限时曝气下pH对SBR亚硝化快速启动及其过程影响

限时曝气条件下,采用SBR反应器处理模拟氨氮废水,通过pH控制实现了SBR系统快速亚硝化启动,并对不同pH下氨氧化过程进行了研究,考察了pH对氨氧化过程中DO变化规律、游离氨及氨氧化速率的影响。结果表明,在pH为7.59~8.12时,可实现氨氧化菌和亚硝态氮快速富集和积累,亚硝态氮积累率可达95%以上;通过pH调节可控制进水游离氨(FA)浓度及氨氧化过程中DO需求,进而影响选择性亚硝化过程。     

高亚硝态氮钝化清洗废水的生物处理及过程控制

钝化清洗废水含有高浓度亚硝态氮,采用普通活性污泥难以进行生物处理。采用亚硝态氮废水富集亚硝态氮氧化菌(NOB),以富含NOB污泥的SBR装置处理模拟化学清洗钝化废水,并提出了该处理工艺的过程控制策略。结果表明:该工艺可以在300 min内完全氧化亚硝态氮浓度高达2000 mg·L-1的钝化废水,高浓度亚硝态氮没有对生物降解过程产生明显抑制;反应过程中DO浓度的变化与亚硝态氮氧化过程存在相关性,溶解氧浓度的移动斜率变化(DO-MSC)可作为亚硝态氮氧化过程控制参数;当DO-MSC0.02时,亚硝态氮氧化过程结束,此时可停止曝气。批次试验结果显示在不同曝气量(0.02~0.125 m3·h-1)和不同温度条件(15~30℃)下,DO-MSC指数均可有效指示亚硝态氮氧化终点。     

生物接触氧化法同步短程硝化反硝化影响因素研究

通过调控系统DO、HRT和p H,成功实现流化床生物接触氧化法同步短程硝化反硝化,并探讨各参数对系统亚硝态氮积累的影响规律。实验结果表明亚硝态氮累积率随着DO和HRT的增加而降低,随p H的升高先增加后降低,峰值出现在p H=7,系统实现同步短程硝化反硝化最佳参数为p H=7,DO=2.5 mg/L,HRT=4 h,且在最优参数条件下能稳定维持亚硝态氮累积率为78%,同时保证氨氮的去除率在90%以上。     

高亚硝态氮钝化清洗废水的生物处理及过程控制

钝化清洗废水含有高浓度亚硝态氮,采用普通活性污泥难以进行生物处理。采用亚硝态氮废水富集亚硝态氮氧化菌（NOB）,以富含NOB污泥的SBR装置处理模拟化学清洗钝化废水,并提出了该处理工艺的过程控制策略。结果表明：该工艺可以在300 min内完全氧化亚硝态氮浓度高达2000 mg·L^-1的钝化废水,高浓度亚硝态氮没有对生物降解过程产生明显抑制;反应过程中DO浓度的变化与亚硝态氮氧化过程存在相关性,溶解氧浓度的移动斜率变化（DO-MSC）可作为亚硝态氮氧化过程控制参数;当DO-MSC >0.02时,亚硝态氮氧化过程结束,此时可停止曝气。批次试验结果显示在不同曝气量（0.02～0.125 m³·h^-1）和不同温度条件（15～30℃）下,DO-MSC指数均可有效指示亚硝态氮氧化终点。     

田间土壤硝态氮快速测定方法的研究与应用前景

对田间土壤硝态氮(NO3N)快速测定方法的研究,包括土壤水分快速测定方法、快速提取技术(经济、易于获得的提取剂和简易、快捷的提取方法)、温度校正系数以及合理使用试纸反射仪的研究。大量的试验结果表明,田间土壤硝态氮快速测定方法所得的土壤硝态氮含量与实验室标准方法所得结果有着良好的线性相关关系。本研究所提出的田间土壤硝态氮快速测定方法具有经济、操作简单、便于农民掌握等特点。目前中国存在着施肥不合理的现象,亟须简易可靠的田间测土技术,因此,田间土壤硝态氮快速测定方法在中国将有着广阔的应用前景。     

