Supervisory control system to enhance partial nitrification in an activated sludge reactor

This work proposes a supervisory control system based on an optimization layer to calculate the optimum pH and dissolved oxygen (DO) set-points for the SISO controller, maintaining the process at stable partial nitrification. Takagi–Sugeno fuzzy multimodels were implemented to estimate ammonium degradation and nitrite accumulation from on-line DO and pH values, and updated using off-line measurements. An activated sludge reactor was operated successfully over 115 consecutive days with the supervisory control system, achieving ammonium degradation and nitrite accumulation values higher than 95% and 80%, respectively. The on-line estimates of the multimodels showed a prediction error of less than 7% at steady state operation, and reflected the tendencies shown in experiment to be caused by changes in pH value and DO concentration.     

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。     

Nonlinear model-based dissolved oxygen control in a biological wastewater treatment process

The dissolved oxygen (DO) concentration has been an important process parameter in the biological wastewater treatment process (WWTP). In this paper, we propose a nonlinear control scheme to maintain the dissolved oxygen level of an activated sludge system. Without any linearization or model reduction, it can directly incorporate the nonlinear DO process model with on-line estimation of the respiration rate (R) and the oxygen transfer rate (K_La). Simulation results show that it outperforms a control performance of the PID controller. Since it incorporates the process disturbance and nonlinearity in the controller design, the suggested method can efficiently deal with the operating condition changes that occur frequently in the wastewater treatment process.     

模糊PID控制器的发展

将模糊控制技术和PID控制相结合，可克服常规PID控制器的不足，使PID控制器具有参数自适应能力。介绍了模糊控制器和PID控制器间的关系，从一维、二维及三维模糊控制器的输入－输出形式上，以及解析分析角度出发，可以说模糊控制器人本质上讲是一种非线性PID控制器。还总结了模糊PID控制器的结构。模糊PID控制器可分成两大类，一类由参数可变的PID控制器及一个模糊控制器共同构成，模糊控制器用于完成PID参数的在线整定，控制信号仍由PID控制器生成；另一类直接用模糊控制器来构造、实现PID控制功能。分别归纳了这两类控制器的多种结构形式。最后还对模糊PID控制器的发展进行了展望，提出了模糊PID 控制器结构的选择、控制器参数调整及控制器评价标准的确定这几方面是今后工作的重点。     

SBR法好氧曝气时间的模糊控制

采用SBR工艺处理化工废水,原水COD浓度变化和控制不同的曝气量水平都将引起有机物降解时间的改变。当有机物达到难降解浓度时,DO和ORP迅速大幅度升高。根据DO、ORP的这一变化特征可有效控制不同条件下有机物降解所需的曝气时间。结合模糊控制理论,将DO,ORP的特征变化用模糊语言变量描述,选择DO误差的大小(EDo)和误差变化的快慢(CEDo)、ORP随时间的变化率CEoRp作为模糊控制器的三个输入变量,给出SBR曝气时间的模糊控制规则,从而实现SBR反应时间的实时控制,在保证出水水质的前提下节约能耗,提高处理效率。     

Temperature control of highly exothermic batch polymerization reactors

In this article a highly exothermic batch polymerization reactor is considered. The reactor is simplified as a mixing tank with the internal heat generation treated as a disturbance. A fuzzy-hybrid-PID-feedback (FH-PID) control structure is developed in which the output of fuzzy hybrid portion is used to adjust the set point of a PID controller to compensate for the effect of the major disturbance, the heat of reaction. In this way, the hybrid portion of the controller does not influence the stability of the original PID control system. A fuzzy model was constructed to estimate the heat of reaction inside the fuzzy hybrid block. The fuzzy parameters of the hybrid portion do not depend on the process model and can be estimated from the transient response obtained with a conventional PID controller. This FH-PID control strategy has been applied to the temperature control of batch solution and batch inverse emulsion polymerizations of acrylamide in a 1 gallon pilot scale reactor. The results show that this fuzzy hybrid—PID-feedback control strategy improves the control performance of the batch polymerization reactor.     

