Spring precipitation is the predominant factor that controls meteorological drought in Inner Mongolia (IM), China. This study used the anomaly percentage of spring precipitation (PAP) as a drought ...index to measure spring drought. A scheme for forecasting seasonal drought was designed based on evidence of spring drought occurrence and speculative reasoning methods introduced in computer artificial intelligence theory. Forecast signals with sufficient lead-time for predictions of spring drought were extracted from eight crucial areas of oceans and 500-hPa geopotential height. Using standardized values, these signals were synthesized into three examples of spring drought evidence (SDE) depending on their primary effects on three major atmospheric circulation components of spring precipitation in IM: the western Pacific subtropical high, North Polar vortex, and East Asian trough. Thresholds for the SDE were determined following numerical analyses of the influential factors. Furthermore, five logical reasoning rules for distinguishing the occurrence of SDE were designed after examining all possible combined cases. The degree of confidence in the rules was determined based on estimations of their prior probabilities. Then, an optimized logical reasoning scheme was identified for judging the possibility of spring drought. The scheme was successful in hindcast predictions of 11 of the 16 (accuracy: 68.8%) spring droughts that have occurred during 1960–2009. Moreover, the accuracy ratio for the same period was 82.0% for drought (PAP ≤ −20%) or not (PAP > −20%). Predictions for the recent 6-year period (2010–2015) demonstrated successful outcomes.
Climate change enforces the integration of distributed renewable energy sources and development of carbon price scheme. Whilst the energy is traded among distributed prosumers, the carbon ...responsibilities and corresponding allowances trading need to be transferred from large-scale energy suppliers to prosumers. During this transformation, the issues of energy imbalance, carbon reduction imbalance, and residential privacy leakage in centralised trading market present serious challenges. In this paper, we propose a fully decentralised blockchain-based peer-to-peer trading scheme coupling energy and carbon markets. We implement pay-to-public-key-hash with multiple signatures as a transaction standard to realise a more secure transaction and reduced storage burden of senders. A script is hashed during the wallet address generation for each new transaction to protect residential privacy. A novel carbon accounting method and corresponding incentive mechanism for carbon reduction are designed to evaluate emission behaviours of distributed prosumers. Case studies demonstrate that the proposed scheme leads a reduced costs and carbon emissions compared to centralised trading systems and existing blockchain-based trading schemes.
While ethylene crackers in Middle East and North America are shifting to ethane feedstocks and the volume of propylene production is becoming less, the market share of on‐purpose propylene production ...technologies keeps on increasing. In Northeast Asia, due to the shortage of naphtha and natural gas, methanol‐to‐olefin and methanol‐to‐propylene technologies attract much attention. In addition, biochemical technologies are impinging the propylene downstream. Using starch, sugar, or biomass as raw materials, important industrial chemicals such as acrylic acid, butanol, and 1,3‐propanediol can be produced. Propylene glycol and epichlorohydrine can be manufactured using glycerine, the byproduct of biodiesel. All these factors are changing the traditional C3 value chain profoundly. The status of the game‐changing technologies related to the C3 value chain is presented. The relevant factors and the implications will be discussed.
The traditional C3 chain is profoundly changed by diverse factors like the increasing on‐purpose propylene production technologies and biotechnological processes. Alternative processes for the production of propylene and its derivatives have been aggressively pursued. The status of the game‐changing technologies related to the C3 value chain and the relevant factors and implications are discussed.
With advances of smart grid, the responsibility of carbon emission reduction can be fairly allocated to each participant in power networks through bidirectional communications. This paper proposes a ...hierarchical carbon market scheduling model to effectively realize carbon emission reduction. The policy makers in the upper level aim to maximize the effects of carbon emission reduction. They set out appropriate monetary incentives and emission allowances for both customers and generators. Considering restrictions from policy makers, both generators and customers in lower levels seek to minimize their operational costs and payment bills, respectively. To achieve these objectives, a multiobjective problem is formulated by forecasting market trends from a behavior learning model. The simulation results demonstrate that through the proposed approach the renewable penetration increases and the carbon emissions decrease. The benefits for each participant are analyzed as well.
