Application of model predictive control to the BSM1 benchmark of wastewater treatment process

Wastewater treatment processes are difficult to be controlled because of their complex and nonlinear behavior. This paper applied model predictive control (MPC) to the Benchmark Simulation Model 1 (BSM1) wastewater treatment process to maintain the effluent quality within regulations-specified limits. Good performance was achieved under steady influent characteristics, especially concerning the nitrogen-related species. In presence of influent disturbances, two approaches have been studied: the addition of a feedforward action based on the measurement of the influent flow rate; the use of nonlinear model predictive controller by addition of a penalty function. The effects of two approaches were visible on the decrease of ammonium and nitrogen concentration which were considered as being of major importance. The results show that MPC can be effectively used for control in wastewater treatment process. By comparing performances, the nonlinear model predictive control strategy with penalty function demonstrates best with small effluent quality index and acceptable aeration and pumping energy consumption.     

Evaluation of multiloop chemical dosage control strategies for total phosphorus removal of enhanced biological nutrient removal process

We developed several control algorithms and compare their control performances for controlling the total phosphorous (TP) concentration in wastewater treatment plant, which has strong influent disturbances and the disturbance effects should be removed while maintaining better effluent quality. An anaerobic - anoxic - oxic (AAO) process, which is a well-known advanced nutrient removal process, was selected as a case study, which is modeled with activated sludge model no. 2. Six control strategies for TP control with a polymer addition were implemented in AAO process and evaluated by the plant’s performance, where the costs of the dosed chemical were compared among the six controllers. The experimental work showed that the advanced control techniques with feedback, feedforward and feedratio controllers were able to control the TP concentration in the effluent, which must be less than 1.50 g P/m³ which is the legal limitation, while reducing the necessary chemical cost. The results showed that the best TP removal performance in the effluent TP removal could be achieved by advanced feedback controller with the tuned control parameters, which showed the best effluent quality and control performance index as well as the cheapest cost of chemical dosage among the six TP control strategies.     

Feedback control design for an anaerobic digestion process

An approach for the design of linear feedback controllers for anaerobic digestion systems is presented. The effluent chemical oxigen demand (COD) concentration and the dilution rate are taken respectively as the regulated and the manipulated variables. The control design is based on simple step‐response models of the process endowed with an input delay to account for dead‐times induced by measurement devices. The resulting feedback controller has a traditional proportional‐integral (PI) control structure, so it can be easily implemented with conventional control technologies. Since the concentration of volatile fatty acids can be easily and quickly measured as compared with COD concentration, it is used as a secondary measurement that is incorporated into the feedback loop scheme to enhance the robustness of the control scheme with respect of influent disturbances. The performance of the proposed control scheme is illustrated via numerical simulations and experimental work. © 2002 Society of Chemical Industry     

A Comparative Study of Combined Feedforward/Feedback Model Predictive Control for Nonlinear Systems

Model predictive control (MPC) provides a natural framework to realize feedforward and feedback control for nonlinear systems where the effect of disturbances (DVs) cannot be separated from that of manipulated variables (MVs). This study examines the performance of MPC with measured DVs as partial inputs of the model used, which is termed as combined feedforward/feedback MPC (CMPC) in contrast to conventional MPC using a model without input of any measured DV. In the simulation of a pH process, we demonstrate the clear superiority of CMPC over MPC. In the experiment with a bench‐scale ethanol and water distillation column, CMPC and MPC using artificial neural network (ANN) models are applied to the dual temperature control problem. External recurrent neural networks (ERNs) with and without a measured DV (feed rate of the column) as their partial input are built and employed in the experiment, with a result that inclusion of the measured DV in the model makes CMPC perform significantly better than MPC. To strengthen practical experience in applying ANN‐based MPC, a detailed procedure of the experiment is also documented.     

