Decoupling internal model control for multivariable systems with multiple time delays

In this paper, the decoupling internal model control (IMC) with stability is investigated for multivariable stable processes with multiple time delays. All the stabilizing IMC controllers which solve this decoupling problem and the resulting closed-loop systems are characterized in terms of the open-loop system's time delays and non-minimum phase zeros. It shows that the inclusion of some time delays and non-minimum phase zeros might be necessary to make a decoupling solution realizable and stabilizing. Based on this characterization, a control design method for best achievable performance is presented. However, owing to the high complexity of the theoretical controller, a practical controller design procedure is developed with the help of the proposed model reduction algorithm. Examples are given to illustrate our analysis and design. Significant performance improvement over the existing multivariable Smith predictor control has been achieved with the proposed approach.     

多变量多时滞非方系统的解耦内模控制

多变量复杂控制系统不仅具有多耦合和多时滞性,还具有结构上的复杂性,即输入输出不等,传递函数为奇异矩阵。传统的多变量内模控制是基于对非奇异对象求逆来进行的,因此很难解决这类问题。针对该情况引入矩阵论中的广义逆概念,通过求对象的广义逆矩阵来设计解耦内模控制器,打破了内模控制只能对方系统进行控制的局限性,并利用泰勒近似很好地解决了多滞后的问题,最后通过设计特殊形式的滤波器,不仅能够消除由纯滞后近似引入的不稳定极点,保证系统的稳定性,且能够保证系统的正则性。仿真结果表明,该方法不仅跟踪迅速,且继承了内模控制的无余差和强鲁棒性,动态解耦效果良好,仅对时滞变化较为敏感。由于系统基于内模控制设计,故模型匹配度越高,系统响应越好。     

A two-tier architecture for networked process control

Traditionally, process control systems utilize dedicated, point-to-point wired communication links using a small number of sensors and actuators to regulate appropriate process variables at desired values. While this paradigm to process control has been successful, chemical plant operation could substantially benefit from an efficient integration of the existing, point-to-point control networks (wired connections from each actuator/sensor to the control system using dedicated local area networks) with additional networked (wired or wireless) actuator/sensor devices. However, augmenting existing control networks with real-time wired/wireless sensor and actuator networks challenges many of the assumptions made in the development of traditional process control methods dealing with dynamical systems linked through ideal channels with flawless, continuous communication. In the context of control systems which utilize networked sensors and actuators, key issues that need to be carefully handled at the control system design level include data losses due to field interference and time delays due to network traffic. Motivated by the above technological advances and the lack of methods to design control systems that utilize hybrid communication networks, in the present work, we present a novel two-tier control architecture for networked process control problems that involve nonlinear processes and heterogeneous measurements consisting of continuous measurements and asynchronous, delayed measurements. This class of control problems arises naturally when nonlinear processes are controlled via control systems based on hybrid communication networks (i.e., point-to-point wired links integrated with networked wired/wireless communication) or utilizing multiple heterogeneous measurements (e.g., temperature measurements which can be taken to be continuous and species concentration measurements which are fed to the control system at asynchronous time instants and frequently involve delays). While point-to-point wired links are very reliable, the presence of a shared communication network in the closed-loop system introduces additional delays and data losses and these issues should be handled at the controller design level. In the two-tier control architecture presented in this work, a lower-tier control system, which relies on point-to-point communication and continuous measurements, is first designed to stabilize the closed-loop system, and an upper-tier networked control system is subsequently designed, using Lyapunov-based model predictive control theory, to profit from both the continuous and the asynchronous, delayed measurements as well as from additional networked control actuators to improve the closed-loop system performance. The proposed two-tier control architecture preserves the stability properties of the lower-tier controller while improving the closed-loop performance. The applicability and effectiveness of the proposed control method is demonstrated using two chemical process examples.     

多液位与加热炉复合系统的协调控制

多液位与加热炉复合系统是一类具有液位和加热炉支路间的强耦合性及大滞后与非线性等特性的复杂工业过程。针对这类系统的多液位控制与加热炉支路温度平衡控制问题,根据解耦与平衡的思想,提出了一种基于总容量平衡的变周期液位控制的协调控制方法,从结构上实现了多液位控制与支路温度平衡控制的分离,耦合性分析也说明了方法的可行性,最终实现了复合系统的整体协调控制。基于HYSYS流程模拟的仿真实验表明了所提出控制方法的有效性。     

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.     

Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition

In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition (SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.     

Identification of Multivariable Delay Processes in Presence of Nonzero Initial Conditions and Disturbances

In this paper, a robust identification method is proposed for multiple‐input and multiple‐output (MIMO) continuous‐time processes with multiple time delays. Suitable multiple integrations are constructed and regression equations linear in the aggregate parameters are derived with the use of the test responses and their multiple integrals. The multiple time delays are estimated by solving some algebraic equations without iteration and the other process model parameters are then recovered. Its effectiveness is demonstrated through simulation and real‐time testing.     

化工双输入输出时滞过程解耦PID控制器解析设计

基于常规单位反馈控制结构,针对具有时滞特性的化工双输入输出过程提出一种解耦PID控制器的解析设计新方法.该解耦PID控制器的突出优点是控制器设计过程简单直观;控制器可以实现标称系统输出间的显著解耦;系统输出分别使用相应控制器对角元素中的调节参数进行单参数整定.同时,对于实际生产过程中常见的被控过程乘性不确定性,分析了系统保持鲁棒稳定的充要条件.仿真实例验证了该方法优于其它方法.     

Multiactor multicriteria decision making for life cycle sustainability assessment under uncertainties

This article aims at developing a generic multiactor multicriteria decision making (MAMCDM) method for life cycle sustainability assessment (LCSA) of industrial systems under uncertainties, which can help multiple stakeholders/decision‐makers to prioritize the alternative industrial systems in a group decision‐making approach. The interval best–worst method, which can address the ambiguity, vagueness and hesitations existing in human's judgments, was developed for determining the weight of the criteria in LCSA. The consensus convergence model was developed for aggregating the relative importance of each criterion determined by different stakeholders/decision‐makers into an interval weight. Afterward, a novel multicriteria decision‐making method which can address the decision‐making matrix with interval numbers was developed to prioritize industrial systems under data uncertainties. An illustrative case has been studied by the developed model, and it reveals that the developed model allows multiple stakeholders/decision‐makers to participate in the decision‐making processes and prioritize industrial systems accurately by using interval numbers. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2103–2112, 2018     

Online polymer molecular weight and conversion monitoring via calorimetric measurements in RAFT emulsion polymerization

BACKGROUND: Online measurements of key emulsion polymerization attributes, such as conversion and molar mass distribution, are unavailable. Costly offline measurements at low sampling frequencies with time delays usually lead to insurmountable challenges in real‐time product quality monitoring and process/product control. RESULTS: We developed an online calorimetric method monitoring the evolution of conversion and molecular weight in complex polymerization reactors. Our experiments were carried out in a 1 L reactor to produce polystyrene homopolymer. Monomer conversion was obtained in real time from polymerization rate, which was estimated from temperature measurements using platinum thermal transducers. The calorimetric model was validated offline for batch and semi‐batch emulsion polymerization of styrene with and without transfer agents. The conversion was validated using offline gravimetry. The molecular weights measured offline via size exclusion chromatography with multiple detectors compared well with those estimated online using the calorimetric method. CONCLUSION: We found that a semi‐batch emulsion polymerization process can be controlled online to approach living polymerization involving transfer agents. Thus our model is suitable as a ‘soft‐sensor’ for real‐time control applications. Copyright © 2009 Society of Chemical Industry     

ROBUST DECOUPLING CONTROL BASED ON EXTERNAL LOOPS

Decoupling control can substantially simplify the design of multivariable control systems. However, decouplers are usually sensitive to modeling errors, especially for the systems with relative gains being far away from unity. Hence, decoupling control is limited in practical application. In the present study, robust control of a decoupling system with external loops is developed. Design of the control system consists of two steps. The first step is to design the decoupler and the controller based on the nominal plant. The second step is to design the filters in the external loops by using a robust stability criterion. The robust control is implemented without changing the original design of the decoupler and the controller. The control system with external loops for decoupling control significantly improves the control performance. The proposed robust control enhances the applicability of decoupling control.     

