Dynamic exergy analysis: From industrial data to exergy flows

As the power and transport sectors decarbonize, industrial emissions will become the main focus of decarbonization efforts. Exergy analysis provides a combined material and energy efficiency approach to assess industrial plants, both of which are necessary to tackle industrial emissions. Existing studies typically use simulated, static data that cannot inform real plant operators. This paper performs an exergy analysis on data spanning 2 years from 311 sensors of a real ammonia production site. We develop methods to overcome unique data challenges associated with real industrial data processing, visualize resource flows in Sankey diagrams, and estimate exergy indicators for both the steam methane reforming plant and its constituent processes. We evaluate average conventional and transit exergy efficiencies for the plant (71%, 15%), primary reformer (86%, 40%), secondary reformer (96%, 71%), high-temperature shift (99.7%, 77%), combustor (56%, 55%), and heat exchange section (85%, 82%). Overall exergy losses are 80 MW; the primary reformer and combustor are the two processes with the highest losses at 35 and 33 MW, respectively. Such an analysis can inform both improvement projects and performance finetuning of a real plant while being applicable to any industrial site. Increased availability of cheap wireless sensors and a shift to Industry 4.0 can enable higher resolution and real-time performance monitoring

A BIM and machine learning integration framework for automated property valuation

Property valuation contributes significantly to market economic activities, while it has been continuously questioned on its low transparency, inaccuracy and inefficiency. With Big Data applications in real estate domain growing fast, computer-aided valuation systems such as AI-enhanced automated valuation models (AVMs) have the potential to address these issues. On the one hand, while the advantages of Machine Learning for property valuation have been recognized by researchers and professionals, the predictive accuracy and model interpretability of current AVMs still need to be improved. On the other hand, the benefits and opportunities of BIM for property valuation have gradually captured the attention, but little effort has been made on standard data interpretation and information exchange in property valuation process. This thesis presents a novel system that leverages a holistic data interpretation, facilitates information exchange between AEC projects and property valuation, and an improved AVM for property valuation. A BIM and Machine Learning (ML) integration framework for automated property valuation was proposed which contains an IFC extension for property valuation, an IFC-based information extraction and an automated valuation model based on genetic algorithm optimized machine learning (GA-GBR). This research contributes to managing information exchange between AEC projects and property valuation and enhancing automated valuation models. The main findings indicated the proposed BIM-ML system: (1) in terms o