污水处理过程溶解氧浓度的自抗扰控制

溶解氧是活性污泥法处理污水的一个关键变量,它关系到污水中有机物的生物降解、微生物生长和出水水质.多数污水处理过程选择溶解氧为被控量.然而,溶解氧浓度受进水流量,进水组分、浓度波动等诸多因素影响,较难控制.本文根据曝气量变化确定溶解氧浓度设定值,以污水进水变化率为控制量,设计线性自抗扰控制实现对溶解氧浓度的跟踪,进而获得对污水出水底物浓度的间接控制.设计两组仿真实验,分别模拟进水底物浓度固定和变化时,线性自抗扰控制对溶解氧浓度和出水底物浓度的控制;同时,设计仿真实验验证线性自抗扰控制对总扰动的估计和补偿效果.仿真结果表明,线性自抗扰控制可获得良好的溶解氧浓度跟踪和出水底物浓度控制效果;在进水水质波动时,线性自抗扰控制亦具有很强的干扰补偿能力,可保证出水水质.     

活性污泥系统中溶解氧模糊控制器的研究

分析了活性污泥系统中溶解氧（DO）控制的重要性及复杂性后，提出用模糊控制的方法控制污水处理中的溶解氧，并设计了溶解氧模糊控制系统，为污水处理的自动控制提供了一种新的设计方法。     

污水处理净化过程的三维细胞自动机动态模拟

针对污水处理过程的复杂性、不可重复性和不可再现性等特点,根据活性污泥的净化机理与动力学特性,在Mckinney等人建立的经典活性污泥法动力学模型的基础上,提出了一种模拟活性污泥净化过程的三维格子气细胞自动机模型.该模型的演化规则根据微生物的增殖规律和经典的动力学模型设计,模型通过模拟有机物和微生物的扩散、反应和沉降过程,反应了活性污泥的整个净化过程.对模型进行的仿真实验结果表明:该模型不仅可以复现污水净化过程,而且更直观地刻画了活性污泥法污水处理过程的动态演化行为,直接反映出活性污泥法系统的表观特征.     

一种用于航天器数据监视的知识表示方法

针对我国目前以人为主的航天器数据监视现状，利用人工智能的一般方法，分析了航天器数据监视中需要用到的各种监视知识，提出一种将各种复杂监视知识表示成计算机可识别形式的方法；该方法采用基于规则的思想，易于接受，并引入了优先级的机制完成冲突消解；该方法能够解决与时间有关，与历史数据有关、基于曲线变化等几类遥测参数的监视问题，具有一定的实用性和推广价值。     

污水处理过程的自适应控制

把自适应控制方法应用到污水生化处理过程中，主要是对溶解氧浓度的控制。使用双线性模型来简化溶解氧动态，对于一些不可以直接测量得到的重要过程参数，采用递推最小二乘算法来估计得到，并设置一步预测输出为期望的输出，从而得到对溶解氧浓度的最小方差自校正控制方案。仿真结果说明了空气流量和氧气吸收率(OUR)对溶解氧浓度的影响。     

基于Clark氧电极的瓦斯微生物传感器的研究

利用氧化细菌在常温中催化瓦斯气体,并产生溶解氧的特性,采用聚乙烯醇固定化甲烷氧化细菌结合Clark氧化电极技术设计一种新的瓦斯微生物传感器检测方法.选用PVA-海藻酸钠-硼酸交联法固定甲烷的氧化细菌,在磷酸盐缓冲液中催化空气的瓦斯气体,通过Clark氧电极对产生的溶解氧进行电化传输到计算机实时记录.针对传感系统进行瓦斯监测实验表明:微生物传感器实时准确地检测出甲烷气体体积分数,是一种简便,快速,灵敏的瓦斯检测办法.     

污水处理过程溶解氧浓度的有限时间抗扰控制

溶解氧浓度是影响污水处理的关键因素,有效控制溶解氧浓度,可减少曝气能耗、提高出水水质.然而,溶解氧浓度受进水流量、组分、浓度、天气等多源干扰的影响.为保证水水质,提出一种改进型有限时间扩张状态观测器,综合有限时间控制与改进型有限时间扩张状态观测器,设计一种改进型有限时间自抗扰控制,以克服污水处理中的扰动和不确定性.在污水处理1号基准仿真模型上,改进型有限时间自抗扰控制获得了期望的溶解氧调控效果.仿真结果表明,改进型有限时间自抗扰控制能有效估计总扰动、提高污水处理效果.     

