Performance evaluation of venturi aeration system

The venturi aeration is an effective practice to increase the dissolved oxygen accessibility in the water bodies. This study aims to optimize the various geometrical parameters of the venturi aeration system. A non-dimensional technique was applied to find the optimum performance of various geometric parameters i.e. throat lengths (t_l), number of air holes (N), and converging and diverging angles (α and β). These experiments have been carried out using 1124 L capacity of tank having dimensions of 105 cm long, 105 cm wide and 102 cm deep. The experiments were conducted at a constant flow velocity of water (1.096 m/s) with varying throat length (t_l = 20–100 mm keeping 20 mm as interval between two consecutive length), number of air holes (N = 1–17 at an equal hole to hole distance of 5 mm between them), and converging and diverging angles (α and β = 10°, 15°, 20° and 25°). Multiple non-linear regression equations were also developed from the linear relation with the dependent variable (Non-dimensional form of standard aeration efficiency, NDSAE) and independent variables (t_l and N). With the geometrically optimized venturi aerator the optimum performance was found for t_l =100 mm, N = 17, and α and β = 15°. The maximum value of standard oxygen transfer rate (SOTR) and standard aeration efficiency (SAE) obtained was 0.0216 kgO₂/h and 0.611 kgO₂/kWh respectively. From the non-dimensional study, it was found that the NDSAE is the function Reynolds number (Re) and Froude number (Fr). The simulation equations were developed on the basis of Re and Fr for NDSAE, and subjected to 7.378 × 10⁻⁶ < Re < 3.689 × 10^-5 and 0.163 < Fr < 0.817, respectively.     

茶树植保机械及减量施药技术研究进展

农药防治是茶树病虫害综合防治的重要组成部分,其在病虫害突发或爆发时具有快速高效的防治优势。茶树叶片表面具有亲水性,常量施药会造成茶叶农残超标、生态环境破坏等问题,实现茶树减量施药是减少茶叶农残的有效手段。系统综述了茶树生物特性、茶树病虫害预测诊断及防治方法、茶树植保机械及施药技术,强调提高茶树低容量喷雾的农药有效利用率是实现茶树减量施药的关键。针对目前茶园地面工况复杂及农药利用率低的问题,本文从低容量仿形喷雾机、茶树病虫害喷雾决策及智能终端等六个方面提出茶树病虫害施药技术及装备的研究建议,指出低量化、精准化及智能化是未来茶树植保喷雾机械及施药技术的发展方向。     

智慧物流在饲料企业管理中的应用研究

饲料行业的竞争日益激烈,其需要学会使用现代科技提升竞争力,智慧物流就是这些技术之一。智慧物流可以提升饲料企业对原材料和产品在物流过程中的管控。本文试图探索智慧物流如何提升饲料企业的管理。通过智慧物流,饲料企业可以改进生产管理的流程和模式,加强采购管理中的风险管理,追踪产品售后的流向和信息,提升企业的产品质量,保证企业财务数据的真实性和可靠性。     

机器学习及其在农业中应用研究的展望

机器学习是一种面向机器的数据分析方法,自动化机器学习的研究促进了人工智能的发展。大数据的快速积累,促进了机器学习算法的井喷式发展。如何选择合适的机器学习算法解决行业问题,成为了当前应用的难点。笔者整理了机器学习新材料,对各种机器算法的特点和算法之间的差异,进行了仔细的梳理,总结了各种算法的需求背景和优缺点,以及主要的应用场合。在此基础上,分析了机器学习在农业应用的案例,综述了机器学习在农业应用,指出了目前存在的发展瓶颈,并提出了进一步研究应用的建议。     

基于云服务的棚室蔬菜智能终端系统设计与实现——以黑龙江省为研究案例

棚室蔬菜产业在黑龙江省农业转方式、调结构和供给侧改革中占有重要的战略地位。黑龙江省棚室蔬菜生产规模近年来发展较快,技术支撑需求也与日俱增。本研究针对黑龙江省棚室蔬菜发展规模与技术服务支撑能力不匹配的现状,提出了基于云服务的棚室蔬菜智能终端系统及关键技术的实现方法。本研究以专家服务为主、数据挖掘技术为辅,以物联网设备为感知手段、以智能手机为用户终端,利用云服务对知识、资源、物联网数据的整合配置能力,提供蔬菜专家及棚室蔬菜用户对信息获取、存储、分析和决策的高效解决方案。本研究的部分内容已在黑龙江省农业科研部门、企业、蔬菜合作社、农户等不同用户群体中实验应用,能够为专家提供棚室蔬菜生产环境的远程问诊手段,适用于各类棚室蔬菜应用场景。本研究还提出了对大规模应用场景下的技术解决方案建议,可在全国的棚室蔬菜生产中推广应用,实现更广泛高效的专家技术服务支撑。     

