成型和灼烧温度对垃圾衍生燃料灰渣理化特性的影响

以生活垃圾衍生燃料(refuse derived fuel,RDF)为研究对象,通过挤压成型的RDF与非成型的RDF在600~1 100℃条件下灼烧,所产生的灰渣进行质量、粒径、化学成分等特性进行分析,探索RDF灰渣随温度变化的规律以及潜在的应用价值。研究结果表明,在1 000℃以下,RDF灰渣随着温度提高,灰分组分发生分解反应,主要是残余有机物、碳酸盐、硫酸盐的分解,灰渣粒径减小;当温度升至1 000℃以上,灰分组分发生化合反应,生成产物以复合硅酸盐为主,灰渣粒径增大甚至结块;灰渣在化学反应过程中,非金属元素C,Cl,S转化为气体化合物逸出主体,使灰渣质量减小;成型RDF灰渣晶体含量比非成型RDF高;成型RDF灰渣粒径比非成型RDF灰渣大。     

Seasonal critical concentration and relationships of leaf phosphorus and potassium status with biomass and yield traits of soybean

Analysis of uppermost fully expanded leaves is useful to detect a deficiency of mineral nutrients such as phosphorus (P) and potassium (K) in soybean. Although, the leaf P or K status aids in fertilizer management, information on nutrient seasonal relationships with growth and yield traits at maturity are limited. To investigate this, soybean was grown under varying P or K nutrition under ambient and elevated CO₂ concentrations. Results show significant relationships of the relative total biomass and yield‐related traits with the foliar P and K concentrations measured several times in the season across CO₂ levels. However, the relationships established earlier in the season showed that the growth period between 25 and 37 d after planting (DAP), representing the beginning of flowering and pod, respectively, is the best for leaf sampling to determine the foliar P or K status. The leaf P and K status as well as the critical leaf P (CLPC) and K (CLKC) concentrations for traits such as seed yield peaked around 30 DAP (R2 stage) and tended to decline thereafter with the plant age. The CLPC and CLKC of seed yield indicate that the leaf P and K concentration of at least 2.74 mg g^?1 and 19.06 mg g^?1, respectively, in the uppermost fully expanded leaves are needed between 25 and 37 DAP for near‐optimum soybean yield. Moreover, the greatest impact of P and K deficiency occurred for the traits that contribute the most to the soybean yield (e.g., relative total biomass, seed yield, pod and seed numbers), while traits such as seed number per pod, seed size, and shelling percentages were the least affected and showed smaller leaf critical concentration. The CLPC or CLKC for biomass and seed yield was greater under elevated CO₂ 24–25 DAP but varied thereafter. These results are useful to researchers and farmers to understand the dynamics of the relationship of pre‐harvest leaf P and K status with soybean productivity at maturity, and in the determination of suitable growth stage to collect leaf samples.     

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.     

Fish‐processing wastes as an alternative diet for culturing the minute rotifer Proales similis de Beauchamp

We evaluated the suitability of fish waste diet (FWD) for culturing the minute rotifer Proales similis through the observation of their population growth and particle size selective feeding. A total of five treatments either with Nannochloropsis oculata or FWD (0.75 and 0.50 g/L) or the combination of N. oculata and FWD were set up in triplicates. P. similis were cultured in diluted natural seawater (8 g/L salinity) and 26 ± 1°C with the diet treatments being applied randomly. We daily monitored the rotifer density, the number of bacteria and the water quality in all the cultures. The population density of P. similis increased exponentially in all treatments, while the mean growth in FWD 0.75 g/L was significantly higher (p < .05) than that in the control. Addition of N. oculata to FWD resulted to lowered P. similis population growth. Bacterial colony count was high in FWD and introduction of P. similis to the diet decreased their density. The estimated bacteria ingestion rates were generally in the range of 6.03 × 10²–1.24 × 10⁴ bacteria/rotifer/hr and there was a positive linear relationship between bacterivory and rotifer population growth. We also observed a shift in the particle size distribution with a reduction in the frequency and concentration of small‐sized particles (<2.5 μm) at day 6. These results accentuate the potential of fish‐processing waste as diet for culturing P. similis which feeds on bacteria and small particles (≤2.5 μm) that are by‐products of degradation of this diet.     

