The determination and matching analysis of pinch point temperature difference in evaporator and condenser of organic rankine cycle for mixed working fluid

Exergo-economic analysis of the pinch point temperature difference (PPTD) in both evaporator and condenser of sub-critical organic Rankine cycle system (ORCs) are performed based on the first and second laws of thermodynamics. Taking mixture R13I1/R601a as a working fluid and the annual total cost per net output power Z as exergo-economic performance evaluation criterion, the effects of PPTD in evaporator ΔT_e, and the PPTD ratio of condenser to evaporator y, on the exergo-economic performance of ORCs are analyzed. Moreover, how some other parameters influence the optimal PPTD in evaporator ΔT_e,opt and the optimal PPTD ratio of condenser to evaporator y_opt are also discussed. It has been found that the exergo-economic performance of ORCs is remarkably influenced by ΔT_e and y, and there exists ΔT_e,opt and y_opt. In addition, ΔT_e,opt and y_opt are affected by heat transfer coefficient ratio of condenser to evaporator ß, the temperature of working fluid at dew point in condenser T_1a, and composition of R13I1/R601a: larger ß and T_1a lead to lower ΔT_e,opt and y_opt; by contraries, larger mass fraction of R13I1 makes ΔT_e,opt and y_opt increase, and y_opt increases linearly. The effects of the temperature of working fluid at bubble point in evaporator T_3a, mass flow rate of exhaust flue gas m_g, and inlet temperature of exhaust flue gas T_gi on ΔT_e,opt and y_opt are very slight. For comparison, three additional working fluids, namely R601a, R245fa, and 0.32R245fa/0.68R601a, are also taken into account.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

The fractionation and geochemical characteristics of rare earth elements measured in ambient size-resolved PM in an integrated iron and steelmaking industry zone

Environmental Science and Pollution Research - Improved understanding of the fractionation and geochemical characteristic of rare earth elements (REEs) from steel plant emissions is important due...     

Combined effects of nitrogen addition and organic matter manipulation on soil respiration in a Chinese pine forest

The response of soil respiration (Rs) to nitrogen (N) addition is one of the uncertainties in modelling ecosystem carbon (C). We reported on a long-term nitrogen (N) addition experiment using urea (CO(NH₂)₂) fertilizer in which Rs was continuously measured after N addition during the growing season in a Chinese pine forest. Four levels of N addition, i.e. no added N (N0: 0 g N m⁻² year⁻¹), low-N (N1: 5 g N m⁻² year⁻¹), medium-N (N2: 10 g N m⁻² year⁻¹), and high-N (N3: 15 g N m⁻² year⁻¹), and three organic matter treatments, i.e. both aboveground litter and belowground root removal (LRE), only aboveground litter removal (LE), and intact soil (CK), were examined. The Rs was measured continuously for 3 days following each N addition application and was measured approximately 3–5 times during the rest of each month from July to October 2012. N addition inhibited microbial heterotrophic respiration by suppressing soil microbial biomass, but stimulated root respiration and CO₂ release from litter decomposition by increasing either root biomass or microbial biomass. When litter and/or root were removed, the “priming” effect of N addition on the Rs disappeared more quickly than intact soil. This is likely to provide a point of view for why Rs varies so much in response to exogenous N and also has implications for future determination of sampling interval of Rs measurement.

     

Plasticity in metamorphic traits of <Emphasis Type="Italic">Rana kukunoris</Emphasis> tadpoles: The interactive effects of food level and rearing temperature

Larval amphibians are particularly likely to encounter variation in rearing temperature and resource availability due to variation in aquatic breeding habitats. In this study, plasticity in growth rates, larval mass, larval period, and size at metamorphosis were examined in Rana kukunoris Nikolskii, 1918 under different combinations of temperature and food level. Larval period and larval body mass was sensitive to food level, and varied with temperature. Tadpoles metamorphosed at an older age at low temperature than those reared at warm temperature. Food level was a significant affect on larval period at low temperature, but not at warm temperature. Mass was heavier for tadpoles reared at low temperatures than those reared at warm temperatures. The effect of food level depended on temperature, because larvae reared at low temperature that were offered a high food level achieved a larger size than larvae offered a low food level, but this did not occur at warm temperature. Therefore, we suggest that high food availability at low temperature prolonged developmental periods, thus larvae are larger as metamorphs than those reared at warm temperatures.     

Decoupling of nonferrous metal consumption from economic growth in China

Nonferrous metal is an important basis material for the development of the national economy, and its consumption directly affects economic development. It has great significance in the effective utilization of nonferrous metals, development of an environment-friendly society, and investigation of the decoupling of nonferrous metal consumption and GDP growth. The decoupling indicators for nonferrous metal consumption and GDP growth (D _r) in China from 1995 to 2010 were calculated in this study, and the results were analyzed. A productive model based on BP neural network was established. Then, the decoupling indicators for nonferrous metal consumption and GDP growth in China for the period of 2011–2020 were predicted. For the period of 1995–2010, the annual average decoupling indicators were <1 for copper, aluminum, zinc, lead, and nickel, except for tin, which was 0.21. The analysis showed that the decoupling of nonferrous metal consumption and GDP growth is in a less optimistic situation to copper, aluminum, zinc, lead, and nickel in China from 1995 to 2010. The annual average decoupling indicator for tin was 0.21, which indicates relative decoupling. For the period of 2011–2020, the predicted decoupling indicators for copper, aluminum, zinc, lead, nickel, and tin were between 0 and 1. This finding indicates the implementation of relative decoupling. However, the total consumption of nonferrous metals did not decouple from GDP growth.     

烟草烟雾对室内空气颗粒物浓度的影响

采用电称冲击低压系统(ELPI)将无烟室和吸烟室内的空气颗粒物(0.03～10.00 μm)分成12级,对其粒子数和质量浓度进行测定.结果表明,吸烟室PM0.03～10.00的日平均粒子数和质量浓度分别是无烟室的1.50、1.13倍.烟草烟雾对室内颗粒物粒子数的影响集中在0.03～1.00 μm粒径段;对室内颗粒物质量浓度的影响表现为双模态结构,峰值分别在0.20～0.70、5.00～8.20 μm粒径段.烟草烟雾颗粒物的粒子数和质量浓度随烟雾消散时间的增加而减少,粒径越小,减小的越明显;烟草烟雾颗粒物在室内消散缓慢,会在长时间内造成影响,应引起足够关注.