System for quasi‐continuous simultaneous measurement of oxygen diffusion rate and redox potential in soil

Oxygen diffusion rate (ODR) and redox potential (E_H) are quantitative indices representing oxygen availability and redox status in soils, which is valuable information for better understanding causes and effects of soil aeration. Because these indices are spatially and temporally highly variable, continuous measurements and adequate numbers of repetitions are essential for accurate in situ monitoring. Here, we present a new, fully automated recording system for in situ measurements where ODR and E_H are measured at the same platinum electrode. The conflict between electrode polarization for ODR and the resulting biased E_H readings is solved by reducing the polarization time and introducing a recovery interval between two consecutive measurement cycles. The shorter polarization time ensures accurate E_H readings. It also results in moderately overestimated ODR readings, but this can be corrected before data analysis. The recovery interval restricts temporal resolution of the E_H‐ODR data pairs to 8 h. We illustrate the use of the system with measurements in a field experiment in Zürich, Switzerland. ODR curves at different depths ran roughly parallel to the corresponding curves of O₂ concentration in soil air but ODR was much more sensitive to precipitation. Low ODR was a necessary but not a sufficient condition for declining E_H. E_H ran parallel to O₂ concentration in soil air rather than to ODR. The fully automated system allows for time series of replicate measurements in multifactorial field studies with reasonable labor requirements. It may be particularly suitable for studies examining the effects of soil tillage, compaction, and irrigation, where structure‐related soil properties such as porosity, gas permeability, and soil aeration play a dominant role.     

面向智能油田的集输管网工艺模拟软件研制

针对集输管网多相输送体系、不同拓扑结构及输送工艺在数字油田智能化建设中的仿真模拟需求,基于图论方法和管网对象设计了可表示复杂连接关系的通用管网数据结构,耦合流动单元水热力数学模型与节点平衡关系建立了普适性的管网非线性方程组,提出了鲁棒性高的求解方法。以油田集输工艺设计、运行及优化等场景中独立使用或在综合管理系统中集成应用为目标研制了计算软件,通过对比商业软件计算结果,验证了所开发软件的适应性和准确性,同时从工程实用方面探讨了其应用前景。     

车载智能土壤采样系统设计与试验

针对传统土壤样本采集操作复杂、劳动强度大、采集精度低和缺乏信息化管理的问题，设计了一种车载智能土壤采样系统。该系统安装于无人驾驶采样车上，系统包括土壤样本自动采集装置和电气控制系统。对系统整个工作过程进行分析，确定了各关键机构及控制硬件的主要工作参数。电气控制系统以运动控制器为控制核心，土壤样本自动采集装置对不同深度范围的土样进行采集，并按地理位置信息分类收集，通过RFID读写器将GPS系统定位所解算的目标采样点的经纬度地理信息和采样深度等信息写入收集土样采样筒底部的电子标签中。性能试验表明，安装于无人驾驶采样车的车载智能土壤采样系统工作运行稳定、可靠，能够对农耕层0~200mm任意深度范围的土壤进行自动分层采样，效率高、精度高、全程自动化，能够按照位置信息进行分类管理，较好地满足了智能化高质量采集土样的需求。     

基于LoRa物联网的智能节水灌溉系统

针对园林与农业的水资源浪费问题,该研究介绍了一种新的灌溉系统用水管理方法,它可以应用于园林或农田,用LoRa无线传感器网络代替人工干预。传统的灌溉系统由于管理和配置不当,平均浪费了30%的用水。该种智能灌溉系统可以提高灌溉过程的效率,不仅为用户节省资金,而且节约水和电等资源,该系统可以实时检索数据,并利用这些数据确定正确的园林与农业用水量。运用该系统,当使用来自环境温湿度和土壤湿度的传感器数据时,可以节省多达33%的水,当仅使用温度输入时,可以节省多达25%。实测结果验证了该设计的可行性和有效性,能够满足园林与农业节水灌溉的需要。     

关于中国智慧管道发展的认识与思考

智慧管道是覆盖油气长输管道全生命周期的综合管理系统,是SCADA系统、电子管道、数字管道、智能管道在物联网、云计算、人工智能时代的延续和发展。剖析了油气管道行业智慧管道的内涵与外延,梳理了智慧管道技术发展的历史脉络,揭示了数字孪生体技术的实质与应用现状,在此基础上,建议中国智慧管道建设宜坚持需求驱动、问题导向、突出重点、体现特色、开放融合、数据共享、滚动发展、持续改进的原则,以期为中国智慧管道的健康持续发展提供参考。