This paper proposes a novel unit commitment (UC) model under smart grid (SG) environment, which intends to strike a balance pursuing minimum carbon emissions for policy maker, minimum costs for ...generators and minimum payment bills for consumers. This leads to a multiobjective optimization problem (MOP) which can be solved through the multiobjective immune algorithm (MOIA). Therefore, the energy market scheduling problem considering low carbon smart grid environment can be analysed. The case studies are conducted to demonstrate the proposed model and present the allocation of power generations as well as the daily energy market scheduling results. It has been proved that the penetration of SG contributes to the mitigation of carbon emissions during the peak demand time by around 500 ton/h. It is also suggested that if the policy maker can provide appropriate monetary compensation for the deployment of SG technologies, generators will be encouraged to participate in the SG deployment.
Macrophages are one of the most important types of immune effector cells and are closely associated with tumor progression and metastasis. In this work, we investigated the influences of oxidized ...multiwalled carbon nanotubes (o-MWCNT) on macrophages that are resting in the normal subcutis tissue or in the tumor microenvironment in vivo as well as on the macrophage cell line of RAW 264.7 treated with combination of IL4, IL10 and IL13 in vitro. The o-MWCNT were characterized with SEM, DLS, FTIR, TGA, and UV-vis-NIR spectroscopy, and their effects on the RWA 264.7 cell line and breast cancer tumor-bearing mice were analyzed using the MTS assay, flow cytometry analysis, and histological and immunohistochemical observations. Our experimental results showed that subcutaneously injected o-MWCNT not only induced phagocytosis of the local resident macrophages, but also competitively recruited macrophages from other tissues. These interactions resulted in macrophage reduction and decreased vessel density around the tumor mass, which together inhibited tumor progression and metastasis in the lung. In the cell line model, the o-MWCNT inhibited the ability of the interleukin treated RAW macrophages to promote tumor cell migration as well as decreased their proliferation rate.
Climate change can lead to more frequent high wind gusts, which represent the future risk that planners for future offshore wind farms (OWF) should consider, as wind turbines (WT) are built to ...standards that rely on current climate conditions. High wind gusts at 35m/s can lead to decreasing the WT operational performance. Given this, we use three climate scenarios corresponding to 1981-2000, 2021-2040, and 2061-2080 to investigate future changes in the WT cut-out wind gust (35m/s) threshold (exceeding the cut-out threshold will lead to no-operation). The future climate change analysis considered the probability of exceeding the 35m/s threshold in the future as the risk ratio (RR) and changes in return time corresponding to the 35m/s threshold in future climate scenarios as the relative change (RC). The research focuses on four regions in the UK exclusive economic zone (EEZ) and uses the 2.2km UK Climate Projection 2018 (UKCP18) hourly maximum wind gust datasets. Using RC calculations, we found most of the locations that have high 35m/s wind gust return periods in the 2021-2040 and 2061-2080 scenarios compared to the baseline scenario lay in the North region with an increased low risk to more frequent 35m/s locations equal to 79.2% and 99.8%, respectively. In RR calculation, we found a spatial correlation with RC locations in two future scenarios indicating that most sites with decreasing risk of occurring 35m/s wind gusts also have a decreasing risk of exceeding the 35m/s threshold in the future.
Energy hub scheduling plays a vital role in optimally integrating multiple energy vectors, e.g., electricity and gas, to meet both heat and electricity demand. A scalable scheduling model is needed ...to adapt to various energy sources and operating conditions. This paper proposes a conditional random field (CRF) method to analyse the intrinsic characteristics of energy hub scheduling problems. Building on these characteristics, a reinforcement learning (RL) model is designed to strategically schedule power and natural gas exchanges as well as the energy dispatch of energy hub. Case studies are performed by using real-time digital simulator that enables dynamic interactions between scheduling decisions and operating conditions. Simulation results show that the CRF-based RL method can approach the theoretical optimal scheduling solution after 50 days training. Scheduling decisions are particularly more dependent on received price information during peak-demand period. The proposed method can reduce 9.76% of operating cost and 1.388 ton of carbon emissions per day, respectively.
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