Engineering

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  • (2022) Yunana, Danladi
    Thesis
    Experimental and probabilistic methods were used to assess the risk of exposure to Legionella sp from aerators used in groundwater treatment plants. Factors considered include an assessment of conditions conducive to Legionella growth, detachment and inhalation by operators; the use of coupon studies to understand temporal changes and biofilm formation; and modelling the risk of Legionella using iterative Bayesian networks (BNs). A survey of 13 groundwater treatment plants (GWTPs) aerators, including tray, open and semi-enclosed systems were identified to feature design and operational risk factors favouring elevated levels of nutrients, water stagnation, challenging water quality, aerosolisation, and inconsistent operation and maintenance. Based on these observations, design considerations for the next generation of safer aerators that can overcome identified Legionella risks factors were outlined. Analysis of 300 sampling events from the aerators over five years indicated an average of 7% increase in colony counts between the inlet and outlet, indicating growth of Legionella within the aerators. In total, 28% of all samples collected from aerator surfaces testing positive for Legionella. However, there was no correlation between the type of aerator and Legionella positivity. Coupons were placed in aerators to assess temporal changes in fouling developed after 6 weeks of operation. The biological activity per unit area (ATP/cm2) was higher for samples collected on the sprayed (vertically placed) coupons (277 ng ATP/cm2) compared with the submerged (horizontally laid) (73 ng ATP/cm2) coupons. Concentrations of dissolved organic carbon (DOC) in the biofilm formed on the coupons were statistically similar for the two tested conditions. Comparing fouling characteristics from the lab and full-scale coupons confirmed the impact of surface orientation and influent characteristics on biofilm formation. In terms of cleaning of the fouled surface, NaOCl at (concentration greater than 6%) was found to achieve 99.9% efficiency in biofilm inactivation. Oxalic acid (concentration greater than 1%) significantly removed inorganic materials like iron and manganese. Combining biocides and antiscalants was therefore recommended to efficiently address fouling challenges in aerators. A BN which considered risk of exposure due to growth and transmission was developed using a fishbone diagram and bowtie analysis. The initial iterative output BN model was elicited deterministically through expert weighted scoring process and discretisation approach and defined relative contributions of risk variables. The BN model also efficiently categorised and differentiated Legionella risk thresholds. A revised BN model conceptually mapped and estimated the causes and consequences of Legionella aerosolisation separately. The Legionella growth sub-model showed weak prediction accuracy with a negative kappa coefficient, signifying inconsistency in predicted and observed Legionella occurrence. The effect of water quality was further explored with a data-driven learning approach using diverse historical water quality records. The optimised BN model utilised the greedy thick thinning approach, complemented with domain knowledge, and achieved superior performance accuracy exceeding 90%. The results indicated that water temperature, free chlorine, season, and heterotrophic plate count can be utilised to track Legionella occurrence in water systems.

  • (2022) Li, Bingnan
    Thesis
    With the rapid development of various geospatial technologies including remote sensing, mobile devices, and Global Position System (GPS), spatio-temporal data are abundantly available nowadays. Extracting valuable knowledge from spatio-temporal data is of crucial importance for many real-world applications such as intelligent transportation, social services, and intelligent distribution. With the fast increase of the amount and resolution of spatio-temporal data, traditional data mining methods are becoming obsolete. In recent years, deep learning models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have made promising achievements in many fields based on the strong ability in automated feature extraction and have been broadly used in different spatio-temporal data mining tasks. Many methods have been developed, and more diverse data were collected in recent decades, however, the existing methods have faced challenges from multi-source geospatial data. This thesis investigates four efficient techniques in different scenarios for spatio-temporal data mining that take advantage of multi-source geospatial data to overcome the limitations of traditional data mining methods. This study investigates spatio-temporal data mining from four different perspectives. Firstly, a multi-elemental geolocation inference method is proposed to predict the location of tweets without geo-tags. Secondly, an optimization model is proposed to detect multiple Areas-of-Interest (AOIs) simultaneously and solve the multi-AOIs detection problem. Thirdly, a multi-task Res-U-Net model with attention mechanism is developed for the extraction of the building roofs and the whole building shapes from remote sensing images, then an offset vector method is used to detect the footprints of the high-rise buildings based on the boundaries of the corresponding building roofs and shapes. Lastly, a novel decoder fusion model is introduced to extract interior road network from remote sensing images and GPS trajectory data. And this method is effective for multi-source data mining. The proposed four methods use different techniques for spatio-temporal data mining to improve the detection performance. Numerous experiments show that the techniques developed in this thesis can detect ground features efficiently and effectively and overcome the limitations of conventional algorithms. The studies demonstrate that exploiting spatial information from multi-source geospatial data can improve the detection accuracy in comparison with single-source geospatial data.