SBR恒曝气量好氧硝化过程动态DO模拟:模型辨识与KLa确定

通过简化活性污泥法1号模型(activated sludge model No.1,ASM1)建立两步硝化反应的数学模型,实现了对序批式反应器(sequencing batch reactor,SBR)恒曝气量好氧过程中溶解氧(dissolved oxygen,DO)动态变化过程的数学模拟,模型辨识科学地区分了可以直接取值的参数包括产率系数、DO饱和常数(或底物饱和常数)和需要重新估计的参数。采用文献推荐参数值模拟了过程中主要状态变量的动力学过程,模拟结果呈现出了多个DO平台,这与实际反应结果数据相符,验证了所建模型的正确性。优化实验设计,获取了典型SBR恒曝气好氧硝化过程动态DO数据,通过理论分析和对数据进行二阶微分处理提出了确定总氧传递系数K_La和相对饱和溶解氧S_O^eq的简单方法,为后续参数估计奠定了基础。     

不同浓度亚硝态氮在弱碱性土壤中的转化规律研究

选取陕西省华县耕作土壤,在实验室条件下研究该弱碱性土壤在不同初始浓度的亚硝态氮(11.2,56,112,280,560 mg/kg土)存在下的氮的转化规律,并进一步阐明亚硝态氮在该土壤中的累积效应。结果表明,土壤中的亚硝态氮的累积效应随着浓度的增大不断增强,即土壤中亚硝态氮转化为硝态氮的转化时间随着亚硝态氮的初始浓度的增加而增加。但是,高浓度的亚硝态氮(560 mg/kg)向硝态氮转化过程中,从第10 d开始,亚硝态氮的转化速率明显下降,低于初始浓度为280 mg/kg时的状态,反应受到强烈的抑制作用;当调整土壤的初始pH值时,该浓度下的抑制作用也几乎不被影响。因此,对出现高浓度亚硝态氮的地区环境需要引起重视,并及时采取一定的措施,以防止其对人类健康产生进一步的影响。     

不同浓度亚硝态氮在弱碱性土壤中的转化规律研究

选取陕西省华县耕作土壤,在实验室条件下研究该弱碱性土壤在不同初始浓度的亚硝态氮(11.2,56,112,280,560 mg/kg土)存在下的氮的转化规律,并进一步阐明亚硝态氮在该土壤中的累积效应。结果表明,土壤中的亚硝态氮的累积效应随着浓度的增大不断增强,即土壤中亚硝态氮转化为硝态氮的转化时间随着亚硝态氮的初始浓度的增加而增加。但是,高浓度的亚硝态氮(560 mg/kg)向硝态氮转化过程中,从第10 d开始,亚硝态氮的转化速率明显下降,低于初始浓度为280 mg/kg时的状态,反应受到强烈的抑制作用;当调整土壤的初始pH值时,该浓度下的抑制作用也几乎不被影响。因此,对出现高浓度亚硝态氮的地区环境需要引起重视,并及时采取一定的措施,以防止其对人类健康产生进一步的影响。     

AO两级生物滤池的同步硝化反硝化脱氮试验

针对现行污水处理厂市政二级出水脱氮效果差的问题,文中构建了具有同步硝化反硝化功能的缺氧-好氧(AO)两级生物滤池工艺,研究了进水碳氮比(C/N)、回流比及溶解氧(DO)浓度对反应器脱氮效果的影响,并在最优工况条件下进行了处理效能试验.结果表明:进水的C/N为4:1时,系统的脱氮效果最佳,并且在缺氧段实现了氨氮与硝态氮同步去除,持续提高C/N,系统整体处理效果并无明显增强;系统的脱氮效能随着回流比的提高而降低,选用最低回流比(100％)为最佳处理工况之一;好氧柱中的DO含量为2 mg/L时,回流至缺氧段的DO浓度最低,系统处理效果最佳.在选定的最优工况条件下,反应器运行稳定,系统出水中氨氮、总氮和化学需氧量(CODCr)的平均质量浓度分别为(0.026±0.001)、(4.210±0.352)mg/L和(12.560±1.033)mg/L.数据表明该系统在最优工况条件下运行情况良好,脱氮效果明显,处理后水质指标达到一级A排放标准.     