基于模糊自适应PID的溶解氧控制系统设计

为了通过曝气使活性污泥得到足够的氧气,水中的可溶性有机污染物被活性污泥吸附,并被存活在活性污泥上的微生物分解,使污水得到净化。针对污水处理中曝气过程溶解氧控制对象的非线性、时变性以及不确定性等特点,提出了一种基于模糊自适应PID的控制方法,引入调整因子的污水处理DO自适应模糊控制系统,通过调节曝气机频率,将DO稳定地控制在理想的位置,该系统以SiemensS7编程实现。结果表明,该控制系统弥补了常规PID和单纯模糊控制的不足,获得了更好的控制效果,提高了污水处理效率。该控制系统有效地解决了曝气生物滤池工艺中曝气过程的时变性、干扰多、变量耦合用常规PID控制效果不理想的状况,对于控制对象模型不好建立的控制对象,具有很好的控制效果。     

基于ESN的多指标DHP控制策略在污水处理过程中的应用

针对污水处理过程(Wastewater treatment process, WWTP)溶解氧(Dissolved oxygen, DO)及硝态氮浓度控制问题, 提出了一种多评价指标的DHP (Dual heuristic dynamic programming)控制策略. 该策略能够降低评价指标的复杂性, 提高评价网络的逼近精度. 采用回声状态网络(Echo state networks, ESNs)实现评价函数及控制策略的逼近, 研究了控制器的在线学习算法. 实验表明, 该策略在控制性能上优于单评价指标的DHP策略及常规PID控制策略.     

木薯酒精发酵过程关键参数的测控研究

木薯酒精发酵过程是一个时变、非线形和多参数影响的动态过程。对发酵过程的关键参数如pH值、溶解氧的测控是非常重要的。设计了由工控机和西门子公司的PLC构成的发酵过程两级计算机集散系统,实现对酒精发酵过程中关键参数pH值、溶解氧等的数据采集与监视,同时利用该系统实现对采集到的数据进行分析与处理,从而达到最终的控制目的。     

A nonlinear observer for activated sludge wastewater treatment process: Invariant observer

An activated sludge wastewater treatment process model is concerned in this paper. In order to estimate the variables that cannot be measured online, an invariant observer for activated sludge wastewater treatment process is presented. The invariant observer can measure biomass concentration, substrate concentration and dissolved oxygen concentration in high accuracy and rapidity. Meanwhile it can be structured by means of typical form, and its robust convergence property is verified by theoretical analysis and numerical simulations (MATLAB ).     

Reduction of aeration costs by tuning a multi-set point on/off controller: A case study

The objective of this work was to select the best configuration for a dissolved oxygen multi-set point on/off controller of a full-scale wastewater treatment plant to ensure the best dissolved oxygen concentration set-point values. The optimal set point values should allow the attainment of a good quality of effluent wastewater and minimise the operational costs related to the aeration process. To achieve this goal, simulation and optimisation software and off-line optimisation techniques were used. As a result of the optimisation of the dissolved oxygen controller, a more stable system was obtained. Because of the stability of the system, the quality of the effluent and activated sludge and the operating life of the aeration equipment was increased. Moreover, the aeration costs were reduced by approximately 17%.     

Dissolved oxygen control for activated sludge processes

Controllers for dissolved oxygen reference trajectory tracking for activated sludge processes are proposed and investigated. A nonlinear model predictive controller and a direct reference adaptive controller are investigated. Both the nutrient and the phosphorous removal from a wastewater by its biological treatment using an activated sludge technology are considered. An approach to the controller design utilises a structure of the dissolved oxygen dynamics and its two time scales: fast and slow. The predictive controllers offer good tracking performance and robustness. The direct model reference adaptive controller is much simpler to implement. However, it is more difficult to compromise between tracking accuracy and rate of change and magnitudes of the control actions. The controllers are validated by simulation using real data sets and an ASM2d model of the biological reactor.     

