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.
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.
Exhaust gas from bright heat treatment furnaces can be used as a fuel for fuel cells because it is rich in hydrogen. However, it contains carbon monoxide (CO); when a CO-containing gas is used as a fuel gas in a proton-exchange membrane fuel cell (PEMFC), cell performance decreases. Therefore, it is necessary to remove contaminants from hydrogen-rich exhaust gas. In this study, the effect of CO contaminants in hydrogen gas on the PEMFC stack performance was firstly evaluated. The experimental results showed a correlation between CO concentration and fuel cell stack performance, wherein performance degraded quickly because of CO poisoning. Furthermore, for improving the performance of the PEMFC stack using the CO-containing hydrogen gas, three methods were evaluated: molecular sieve adsorption, methanation reaction, and air bleeding. These methods were effective against CO poisoning and delayed performance deterioration. In particular, methanation refining was observed to be the most effective method for reducing CO poisoning.
Flow induced vibrations (FIVs) of four tandem circular cylinders with roughness strips are numerically studied in Reynolds number range of 30,000â‰¤Reâ‰¤100,000. The power conversion and FIV responses are discussed. The VIV initial branch, VIV upper branch, VIV to galloping transition, and galloping are clearly observed for first cylinder. The amplitude ratio curves of third and fourth cylinders show an upward trend and the branches of FIV are not obvious. Both the converted power and conversion efficiency of four cylinders are higher than those of one, two and three cylinders when 62,049â‰¤Reâ‰¤90,000. The maximum total converted power reaches to 103W when Re=100,000.
As a new geothermal heat utilization mode, the research and application of single well pumping system are in the initial stage, so it is necessary to establish the system feasibility evaluation standard. Based on the refraction law, an empirical correlation of the heat affected radius of the aquitard is established in this paper. It is found that the heat affected radius of the aquitard can be characterized by the flow parameters (flow rate, recharge temperature), geological parameters (permeability, porosity, thickness) and physical parameters (thermal diffusivity) of the pumping and recharging layer. It is found that the most sensitive factors of thermal performance are permeability, recharge temperature and circulation flow.
We propose a novel framework to address the problem of detecting anomalies in building electricity consumption profiles. Our method is based on two sequential steps, which combine machine learning clustering and regression methods. The first step separates weekly anomalous consumption profiles from
regular ones, for a selected timespan. This is achieved through an unsupervised machine learning clustering method applied on a representation of weekly profiles in a two-dimensional space. The results of the clustering method are used to train a regression model which predicts the future behavior of the time series. Any measured consumption which deviates from the predicted value of the regression model is flagged as anomalous, and this could potentially trigger an alarm in the system. Results are discussed and performances are compared with respect to a simple regression model. Possible applications of this method for real-time anomaly detection are briefly discussed.
This study evaluates the cooling effect of latent heat storage (PCM) on integrated PV cells between two glass layers in a double-glazed ventilated building-integrated photovoltaic (BIPV) faÃ§ade. Transient CFD simulations were performed to investigate the potential of thermal management. The varying parameters were: PCM thickness, PCM thermal conductivity, PCM latent heat storage capacity, BIPV glass thickness, and air velocity in the ventilated gap. A regression model with interactions was made for evaluating the relative impact of thermal management on overheating hours compared to a reference system without PCM. The results show the importance of enhancing the thermal conductivity of PCM, with the greatest impact in the initial enhancement and neglectable effect at higher values above roughly 1 W/mK.
A data mining approach is proposed for evaluating the effects of battery production factors in cathode coating stage on both battery capacity and internal resistance for the first time. Specifically, an effective neural network model is built based on real data form designed experiments for obtaining reference cathode coating for coin cells. The purpose is to analyze and predict how the battery quality in both charge and discharge scenarios changes with respect to the key factors of coating including its weight and thickness. The results highlight the correlation between mentioned factors and battery quality indices, which could guide manufacturer to identify efficient ways for producing high-quality batteries.
Recently, with the introduction of DER (Distributed Energy Resources) such as wind, solar, energy storage systems, and electric vehicles, distribution systems have also started to produce and trade electricity. In order to cope with the changing system environment, countries around the world recognize that new functions for distribution system operation are needed, and application research based on AMI (Advanced Metering Infrastructure) is underway. However, it is still in the research stage and there is no specific functional design. In this paper, in line with the spread of AMI in Korea, four business use cases that can be implemented using AMI are defined. These are related to the distribution system and DER control, and the information exchange sequence was designed and represented as a Unified Modeling Language diagram. DSO (Distribution System Operator) can establish a foundation for improving distribution system operation and secure LV (Low Voltage) distribution system visibility through the implementation of this business use case.