区间二型模糊免疫PID在环己烷无催化氧化温度控制系统中的应用

环己烷无催化氧化过程具有非线性、多变量耦合、大时滞等特点,使用常规比例积分微分(PID)控制方案无法达到理想的控制性能。提出了一种区间二型模糊免疫PID控制器,其本质上是一种基于免疫PID的非线性控制器,利用区间二型模糊逻辑系统来逼近免疫反馈律中的非线性函数,以提升控制器处理和逼近复杂不确定非线性系统的能力。将所提出的控制器应用于环己烷无催化氧化温度控制系统,仿真结果表明该方法是有效的。     

Aeration control of a wastewater treatment plant using hybrid NMPC

In the operation of wastewater treatment plants a key variable is dissolved oxygen (DO) content in the bioreactors. As oxygen is consumed by the microorganisms, more oxygen has to be added to the water in order to comply with the required minimum dissolved oxygen concentration. This is done using a set of aerators working on/off that represents most of the plant energy consumption. In this paper a hybrid nonlinear predictive control algorithm is proposed, based on economic and control aims. Specifically, the controller minimizes the energy use while satisfying the time-varying oxygen demand of the plant and considering several operation constraints. A parameterization of the binary control signals in terms of occurrence time of events allows the optimization problem to be re-formulated as an nonlinear programming (NLP) problem at every sampling time. Realistic simulation results considering real perturbations data sets for the inlet variables are presented.     

区间二型模糊免疫PID在环己烷无催化氧化温度控制系统中的应用

环己烷无催化氧化过程具有非线性、多变量耦合、大时滞等特点,使用常规比例积分微分(PID)控制方案无法达到理想的控制性能。提出了一种区间二型模糊免疫PID控制器,其本质上是一种基于免疫PID的非线性控制器,利用区间二型模糊逻辑系统来逼近免疫反馈律中的非线性函数,以提升控制器处理和逼近复杂不确定非线性系统的能力。将所提出的控制器应用于环己烷无催化氧化温度控制系统,仿真结果表明该方法是有效的。     

面向可持续发展的半导体制造的集成产品和过程控制

Semiconductor fabrication is a manufacturing sequence with hundreds of sophisticated unit operations and it is always challenged by strategy development for ensuring the yield of defect-free products. In this paper, an advanced control strategy through integrating product and process control is established. The proposed multiscale scheme contains three layers for coordinated equipment control, process control and product quality control. In the upper layer, online control performance assessment is applied to reduce the quality variation and maximize the overall product performance (OPP). It serves as supervisory control to update the recipe of the process controller in the middle layer. The process controller is designed as an exponentially weighted moving average (EWMA) run-to-run controller to reject disturbances, such as process shift, drift and tool worn out, that are exerted to the op-eration. The equipment in the process is individually controlled to maintain its optimal operational status and maximize the overall equipment effectiveness (OEE), based on the set point given by the process controller. The ef-ficacy of the proposed integrated control scheme is demonstrated through case studies, where both the OPP (for product) and the OEE (for equipment) are enhanced.     

基于模糊神经网络的污水处理生化除磷过程控制

针对污水处理生化除磷过程中出水总磷难以实时达标的问题,提出了一种基于模糊神经网络(fuzzy neural network,FNN)的出水总磷控制方法。首先,通过分析污水处理生化除磷机理,确定了控制器的操作变量为生化反应池第五分区外部碳源(external carbon, EC)与溶解氧(dissolved oxygen, DO)传递系数。其次,设计了一种基于FNN的出水总磷控制器,采用梯度下降算法更新控制器参数;最后,将基于FNN的出水总磷控制器应用于污水处理过程基准仿真平台(benchmark simulation model No.1,BSM1),实验结果表明,基于FNN的出水总磷控制器能够保证出水总磷的达标排放,具有较好的控制效果。     

