Research on Purification of Domestic Sewage by Artificially Strengthened Ecological Filter Bed in North China

Artificially strengthened filter bed is an innovative wastewater treatment technology based on the coupling of eco-contact oxidation filters and artificial wetlands purification mechanism.By small scale laboratory equipment,the effects of cascade aeration,filter type,filter clogging and other ecological factors on the operation effect of artificial filter bed were studied.As indicated by the results,the pretreatment of cascade aeration had obvious effect and could satisfy the oxygen requirements of artificially strengthened ecological filter bed.Through the analysis on the purification results of volcanic and gravel filter,the effluent quality of volcanic filter was better than that of gravel filter.With the advantages of low operations costs and good effluent quality,the artificially strengthened ecological filter bed has great value to be popularized in North China.

Prediction of Pulsation Frequency of Pulsing Flow in Trickle Beds Based on Artificial Neural Network

An extensive database (946 measurements) for the frequency of pulsing flow in trickle beds was established by collecting the experimental results published over past 30 years. A new correlation based on artificial neural network (ANN) to predict the pulsation frequency was developed. Seven dimensionless numbers (groups) employed in the proposed correlation were liquid and gas Reynolds, liquid Weber, liquid Eotvos, gas Froude, and gas Stokes numbers and a bed correction factor. The comparisons of performance reported in the of literature and present correlations show that ANN correlation is a significant improvement in predicting pulsation frequency with an average absolute relative error (AARE) of 10% and a standard deviation less than 18%.

Estimation of hydraulic jump on corrugated bed using artificial neural networks and genetic programming

Artificial neural networks （ANNs） and genetic programming （GP） have recently been used for the estimation of hydraulic data. In this study, they were used as alternative tools to estimate the characteristics of hydraulic jumps, such as the free surface location and energy dissipation. The dimensionless hydraulic parameters, including jump depth, jump length, and energy dissipation, were determined as functions of the Froude number and the height and length of corrugations. The estimations of the ANN and GP models were found to be in good agreement with the measured data. The results of the ANN model were compared with those of the GP model, showing that the proposed ANN models are much more accurate than the GP models.

Simulation on hydrodynamics of non-spherical particulate system using a drag coefficient correlation based on artificial neural network

Fluidization of non-spherical particles is very common in petroleum engineering.Understanding the complex phenomenon of non-spherical particle flow is of great significance.In this paper,coupled with two-fluid model,the drag coefficient correlation based on artificial neural network was applied in the simulations of a bubbling fluidized bed filled with non-spherical particles.The simulation results were compared with the experimental data from the literature.Good agreement between the experimental data and the simulation results reveals that the modified drag model can accurately capture the interaction between the gas phase and solid phase.Then,several cases of different particles,including tetrahedron,cube,and sphere,together with the nylon beads used in the model validation,were employed in the simulations to study the effect of particle shape on the flow behaviors in the bubbling fluidized bed.Particle shape affects the hydrodynamics of non-spherical particles mainly on microscale.This work can be a basis and reference for the utilization of artificial neural network in the investigation of drag coefficient correlation in the dense gas-solid two-phase flow.Moreover,the proposed drag coefficient correlation provides one more option when investigating the hydrodynamics of non-spherical particles in the gas-solid fluidized bed.

Anaerobic tapered fluidized bed reactor for starch wastewater treatment and modeling using multilayer perceptron neural network

treatability of synthetic sago wastewater was investigated in a laboratory anaerobic tapered fluidized bed reactor （ATFBR） with a mesoporous granular activated carbon （GAC） as a support material. The experimental protocol was defined to examine the effect of the maximum organic loading rate （OLR）, hydraulic retention time （HRT）, the efficiency of the reactor and to report on its steady- state performance. The reactor was subjected to a steady-state operation over a range of OLR up to 85.44 kg COD/（m^3·d）. The COD removal efficiency was found to be 92% in the reactor while the biogas produced in the digester reached 25.38 m^3/（m^3·d） of the reactor. With the increase of OLR from 83.7 kg COD/（m^3·d）, the COD removal efficiency decreased. Also an artificial neural network （ANN） model using multilayer perceptron （MLP） has been developed for a system of two input variable and five output dependent variables. For the training of the input-output data, the experimental values obtained have been used. The output parameters predicted have been found to be much closer to the corresponding experimental ones and the model was validated for 30% of the untrained data. The mean square error （MSE） was found to be only 0.0146.