A delay‐dependent robust fuzzy MPC approach for nonlinear CSTR

Based on Takagi–Sugeno (T–S) fuzzy models, a robust fuzzy model predictive control (MPC) algorithm is presented for a class of nonlinear time‐delay systems with input constraints. Delay‐dependent sufficient conditions for the robust stability of the closed‐loop system are derived, and the condition for the existence of the fuzzy model predictive controller is formulated in terms of nonlinear matrix inequality via the parallel distributed compensation (PDC) approach. By using a novel matrix transform technique, a receding optimization problem with linear matrix inequality (LMIs) constraints is constructed to design the desired controllers with an on‐line optimal receding horizon guaranteed cost. Finally, an example of continuous stirred tank reactors (CSTR) is given to demonstrate the effectiveness of the proposed results.     

TWO-WAY OR ONE-WAY DECOUPLING IN DISTILLATION?†

A systematic simulation study of dual composition control of distillation has been carried out in order to compare different decoupling strategies and contrast them with well-tuned SISO-controllers.

Two column models are studied. One describes the experimental pilot plant used by Wood and Berry (1973) and the other the experimental pilot plant investigated by Luyben and Vinante (1972).

A better control quality-compared to the control quality obtained by SISO-control-may be achieved if suitably chosen decoupling elements are introduced. The best control quality does not, however, necessarily correspond to the best decoupling. In the studied systems, where the controllers of the decoupled systems have been tuned according to the Ziegler-Nichols settings for the effective process transfer functions, there is no incentive to use two decouplers (two-way decoupling): the same or better control quality is obtained with the simpler scheme utilizing only one decoupler (one-way decoupling). Actually, for the Luyben and Vinante system SISO-control is superior to two-way decoupling, whereas one-way decoupling is superior to SISO-control.     

Interaction analysis and geometric interconnection decoupling for networks of process systems

The effects of interactions between nonlinear subprocesses on the stabilizability of plantwide systems via the concept of dissipative systems are studied. Conditions for which controlled variables of each interconnected subprocess can be driven to and maintained at their desired values are established through the application of interconnection decoupling techniques. The resulting decoupling feedback law encodes the interaction effects between subprocesses and determines the required information structure for achieving desired control performance using distributed control laws. The proposed constructive approach leads to new criteria for the selection of manipulated and controlled variables that guarantee plantwide stability. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2795–2809, 2013     

Integrating data‐based modeling and nonlinear control tools for batch process control

A data‐based multimodel approach is developed in this work for modeling batch systems in which multiple local linear models are identified using latent variable regression and combined using an appropriate weighting function that arises from fuzzy c‐means clustering. The resulting model is used to generate empirical reverse‐time reachability regions (RTRRs) (defined as the set of states from where the data‐based model can be driven inside a desired end‐point neighborhood of the system), which are subsequently incorporated in a predictive control design. Simulation results of a fed‐batch reactor system under proportional‐integral (PI) control and the proposed RTRR‐based design demonstrate the superior performance of the RTRR‐based design in both a fault‐free and faulty environment. The data‐based modeling methodology is then applied on a nylon‐6,6 batch polymerization process to design a trajectory tracking predictive controller. Closed‐loop simulation results illustrate the superior tracking performance of the proposed predictive controller over PI control. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Vinyl Sulfonates: A Click Function for Coupling‐and‐Decoupling Chemistry and their Applications

The term coupling‐and‐decoupling (CAD) chemistry refers to applications in which efficient bond formation and subsequent cleavage between two moieties is required. Within this context, the scope of the vinyl sulfonate (VSO) group as an efficient tool for CAD chemistry is reported. The coupling step relies on the click features of the Michael‐type addition of diverse nucleophiles to vinyl sulfonates as a valuable methodology. The feasibility of this strategy has been proved by the high yields obtained in mild conditions with model VSO derivatives. Cleavage of the resulting sulfonate adducts either through nucleophilic substitution with different nucleophiles (for alkyl VSO groups) or through hydrolysis (for both alkyl and aryl VSO) are successful strategies for the decoupling step, the former being the most promising, as the reaction proceeds under milder conditions with thiol nucleophiles. Moreover, the click VSO coupling chemistry proves to be orthogonal with the click CuAAC reaction, which enables the VSO‐CAD methodology for the preparation of hetero‐bifunctional clickable and cleavable linkers for double click modular strategies. The potential of the VSO‐CAD chemistry is demonstrated in two biologically relevant examples: the decoupling of sulfonates with glutathione (GSH) under conditions compatible with those of living systems; and the synthesis of homo‐ and heterogeneous multivalent glycosylated systems from 1‐thio and 1‐azido or 1‐azidoethyl sugar derivatives and bis‐vinyl sulfonates (homo systems) or alkynyl‐VSO bifunctional clickable‐cleavable linkers (hetero systems). As proof of concept, the cleavable character of these multivalent systems was demonstrated by using one of them as a reversible linker for the non‐covalent assembling and chemical decoupling of two model lectins.