Intensive organic vegetable production increases soil organic carbon but with a lower carbon conversion efficiency than integrated management

Intensive vegetable production in greenhouses has rapidly expanded in China since the 1990s and increased to 1.3 million ha of farmland by 2016, which is the highest in the world. We conducted an 11‐year greenhouse vegetable production experiment from 2002 to 2013 to observe soil organic carbon (SOC) dynamics under three management systems, i.e., conventional (CON), integrated (ING), and intensive organic (ORG) farming. Soil samples (0–20 and 20–40 cm depth) were collected in 2002 and 2013 and separated into four particle‐size fractions, i.e., coarse sand (> 250 µm), fine sand (250–53 µm), silt (53–2 µm), and clay (< 2 µm). The SOC contents and δ¹³C values of the whole soil and the four particle‐size fractions were analyzed. After 11 years of vegetable farming, ORG and ING significantly increased SOC stocks (0–20 cm) by 4008 ± 36.6 and 2880 ± 365 kg C ha^?1 y^?1, respectively, 8.1‐ and 5.8‐times that of CON (494 ± 42.6 kg C ha^?1 y^?1). The SOC stock increase in ORG at 20–40 cm depth was 245 ± 66.4 kg C ha^?1 y^?1, significantly higher than in ING (66 ± 13.4 kg C ha^?1 y^?1) and CON (109 ± 44.8 kg C ha^?1 y^?1). Analyses of ¹³C revealed a significant increase in newly produced SOC in both soil layers in ORG. However, the carbon conversion efficiency (CE: increased organic carbon in soil divided by organic carbon input) was lower in ORG (14.4%–21.7%) than in ING (18.2%–27.4%). Among the four particle‐sizes in the 0–20 cm layer, the silt fraction exhibited the largest proportion of increase in SOC content (57.8% and 55.4% of the SOC increase in ORG and ING, respectively). A similar trend was detected in the 20–40 cm soil layer. Over all, intensive organic (ORG) vegetable production increases soil organic carbon but with a lower carbon conversion efficiency than integrated (ING) management.     

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R2 adj), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH≥15.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.     

麻山药种植田沙壤土流动性离散元模型颗粒放尺效应

为简化麻山药-沙壤土复合体离散元模型，提高离散单元法在农业领域中的计算效率，以沙壤土为研究对象，在EDEM离散元软件中构建非球形颗粒，进行双目标参数标定试验，采用放大颗粒粒径方法，利用转鼓、坍塌与FT4流变仿真试验，从颗粒群动态堆积角、流动质量、流动速率以及能量等方面探究了颗粒放尺效应对颗粒群物理特性的影响。试验结果表明：干燥处理后的2 mm粒径沙壤土基质静态堆积角和动态堆积角平均值分别为32.16°和35.02°；与独立标定试验相比，双目标标定试验获得的仿真参数更具准确性与唯一性；在动态堆积角试验中，真实沙壤土颗粒在转鼓中所形成的动态堆积角随粒径和旋转速度的增大而减小，而在仿真试验中，转鼓转速相同情况下，非球形颗粒群在增大粒径的情况下所产生的动态休止角差异较小；坍塌试验中，不同粒径颗粒群在流动过程中的流动质量与平均流速变化趋势基本一致，但误差随粒径增大而增大；颗粒质量相同时，将粒径分别放大2倍及4倍，颗粒数量同比减少87.24%、98.92%，仿真时间明显缩短，计算效率显著提高；FT4流变试验表明，当放尺因子S为2时，阻力FV及其力矩T随时间变化的拟合曲线值约为原尺时的2倍，而当放尺因子S为4时，与原尺相比，拟合曲线斜率差异显著，相关性明显降低。研究结果可为构建沙壤土离散元放尺模型提供理论依据，同时也可为农业工程离散元放尺仿真计算提供一定参考。