Disturbance rejection of ball mill grinding circuits using DOB and MPC

Ball mill grinding circuit is essentially a multivariable system with couplings, time delays and strong disturbances. Many advanced control schemes, including model predictive control (MPC), adaptive control, neuro-control, robust control, optimal control, etc., have been reported in the field of grinding process. However, these control schemes including the MPC scheme usually cannot achieve satisfying effects in the presence of strong disturbances. In this paper, disturbance observer (DOB), which is widely used in motion control applications, is introduced to estimate the disturbances in grinding circuit. A compound control scheme, consisting of a feedforward compensation part based on DOB and a feedback regulation part based on MPC (DOB-MPC), is thus developed. A rigorous analysis of disturbance rejection performance is given with the considerations of both model mismatches and external disturbances. Simulation results demonstrate that when controlling the ball mill grinding circuit, the DOB-MPC method possesses a better performance in disturbance rejection than that of the MPC method.     

Performance assessment of cascade controllers for nitrate control in a wastewater treatment process

A cascade control strategy is proposed to the benchmark simulation model 1 (BSM1) to enhance the treatment performance of nitrogen removal in a biological wastewater treatment plant. The proposed control approach consists of two control loops, a primary outer loop and a secondary inner loop. The method has two controllers of which the primary loop has a model predictive control (MPC) controller and the secondary loop has a proportional-integralderivative (PID) controller, which is a cascade MPC-PID controller. The primary MPC controller is to control the nitrate concentration in the effluent, and the secondary PID controller is to control the nitrate concentration in the final anoxic compartment. The proposed method controls the nitrate concentrations in the effluent as well as in the final anoxic reactor simultaneously to strictly satisfy the quality of the effluent as well as to remove the effects of disturbances more quickly by manipulating the external carbon dosage rate. Because the control performance assessment (CPA) technique has the features of determining the capability of the current controller and locating the best achievable performance, the other novelty of this paper is to suggest a relative closed-loop potential index (RCPI) which updates the CPA technology into a closed-loop cascade controller. The proposed method is compared with a cascade PID-PID control strategy and the original PID controller in BSM1 and an improved performance of the suggested cascade MPC-PID controller is obtained by using the CPA approach.     

Model predictive control for multivariable unstable processes with constraints on manipulated variables

The original MPC(Model Predictive Control) algorithm cannot be applied to open loop unstable systems, because the step responses of the open loop unstable system never reach steadystates. So when we apply MPC to the open loop unstable systems, first we have to stabilize them by state feedback or output feedback. Then the stabilized systems can be controlled by MPC. But problems such as valve saturation may occur because the manipulated input is the summation of the state feedback output and the MPC output. Therefore, we propose Quadratic Dynamic Matrix Control(QDMC) combined with state feedback as a new method to handle the constraints on manipulated variables for multivariable unstable processes. We applied this control method to a single-input-single-output unstable nonlinear system and a multi-input-multi-output unstable system. The results show that this method is robust and can handle the input constraints explicitly and also its control performance is better than that of others such as well tuned PI control. Linear Quadratic Regulator (LQR) with integral action.     

Model predictive control for multivariable unstable processes with constraints on manipulated variables

The original MPC(Model Predictive Control) algorithm cannot be applied to open loop unstable systems, because the step responses of the open loop unstable system never reach steady states. So when we apply MPC to the open loop unstable systems, first we have to stabilize them by state feedback or output feedback. Then the stabilized systems can be controlled by MPC. But problems such as valve saturation may occur because the manipulated input is the summation of the state feedback output and the MPC output. Therefore, we propose Quadratic Dynamic Matrix Control(QDMC) combined with state feedback as a new method to handle the constraints on manipulated variables for multivariable unstable processes. We applied this control method to a single-input-single-output unstable nonlinear system and a multi-input-multi-output unstable system. The results show that this method is robust and can handle the input constraints explicitly and also its control performance is better than that of others such as well tuned PI control. Linear Quadratic Regulator (LQR) with integral action.     

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Based on the two-dimensional (2D) systemtheory, an integrated predictive iterative learning control (2D-IPILC) strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control (ILC) andmodel predictive control(MPC) is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By minimizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type (Ptype) ILC despite the model error and disturbances.     

