall internal consistency. Specifically, the model extension aimed to ensure mass balance verification across Chemical Oxygen Demand (COD), Sulfur (S), Phosphorus (P), and Nitrogen (N), and to verify the extended model using empirical data from the Hong Kong SANI pilot plant. The methodology involved an extension of the PWM_SA model using the existing Gu jer matrix framework, with the addition of a steady-state model to represent Biological Sulfate Reduction (BSR). This model component employed COD-based kinetics to cap ture the removal of biodegradable COD and sulfate, with sewage sludge serving as the carbon source. The model structure includes three key components: (1) a COD-based i anaerobic hydrolysis kinetics module that simulates the removal of biodegradable COD and sulfate under varying Hydraulic Retention Times (HRT) and Sludge Retention Times (SRT); (2) a stoichiometric module that balances key elements (C, H, O, N, P, S), COD, and charge, which enables the prediction of concentrations for essential parameters—including alkalinity, COD, sulfate (SO4 2 – ), sulfide (H2S), nitrate (NO3 – ), and Free Saline Ammonia (FSA)—across sulfate reduction and autotrophic denitrification stages; and (3) a mixed weak acid/base chemistry module, which accounts for inorganic carbon (HCO3 – ) and sul fide species (H2S/HS– ), ensuring accurate pH predictions. The biochemical processes for sulfate reduction and autotrophic denitrification were integrated into the matrix along with their corresponding stoichiometric and kinetic pa rameters, offering a detailed representation of the biochemical interactions and transfor mations occurring in the system. The Gujer matrix structure allowed for the systematic classification of components based on solubility, degradability, and organic or inorganic characteristics, facilitating model verification and consistency through elemental mass bal ance. The extended model was implemented and verified in the WEST software, utilizing experimental data from the SANI pilot plant in Hong Kong to align the model with real world treatment conditions and verify the accuracy of kinetic rates and parameters. This approach established a reliable basis for modelling saline wastewater treatment within the PWM_SA framework. The overall results of this study demonstrated that the adapted PWM _SA model, now incorporating SANI-specific bioprocesses achieved high accuracy in simulating COD, S, N, and P transformations within the treatment system, closely mirroring empirical data and ensuring robust mass balance across key elements. With a COD mass balance reaching 100% compared to a measured balance of 90%, the model shows strong consistency in predicting organic matter utilization, and component transformations through sulfate reduction, autotrophic denitrification, and nitrification. Additionally, the effluent COD values supported the model's predictive accuracy, although minor deviations underscored inherent limitations in the steady-state approach for capturing dynamic effluent characteristics. In conclusion, this study successfully integrated the SANI system's bioprocesses into the PWM_SA model, creating a robust framework for saline wastewater treatment in South Africa. Although the steady-state model provided meaningful insights, some limitations highlight the potential for future dynamic modelling to capture transient fluctuations and real-time responses to influent variability. This research establishes a critical foundation for the implementation of SANI-based saline sewage treatment in South Africa, supporting efforts to enhance water sustainability and manage pollution effectively in saline and water-stressed environments.