一种基于高氨氮、高硝态氮废水的新型脱氮技术

针对某类高氨氮、高硝态氮、低C/N比的废水,提出了一种全新的脱氮技术-半反硝化-厌氧氨氧化联合脱氮技术,阐述了其研究现状、技术原理及工艺特点,重点论述了半反硝化过程中亚硝态氮累积的研究现状及影响因子,展望了该新型生物脱氮技术的应用前景并指出今后的研究方向.     

五箱一体化活性污泥工艺的数学模拟与校正

对五箱一体化活性污泥工艺进行了数学模拟研究, 针对模型中的动力学参数提出了一种迭代计算的校正方法。该方法结合了6个批式实验模拟(氨氮吸收速率AUR、硝氮吸收速率NUR、耗氧速率OUR、厌氧释磷速率PRR、好氧吸磷速率PUR_aerobic和缺氧吸磷速率PUR_anoxic)、灵敏度分析以及数学优化方法(遗传算法):通过迭代计算确定合适的污泥组分比例, 完成对6个批式实验的模拟;灵敏度分析可以分别确定各个批式实验模拟中参数对模拟结果的影响程度, 挑选待优化参数, 完成参数识别;数学优化方法可以自动对参数进行校正。结果显示, 校正后的模型对五箱一体化活性污泥工艺的模拟效果较好:6个批式实验的模拟值与测量值之间的平均相对误差(ARD)分别为5.65%、17.27%、6.02%、7.11%、13.07%和6.98%;30 d动态出水COD、NH₄⁺-N、TN和TP的ARD值分别为4.72%、18.87%、9.45%和38.11%。研究结果表明, 提出的迭代方法可以用来对活性污泥模型进行校正, 而且校正效果较好。     

游离氨耦联溶解氧调控高氮豆制品废水脱氮探究

为了实现高氮豆制品废水短程硝化的快速启动与稳定运行,采用了游离氨(FA)耦联溶解氧(DO)的策略进行调控。结果表明,当ρ(DO)在0.2~1.2 mg/L时,豆制品废水均能成功启动亚硝化,并且随着ρ(DO)的增加,达到完全亚硝化的周期数增加。ρ(DO)的升高促进硝化速率,并且当ρ(DO)在1.2~1.5 mg/L时,初始硝化速度为8.5 mg/(g·h),显著高于其他实验组。在FA耦联DO控制豆制品废水短程硝化过程中,而当ρ(FA)控制在6.5~7.0 mg/L时,豆制品废水短程硝化进入稳定期,Nitrosomonas europaea和Nitrosomonas stercoris增加至30.5%和21.3%。     

KINETIC PARAMETER ESTIMATION BY STANDARD OPTIMIZATION METHODS IN CATALYTIC CONVERTER MODELING

The application of mathematical models to the prediction of the performance of automotive catalytic converters is gaining increasing interest, both for gasoline and diesel engined-vehicles. This article addresses converter modeling in the transient state under realistic experimental conditions. The model employed in this study relies on Langmuir-Hinshelwood kinetics, and a number of apparent kinetic parameters must be tuned to match the behavior of each different catalyst formulation. The previously applied procedure of manually tuning kinetics parameters requires significant manpower. This article presents a methodology for kinetic parameter estimation that is based on standard optimization methods. The methodology is being applied in the exploitation of synthetic gas experiments and legislated driving cycle tests and the assessment of the quality of information contained in the test results. Although the optimization technique employed for parameter estimation is well known, the development of the specific parameter estimation methodology that employs the results of the available types of experiments is novel and required significant development. Application of this refined tuning methodology increases the quality and reliability of prediction and also greatly reduces the required manpower, which is important in the specific engineering design process. The parameter estimation procedure is applied to the example of modeling of a diesel catalytic converter with adsorption capabilities, based on laboratory experiments and vehicle driving cycle tests.     