FPGA based QDMC control for reverse-osmosis water desalination system

Model-based predictive control (MPC) is a widely used advanced control algorithm in process industries, such as chemical plants and oil refineries. Quadratic dynamic matrix control (QDMC) is one of the popular MPC strategies. In this paper, the QDMC controller is used to control a simulated reverse-osmosis (RO) water desalination system with spiral wound element (SWM). A cascaded control system was designed with the QDMC controller and a PID controller for the desalination process, where the QDMC controller optimizes the set point of the PID controller and directly controls one output. In order to make the implementation more convenient, this paper also studies the implementation of QDMC control system based on a field programmable gate array (FPGA) chip. A support software is developed to help engineers adjust the parameters of the QDMC controller. Since the QDMC controller is implemented in an embedded system, the overall system cost is reduced, which is helpful to the application of RO desalination system.     

基于改进DOB的矿物研磨过程最优运行先进反馈控制(英文)

Advanced feedback control for optimal operation of mineral grinding process is usually based on the model predictive control (MPC) dynamic optimization. Since the MPC does not handle disturbances directly by controller design, it cannot achieve satisfactory effects in controlling complex grinding processes in the presence of strong disturbances and large uncertainties. In this paper, an improved disturbance observer (DOB) based MPC advanced feedback control is proposed to control the multivariable grinding operation. The improved DOB is based on the optimal achievable H 2 performance and can deal with disturbance observation for the nonminimum-phase delay systems. In this DOB-MPC advanced feedback control, the higher-level optimizer computes the optimal operation points by maximize the profit function and passes them to the MPC level. The MPC acts as a presetting controller and is employed to generate proper pre-setpoint for the lower-level basic feedback control system. The DOB acts as a compensator and improves the operation performance by dynamically compensating the setpoints for the basic control system according to the observed various disturbances and plant uncertainties. Several simulations are performed to demonstrate the proposed control method for grinding process operation.     

ANAMMOX过程性能与电导率变化

研究了电导率变化与离子强度及其厌氧氨氧化(ANAMMOX)过程性能的关系,结果表明:电导率与模拟废水离子强度近似呈正比,与主要成分浓度之间存在着显著的线性关系;电导率变化与ANAMMOX过程性能变化具有很好一致性;电导率能反映ANAMMOX工艺容积负荷与容积氮去除速率、进水浓度与出水浓度的大小。电导率可用于指示ANAMMOX过程性能的变化,也可用于辅助ANAMMOX的过程控制。     

废水处理过程的抗扰控制研究

厌氧消化废水处理过程中,动态变化的进水组分、组分浓度,组分间耦合等各种不确定因素,使得简单的闭环控制无法获得理想的污水处理效果。为使污水处理出水水质达标,对污水处理过程模型依赖小、对各种变化（扰动）因素鲁棒性强、动态响应好的控制方法可满足工程要求。为此,设计一种能够主动估计并消除扰动的抗扰控制方法,有效估计并补偿污水处理过程中存在的各种不确定因素,以获得期望的污水处理效果。数值仿真结果表明,抗扰控制具有较好的抗干扰能力,能够满足控制要求,是一种可行的污水处理控制方案。     

Experimental evaluation of feedback,feedforward and combined feedforward-feedback binary distillation column control

The control behavior of a nine inch diameter eight tray bubble cap column separating a methanol-water mixture has been studied using feedback, feedforward and combined feedforward-feedback control. Top product composition was controlled by manipulation of the reflux rate to compensate for feed flow disturbances. Pulse testing was used to determine the process transfer functions. Gain, gain plus time delay and gain plus time lag feedforward control were implemented by means of an IBM 1800 digital computer. Combined feedforward-feedback control, using gain and gain plus time lag feedforward control action, was also studied. The improved column control behavior is compared with results obtained using only feedback control.     

