Study Progress Analysis of Effluent Quality Prediction in Activated Sludge Process Based on CiteSpace

In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.

Extended activated sludge model no. 1 (ASM1) for simulating biodegradation process using bacterial technology

Phosphorus is one of the most important nutrients required to support various kinds of biodegradation processes. As this particular nutrient is not included in the activated sludge model no. 1 （ASM1）, this study extended this model in order to determine the fate of phosphorus during the biodegradation processes. When some of the kinetics parameters are modified using observed data from the restoration project of the Xuxi River in Wuxi City, China, from August 25 to 31 in 2009, the extended model shows excellent results. In order to obtain optimum values of coefficients of nitrogen and phosphorus, the mass fraction method was used to ensure that the final results were reasonable and practically relevant. The temporal distribution of the data calculated with the extended ASM1 approximates that of the observed data.

Application of BP NN and RBF NN in Modeling Activated Sludge System

Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed respectively and the ability of convergence and generalization has been analyzed separately. As for BP NN, the effects of numbers of layers and nodes have been studied; as for RBF NN, the influences of the number of nodes and the RBF′s width have been studied. It is concluded that BP NN has converged much slowly in comparison with RBF NN. The conclusion that the RBF NN is suitable for modeling activated sludge system has been drawn. An automatically optimum design program for RBF NN has been developed, through which the RBF NN model of traditional activated sludge system has been established.

An Adaptive Time-Step Backward Differentiation Algorithm to Solve Stiff Ordinary Differential Equations: Application to Solve Activated Sludge Models

A backward differentiation formula (BDF) has been shown to be an effective way to solve a system of ordinary differential equations (ODEs) that have some degree of stiffness. However, sometimes, due to high-frequency variations in the external time series of boundary conditions, a small time-step is required to solve the ODE system throughout the entire simulation period, which can lead to a high computational cost, slower response, and need for more memory resources. One possible strategy to overcome this problem is to dynamically adjust the time-step with respect to the system’s stiffness. Therefore, small time-steps can be applied when needed, and larger time-steps can be used when allowable. This paper presents a new algorithm for adjusting the dynamic time-step based on a BDF discretization method. The parameters used to dynamically adjust the size of the time-step can be optimally specified to result in a minimum computation time and reasonable accuracy for a particular case of ODEs. The proposed algorithm was applied to solve the system of ODEs obtained from an activated sludge model (ASM) for biological wastewater treatment processes. The algorithm was tested for various solver parameters, and the optimum set of three adjustable parameters that represented minimum computation time was identified. In addition, the accuracy of the algorithm was evaluated for various sets of solver parameters.

Activated sludge process based on artificial neural network

Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.

Stoichiometric deduction of activated sludge process for organic carbon and nitrogen removal

The activated sludge process （ASP） is the most generally applied biological wastewater treatment method. The ASP for the removal of organic carbon and nitrogen can be looked as the combination of eight processes. In order to set up an ASP model, the stoichiometric coefficients should be deduced so that the stoichiometric matrix can be presented. The important assumptions and simplifications behind the model for ASP are enumerated. Using the matrix, mass balance equation and consistent units, the stoichiometric coefficients in the eight processes are exclusively deduced one by one.

Design and Comparison of Wastewater Treatment Plant Types (Activated Sludge and Membrane Bioreactor), Using GPS-X Simulation Program: Case Study of Tikrit WWTP (Middle Iraq)