SBR法短程硝化及过程控制研究

考察采用SBR法处理氨氮浓度较高的化工废水时供氧方式对硝化过程中溶解氧(DO)质量浓度、氧化还原电位(ORP)和pH变化规律的影响。试验结果表明,在曝气流量恒定的条件下,可以硝化过程中DO质量浓度和pH升高速率的不同表征反应的进行程度,即当氨氮浓度接近零时,DO质量浓度和pH升高速率或变化幅度加大,二者可以作为SBR硝化反应时间的控制参数在DO质量浓度恒定的情况下,pH在整个硝化反应过程中都是缓慢下降或趋于稳定的,当硝化反应结束时突然升高,因此pH也可作为SBR硝化反应时间较好的控制参数。     

基于参数估计的动态系统过失误差侦破与识别

The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a powerful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in efficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the presence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be estimated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of decision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a continuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.     

A quasi-sequential parameter estimation for nonlinear dynamic systems based on multiple data profiles

A three-stage computation framework for solving parameter estimation problems for dynamic systems with multiple data profiles is developed. The dynamic parameter estimation problem is transformed into a nonlinear programming (NLP) problem by using collocation on finite elements. The model parameters to be estimated are treated in the upper stage by solving an NLP problem. The middle stage consists of multiple NLP problems nested in the upper stage, representing the data reconciliation step for each data profile. We use the quasi-sequential dynamic optimization approach to solve these problems. In the lower stage, the state variables and their gradients are evaluated through integrating the model equations. Since the second-order derivatives are not required in the computation framework this proposed method will be efficient for solving nonlinear dynamic parameter estimation problems. The computational results obtained on a parameter estimation problem for two CSTR models demonstrate the effectiveness of the proposed approach.     

A new Bayesian approach to quantile autoregressive time series model estimation and forecasting

This paper proposes a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. We establish that the joint posterior distribution of the model parameters and future values is well defined. The associated Markov chain Monte Carlo algorithm for parameter estimation and forecasting converges to the posterior distribution quickly. We also present a combining forecasts technique to produce more accurate out‐of‐sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to check the quality of the estimated conditional quantiles is developed. We verify our methodology using simulation studies and then apply it to currency exchange rate data. The results obtained show that an unequally weighted combining method performs better than other forecasting methodology.     

基于区间二型模糊神经网络污水处理过程溶解氧浓度控制

针对城市污水处理过程溶解氧浓度难以精确控制的问题,提出了一种基于区间二型模糊神经网络（interval type-2 fuzzy neural networks,IT2FNN）的溶解氧浓度控制方法。先将IT2FNN应用在城市污水处理过程溶解氧浓度控制器的设计,获得了一种IT2FNN溶解氧浓度控制器。后采用自适应学习算法在线调整控制器的参数,提高了控制器的自适应能力。最后将提出的IT2FNN溶解氧浓度控制器应用于基准仿真2号模型（benchmark simulation model no.2,BSM2）平台,结果表明,IT2FNN控制器能够实现第5分区溶解氧浓度精确控制,具有较好的控制效果。     

On the feasibility and reliability of nonlinear kinetic parameter estimation for a multi-component photocatalytic process

Nonlinear kinetic parameter estimation plays an essential role in kinetic study in reaction engineering. In the present study, the feasibility and reliability of the simultaneous parameter estimation problem is investigated for a multi-component photocatalytic process. The kinetic model is given by the L-H equation, and the estimation problem is solved by a hybrid genetic-simplex optimization method. Here, the genetic algorithm is applied to find out, roughly, the location of the global optimal point, and the simplex algorithm is subsequently adopted for accurate convergence. In applying this technique to a real system and analyzing its reliability, it is shown that this approach results in a reliable estimation for a rather wide range of parameter value, and that all parameters can be estimated simultaneously. Using this approach, one can estimate kinetic parameters for all components from data measured in only one time experiment.