厌氧—好氧工艺处理饮料生产废水的工程应用

文章介绍了采用厌氧—好氧工艺处理某饮料厂生产废水的工程设计、工艺调试。在高浓度废水COD浓度为13830mg/L、生活污水COD浓度为229mg/L时,出水COD小于60mg/L,达到《广东省地方标准水污染物排放限值》(DB44/26-2001)(第二时段)一级标准。实践证明:该处理工艺可行,处理效率高,运行稳定、可靠安全、管理与维护方便。     

分段进水A/O工艺流量分配专家系统的建立与应用

分段进水A/O工艺中,进水流量分配是重要控制参数,是该工艺稳定运行并发挥优势的关键。本文提出3种流量分配思想:等负荷流量分配法、流量分配系数法及末端集中进水,并根据实际生活污水进行试验得出的大量试验数据,辅以相关领域文献及污水处理领域专家的经验作为参考,建立分段进水A/O工艺流量分配专家决策系统。该专家系统可以根据不同的进水水量、水质及出水特点,对合适的流量分配方法进行决策。最后,针对实际污水处理厂一天内典型的水质水量变化情况,对专家系统的应用进行说明。     

基于MPC控制技术优化VPSA制氧工艺的模拟

针对真空变压吸附制氧在gPROMS软件中建立了严格的数学模型,基于LiLSX吸附剂设计了两塔八步的真空变压吸附流程生产纯度为92%的O ₂。对此流程进行优化,其纯度和回收率有了明显的改进。在此基础上,引入实际生产中经常存在的如进料流量的变化以及吸附性能降低等扰动因素,使模拟工作更接近实际。根据产品气中O ₂纯度的反馈,采用模型辨识技术设计了MPC控制器,用于预测控制VPSA过程的动态行为。开环和闭环控制结果的对比显示,流程在设计的MPC控制下展现出更好的结果,这表明MPC控制策略可以明显改善空气分离制氧的生产过程。     

水解酸化-A2/O -MBR-BAC组合工艺处理焦化废水试验研究

为解决焦化行业废水处理不达标的问题,试验研究了水解酸化-A2/O -膜生物反应器(MBR)-活性炭过滤(BAC)的组合工艺处理焦化废水的可行性.结果表明,进水NH3-N的质量浓度为88 mg/L左右时,出水NH3-N的质量浓度稳定在3 mg/L左右,组合工艺对NH3-N的去除率能达到96％.同时,进水CODCr的质量浓...     

水解酸化-A2/O-MBR-BAC组合工艺处理焦化废水试验研究

为解决焦化行业废水处理不达标的问题,试验研究了水解酸化-A2/O-膜生物反应器（MBR）-活性炭过滤（BAC）的组合工艺处理焦化废水的可行性。结果表明,进水NH3-N的质量浓度为88 mg/L左右时,出水NH3-N的质量浓度稳定在3 mg/L左右,组合工艺对NH3-N的去除率能达到96%。同时,进水CODCr的质量浓度在970mg/L左右,出水CODCr去除率能达到90%,出水满足GB 8978—1996《污水综合排放标准》一级标准,该工艺对焦化废水有很好的处理效果。     

Dynamic Modelling and Process Control of ZnS Precipitation

Abstract

This paper presents the dynamic modelling and design of a control strategy for the ZnS precipitation process. During lab‐scale experiments, the sulfide concentration in a precipitator was controlled at a prespecified pS value by manipulating the flow from a buffer vessel. Batch tests showed that the optimal condition for zinc sulfide precipitation is at a sulfide concentration of 10^?15 mole/l (pS 15). Experiments with the precipitator showed that the sulfide concentration highly deviates from a given setpoint when proportional (P) control is used, but this deviation can be decreased using a Proportional Integral (PI) controller. Moreover, the PI controller was able to handle sudden disturbances in the process conditions (pH, influent flow rate, or zinc and sulfide concentration). Additional precipitation experiments were conducted using effluent from a sulfate reducing gas‐lift reactor to determine if the compounds present in the effluent influence the control process. With the gas‐lift reactor effluent and a PI controller, the desired sulfide concentration was reached almost instantaneously (within 15 minutes) within acceptable margins (2–5%).