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

Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review

As the adoption of machine learning models increases, ensuring robust models against adversarial attacks is increasingly important. With unsupervised machine learning gaining more attention, ensuring it is robust against attacks is vital. This paper conducts a systematic literature review on the robustness of unsupervised learning, collecting 86 papers. Our results show that most research focuses on privacy attacks, which have effective defenses; however, many attacks lack effective and general defensive measures. Based on the results, we formulate a model on the properties of an attack on unsupervised learning, contributing to future research by providing a model to use.Comment: 38 pages, 11 figure

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

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

Signaling from the plasma-membrane localized plant immune receptor RPM1 requires self-association of the full-length protein

Pathogen recognition first occurs at the plasma membrane, where receptor-like kinases perceive pathogen-derived molecules and initiate immune responses. To abrogate this immune response, pathogens evolved effector proteins that act as virulence factors, often following delivery to the host cell. Plants evolved intracellular receptors, known as NOD-like receptors (NLRs), to detect effectors, thereby ensuring activation of effector-triggered immunity. However, despite their importance in immunity, the molecular mechanisms underlying effector recognition and subsequent immune activation by membrane-localized NLRs remain to be fully elucidated. Our analyses reveal the importance of and need for self-association and the coordinated interplay of specific domains and conserved residues for NLR activity. This could provide strategies for crop improvement, contributing to effective, environmentally friendly, and sustainable solutions for future agriculture

Selective Nanoshaving of Self-Assembled Monolayers of 2-(4-Pyridylethyl)Triethoxysilane

Single molecular layers of 2-(4-pyridylethyl)triethoxysilane have been deposited on native oxide surfaces of silicon, with the triethoxysilylethyl groups towards the silicon oxide interface and pyridine at the surface. It is possible to “shave” or mechanically break the molecular bonds at the alkoxy-silane (Si–C) bond using scanning atomic force microscope, leaving large swaths of surface area cut to a depth of 0.64 ± 0.06 nm, exposing the silicon of the alkoxy-silane ligand. Mechanical cleavage of the pyridine ligand alone is also possible, but more difficult to control selectively

Quantized event-driven simulation for integrated energy systems with hybrid continuous-discrete dynamics

Effective simulation methods are becoming critically essential for the analysis of integrated energy systems (IESs) to reveal the interactions of multiple energy carriers. The incorporation of various energy technologies and numerous controllers make the IES a heterogeneous system, which poses new challenges to simulation methods. This paper focuses on the simulation of an IES with hybrid continuous-discrete properties and heterogeneous characteristics. First, a modified third-order quantized state system (MQSS3) method is proposed for the simulation of district heating systems (DHSs), in which quantized state system (QSS) and time-discretized integration are integrated to efficiently manage numerous discrete control actions. Second, an event-driven framework is established to integrate MQSS3 into the simulation of the electricity-heat integrated energy system (EH-IES). This framework enables the adoption of the most suitable models and algorithms for different systems to accommodate the heterogeneous properties of an IES. Case studies of an EH-IES with maximum 80% PV penetration and 210 buildings demonstrate that the dynamic interactions between the DHS and the power distribution network are accurately illustrated by the proposed simulation methods, in which MQSS3 indicates the highest simulation efficiency. It is also demonstrated in the simulation results that the flexibility from DHS can be utilized as demand-side resource to support the operation of power distribution network in aspects such as consuming the surplus PV generations

Data-driven coordinated voltage control method of distribution networks with high DG penetration

The highly penetrated distributed generators (DGs) aggravate the voltage violations in active distribution networks (ADNs). The coordination of various regulation devices such as on-load tap changers (OLTCs) and DG inverters can effectively address the voltage issues. Considering the problems of inaccurate network parameters and rapid DG fluctuation in practical operation, multi-source data can be utilized to establish the data-driven control model. In this paper, a data-driven coordinated voltage control method with the coordination of OLTC and DG inverters on multiple time-scales is proposed without relying on the accurate physical model. First, based on the multi-source data, a data-driven voltage control model is established. Multiple regulation devices such as OLTC and DG are coordinated on multiple time-scales to maintain voltages within the desired range. Then, a critical measurement selection method is proposed to guarantee the voltage control performance under the partial measurements in practical ADNs. Finally, the proposed method is validated on the modified IEEE 33-node and IEEE 123-node test cases. Case studies illustrate the effectiveness of the proposed method, as well as the adaptability to DG uncertainties