Mathematical models and simulation are considered a powerful tool in engineering practice. Those tools are becoming increasingly used for the improvement of wastewater treatment plants design because the conceptual design is complex and ill-defined. In this paper, three alternatives: 1) complete mix activated sludge without nitrogen removal (CAS);2) complete mix activated sludge with nitrogen removal (CAS-N) and;3) membrane bioreactor (MBR) processes were designed into two steps: first concept design to calculate the size of process units, then second implement modeling and simulation to improve the accuracy of the conceptual design. In brief, the treatment process design has been verified by using the activated sludge model No. 1 (ASM1) in GPS-X (v.7) simulation software. This application helps not only in sizing the treatment units but also in understanding the plant’s capacity. In the same time, it can assist in studying the future expansion works required for increased hydraulic and organic loadings. For this purpose, Tikrit WWTP was selected as a case study. The used model was validated by comparing the designed values of the plant and the modeling data. The verification of the obtained results from both hand calculations and the results of the program showed a good agreement. A significant difference in the volume of secondary treatment was obtained from design calculations, where the CAS without denitrification system was 9244 m3 (aerobic and secondary tanks), CAS with denitrification system was 11,324 m3 (anoxic, aerobic and secondary tanks) and for MBR system was 7468 m3 (anoxic, aerobic and immersed membrane tanks). From the obtained results point of view, it can be concluded that mathematical models can be considered as worthy tools to complement the established wastewater treatment plant design procedures.

LOCALIZATION OF OBJECT (SPINE) IN MEDICAL IMAGE USING ACTIVE SHAPE MODELS

Active shape models (ASM), consisting of a shape model and a local gray-level appearance model, can be used to locate the objects in images. In original ASM scheme, the model of object′s gray-level variations is based on the assumption of one-dimensional sampling and searching method. In this work a new way to model the gray-level appearance of the objects is explored, using a two-dimensional sampling and searching technique in a rectangular area around each landmark of object shape. The ASM based on this improvement is compared with the original ASM on an identical medical image set for task of spine localization. Experiments demonstrate that the method produces significantly fast, effective, accurate results for spine localization in medical images.

Experimental study on zero excess sludge production process with ozonation unit in MBR process

In order to investigate the influence factors of zero excess activated sludge (EAS) process by ozonation, a 100 L membrane bioreactor coupled with a sludge ozonation unit (MBR-SO) was performed for 80 d without EAS wasting. Some mathematical models were developed to elucidate the relationship between process parameters and the operating effects. It is considered that the sludge lysing ratio (ξ), produced COD per unit mass lysed MLSS (η), observed sludge yield coefficient (Yobs) and intrinsic yield coefficient for COD produced by lysed sludge (Y2) significantly affect the flowrate to ozonation unit (q). When q is 0.0067 times of influent flowrate (Q) and ξ is about 0.72 for each batch ozonation, a relatively stable MLSS concentration of 8168 mg/L and zero Yobs are achieved in the MBR-SO system. The calculation of sludge disintegration number indicates that the high ξ can apparently decrease the sludge amount needed for ozonation. At the same ozone dose, the low input ozone concentration and high flowrate can enhance the sludge lysing effects and a low energy consumption of 0.041 Yuan/m3 wastewater is obtained.

Active Shape Models Using Scale Invariant Feature Transform

A new active shape models (ASMs) was presented, which is driven by scale invariant feature transform (SIFT) local descriptor instead of normalizing first order derivative profiles in the original formulation, to segment lung fields from chest radiographs. The modified SIFT local descriptor, more distinctive than the general intensity and gradient features, is used to characterize the image features in the vicinity of each pixel at each resolution level during the segmentation optimization procedure. Experimental results show that the proposed method is more robust and accurate than the original ASMs in terms of an average overlap percentage and average contour distance in segmenting the lung fields from an available public database.

Active Shape Model of Combining Pca and Ica: Application to Facial Feature Extraction

Active Shape Model （ASM） is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis （PCA） to statistically model the variability in the training set of example shapes. Independent Component Analysis （ICA） has been proven to be more efficient to extract face features than PCA. In this paper, we combine the PCA and ICA by the consecutive strategy to form a novel ASM. Firstly, an initial model, which shows the global shape variability in the training set, is generated by the PCA-based ASM. And then, the final shape model, which contains more local characters, is established by the ICA-based ASM. Experimental results verify that the accuracy of facial feature extraction is statistically significantly improved by applying the ICA modes after the PCA modes.

