Identification of Wastewater Primary Sludge Composition using Augmented Batch Tests and Mathematical Models - Gaszynski, Christopher (Department of Civil Engineering, 2021)
Over the past 40 years, there has been a continuous progression in generating concrete solutions in the design and operation of wastewater treatment systems. This progression has included the development of various integrated and logical-mathematical glass box models geared towards the recovery of resources. However, the accuracy of these models relies on characterising the influent waste stream accurately, which includes identifying the molecular composition of the biodegradable organics. Municipal wastewater contains an array of organics, and it is crucial to accurately identify the composition of these organics before they are fed into the virtually replicated (i.e. modelled) system. This identification of influent organics ensures that the performance of the system can be predicted, and system upset, or failure can be avoided. The overarching aim of this project was to characterise the composition of the organics present in municipal wastewater using experimental analysis and mathematical bioprocess modelling. The project extended and improved current analytical methods by developing: 1. The Augmented Biochemical Methane Potential (AugBMP) Test: A test run identically to the BMP test but extended to include the methane and carbon dioxide production, the aqueous phase volatile fatty acids (VFAs), H2CO3 and total alkalinity, ammonia and orthophosphate concentrations and the reactor pH. 2. The Augmented Biochemical Sulphide Potential (AugBSP) Test: A test aimed to improve the accuracy of the AugBMP test, as methanogenesis was replaced by Biological Sulphate Reduction (BSR), minimising the gas measurement error commonly experienced during the BMP test. The proposed AugBMP and AugBSP tests coupled with mathematical bioprocess modelling, provides a powerful tool for determining the composition of the biodegradable organics present in wastewater substrates. The project incorporated a holistic approach which included fabricating the proposed testing vessels, performing multiple experiments and as accurately as possible, simulating the observed outcome using mathematical models. The outcome of this project allowed for the composition of unknown substrates, such as the organics present in primary sewage sludge to be identified confidently. This thesis unpacks and describes this new methodology developed to characterise substrates with unknown composition.