On-Line Prediction of a Fixed-Bed Reactor Using K-L Expansion and Neural Networks

An on-line prediction scheme combining the Karhunen-Love expansion and a recurrent neural network for a wall-cooled fixed-bed reactor is presented.Benzene oxidation in a pilotscale,single tube fixed-bed reactor is chosen as a working system and a pseudo-homogeneous twodimensional model is used to generate simulation data to investigate the prediction scheme presentedunder randomly changing operating conditions.The scheme consisting of the K-L expansion andneural network performs satisfactorily for on-line prediction of reaction yield and bed temperatures.

基于人工智能的交互式护理床设计

当今社会,随着我国人口老龄化的加剧,卧床人数也在不断增加,所以医护人员的需求量也在增加,同时卧床还会导致意外坠床、压疮等情况。针对于上述问题,该项目设计一款基于人工智能技术和物联网技术的交互式护理床。该项目采用STM32作为核心处理器,病人可以通过语音指令对床板进行控制,防止出现压疮;摄像头采集病人的图像信息交给树莓派进行处理,从而防止病人坠床;再加上物联网平台的使用,病人的状态以及体征可以通过APP和网页传达给医护人员,医护人员还可以发送指令到APP,实现病人、床、医护人员三者之间的智能交互。

Comparison of artificial neural networks with empirical correlations for estimating the average cycle time in conical spouted beds

Conventional spouted beds have been extensively used in many real-life applications but are not suited for all types of materials, especially fine particles, which require internal devices to improve their motion in the spouted bed. However, unlike conventional spouted beds, there are almost no mechanistic or empirical models available for the design of spouted beds with internals. Given the availability of an extensive but not experimentally designed database, the main purpose of this study is to present an analysis of neural networks and empirical models in terms of their suitability to fit and predict average cycle times in conical spouted beds with and without draft tubes. The parameters investigated are particle size, density, contactor angle, gas inlet diameter, static bed height, and draft tube features. Although the amount of information is always a key factor when fitting models, the size of the database used in this study strongly affects the fitting performance of empirical models, whereas artificial neural networks are more influenced by how the data are scaled. Results of model verification show that both techniques are suitable for predicting average cycle times for data outside the range covered by the database.

Prediction of bed load sediments using different artificial neural network models

Modeling and prediction of bed loads is an important but difficult issue in river engineering.The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data.In this paper,three different artificial neural networks(ANNs)including multilayer percepterons,radial based function(RBF),and generalized feed forward neural network using five dominant parameters of bed load transport formulas for the Main Fork Red River in Idaho-USA were developed.The optimum models were found through 102 data sets of flow discharge,flow velocity,water surface slopes,flow depth,and mean grain size.The deficiency of empirical equations for this river by conducted comparison between measured and predicted values was approved where the ANN models presented more consistence and closer estimation to observed data.The coefficient of determination between measured and predicted values for empirical equations varied from 0.10 to 0.21 against the 0.93 to 0.98 in ANN models.The accuracy performance of all models was evaluated and interpreted using different statistical error criteria,analytical graphs and confusion matrixes.Although the ANN models predicted compatible outputs but the RBF with 79%correct classification rate corresponding to 0.191 nctwork error was outperform than others.

人工生态浮床技术在城市水环境治理中的应用研究

城市可持续发展离不开生态环境建设,水环境治理是城市生态环境建设的重要环节。本文结合实际案例详细研究人工浮床技术在城市河道黑臭、河湖富营养化等水环境问题中的应具体应用及效果,以期能够促进城市更好地开展生态环境建设,更好地实现可持续发展。

