Metagenomic analysis reveals the effects of cotton straw–derived biochar on soil nitrogen transformation in drip-irrigated cotton field

Environmental Science and Pollution Research - Biochar has been widely accepted as a soil amendment to improve nitrogen (N) use efficiency, but the effect of biochar on N transformation metabolic...     

走向工作面贯通上山与支架快速回撤技术

针对走向工作面停采线与上山之间的护巷煤柱造成煤炭损失，支架回撤工艺复杂、巷道掘进量大等问题，提出走向工作面贯通上山与支架快速回撤技术。工作面贯通采区上山，利用上山回撤支架。在贯通前，利用压力拱模型分析剩余煤柱应力变化规律，并结合极限平衡区公式，确定让压煤柱为8 m；对贯通前基本顶合理断裂位置、让压调节机制、末采阶段采高进行了分析确定，并采取上山巷内补强支护及挂绳铺网措施。贯通上山后，首先利用支架对上山留巷，完成留巷后，利用上山回撤支架，先将上部端头3架逆时针旋转90°作为掩护架，在对剩余支架逐架回撤时，3个掩护架呈现台阶型。提出的技术取消了护巷煤柱及回撤通道，提高了煤炭采出率，减少了巷道掘进量，简化了回撤工艺。     

电晕放电等离子体技术处理水中四环素的研究

采用电晕放电等离子体技术降解水中的四环素,研究了在反应体系中,初始浓度、输入功率、电极间距、空气流量及初始pH对四环素去除效率的影响.同时,还对四环素在降解过程中不同时段的COD、TOC和B/C变化进行了研究,并对其降解产物进行了分析和讨论.实验结果表明:电晕放电等离子体对水中四环素具有较好的去除效果,在四环素初始浓度为200 mg·L-1、pH=2.47、初始电导率为1.50 mS·m-1、空气流量为0.06 m3·h-1、电极间距为4 mm、输入功率为45.0 W的条件下,反应20 min后,四环素的去除率可达到99.1%,COD去除率可达31.2%,TOC去除率可达80%左右,其B/C比提高为0.30,有效地改善了废水的可生化性.     

活性炭纤维负载TiO2催化剂的制备及其电催化性能

采用溶胶-凝胶法制备了活性炭纤维负载TiO₂催化剂（TiO₂/ACF），并通过SEM和XRD等手段对TiO₂/ACF进行了表征。以邻苯二甲酸二甲酯（DMP）为目标降解物，考察了催化材料的电催化活性。实验结果表明：在反应温度为25 ℃、初始DMP质量浓度为100.0 mg/L、电解质Na₂SO₄质量浓度为100 mg/L、电流密度为62.5 mA/cm²、电极板间距为4 cm的条件下，经过40 min的降解，DMP质量浓度为1.8 mg/L，DMP去除率为98.2%;TiO₂/ACF在反应过程中可以原位再生，经6次重复使用后仍保持很高的催化活性，对DMP的去除率仍达90%以上。     

酸析法和碱析法处理草浆造纸黑液的效果对比

对比了酸析法和碱析法对碱法草浆造纸黑液中木质素的去除效果，并分析了聚合氯化铝(PAC)絮凝剂对酸析和碱析后木质素的絮凝效果。实验结果表明:酸析法和碱析法都能有效析出造纸黑液中的木质素，酸析法对色度的去除效果要优于碱析法;当造纸黑液pH为3时，COD去除率达到72.0%，色度去除率为97.7%;当造纸黑液pH为13时，COD去除率达到55.0%，色度去除率为22.3%;PAC对于酸析后木质素有较好的絮凝效果，当废水温度为55℃、PAC加入量为45mL/L时，COD去除率可以达到79.5%，色度去除率为98.5%;PAC絮凝剂对于碱析后的造纸黑液色度的去除效果较差。     

TiO2纳米管催化降解4，4’-二溴联苯的影响因素和动力学研究

利用TiO2纳米管催化降解水中的4，4’-二溴联苯，对催化降解过程和影响因素进行研究。结果表明，TiO2纳米管对其有较高的催化降解效率且降解过程符合Langmuir—Hinshelwood动力学模式。不同光源、4，4’-二溴联苯的初始浓度、纳米管添加量和pH值对催化降解过程都有较大影响，其中pH值的影响最为明显。反应液在中性状态下的降解率明显低于pH=1或11的情况。在pH=1时，4，4’一二溴联苯的降解率达86％。     

用好氧-厌氧反复耦合固定床生物反应器处理肌苷生产废水

利用由多孔微生物载体构建的好氧-厌氧反复耦合固定床生物反应器进行了高浓度肌苷生产废水处理中试研究。连续84d的运行结果表明，当进水COD为1500-2700mg／L、水力停留时间为22．1h时，出水COD可维持在150mg／L左右，COD去除率达90％～95％。装置运行稳定后，未经沉淀的出水中的固体悬浮物质量浓度小于50mg／L，表明该反应器可避免剩余污泥的产生。中试结果验证了该反应器处理高浓度肌苷废水的可行性和优势，同时为装置放大提供了理论依据。     

Density estimation using ranked-set sampling data

The ranked-set sampling (RSS) is applicable in practical problems where the variable of interest for an observed item is costly or time-consuming but the ranking of a set of items according to the variable can be easily done without actual measurement. In the context of RSS, the need for density estimation arises in certain statistical procedures. The density estimation also has its own interest. In this article, we develop a method for the density estimation using RSS data. We derive the properties of the resulted density estimate and compare it with its counterpart in simple random sampling (SRS). It is shown that the density estimate using RSS data provides a better estimate of the density than the usual density estimate using SRS data. The density estimate developed in this article can well serve various purposes in the context of RSS.     

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

● Hybrid deep-learning model is proposed for water quality prediction. ● Tree-structured Parzen Estimator is employed to optimize the neural network. ● Developed model performs well in accuracy and uncertainty. ● Usage of the proposed model can reduce carbon emission and energy consumption. Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m³/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of COD_eff and biogas yield, R² values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.     

高压脉冲放电等离子体对水中土霉素的降解

采用针-板式高压脉冲放电等离子体降解水中土霉素,考察了放电输出功率、空气流量、电极间距、溶液初始浓度、初始pH、初始电导率和添加Fe2+的量对土霉素去除率的影响。实验结果表明,高压脉冲放电等离子体对水中土霉素有较好的去除效率,在初始浓度200 mg/L,放电功率64 W,电极间距8 mm,空气流速0.05 m3/h,初始pH为2.6,初始电导率1.083 mS/cm条件下,反应12 min后,土霉素的去除率可达97%。向溶液中添加Fe2+,可提高土霉素的去除率。TOC随着反应时间的延长逐渐减小,反应12 min时,TOC去除率可达57%,说明大分子物质被降解为小分子物质,部分被完全矿化为CO2和H2O。