Property Impacts on Performance of CO2 Pipeline Transport

AbstractCarbon Capture and Storage (CCS) is one of the most potential technologies to mitigate climate change. Usingpipelinesto transport CO2 from emission sources to storage sitesis one of common and mature technologies. The design and operation of pipeline transport process requires careful considerations of thermo-physical properties.This paper studied the impact of properties, including density, viscosity, thermal conductivity and heat capacity, onthe performance of CO2 pipeline transport. The pressure loss and temperature dropin steady state were calculated by using homogenous friction model and Sukhof temperature drop theory, respectively. The results of sensitivity study show thatover-estimating density and viscosity increases the pressure loss while under-estimating of density and viscosity decreases it. Over-estimating density and heat capacity leads to lower temperature drop while under-estimating of density and heat capacity result in higher temperature drop.This study suggests that the accuracy of property models for example, more accurate density model, should be developed for the CO2 transport design

Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data

Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize multi-scale information, both spatially and temporally. Spatially, there is a lack of integration between individual monitoring stations and city-wide scales. Temporally, the periodic nature of air quality variations is often overlooked or inadequately considered. To address these limitations, we present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2), bridging the gap in air quality forecasting across spatial and temporal scales. The proposed framework consists of two modules: Multi-scale Spatial GCN (MS-GCN) for spatial information fusion and Multi-scale Temporal GRU(MT-GRU) for temporal information integration. In the spatial dimension, the MS-GCN module employs a bidirectional learnable structure and a residual structure, enabling comprehensive information exchange between individual monitoring stations and the city-scale graph. Regarding the temporal dimension, the MT-GRU module adaptively combines information from different temporal scales through parallel hidden states. Leveraging meteorological indicators and four air quality indicators, we present comprehensive comparative analyses and ablation experiments, showcasing the higher accuracy of M2G2 in comparison to nine currently available advanced approaches across all aspects. The improvements of M2G2 over the second-best method on RMSE of the 24h/48h/72h are as follows: PM2.5: (7.72%, 6.67%, 10.45%); PM10: (6.43%, 5.68%, 7.73%); NO2: (5.07%, 7.76%, 16.60%); O3: (6.46%, 6.86%, 9.79%). Furthermore, we demonstrate the effectiveness of each module of M2G2 by ablation study

System dynamics of oxyfuel power plants with liquid oxygen energy storage

Traditional energy storage systems have a common feature: the generating of secondary energy (e.g. electricity) and regenerating of stored energy (e.g. gravitational potential, and mechanical energy) are separate rather than deeply integrated. Such systems have to tolerate the energy loss caused by the second conversion from primary energy to secondary energy. This paper is concerned with the system dynamics of oxyfuel power plants with liquid oxygen energy storage, which integrates the generation of secondary energy (electricity) and regeneration of stored energy into one process and therefore avoids the energy loss caused by the independent process of regeneration of stored energy. The liquid oxygen storage and the power load of the air separation unit are self-adaptively controlled based on current-day power demand, day-ahead electricity price and real-time oxygen storage information. Such an oxyfuel power plant cannot only bid in the day-ahead market with base load power but also has potential to provide peak load power through reducing the load of the air separation unit in peak time. By introducing reasoning rules with fuzzy control, the oxygen storage system has potential to be further extended by integrating renewable energy resources into the system to create a cryogenic energy storage hub

cfd investigation of the open center on the performance of a tidal current turbine

In the present paper, a revision of the layout of an innovative open center self-balancing tidal turbine is presented. Initially, the design was characterized by a central deflector, responsible fo ..

Open center tidal turbine: How a new mooring system concept affects the performances

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Characterization of the fertilization independent endosperm (FIE) gene from soybean

Reproduction of angiosperm plants initiates from two fertilization events: an egg fusing with a sperm to form an embryo and a second sperm fusing with the central cell to generate an endosperm. The tryptophan-aspartate (WD) domain polycomb protein encoded by fertilization independent endosperm (FIE) gene, has been known as a repressor of hemeotic genes by interacting with other polycomb proteins, and suppresses endosperm development until fertilization. In this study, one Glycine max FIE (GmFIE) gene was cloned and its expression in different tissues, under cold and drought treatments, was analyzed using both bioinformatics and experimental methods. GmFIE showed high expression in reproductive tissues and was responsive to stress treatments, especially induced by cold. GmFIE overexpression lines of transgenic Arabidopsis were generated and analyzed. Delayed flowering was observed from most transgenic lines compared to that of wild type. Overexpression of GmFIE in Arabidopsis also leads to semi-fertile of the plants.Keywords: Polycomb proteins, fertilization independent endosperm (FIE), Glycine max, Arabidopsis thalian

Modelling the impact of social network on energy savings

It is noted that human behaviour changes can have a significant impact on energy consumption, however, qualitative study on such an impact is still very limited, and it is necessary to develop the corresponding mathematical models to describe how much energy savings can be achieved through human engagement. In this paper a mathematical model of human behavioural dynamic interactions on a social network is derived to calculate energy savings. This model consists of a weighted directed network with time evolving information on each node. Energy savings from the whole network is expressed as mathematical expectation from probability theory. This expected energy savings model includes both direct and indirect energy savings of individuals in the network. The savings model is obtained by network weights and modified by the decay of information. Expected energy savings are calculated for cases where individuals in the social network are treated as a single information source or multiple sources. This model is tested on a social network consisting of 40 people. The results show that the strength of relations between individuals is more important to information diffusion than the number of connections individuals have. The expected energy savings of optimally chosen node can be 25.32% more than randomly chosen nodes at the end of the second month for the case of single information source in the network, and 16.96% more than random nodes for the case of multiple information sources. This illustrates that the model presented in this paper can be used to determine which individuals will have the most influence on the social network, which in turn provides a useful guide to identify targeted customers in energy efficiency technology rollout programmes

Enhancing Building Semantic Segmentation Accuracy with Super Resolution and Deep Learning: Investigating the Impact of Spatial Resolution on Various Datasets

The development of remote sensing and deep learning techniques has enabled building semantic segmentation with high accuracy and efficiency. Despite their success in different tasks, the discussions on the impact of spatial resolution on deep learning based building semantic segmentation are quite inadequate, which makes choosing a higher cost-effective data source a big challenge. To address the issue mentioned above, in this study, we create remote sensing images among three study areas into multiple spatial resolutions by super-resolution and down-sampling. After that, two representative deep learning architectures: UNet and FPN, are selected for model training and testing. The experimental results obtained from three cities with two deep learning models indicate that the spatial resolution greatly influences building segmentation results, and with a better cost-effectiveness around 0.3m, which we believe will be an important insight for data selection and preparation