Enhanced-efficiency operating variables selection for vapor compression refrigeration cycle system

In this paper, a novel enhanced-efficiency selection of operating variables based on self-optimizing control (SOC) method for the vapor compression refrigeration cycle (VCC) system is proposed. An objective function is proposed to maximize the energy efficiency of the VCC system while meeting with the demand of indoor thermal comfort. With the detailed analysis of operating variables, three unconstrained degrees of freedom are selected among all the candidate operating variables. Then two SOC methods are applied to determine the optimal individual controlled variables (CVs) and measurement combinations as CVs. The model predictive control (MPC) method based controllers and PID controllers are designed for different sets of CVs, and the experimental results indicate that the proposed selection of CVs can achieve a good trade-off between optimal (or near optimal) stable operation and enhanced-efficiency of the synthesized control structure.     

中央空调制冷系统运行优化模型及实例分析

中央空调制冷系统能耗在建筑能耗中占有很大比重,而由于设计不合理、运行控制不合理等诸多因素的存在,导致中央空调制冷系统的能耗偏大,不符合我国可持续发展的原则。因此有必要研究建立中央空调制冷系统研究运行优化模型并在此基础上找到中央空调制冷系统优化控制策略。本文通过建立中央空调制冷系统运行优化的数学模型,并结合实际工程案例,计算出中央空调系统优化控制策略,以期为中央空调制冷系统优化运行提供参考。     

Thermodynamic performance assessment of vacuum membrane-based dehumidification and air carrying energy radiant air-conditioning system(VMD-ACERS)

Temperature and humidity independent control(THIC)air-conditioning system is a promising technol-ogy.In this work,a novel temperature and humidity independent control(THIC)system is proposed,namely VMD-ACERS,which integrates vacuum membrane-based dehumidification and air carrying energy radiant air-conditioning system.This work establishes a novel coefficient of performance(COP)model of VMD-ACERS.The main parameters affecting the COP of conventional fan coil unit cooling sys-tem(FCUCS)and VMD-ACERS are investigated.The performance of FCUCS and VMD-ACERS are com-pared,and the energy-saving potential of VMD-ACERS is proved.Results indicate that,for FCUCS,the importance ranking of parameters is basically stable.However,for VMD-ACERS,the importance ranking will be affected by FCU and refrigerant.The most important parameters of VMD-ACERS are condensation temperature and permeate side pressure.On the contrary,superheating,subcooling are relatively less important parameters.For VMD-ACERS,it is not necessary to pursue the membrane with very high selec-tivity,because the selectivity of membrane would also be a less important parameter when it reaches 500.The COP of VMD-ACERS is higher than that of FCUCS when the permeate side pressure is higher than 8 kPa.The VMD-ACERS solves two technical problems about power-saving and thermal comfort of con-ventional THIC,and can extend the application of THIC air-conditioning system.     

流化床反应器中气相丙烯聚合反应的动力学和预测控制(英文)

A two-phase dynamic model, describing gas phase propylene polymerization in a fluidized bed reactor, was used to explore the dynamic behavior and process control of the polypropylene production rate and reactor temperature. The open loop analysis revealed the nonlinear behavior of the polypropylene fluidized bed reactor, jus- tifying the use of an advanced control algorithm for efficient control of the process variables. In this case, a central- ized model predictive control （MPC） technique was implemented to control the polypropylene production rate and reactor temperature by manipulating the catalyst feed rate and cooling water flow rate respectively. The corre- sponding MPC controller was able to track changes in the setpoint smoothly for the reactor temperature and pro- duction rate while the setpoint tracking of the conventional proportional-integral （PI） controller was oscillatory with overshoots and obvious interaction between the reactor temperature and production rate loops. The MPC was able to produce controller moves which not only were well within the specified input constraints for both control vari- ables, but also non-aggressive and sufficiently smooth for practical implementations. Furthermore, the closed loop dynamic simulations indicated that the speed of rejecting the process disturbances for the MPC controller were also acceotable for both controlled variables.     