ADAPTIVE FUZZY MODEL BASED PREDICTIVE CONTROL OF AN EXOTHERMIC BATCH CHEMICAL REACTOR*

An adaptive fuzzy model based predictive control (AFMBPC) approach is presented to track the desired temperature trajectories in an exothermic batch chemical reactor. The AFMBPC incorporates an adaptive fuzzy modeling framework into a model based predictive control scheme to derive analytical controller output. This approach has the flexibility to cope with different fuzzy model structures whose choice also lead to improve the controller performance. In this approach, adaptation of fuzzy models using dynamic process information is carried out to build a predictive controller, thus eliminating the determination of a predefined fixed fuzzy model based on various sets of known input-output relations. The performance of the AFMBPC is evaluated by comparing to a fixed fuzzy model based predictive controller (FFMBPC) and a conventional PID controller. The results show the better suitability of AFMBPC for the control of highly nonlinear and time varying batch chemical reactors.     

A method for robustness analysis of controlled nonlinear systems

This paper presents an approach to analyzing robustness properties of nonlinear systems under feedback control. The core idea is to apply numerical bifurcation analysis to the closed-loop process, using the controller/observer tuning parameters, the set points, and parameters describing model uncertainty (parametric as well as unmodeled dynamics) as bifurcation parameters. By analyzing the Hopf bifurcation and saddle-node bifurcation loci with respect to these parameters, bounds on the controller tuning are identified which can serve as a measure for the robustness of the controlled system. These bounds depend upon the type as well as the degree of mismatch that exists between the plant and the model used for controller design.The method is illustrated by analyzing three control systems which are applied to a continuously operated stirred tank reactor: a state feedback linearizing controller and two output feedback linearizing controllers. While model uncertainty has only a minor effect on the tuning of the state feedback linearizing controller, this does not represent a very realistic scenario. However, when an observer is implemented in addition to the controller and an output feedback linearizing scheme is investigated, it is found that the plant-model mismatch has a much more profound impact on the tuning of the observer than it has on the controller tuning. In addition, two observer designs with different level of complexity are investigated and it is found that a scheme which makes use of additional knowledge about the system will not necessarily result in better stability properties as the level of uncertainty in the model increases. These investigations are carried out using the robustness analysis scheme introduced in this paper.     

Dynamic optimization oriented modeling and nonlinear model predictive control of the wet limestone FGD system

Nonlinear model predictive control (NMPC) scheme is an effective method of multi-objective optimization control in complex industrial systems. In this paper, a NMPC scheme for the wet limestone flue gas desulphurization (WFGD) system is proposed which provides a more flexible framework of optimal control and decision-making compared with PID scheme. At first, a mathematical model of the FGD process is deduced which is suitable for NMPC structure. To equipoise the model's accuracy and conciseness, the wet limestone FGD system is separated into several modules. Based on the conservation laws, a model with reasonable simplification is developed to describe dynamics of different modules for the purpose of controller design. Then, by addressing economic objectives directly into the NMPC scheme, the NMPC controller can minimize economic cost and track the set-point simultaneously. The accuracy of model is validated by the field data of a 1000 MW thermal power plant in Henan Province, China. The simulation results show that the NMPC strategy improves the economic performance and ensures the emission requirement at the same time. In the meantime, the control scheme satisfies the multiobjective control requirements under complex operation conditions (e.g., boiler load fluctuation and set point variation). The mathematical model and NMPC structure provides the basic work for the future development of advanced optimized control algorithms in the wet limestone FGD systems.     