Identical full-scale biogas-lift reactors(BLRs) with anaerobic granular sludge and residual activated sludge for brewery wastewater treatment and kinetic modeling

Two identical full-scale biogas-lift reactors treating brewery wastewater were inoculated with different types of sludge to compare their operational conditions, sludge characteristics, and kinetic models at a mesophilic temperature. One reactor （R1） started up with anaerobic granular sludge in 12 weeks and obtained a continuously average organic loading rate （OLR） of 7.4 kg chemical oxygen demand （COD）/（m3.day）, COD removal efficiency of 80%, and effluent COD of 450 mg/L. The other reactor （R2） started up with residual activated sludge in 30 weeks and granulation accomplished when the reactor reached an average OLR of 8.3 kg COD/（m^3·day）, COD removal efficiency of 90%, and effluent COD of 240 mg/L. Differences in sludge characteristics,biogas compositions, and biogas- lift processes may be accounted for the superior efficiency of the treatment performance of R2 over R1. Grau second-order and modified StoverKincannon models based on influent and effluent concentrations as well as hydraulic retention time were successfully used to develop kinetic parameters of the experimental data with high correlation coefficients （R2 〉 0.95）, which further showed that R2 had higher treatment performance than R1. These results demonstrated that residual activated sludge could be used effectively instead of anaerobic granular sludge despite the need for a longer time.

Modification of the activated sludge model for chemical dosage

Full-scale experiments have been carried out to adapt the activated sludge model ASM2d to include the influence of metal dosage （Fe^3＋ and Al^3＋） for phosphorus removal. Phosphorus removal rates, nitrification rates, as well as pH and sludge settling performance, were evaluated as functions of the metal dosages. Furthermore, models relating certain parameters to the dosage of chemicals have been derived. Corresponding parameters in the ASM2d and the secondary settler models, included in the Benchmark Simulation Model No 1 （BSM1）, have been modified to take the metal influence into consideration. Based on the effluent limits and penalty policy of China, an equivalent evaluation method was derived for the total cost assessment. A large number of 300-day steady-state and 14-day open-loop dynamic simulations were performed to demonstrate the difference in behavior between the original and the modified BSM1. The results show that 1） both in low and high mole concentrations, Fe^3＋ addition results in a higher phosphorus removal rate than Al^3＋; 2） the sludge settling velocity will increase due to the metal addition; 3） the respiration rate of the activated sludge is decreased more by the dosage of Al^3＋ than Fe^3＋; 4） the inhibition of Al^3＋ on the nitrification rate is stronger than that of Fe^3＋; 5） the total operating cost will reach the minimum point for smaller dosages of Fe^3＋, but always increase with Al^3＋ addition.

基于活性污泥数学模型(ASM_s)的污水处理系统不确定性分析研究进展

活性污泥数学模型(ASM_s)在应用过程中由于未能充分考虑污水处理系统的不确定性而降低了其可靠性和决策的准确性,目前国内外对基于ASM_s的不确定性分析尚处于研究的初级阶段。本文概述了不确定性分析的两种常用方法,介绍了污水处理系统中基于ASM_s的不确定性源的识别与分类、不确定性指标量化的研究现状,以及不确定性分析在污水处理厂优化设计、工艺改造等方面的应用。指出不确定性源的识别与分类目前仍然没有规范统一的分类机制和识别方法,不确定性指标的量化是模型应用的关键环节,尚需开展深入研究和分析;污水处理系统的不确定性分析将是今后ASM_s应用和发展的研究重点,不确定性分析可以帮助研究者更好地了解和把握污水处理系统模拟预测结果的不确定性范围,从而可以进行有效的风险评估和提高决策支持过程。

