Preventive maintenance plays a vital role in optimizing industrial operations. However, detecting equipment needing such maintenance using available data can be particularly challenging due to the class imbalance prevalent in real-world applications. The datasets gathered from equipment sensors primarily consist of records from well-functioning machines, making it difficult to identify those on the brink of failure, which is the main focus of preventive maintenance efforts. In this study, we employ neural network algorithms to address class imbalance and cost sensitivity issues in industrial scenarios for preventive maintenance. Our investigation centers on the "APS Failure in the Scania Trucks Data Set," a binary classification problem exhibiting significant class imbalance and cost sensitivity issues—a common occurrence across various fields. Inspired by image detection techniques, we introduce a novel loss function called Focal loss to traditional neural networks, combined with techniques like Cost-Sensitive Learning and Threshold Calculation to enhance classification accuracy. Our study's novelty is adapting image detection techniques to tackle the class imbalance problem within a binary classification task. Our proposed method demonstrates improvements in addressing the given optimization problem when confronted with these issues, matching or surpassing existing machine learning and deep learning techniques while maintaining computational efficiency. Our results indicate that class imbalance can be addressed without relying on conventional sampling techniques, which typically come at the cost of increased computational cost (oversampling) or loss of critical information (undersampling). In conclusion, our proposed method presents a promising approach for addressing class imbalance and cost sensitivity issues in industrial datasets heavily affected by these phenomena. It contributes to developing preventive maintenance solutions capable of enhancing the efficiency and productivity of industrial operations by detecting machines in need of attention: this discovery process we term predictive maintenance. The artifact produced in this study showcases the utilization of Focal Loss, Cost-Sensitive Learning, and Threshold Calculation to create reliable and effective predictive maintenance solutions for real-world applications. This thesis establishes a method that contributes to the body of knowledge in binary classification within machine learning, specifically addressing the challenges mentioned above. Our research findings have broader implications beyond industrial classification tasks, extending to other fields, such as medical or cybersecurity classification problems. The artifact (code) is at: https://shorturl.at/lsNSY

Over the last two decades, implementation of membrane bioreactors (MBRs) has increased due to their superior effluent quality and low plant footprint. However, they are still viewed as a high-cost option, both with regards to capital and operating expenditure (capex and opex). The present thesis extends the understanding of the impact of design and operational parameters of membrane bioreactors on energy demand, and ultimately whole life cost. A simple heuristic aeration model based on a general algorithm for flux vs. aeration shows the benefits of adjusting the membrane aeration intensity to the hydraulic load. It is experimentally demonstrated that a lower aeration demand is required for sustainable operation when comparing 10:30 to continuous aeration, with associated energy savings of up to 75%, without being penalised in terms of the fouling rate. The applicability of activated sludge modelling (ASM) to MBRs is verified on a community-scale MBR, resulting in accurate predictions of the dynamic nutrient profile. Lastly, a methodology is proposed to optimise the energy consumption by linking the biological model with empirical correlations for energy demand, taking into account of the impact of high MLSS concentrations on oxygen transfer. The determining factors for costing of MBRs differ significantly depending on the size of the plant. Operational cost reduction in small MBRs relies on process robustness with minimal manual intervention to suppress labour costs, while energy consumption, mainly for aeration, is the major contributor to opex for a large MBR. A cost sensitivity analysis shows that other main factors influencing the cost of a large MBR, both in terms of capex and opex, are membrane costs and replacement interval, future trends in energy prices, sustainable flux, and the average plant utilisation which depends on the amount of contingency built in to cope with changes in the feed flow.