基于加权偏离度统计方法的预测控制性能评估算法

针对带区域约束条件的预测控制系统性能评估问题,在考虑过程输出变量约束类型的基础上,提出了基于加权偏离度统计方法的控制性能评估算法。该方法依据控制要求的不同,将输出变量分为质量变量和约束变量,并结合工程经验合理选择变量的权重。基于系统闭环运行数据和约束设置,通过计算变量的加权偏离度得到控制系统的性能评估指标,从而为预测控制器的参数调整和性能提升提供了决策依据。系统仿真实例和工程应用证明了该评估算法对区域预测控制系统性能评估的有效性。     

Multivariable model predictive control of a novel rapid pressure swing adsorption system

A multivariable model predictive control (MPC) algorithm is developed for the control and operation of a rapid pressure swing adsorption (RPSA)‐based medical oxygen concentrator. The novelty of the approach is the use of all four step durations in the RPSA cycle as independent manipulated variables in a truly multivariable context. The RPSA has a complex, cyclic, nonlinear multivariable operation that requires feedback control, and MPC provides a suitable framework for controlling such a multivariable system. The multivariable MPC presented here uses a quadratic optimization program with integral action and a linear model identified using subspace system identification techniques. The controller was designed and tested in simulation using a complex, highly coupled, nonlinear RPSA process model. The model was developed with the least restrictive assumptions compared to those reported in the literature, thereby providing a more realistic representation of the underlying physical phenomena. The resulting MPC effectively tracks set points, rejects realistic process disturbances and is shown to outperform conventional PID control. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1234–1245, 2018     

Development of a novel control strategy for a highly segmented injection mold tempering for inline part warpage control

The demand of precise injection-molded parts is steadily increasing and is today one of the most relevant challenges, due to local variations in temperature and pressure during the production of the part. These variations can lead to a significant change of the local specific volume, shrinkage potential, and inner stress, which ultimately results in part warpage. By homogenizing the local specific volume over the part according to the specific pvT-behavior of the polymer, warpage is expected to be reduced. The following work describes a new approach to control the local specific volume by a newly developed segmented and highly dynamic mold temperature control based on rapid heating ceramics and CO₂ evaporation chambers. Since injection molding is a dynamic process and heat transfer inside the mold is comparably slow, a special control strategy is necessary to activate the heating and cooling elements in advance. For this, a novel prediction strategy based on a discretization of the one-dimensional heat equation has been developed. Experimental trials including a classical PID controller and a model predictive control approach (MPC) show that the MPC is superior regarding the process stability.     

Rotary Dryer Control Using a Grey-Box Neural Model Scheme

The application of a Grey-box Neural Model (GNM) in a nonlinear model predictive control scheme (NMPC) of a direct rotary dyer is presented in this work. The GNM, which is based on the combination of phenomenological models and empirical artificial neural network (ANN) models, was properly developed and validated by using experimental fish-meal rotary drying information. The GNM was created by combining the rotary dryer mass and energy balances and a feed forward neural network (FFNN), trained off-line to estimate the drying rate and the volumetric heat transfer coefficient. The GNM results allowed us to obtain the relation between the controlled variable (solid moisture content) and the manipulated variable (gas phase entrance temperature) used in the predictive control strategy. Two NMPC control strategies, one with a fixed extended prediction horizon and another with an extended range prediction horizon, were applied to a simulated industrial fish-meal drying process. The results showed that a correct rotary dryer representation can be obtained by using a GNM approach. Due to the representation capability of the GNM approach, excellent control performances of the NMPCs were observed when the process variables were subject to disturbances. As analyzed in this work, the fixed extended prediction horizon MPC surpassed recognized control methodologies (quadratic dynamic matrix control).     

双冷源温湿分控空调系统在粘胶长丝生产中的应用

根据温湿度独立控制的原理,设计了双冷源温湿分控空调系统,应用于粘胶长丝络筒车间,并与温湿联控的传统空调系统进行比较.结果表明:双冷源温湿分控空调系统的耗冷量降低26.9％,冷冻水用量减少11.69％,综合循环性能系数提高17.95％.温湿分控空调系统在节能方面有优势,且对空气参数进行有效控制,确保粘胶长丝络筒车间的恒温...     

