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mineral separation from ore machines

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[0013] Fig.0 K magnification. 4: (a) Transmission electron micrograph of nanoform kaolin clay mineral depicting the laminar hexagonal sheet structure at 12.0 K magnification (b) Transmission electron micrograph of nanoform kaolin clay mineral at 20.

mineral separation from ore machines

(4) in this system, be provided with closed warehouse of sludge and coal bunker, therefore can avoid mud stink and coal dust to the diffusion of atmospheric environment, improved environment.

It combines all three production factors as inputs and measures single-factor efficiency in a total-factor environment., 2016 [17]). For example, based on a non-radial DEA framework, Zhou et al. All these factors result in high energy demand and low energy efficiency in the Chinese economy. As a result of considerable increase in energy prices and concerns about sustainable development, the issue of energy efficiency gained much of the attention, public as well as scientific, during the last two decades. In addition, in recent years, China has increased its exports of energy-intensive products such as automobiles, machinery, and steel. While these traditional energy efficiency indexes take only energy into account as a singe input to produce output (GDP) while other inputs such as labor and capital are ignored, a new approach known as total-factor energy efficiency (TFEE) has been developed by Hu and Wang (2006) [6] to overcome the disadvantages of the traditional partial-factor energy efficiency and since then a number of papers have been published. (2013) [18] proposed a non-radial DEA approach by integrating the entropy weight and SBM to access the environmental efficiency of the Chinese power industry at the provincial levels from 2005 to 2010; and Zhang and Choi (2013) [19] improved two SBM efficacy guides for modeling China’s local energy and ecological activity during the 2001–2010. Some scholars use SBM (Slacks-Based Measure) model to make up for these shortcomings: Zhou et al. However, none of the above studies meet the requirements of Chinese energy reduction, and are also biased estimations (Fukuyama et al. Meanwhile, China’s industry has basically completed mid-stage industrialization and has entered its late stage. China’s soaring demand for energy results from several factors that are central to the structure of the Chinese economy, and thus are quite clear. In this context, the Chinese Government decisively put forward a legally binding energy efficiency target of decreasing energy intensity by 20% during the 12th five year plan time [1] and allocated the target to the provincial level. Lesage and Pace (2010) [22] argued that ignoring spatial correlation will result in the omission of variables and increment of the total amount of biased factors, which may make estimation biased. These imply the rigidity growth of energy consumption and serious environment pollution are dual constraints of Chinese economy growth. (2016) [23]). In other words, even though all of the above evaluation methods account for the environment factors, it is difficult to ensure an accurate and fair assessment because of ignoring spillover impact of space among regions (a comprehensive review of the DEA approach in energy efficiency has been written by Mardani et al. (2008) [10] develop several DEA-based environmental efficiency evaluation models for the measurement of the carbon emission efficiency of several world regions. Moreover, some scholars consider undesirable outputs such as SO2 and CO2 to evaluate the environmental efficiency. Bian and Yang (2010) [11] proposed several DEA models to simultaneously measure resource (energy) and environmental efficiency and applied their models in the resource and environmental efficiency evaluation problem of 30 Chinese provinces. The effective way to break the constraints is to find paths to improve Chinese energy efficiency while the goal of research is to identify the main areas needing to improve energy efficiency. The factors of labor, primary energy consumption, GDP, CO2, SO2, NOx and CO were all included as input or output factors in their study. The relocation of energy-intensive industries from the coastal region to the inland areas reduces the overall energy efficiency of the Chinese economy as well. (2007) [9] evaluate the energy and environmental efficiency of 26 OECD countries from 1995 to 1997. The total energy consumption has grown at a high rate, making China the second-largest energy consumer and largest CO2 emitter in the world. Although multi-stage DEA model separates environment factors and management factors, other issues still exist that make method biased and unfair, for example ignoring environment constraints. (Some researchers (Honma and Hu, 2009 [7]) concluded that the partial-factor energy efficiency estimation is misleading and cannot give the appropriate benchmark. However, as SBM model only attributes factors affecting the energy efficiency of cities to input factors, and does not eliminate external environment factors, internal management factors and random error factors, its results are also biased and unfair (Timmer, 1997 [20]). Since the first oil crisis (1973/1974) there has been a growing body of research in the field of energy studies (Tonini, 2016 [3]) and the issue of energy efficiency has become crucial component of energy strategy in many countries and regions for the last two decades. In accordance with the investigation of the World Bank (2015) [2], China’s energy cost is still between 8% and 12% of GDP.5% of global GDP in 2015, China has turned into the largest manufacturing power in the world. By adopting a comprehensive approach involving legal, administrative and economic means, the energy efficiency initiative has achieved remarkable results including a 19. These prior studies enhance our knowledge, but they mainly focused on regional or sectional total energy efficiency, using gross domestic product as yield, with a few studies focusing on energy efficiency analysis of Chinese cities.1% drop in energy intensity [1]. An important focus of these studies is how to improve energy efficiency without harming economic growth, which is the main problem that China is facing. This TFEE index provides a useful alternative to the traditional energy efficiency indicators mentioned above., 2015 [1]). Zhou et al. China is reliant on the fast expansion of the secondary industry, and particularly heavy industry., 2009 [16]), because they do not include non-consensual out-production of environment pollution (Bian et al. Assessing energy efficiency in macro-level policy analysis is usually done by two indicators: energy intensity (Bosseboeuf, Chateau and Lapillonne, 2000 [4]) and energy efficiency (Barcelona, 2007 [5]). China is in the rapid development period of urbanization and industrialization; with 15.Energy is always the important component for economic growth and social development, which provides not only an important source of power for life and production, but also important industrial raw materials.) For example, Hu and Kao (2007) [8] calculate the energy saving ratio and energy savings per capita of 17 countries from Asia-Pacific Economic Cooperation (APEC) between 1991 and 2000 with TFEE. However, accompanied with the high-speed development of manufacturing, China has paid enormous costs for energy over-consumption (Figure 1 and Figure 2) and serious environmental pollution. Honma and Hu (2009) [7] obtain the regional TFEE of Japan with the DEA model based on a dataset of 47 prefectures during the period 1993–2003. The other problem is that the second stage of multi-stage DEA model is based on the space independence among evaluation units, but Pan (2012) [21] has shown Chinese TFEE has spatial correlation. However, how to evaluate regional energy efficiency has become a primary problem. However, due to the lack of sufficient theoretical support and experience, most provinces have carried out their work blindly, promoted energy efficiency ineffectively, and faced unique difficulties not addressed by the “one-size-fits-all” national policy (Lu et al. These studies include those of Li and Hu (2012) [12], who estimated the TFEE of Chinese 210 cities from 1995 to 2006; Sun and Xiao (2012) [13], who studied the TFEE of Chinese Yangtze River Delta region from 1992 to 2010; Qin (2014) [14], who analyzed the TFEE of Chinese 230 element cities at the beginning of 2000s; and Lin and Du (2015) [15], who estimated the affection of market-oriented reforms on Chinese energy and CO2 emissions performance.