交汇区水流结构对底泥磷分布特征影响机制

交汇区的水流结构极为复杂,揭示交汇区底泥磷分布特征难度较大。设计交汇区底泥磷分布实验系统及其检测方法,测量交汇区的三维流场和底泥中总磷含量,分析交汇区水流分区与底泥中总磷空间分布的关系,并基于交汇水槽实验中204组水动力参数与底泥中总磷含量数据,建立人工神经网络模型,定量分析流场中水动力参数对底泥中总磷含量的影响,辨识其中的关键水动力参数。结果表明:分离区内水流水平流速较小,底泥磷含量较高;位于分离区外侧的加速区水平流速较大,底泥磷含量较低,但加速区的局部区域(剪切层区)存在显著的底泥磷含量偏高现象;通过人工神经网络模型的参数敏感性分析,定量解释局部底泥磷含量高值现象与该区域较大的向下水流有关。

小流域富营养化水体修复研究——以海马齿联合多种基质的人工湿地为例

流域水体富营养化已成为水环境的重要问题。构建基于多类型基质与浮床植物组合的水平潜流人工湿地,考察其对小流域富营养化水体的生态修复性能,并通过高通量测序等方法解析该湿地系统微生物群落变化特征。研究结果表明,以基质和浮床植物组合的复合生态系统可在更短时间完成启动,对小流域富营养化水体中的总氮(Total Nitrogen,TN)、总磷(Total Phosphorus,TP)和氨氮(NH_(3)-N)表现出较强的去除效果;多组合基质投至水底,与底泥接触,其凭借吸附及截留作用,在短时间内能有效净化水中的氮磷;水平潜流人工湿地中,海马齿浮床根际为水体微生物的固定提供良好场所,使微生物多样性增加,能加速有机污染物的降解,改善水质。

基于循证理念的舒适护理结合术后离床锻炼对人工髋关节置换术患者快速康复的效果评价

目的探讨基于循证理念的舒适护理结合术后离床锻炼对人工髋关节置换术患者快速康复的临床效果评价。方法便利选取2021年1月—2022年4月湖北省孝感市孝南区第二人民医院收治的45例行择期髋关节置换术的患者为对照组,2022年5月—2023年8月本院收治的47例行择期髋关节置换术的患者为观察组,对照组予以常规护理,观察组施以基于循证理念的舒适护理结合术后离床锻炼干预,比较两组患者干预前后的快速康复效果、舒适度及预后情况。结果干预后,两组患者疼痛视觉模拟评分低于干预前,Berg平衡量表评分高于干预前,且观察组优于对照组,差异有统计学意义(P均<0.05)。观察组简化舒适状况量表评分为(93.24±6.15)分,高于对照组的(80.15±7.27)分,差异有统计学意义(t=9.338,P<0.05)。干预后,两组患者日常生活能力量表评分、简化世界卫生组织生活质量测定量表评分均高于干预前,且观察组高于对照组,差异有统计学意义(P均<0.05)。观察组并发症总发生率为2.13%(1/47),低于对照组的17.78%(8/45),差异有统计学意义(χ^(2)=4.730,P<0.05)。结论人工髋关节置换术患者应用基于循证理念的舒适护理结合术后离床锻炼干预,有助于提升患者快速康复效果,获得较高舒适度,且预后良好。

人工海水条件下移动床生物膜反应器快速挂膜启动试验

海水循环水产养殖系统中,生物膜反应器的挂膜时间通常较长。为实现人工海水条件下移动床生物膜反应器(moving-bed biofilm reactor,MBBR)滤片的快速挂膜,通过搅拌滤片,采用低成本的海水配方、专用营养液以及选择多样化的菌种等方法和措施进行了试验,并建立了以氨氮、亚硝酸盐氮水平、滤片颜色和孔隙生成物为指标的评估体系。试验结果表明,相比于化学营养组的56 d,发酵营养组经过46 d即完成了挂膜。挂膜过程中氨氮质量浓度最高为20.36~25.12 mg/L,亚硝酸盐氮质量浓度最高为45.28~53.20 mg/L。

Assessment of pressure drop in conical spouted beds of biomass by artificial neural networks and comparison with empirical correlations

Pressure drop is an essential parameter in the operation of conical spouted beds(CSB)and depends on its geometric factors and materials used.Irregular materials,like biomass,are complex to treat and,unlike other gas–solid contact methods,CSB turn out to be a suitable technology for their treatment.Artificial neural networks were used in this study for the prediction of operating and peak pressure drops,and their performance has been compared with that of empirical correlations reported in the literature.Accordingly,a multi-layer perceptron network with backward propagation was used due to its ability to model non-linear multivariate systems.The fitting of the experimental data of both operating and peak pressure drop was significantly better than those reported in the literature,specifically in the case of the peak pressure drop,with R^(2) being 0.92.Therefore,artificial neural networks have been proven suitable for the prediction of pressure drop in CSB.