模糊自适应PI控制在空调系统中的研究

以空气焓差法试验台空调系统的温度控制系统为具体仿真对象建立了数学模型,该空调系统可以看作是一阶惯性加纯滞后的环节,而且对象的过程参数和时延时间是时变的,传统的PID控制无法获得理想的控制效果。提出了一种无需辨识环节的具有智能的模糊自适应PI的控制算法并将其应用在该空调系统中,该算法对模糊控制和PI控制进行有机结合,根据实际控制经验,通过模糊控制规则对控制回路中PI控制器的参数进行实时整定,并将该控制算法和经过良好整定的PI控制器在空调系统中的控制性能进行比较。仿真结果表明,模糊自适应PI控制提高了系统的鲁棒性、减小了超调量、提高了抗干扰能力、缩短了调整时间。     

Guaranteed cost distributed fuzzy observer‐based control for a class of nonlinear spatially distributed processes

The guaranteed cost distributed fuzzy (GCDF) observer‐based control design is proposed for a class of nonlinear spatially distributed processes described by first‐order hyperbolic partial differential equations (PDEs). Initially, a T–S fuzzy hyperbolic PDE model is proposed to accurately represent the nonlinear PDE system. Then, based on the fuzzy PDE model, the GCDF observer‐based control design is developed in terms of a set of space‐dependent linear matrix inequalities. In the proposed control scheme, a distributed fuzzy observer is used to estimate the state of the PDE system. The designed fuzzy controller can not only ensure the exponential stability of the closed‐loop PDE system but also provide an upper bound of quadratic cost function. Moreover, a suboptimal fuzzy control design is addressed in the sense of minimizing an upper bound of the cost function. The finite difference method in space and the existing linear matrix inequality optimization techniques are used to approximately solve the suboptimal control design problem. Finally, the proposed design method is applied to the control of a nonisothermal plug‐flow reactor. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2366–2378, 2013     

FUZZY GAIN SCHEDULED CSTR WITH GA-BASED PID

In this article, a new control scheme, the gain scheduled genetic algorithm (GA)-based PID is proposed for a continuous stirred tank reactor (CSTR). A CSTR is a highly nonlinear process that exhibits stability in certain regions and instability in other regions. The proposed control scheme implements the characteristics of the genetic algorithm's (GA) global optimization to optimize the PID's three control parameters, k_p, k_i, k_d, to obtain the best control effect by minimizing the integral square error online. The PID controller parameters tuned by the GA for each region are gain scheduled by a fuzzy logic scheduler. Fuzzy gain scheduling is a special form of fuzzy control that uses linguistic rules and fuzzy reasoning to determine the controller parameter transition policy for the dynamic plant subject to large changes in its operating state. Simulation results show the feasibility of using the proposed controller for the control of the dynamical nonlinear CSTR.     

一种比例因子自调整的模糊预测函数控制

针对由主、副被控对象级联而成的大纯滞后系统,提出一种内回路采用PI控制、外回路采用具有比例因子自调整的模糊增量型预测函数控制策略,它将预测函数控制和具有比例因子自调整的模糊控制进行综合来共同控制由内回路和主被控对象构成的广义被控对象。大量的仿真实验表明:本方法较其他方法控制效果更好,即使在模型严重失配时,本方法仍具有很强的鲁棒性和抗干扰能力。     

Controlling of Proton Exchange Membrane Fuel Cell by Model Predictive Controller Based on ANFIS Model

Proton exchange fuel cell is one of the most promising new technologies in electrical energy production. Due to slow dynamic, nonlinearity and dependency of time changing variables of proton exchange membrane fuel cell (PEMFC), its control issue is a challenging problem. In this paper, model predictive controller (MPC) based on the adaptive neuro‐fuzzy interface model of the PEMFC is proposed to control the output voltage. First the adaptive neuro‐fuzzy interference system (ANFIS) model is identified to approximate the dynamic behavior of the PEMFC system with a set of data which are taken from a physical model of a 5 kW PEMFC setup plant. Then the branch‐and‐bound method and the greedy algorithm are used to solve the constrained optimization function of the predictive control problem. The results reveal that the ANFIS model can effectively approximate the dynamic behavior of the PEMFC and the predictive controller based on this model can successfully control the output and satisfy the constraints.