一体化活牲污泥法处理城市污水

一体化活性污泥法(UNITANK)是一种具有脱氮除磷功能的以曝气-非曝气方式运行的间歇活性污泥处理工艺。分析介绍了UNITANK工艺的运行特性、工艺设计及应用情况。     

采用间歇曝气改进AB工艺氮磷脱除的研究

连续流间歇曝气工艺是在对传统活性污泥法的改造中发展起来的一种工艺。笔者针对 AB法工艺氮磷脱除功能较弱的特点 ,提出了采用间歇曝气工艺改进 AB法工艺氮磷脱除功能的试验方案 ,并以 AB法工艺污水厂实际污水为研究对象进行了小试研究。由试验结果可以看出 ,连续流间歇曝气工艺简便易行 ,能明显提高氨氮和总氮的去除率 ,在碳源满足脱氮除磷要求的条件下 ,可达到良好的氮磷脱除效果     

单槽式嫌气好气活性污泥法

单槽式嫌气好气活性污泥法单槽式嫌气好气活性污泥法是在单个完全混合型反应槽内设置水下曝气机间歇供气，在时间序列中形成嫌气好气状态进行污水处理。此法是日本在最近几年内开发的新型活性污泥法，适用于小型生活污水处理。该法具有下列特点：①确保反应槽内好气状态的...     

活性污泥系统中曝气机的速度控制

针对活性污泥法处理废水的特点，讨论了曝气机速度控制的简化模型。采用专家模糊控制器，构成了曝气机速度的ＥＦＣ控制系统。仿真研究表明：采用ＥＦＣ控制，系统有较强的适应性，且实施简单，有一定实际意义。     

OMNIFLO型定序间歇曝气池设计运转

定序间歇曝气法是非稳定态活性污泥法的一种。进水、曝气、沉淀、滗析和置闲,按定时顺序在一个池内完成,故减少了常规处理工艺中的构筑物以及水泵与管道等材料设备费用,因而占地面积少、投资省、运行费用低。同时,由於活性污泥沉降性能好、沉淀面积大以及固液分     

无锡太湖新城污水厂二期生物池溶解氧控制优化

污水处理厂生物池的溶解氧控制对于活性污泥法脱氮除磷工艺(如改良A2/O工艺)的运行十分重要.结合活性污泥模型软件计算不同工况下的曝气量,对无锡太湖新城污水厂二期生物池的曝气管路系统进行了设计优化,并采用生物工艺智能优化系统( BIOS)在实际运行中根据进水负荷动态设定溶解氧目标值,保证工艺稳定和排放达标,并可节省曝气能耗.     

改良型氧化沟间歇组合曝气生产性试验研究

重庆市某区县城市污水处理厂采用恒定曝气方式运行,能耗和药耗偏高,且出水TN、TP浓度存在超标风险,为此根据进水水质、水量变化特征以及曝气系统的特点,提出了时间与空间上的间歇组合曝气运行方式。分别考察了间歇曝气氧化沟不同位置曝气机组合形成的3种模式的运行效果。结果表明,模式2为最优间歇组合曝气方式,与恒定曝气相比,全天平均DO浓度下降0.47 mg/L,对TN的去除率提高了20.1%,出水TN为10.36 mg/L,生物除磷率也相对恒定曝气提高了8.4%,相应的化学除磷剂投量从23.5 mg/L降低到14.5 mg/L,药耗下降了38.3%,电耗相比下降了12.2%。间歇组合曝气解决了运行中存在的问题,达到降低能耗、药耗和提高出水水质的双重效果。     

同时去除氯和磷的污水处理工艺

一般活性污泥连续曝气法的脱氮率只有20％左右,磷几乎不能去除。小规模污水处理的氧化沟法脱氮率为40～80％,间歇活性污泥法的脱氮率为50～80％,它们对磷都不能达到预定的去除效果。 日本(株)西原环境卫生研究所研究开发了一种新的生物处理工艺,即在一个反应槽内,根据生物反应的机制,     

曝气/间歇曝气两级生物滤池去除COD和TP研究

为提高曝气生物滤池的除磷能力，采用曝气／间歇曝气两级生物滤池处理模拟生活污水，考察了系统对COD和TP的去除效果。结果表明，当间歇曝气生物滤池的曝气和停曝时间分别为2h和1h时，系统对COD的去除效果不会受到明显的负面影响，出水COD达到《城镇污水处理厂污染物排放标准》（GB18918—2002）中的一级B标准。对TP的去除主要集中在采用间歇曝气的生物滤池中，其对TP的去除率平均为59％，系统对TP的去除率平均为72％。曝气／间歇曝气两级生物滤池在保证对COD去除效果的前提下大大提高了系统的除磷率，解决了传统曝气生物滤池除磷率低的问题。     

