The main aim of this paper is to develop a vision-based deep learning method for real-time occupancy activity detection and recognition to help the operations of building energy systems. A faster region-based convolutional neural network was developed, trained, and deployed to an artificial intelligence (AI)-powered camera for the application of real-time occupancy activity detection. Initial experimental tests were performed within an office space of a selected case study building. The detection provided correct detections for the majority of the time (97.32%). Average detection accuracy of 92.20% was achieved for all activities. Building energy simulation of the case study building was performed to assess the potential energy savings that can be achieved. The impact of using the typical schedules and deep learning influenced profiles (DLIP) were assessed. The work has shown that the generation of the real-time DLIP from the ability to enable prediction and generation of real-time occupancy activity-based internal heat gains data can inform building energy management systems and controls of the heating, ventilation and air-conditioning (HVAC) for a more accurate and optimized operation.
The steel sector is one of the largest industrial sources of CO2 emissions, contributing around 28% of the global industry sector’s direct greenhouse gas emissions. One crucial technological option for decreasing emissions is carbon capture and storage (CCS). ‘CCS readiness’ or ‘CO2 Capture Readiness (CCSR)’ is a design concept requiring minimal up-front investment in the present to maintain the potential for CCS retrofit in the future. As such, capture readiness avoids a carbon lock-in effect in the steel industry. This report outlines the essential technical and design requirements to ensure that a steel plant is capture-ready. Through a case study for a hypothetical CCSR project for capturing 0.5 million tonnes of CO2 using ASPEN Plus, a conceptual design for meeting the requirements of a carbon capture-ready steel plant is developed. The space required for the capture unit at a 0.5 million tonnes level is estimated at around 4,000m2. The comprehensive utilisation of waste heat would be advantageous for CCS applications in China’s steel production. It is recommended that back-pressure steam turbines are used to drive multi-stage CO2 compression instead of electric-motor-driven compressors with huge power loads of 7,100kW. Potential pre-investment options are identified to ease future capture retrofit.
Ammonia as energy carrier has been recognized and is a hot research topic in recent years. NH3-N often contaminated in municipal wastewater and caused eutrophication of water body. The use of an electrochemical deionizing and ingathering (EDI) method exhibits the ability to segregate targeted ions absorbed in a dilute solution. Through multistage experiment, we demonstrated a concentration of NH4 of 836.3 mg/L and 1734.8 mg/L, 1032 and 953 times of magnitude respectively in the multistage process. Besides, we analyzed the possibility of separation of these ions by increasing applied voltage. This technology provides a feasibility of retrieving ammonia from a very low concentration in wastewater to be as energy resources.