海上裸露基岩斜桩嵌岩稳桩关键技术研究

斜桩嵌岩是在基岩埋藏深度较浅或者基本无覆盖层的地质条件下采用一定斜率的嵌岩灌注桩技术。钢管桩作为嵌岩灌注桩的钢护筒,受海底地形影响,钢护筒的入土深度不足以形成桩基自身稳定的地质条件。文章通过分析一定斜率的钢护筒在受到自身重力、钢护筒内水的重力、波浪力、水流力、土压力等的作用下,通过最不利工况计算出桩的最小入土深度,形成人造基床,达到桩基自身的稳桩措施,解决了在海上裸露基岩或覆盖层较薄的地基上进行斜桩嵌岩施工技术难题,为斜桩嵌岩施工提供了理论依据。

组合型生态浮床的动态水质净化特性

研究开发了一种由水生植物、水生动物及微生物膜构建的组合型浮床生态系统.通过中试研究,考察了该浮床对富营养化湖泊水体在动态条件下的净化效果.结果表明,水体交换时间为7d时TN、TP、高锰酸盐指数的去除率分别为53.8%、86.0%和35.4%.污染物的直接净化主体为人工介质和水生植物单元,但在生态浮床中引入河蚬增加水生动物单元,通过食物链的'加环'作用,提高了颗粒性有机物的可溶化和无机化(氨化)以及可生化性,改善了植物吸收以及人工介质单元生物膜中微生物的基质条件,促进微生物的生长和活性,提高了浮床的净化效果.

浮床栽培植物生长特性的研究

在不施肥条件下,利用富营养化鱼塘人工浮床无土栽培水稻(O.sativaL.)、香根草[Vetiveriazizanioides(L.)Nash]、美人蕉(CannaindicaL.)和水芋头[Colocasiaesculenta(L.)Schott].与陆地常规栽培相比,4种植物均能生长,其中水稻、美人蕉的生物量、株高、叶绿素含量、根系活力等变化较小,香根草的生物量下降明显,水芋头的根冠比失调、球茎产量下降,美人蕉可获得和陆栽相仿甚至更好的株型和生物量,表明水稻、美人蕉适宜在富营养化水体浮床无土栽培.

组合生态浮床技术对富营养化水源水质的改善效果

通过延长浮床生态系统的食物链和强化浮床的微生物富集特性,构筑了以水生植物、水生动物及微生物生态系统为主体的组合生态浮床,考察了其对富营养化水源水质的改善效果。研究表明,当水体交换时间为7d、水力负荷为1.85m3/(m2.d)时,组合生态浮床对TN、TP、TOC、Chl-a的平均去除率分别为52.7%、54.5%、49.2%、80.2%,相应的去除负荷分别为4.03、0.79、0.49g/(m2.d)和25.31mg/(m2.d);对总藻毒素和胞外藻毒素的去除率分别为77.4%和68%。可见,该组合生态浮床是改善富营养化水源水质的有效方法。

人工浮床处理重污染河水的效能分析

试验采用填料人工浮床的方式种植菖蒲、风车草、香根草3种植物,分3池进行试验,采用重力自流方式分别进水,对浮床出水进行水质监测。监测结果表明菖蒲与风车草混种及菖蒲浮床系统对试验河水中有机物和N、P的去除较好。试验数据表明植物直接吸收不是氮、磷去除的主要途径;试验进水为劣V类重污染河水时,菖蒲、混种菖蒲与风车草、香根草浮床的COD、TP、TN平均去除率分别可达20.46%、23.3%、18.9%,38.35%、45.46%、44.8%与42.34%、45.82%、38.6%;控制试验进水污染负荷,采用菖蒲、混种菖蒲与风车草、香根草的人工浮床生态工程技术治理重污染河水是可行的。