基于国际评价基准的溶解氧控制方法研究

针对活性污泥污水处理过程溶解氧浓度(DO)控制的非线性特性，以DO为控制对象、国际评价基准（benchmark）为平台，将PID控制、增益调度控制和一般模型控制(GMC)方法应用于DO控制中，并进行了控制器性能的比较．仿真结果表明，所提一般模型非线性控制方法的性能优于其他两种控制方法，体现出该方法的有效性和优越性．     

案例推理方法在污水处理中的应用

以活性污泥法工艺过程为研究背景,采用案例推理的方法,为技术推广提供决策支持,实现了活性污泥法污水处理工艺流程故障诊断。     

Distributed digital processing and closed loop computer control of wastewater treatment

The application of digital computer control to municipal wastewater treatment processes is steadily gaining popularity. Within the next few years this mode of control is expected to become a standard feature of larger wastewater treatment plants. The purpose of this paper is threefold: first, to acquiant those outside the wastewater industries of the general concepts of wastewater treatment so they may appreciate some of the requirements imposed on the control computer; second, to present the overall control laws that regulate the feedback control of pure oxygen activated sludge and anaerobic digestion processes; and third, to describe the control system architecture and present the general philosophy of on-line closed loop control of Metro Denver's high purity oxygen activated sludge process.     

SBR系统中的模糊神经网络控制器设计

将模糊控制与神经网络相结合,设计4层模糊神经网络控制器,分析其结构及算法。利用神经网络的自学习能力,在线动态调整模糊变量的隶属函数,优化控制规则,并对曝气池中溶解氧浓度与活性污泥浓度进行控制。通过Matlab对溶解氧的控制进行数字仿真实验,结果表明,具有学习能力的模糊神经网络控制可在污水处理系统的应用中获得更优的性能。     

Control of an ideal activated sludge process in wastewater treatment via an ODE–PDE model

The activated sludge process (ASP), found in most wastewater treatment plants, consists basically of a biological reactor followed by a sedimentation tank, which has one inlet and two outlets. The purpose of the ASP is to reduce organic material and dissolved nutrients (substrate) in the incoming wastewater by means of activated sludge (microorganisms). The major part of the discharged flow through the bottom outlet of the sedimentation tank is recirculated to the reactor, so that the biomass is reused. Only two material components are considered; the soluble substrate and the particulate sludge. The biological reactions are modelled by two nonlinear ordinary differential equations and the continuous sedimentation process by two hyperbolic partial differential equations (PDEs), which have coefficients that are discontinuous functions in space due to the inlet and outlets. In contrast to previously published modelling-control aspects of the ASP, the theory for such PDEs is utilized. It is proved that the most desired steady-state solutions can be parameterized by a natural control variable; the ratio of the recirculating volumetric flow to the input flow. This knowledge is a key ingredient in a two-variable regulator, with which the effluent dissolved nutrients concentration and the concentration profile in the sedimentation tank are controlled. Theoretical results are supported by simulations.     

Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural network

The dissolved oxygen (DO) concentration in activated sludge wastewater treatment processes (WWTPs) is difficult to control because of the complex nonlinear behavior involved. In this paper, a self-organizing radial basis function (RBF) neural network model predictive control (SORBF-MPC) method is proposed for controlling the DO concentration in a WWTP. The proposed SORBF can vary its structure dynamically to maintain prediction accuracy. The hidden nodes in the RBF neural network can be added or removed on-line based on node activity and mutual information (MI) to achieve the appropriate network complexity and the necessary dynamism. Moreover, the convergence of the SORBF is analyzed in both the dynamic process phase and the phase following the modification of the structure. Finally, the SORBF-MPC is applied to the Benchmark Simulation Model 1 (BSM1) WWTP to maintain the DO concentration. The results show that SORBF-MPC effectively provides process control. The performance comparison also indicates that the proposed model's predictive control strategy yields the most accurate for DO concentration, better effluent qualities, and lower average aeration energy (AE) consumption.