除磷脱氮SBR系统的污泥特性分析

通过对除磷脱氮SBR系统活性污泥性能及活性的研究，认为该系统特有的物质转化方式是导致活性污泥的性能发生周期性变化的主要原因。试验结果表明，单位体积混合液中好氧污泥比厌氧污泥浓度低，好氧污泥中挥发性固体物质的比例比厌氧污泥低；在间歇曝气系统中分子氧的不连续输入使氧化酶传递氧的过程受阻，导致耗氧呼吸速率(SOUR)和脱氢酶活性具有不同的变化规律：前者在厌氧段增加而在好氧段减小，后者在好氧段增加而在缺氧段减小。     

Application of artificial neural network for the prediction of jaggery mass during drying inside the natural convection greenhouse dryer

In this paper, an attempt is made to predict the hourly mass of jaggery during the process of drying inside greenhouse dryer under the natural convection mode. Jaggery was dried until the constant variation in the mass of jaggery. Artificial neural network (ANN) is used to predict the mass of the dried jaggery on hourly basis. Solar radiation, ambient temperature and relative humidity are input parameters for the prediction of jaggery mass in each hour in the ANN modelling. The results of the ANN model are also validated with experimental drying data of jaggery mass. The statistical parameters such as root mean square error and correlation coefficient (R²) are used to measure the difference between values predicted by the ANN model and the values actually observed from the experimental study. It was found that the results of the ANN model and experimental are shown fairly good agreement.     

人工神经网络在油漆废水混凝氧化处理建模中的应用

利用改进的B－P算法，对油漆废水混凝氧化处理系统建立了人工神经网络模型，并利用该模型拟合、预测了一些实验数据。结果表明，模型的计算值与实测数据之间的误差很小，而且能正确反映各影响因素作用的内部机理。     

Neural network prediction of concrete degradation by sulphuric acid attack

Microbiologically induced corrosion is a leading cause of the deterioration of wastewater collection, transmission and treatment infrastructure around the world. This paper examines the feasibility of using artificial neural networks (ANNs) to predict the compressive strength of concrete and its degradation under exposure to sulphuric acid of various concentrations. A database incorporating 78 concrete mixtures performed by the authors was developed to train and test the ANN models. Data were arranged in a patterned format in such a manner that each pattern contains input variables (concrete mixture parameters) and the corresponding output vector (weight loss of concrete by H₂SO₄ attack and compressive strength at different ages). Results show that the ANN model I successfully predicted the weight loss of concrete specimens subjected to sulphuric acid attack, not only for mixtures used in the training process, but also for new mixtures unfamiliar to the ANN model designed within the practical range of the input parameters used in the training process. Root-mean-squared error (RMSE) and average absolute error (AAE) for ANN predictions of weight loss due to sulphuric acid attack were 0.013 and 8.45%, respectively. The ANN model II accurately predicted the compressive strength of the various concrete mixtures at different ages with RMSE and AAE of 2.35 MPa and 4.49%, respectively. A parametric study shows that both models I and II can successfully capture the sensitivity of output variables to changes in input parameters.     

Simulating pavement structural condition using artificial neural networks

This article aims to investigate the feasibility of incorporating of an artificial neural network (ANN) as an innovative technique for modelling the pavement structural condition, into pavement management systems. For the development of the ANN, strain assessment criteria are set in order to characterise the structural condition of flexible asphalt pavements with regards to fatigue failure. This initial task is directly followed with the development of an ANN model for the prediction of strains primarily based on in situ field gathered data and not through the usage of synthetic databases. For this purpose, falling weight deflectometer (FWD) measurements were systematically conducted on a highway network, with ground-penetrating radar providing the required pavement thickness data. The FWD data (i.e. deflections) were back-analysed in order to assess strains that would be utilised as output data in the process of developing the ANN model. A paper exercise demonstrates how the developed ANN model combined with the suggested conceptual approach for characterising pavement structural condition with regard to strain assessment could make provisions for pavement management activities, categorising network pavement sections according to the need for maintenance or rehabilitation. Preliminary results indicate that the ANN technique could help assist policy decision makers in deriving optimum strategies for the planning of pavement infrastructure maintenance.     

An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag power

The tensile behavior of hybrid fiber reinforced concrete (HFRC) is important to the design of HFRC and HFRC structure. This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC. This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23 features of HFRC. In the model, three methods to process output features (no-processed, mid-processed, and processed) are discussed and the mid-processed method is recommended to achieve a better reproduction of the experimental data. This means the strain should be normalized while the stress doesn’t need normalization. To prepare the database of the model, both many direct tensile test results and the relevant literature data are collected. Moreover, a traditional equation-based model is also established and compared with the ANN model. The results show that the ANN model has a better prediction than the equation-based model in terms of the tensile stress-strain curve, tensile strength, and strain corresponding to tensile strength of HFRC. Finally, the sensitivity analysis of the ANN model is also performed to analyze the contribution of each input feature to the tensile strength and strain corresponding to tensile strength. The mechanical properties of plain concrete make the main contribution to the tensile strength and strain corresponding to tensile strength, while steel fibers tend to make more contributions to these two items than PVA fibers.     