膜生物反应器ASM-CFD耦合仿真研究进展

膜生物反应器(membrane bioreactor,MBR)出水水质和运行能耗受生化反应和水力条件的共同影响,开展基于生化反应动力学与水力输运过程相互作用下的数字化MBR仿真模拟,对MBR节能减耗、结构优化及创新具有重要意义.文章介绍在MBR生物-水力场仿真模拟中常用的生化反应动力学模型,活性污泥模型(activated sludge model,ASM)及计算流体力学(computational fluid dynamics,CFD)模型,综述ASM-CFD耦合模型对MBR生物-水力场仿真研究的最新进展.综述结果表明,ASM-CFD可以较好地模拟短污泥龄(sludge residence time,SRT)MBR生物-水力场,能提供准确、丰富的流场和污水组分浓度分布信息.

ASM2d模型在北京高碑店污水处理厂的应用

描述了GPS-X模拟软件中国际水协(IWA)的ASM2d模型在北京高碑店污水处理厂应用的实例。该研究的目的包括:建立一个大型污水处理厂的模拟模型,以更好地理解生物处理过程;使用建立的模型寻求改善和优化污水处理厂脱氮除磷的方案。在应用中,模型的校准和验证是在已有资料基础上进行的,包括污水处理厂的设计和日常运行监测数据,这样就避免了在模拟大型污水处理厂的前期进行大量的专门研究工作。但是已有的数据不能直接作为模型的输入参数,需要把进水的COD、氮和磷的数据转化成适合于模型输入的各种组分。最后,使用经校准和验证的模型对改善污水处理厂的脱氮除磷进行了初步探讨。

基于ASM1的城市污水厂优化诊断与改造

为满足新的污水排放标准,同时考虑降低运行成本、节省基建费用等因素,通过国际水协推出的活性污泥数学模型ASM1建立适用于污水厂实际运行状况的水质模型,对上海市长桥污水处理厂进行了工艺优化诊断,并提出了改造方案,确保工艺运行经济、高效,出水水质能够稳定达标。

基于改进ASM的三维人脸自动对齐算法

针对传统三维人脸对齐算法需要大量人工操作和数据冗余的不足,提出一种基于改进主动形状模型的三维人脸自动对齐算法。该算法对二维人脸进行快速自动特征定位,根据三维人脸柱面展开的二维纹理图进行自动特征定位并分片。采用基于平面模板的重采样算法,自动实现三维人脸顶点稠密对齐。实验结果表明,该算法在提高运算速率的同时,得到的归一化结果与原始数据仍能保持99%形状相似度。

基于ASM的多特征融合驾驶员疲劳检测方法

为了提高对驾驶员疲劳程度检测的准确性与鲁棒性,提出了一种基于主动形状模型的多个特征融合疲劳检测算法。首先利用简单类Haar特征的级联Adaboost算法快速检测出人脸位置,然后对检测到的人脸进行基于主动形状模型(active shape model,ASM)的特征点定位,利用12个ASM特征标记点,得出眼睛、嘴部和头部的状态参数,再相应地计算出PERCLOS(percentage of eyelid closure over the pupil over time)、AECS(average eye closure speed)、哈欠频率、点头频率等4个疲劳特征,最后利用自适应神经模糊推理系统(adaptive network based fuzzy inference system,ANFIS)判决出驾驶员的3级疲劳程度(清醒、疲劳和严重疲劳)。实验结果表明,本方法对驾驶员疲劳检测准确率达93.3%,具有较高的准确性与鲁棒性。

ASM1中化学计量系数与动力学参数的测定

为了应用活性污泥1号模型模拟印染废水为主的污水集中处理厂,针对该厂实际废水,选取关键的化学计量系数YH和动力学参数bH及^μH进行测定。详细叙述了YH、bH和^μH的测定原理及具体操作过程。YH、bH和^μH的实测结果分别为0.65、0.50、3.23,这些值不同于ASM1中20℃下城市污水的推荐值0.67、0.62、6.0。