Dantzig–Wolfe decomposition and plant-wide MPC coordination

Due to the enormous success of model predictive control (MPC) in industrial practice, the efforts to extend its application from unit-wide to plant-wide control are becoming more widespread. In general, industrial practice has tended toward a decentralized MPC architecture. Most existing MPC systems work independently of other MPC systems installed within the plant and pursue a unit/local optimal operation. Thus, a margin for plant-wide performance improvement may be available beyond what decentralized MPC can offer. Coordinating decentralized, autonomous MPC has been identified as a practical approach to improving plant-wide performance. In this work, we propose a framework for designing a coordination system for decentralized MPC which requires only minor modification to the current MPC layer. This work studies the feasibility of applying Dantzig–Wolfe decomposition to provide an on-line solution for coordinating decentralized MPC. The proposed coordinated, decentralized MPC system retains the reliability and maintainability of current distributed MPC schemes. An empirical study of the computational complexity is used to illustrate the efficiency of coordination and provide some guidelines for the application of the proposed coordination strategy. Finally, two case studies are performed to show the ease of implementation of the coordinated, decentralized MPC scheme and the resultant improvement in the plant-wide performance of the decentralized control system.     

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.     

A robustly stabilizing fault-tolerant MPC: A performance improvement-based approach

In terms of model predictive control (MPC) performance degradation caused by operational faults, in this article, a robust MPC strategy with active fault tolerance properties is proposed. The proposed strategy incorporates a fault supervision layer into the structure of conventional cost-contracting formulation-based robust MPC for the online update of the nominal controller model in the event of faults. The robust MPC is based on multiplant uncertainty, while the supervisory layer consists of a bank of unknown input observers and a decision-making algorithm. Simulation results in a nonlinear polymerization reactor subject to process faults demonstrate that the proposed approach offers superior performance compared to the conventional strategy.     

Control of plastic melt temperature: A multiple input multiple output model predictive approach

Good control of plastic melt temperature for injection molding is very important in reducing operator setup time, ensuring product quality, and preventing thermal degradation of the melt. The controllability and setpoints of other process parameters also depend on the precise monitoring and control of plastic melt temperatures. We experimentally investigated the thermal interactions between the barrel zones of an instrumented plastic injection molding machine (IMM). These interactions result from the zone temperature differences that are used in normal machine operations. From these experimental interactions, multiple-input-multiple-output (MIMO) and single-input-single-output (SISO) models were derived for controlling these zone temperatures using a Model Predictive Control (MPC) strategy. An experimental comparison was made between MIMO MPC and SISO MPC of plastic melt temperature, which showed that the MIMO MPC scheme is more energy efficient, having zero overshoot.     

Lexicographic optimization based MPC: Simulation and experimental study

Multi-variable prioritized control study is carried out using model predictive control (MPC) algorithms. The conventional MPC algorithm implements multi-variable control through one augmented objective function and requires weights adjustment for required performance. In order to implement explicit prioritization in multiple control objectives, we have used lexicographic MPC. To achieve better tracking performance, we have used a new MPC algorithm, by modifying the lexicographic constraint, referred to as MLMPC, where tuning of weights is not required. The effectiveness of MLMPC algorithm is demonstrated on a PMMA reactor for controlling the number average molecular weight and the reactor temperature. We have also verified the benefits of proposed algorithm on an experimental single board heater system (SBHS) for controlling temperature of a thin metal plate. These simulation and experimental studies demonstrate the superiority of the proposed method over conventional MPC and lexicographic MPC. Finally, we have presented generalized mathematical solutions to the optimization problem in MLMPC.     