Modelling of synthesis of waste cooking oil methyl esters by artificial neural network and response surface methodology

This present study was carried out to investigate the application of artificial neural network (ANN) and response surface methodology (RSM) as modelling tools for predicting the waste cooking oil methyl esters (WCOME) yield obtained from alkali-catalysed methanolysis of waste cooking oil (WCO). The impact of process parameters involved was studied by a central composite rotatable design. A comparison of the two developed models for the methanolysis process was carried out based on pertinent statistical parameters. The calculated values of coefficient of determination (R²) of 0.9950 and the average absolute deviation (AAD) of 0.4930 for the ANN model compared with R² of 0.9843 and AAD of 0.9376 for the RSM model demonstrated that the ANN model was more accurate than the RSM model. The actual maximum WCOME yield of 94?wt% was obtained at a reaction temperature of 55°C, a catalyst amount of 1?w/v, a reaction time of 70 min and a methanol-to-oil ratio of 6:1.

Abbreviations/Nomenclature CV: coefficient of variance; FFA: free-fatty acid; R: correlation coefficient; R²: coefficient of determination     

Modelling of water reserves in mineral soils with different retention properties

This work focuses on modelling soil water reserves using an Artificial Neural Network (ANN). Four model variants were established based on 843 records (verified through 268 measurements) of soil water content (SWC) measured at full‐scale field sites located in Southwest Poland. It is revealed that commonly recorded climatic data (precipitation and temperature) linked with SWC and field water capacity (FWC) are applicable in the ANN modelling. The basic model (utilising the meteorological data) was the most suitable for soil profiles with thicknesses of 0–25 cm, while in profiles with thicknesses of 0–50 cm and 0–100 cm the comprehensive ANN model (linking climatic data, FWC and SWC) was the most appropriate. Furthermore, comparative studies of the measured and modelled data indicated their statistical convergence, thus providing support for the practical implementation of the proposed ANN modelling.     

Comparing the linear and logarithm normalized artificial neural networks in inverse design of aircraft cabin environment

When the indoor environment is designed by genetic algorithm (GA) and computational fluid dynamics (CFD), the artificial neural network (ANN) plays a role of surrogate model of CFD to reduce the computational cost. To improve the performance of ANN, a self-updating logarithm normalized method was proposed to enhance the local prediction of ANN in the inverse design based on GA and ANN. An MD-82 aircraft cabin was used to test the performance of the proposed method, and different environmental parameters were chosen to be the objectives of the cabin environment. The success rate (SR) was used to evaluate the local prediction ability of ANN. Instead of linear normalized ANN, SR was found to be increased by 10.5% with the logarithm normalized ANN and the computational cost was reduced by 23.2% for the same quality of solution.     

Long-term deflection of cracked composite beams with nonlinear partial shear interaction — A study using neural networks

A new study of the short- and long-term deflections of simply-supported composite beams using finite element analysis and artificial neural networks (ANNs) is presented. In this study, two ANN models are developed and trained using the results of a finite element model developed by the authors in a companion paper. The finite element model accounted for the nonlinear load–slip relationship of shear connectors as well as the creep, shrinkage, and cracking of concrete slabs. The effects of creep and shrinkage of the concrete slab are considered only for non-cracked concrete. A large database representing a wide range of different design parameters was constructed for the purpose of training and verifying the two ANN models. It was found that the two ANN models were capable of predicting deflections of composite beams not used as part of the training process. The ANN models were then used to evaluate the effects of non-geometric design variables on the short- and long-term deflections of simply-supported composite beams. Finally, the short- and long-term deflections computed based on the approaches given in the AISC specification and Eurocode 4 were assessed using the results of the finite element model. It was found that the AISC approach underestimates short-term deflections and overestimate long-term deflections when compared with the results of the finite element method.     

基于外海环境预报的近岸岛礁桥址区波高ANN推算模型

中国跨海桥梁多建于近岸岛礁海域,桥址区的波浪要素随时空演变复杂。桥址区波高的准确推算对于桥梁结构设计和施工组织具有十分重要的意义。提出一种基于外海环境预报数据的近岸岛礁桥址区波高人工神经网络（ANN）推算模型,并以平潭海峡公铁两用大桥桥址海域为研究对象,运用ANN算法中常用的BP神经网络对外海海洋预报台提供的波高、风速数据以及在桥址区实测波高数据进行训练,建立二者之间的映射关系及ANN推算模型。为验证推算模型的可行性和有效性,运用上述模型对桥址区连续80 d的海浪波高进行推算,通过对比前人模型和实测数据发现,推算波高和实测波高的变化趋势基本吻合,均方根误差满足预测要求,获得了理想的预测效果。研究表明,提出的波高ANN推算模型可以利用外海预报信息进行近岸岛礁桥址区的波高推算,且建模过程较为简单。