The water can cause the organic silicate to hydrolyze and polymerize. Some chemical materials may be sensitive to formation sand and fluid type. The second stage, non-polymeric materials like sodium silicate solution was injected through the wellbore into the sand zone. In the same phase, the viscosity of sodium silicate solution was decreased by using a dilution fluid. Alcohol has low surface tension reaction with water, so it is preferable preflush for the formation. Finally, a water-miscible organic solvent including inorganic salt and an alkylpolysilicate was injected into the zone. The experimental investigation on formation samples after the application of sodium silicate method disclosed that permeability retention was (70–80)% and the compressive strength was (284–427) psi at 60 °C [77]. The final stage, the nitrogen gas was injected as an overflush removing the extra of silicate solution from the porespaces and drying the sodium silicate coating sand grains and to promote sodium silicate salts precipitation. Some advantages and disadvantages of silicate polymer methods as reported in literature are summarized in Table 10. He concluded that it is necessary to use new consolidating materials, which are safer and less sensitive to formation and sand fluid, to avoid the problems of safety issues and the negative effect on the formation [74]. The alcohol-silicate solution reacts with residual formation water, present in less mobile areas of the formation, not swept by both alcohol and silicate solution and water. Cobianco et al. Initially, an aqueous silicate solution was injected into the production zone through the perforated casing. [77] used a three-stages process to control sand production in oil fields. Firstly, organic silicate and alcohol are injected into the target formation followed by water. Initially, 3% KCl was injected as a preflush to avert any contact between the formation water and the sodium silicate. The aqueous silicate solution is an ammonium silicate solution. The rock that had undergone aqueous silicate solution consolidation was examined at 79.As an application to oxidation, Anthony [74] used organic silicate, alcohol and water for hydrolyzing and forming a coating-like binding agent which could consolidate and strengthen unconsolidated sand formation in two steps, Table 9. They reported that the compressive strength resulted from the consolidation is in the range of 0–398 psi at temperature ranging from 20 to 60 °C. A weak organic acid solution was injected in order to stabilize the amorphous silica network. After that, a water-immiscible hydrocarbonaceous liquid as a spacer volume was injected to the zone. Dwivedi and Singh [76] analyzed non-polymeric materials, such as sodium silicate. Shu [75] applied three phases to consolidate the sand formation.5 °C, and the retention permeability is high without a specific value of permeability retention [75]. Therefore, it is needed to preflush the target formation with a liquid, such as alcohol that will totally displace the formation water. He utilized the proton source water which contains a weak base and a strong acid, where the strong acids are unsafe aspects to be used in the treatment.

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