Economic model predictive control of nonlinear process systems using Lyapunov techniques

In this work, we develop model predictive control (MPC) designs, which are capable of optimizing closed‐loop performance with respect to general economic considerations for a broad class of nonlinear process systems. Specifically, in the proposed designs, the economic MPC optimizes a cost function, which is related directly to desired economic considerations and is not necessarily dependent on a steady‐state—unlike conventional MPC designs. First, we consider nonlinear systems with synchronous measurement sampling and uncertain variables. The proposed economic MPC is designed via Lyapunov‐based techniques and has two different operation modes. The first operation mode corresponds to the period in which the cost function should be optimized (e.g., normal production period); and in this operation mode, the MPC maintains the closed‐loop system state within a predefined stability region and optimizes the cost function to its maximum extent. The second operation mode corresponds to operation in which the system is driven by the economic MPC to an appropriate steady‐state. In this operation mode, suitable Lyapunov‐based constraints are incorporated in the economic MPC design to guarantee that the closed‐loop system state is always bounded in the predefined stability region and is ultimately bounded in a small region containing the origin. Subsequently, we extend the results to nonlinear systems subject to asynchronous and delayed measurements and uncertain variables. Under the assumptions that there exist an upper bound on the interval between two consecutive asynchronous measurements and an upper bound on the maximum measurement delay, an economic MPC design which takes explicitly into account asynchronous and delayed measurements and enforces closed‐loop stability is proposed. All the proposed economic MPC designs are illustrated through a chemical process example and their performance and robustness are evaluated through simulations. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

毛细管网辐射空调系统特点及发展趋势

毛细管辐射空调系统是温湿度独立控制的新型空调技术,是对传统空调技术的挑战和创新。主要介绍了毛细管辐射空调系统的工作原理、系统组成及末端特点,分析了其节能、舒适、环保的优势,并对其今后的发展趋势与市场前景提出了一些看法,毛细管辐射技术在我国一定会有很好的发展前景。     

Temperature control in injection molding. Part II: Controller design,simulation, and implementation

This paper presents a new approach for temperature control of an injection molding machine (IMM) that uses a model predictive control (MPC) strategy. The control system consists of a number of single‐input‐single‐output model predictive controllers, each associated with a particular temperature zone. What distinguishes this approach from others is how the MPC strategy exploits knowledge of temperature interaction between adjacent zones and the effects of back pressure, to develop individual temperature controllers for each zone. This is achieved by decoupling the interaction between zones. The new thermal controller was simulated and implemented with good results on a 150‐tonne IMM using a series of comparative experiments. Polym. Eng. Sci. 44:2318–2326, 2004. © 2004 Society of Plastics Engineers.     

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。     

Model predictive control with non-uniformly spaced optimization horizon for multi-timescale processes

Many chemical processes exhibit disparate timescale dynamics with strong coupling between fast, moderate and slow variables. To effectively handle this issue, a model predictive control (MPC) scheme with a non-uniformly spaced optimization horizon is proposed in this paper. This approach implements the time intervals that are small in the near future but large in the distant future, allowing the fast, moderate and slow dynamics to be included in the optimization whilst reducing the number of decision variables. A sufficient condition for ensuring stability for the proposed MPC is developed. The proposed approach is demonstrated using a case study of an industrial paste thickener control problem. While the performance of the proposed approach remains similar to a conventional MPC, it reduces the computational complexity significantly.     

Composition control and temperature inferential control of dividing wall column based on model predictive control and PI strategies

The dividing wall column (DWC) is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control (MPC) is an advanced control strategy that has acquired extensive applications in various industries. In this study, MPC is applied to the process for separating ethanol, n-propanol, and n-butanol ternary mixture in a fully thermally coupled DWC. Both composition control and temperature inferential control are considered. The multiobjective genetic algorithm function “gamultiobj” in Matlab is used for the weight tuning of MPC. Comparisons are made between the control performances of MPC and PI strategies. Simulation results show that although both MPC and PI schemes can stabilize the DWC in case of feed disturbances, MPC generally behaves better than the PI strategy for both composition control and temperature inferential control, resulting in a more stable and superior performance with lower values of integral of squared error (ISE).