     

DECOUPLER DESIGN AND CONTROL SYSTEM TUNING BY INA FOR DISTILLATION COMPOSITION CONTROL†

Six distillation column models have been used in studies of the usefulness of the Inverse Nyquist Array (INA) method for design of dual composition control for distillation. Five of the column models are experimental, one describing an industrial column, the others four pilot plants. One column model is obtained by modeling from first principles.

The control strategies investigated and compared are multiloop SISO, 2-way decoupling and l-way decoupling. The control variables are the standard ones, i.e. reflux flow and boilup.

In most of the cases INA has been found to be a useful tool for design of the loops and for comparison of the different control approaches. INA has also been found to be useful for simultaneous tuning of the decouplers and the feedback controllers.

For 2-way decoupling the criterion to minimize interaction at the critical frequency for each primary feedback loop has been found useful in most, but not all, cases. The scheme can be designed by pure gains in the decouplers and there is no reason to introduce dynamics into the decouplers—the systems studied are already sufficiently rich in dynamics.

INA has been found to be a suitable vehicle for the choice between the two possible l-way decoupling schemes.

With the design approach taken, l-way decoupling has been found to provide considerably better control quality than 2-way decoupling in two of the six systems, the differences in control quality being small in the other cases.

The effect of model mismatching on the results, caused e.g. by process nonlinearities, is discussed and simple rules to decrease the parameter sensitivity for 2-way decoupling schemes are given. When model mismatching is considered, the advantage of l-way decoupling over 2-way decoupling seems to increase.

One deficiency of the INA design as used in this paper is that it does not single out the design which is to be preferred on the grounds of robustness.     

RECURRENT NEURAL NETWORKS IN DECOUPLING CONTROL OF MULTIVARIABLE NONLINEAR SYSTEMS

In this work we focus on the synergy between modeling with RNNs, and nonlinear controller design for decoupling control. The thesis of the paper is that recurrent neural networks (RNNs) can be conveniently used in an integrated black-box modeling and controller design methodology for decoupling control of multivariable nonlinear systems. A simulation example on a multivariable continuous-stirred-tank-reactor (CSTR) is provided to elucidate related issues. The effects of modeling uncertainty and state reconstruction on decoupling performance are specifically discussed.     

Dynamic risk analysis using alarm databases to improve process safety and product quality: Part II—Bayesian analysis

Part II presents step (iii) of the dynamic risk analysis methodology; that is, a novel Bayesian analysis method that utilizes near‐misses from distributed control system (DCS) and emergency shutdown (ESD) system databases—to calculate the failure probabilities of safety, quality, and operability systems (SQOSs) and probabilities of occurrence of incidents. It accounts for the interdependences among the SQOSs using copulas, which occur because of the nonlinear relationships between the variables and behavior‐based factors involving human operators. Two types of copula functions, multivariate normal and Cuadras–Augé copula, are used. To perform Bayesian simulation, the random‐walk, multiple‐block, Metropolis–Hastings algorithm is used. The benefits of copulas in sharing information when data are limited, especially in the cases of rare events such as failures of override controllers, and automatic and manual ESD systems, are presented. In addition, product‐quality data complement safety data to enrich near‐miss information and to yield more reliable results. Step (iii) is applied to a fluidized‐catalytic‐cracking unit (FCCU) to show its performance. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Planning and scheduling of flexible process networks under uncertainty with stochastic inventory: MINLP models and algorithm

The tactical planning and scheduling of chemical process networks consisting of both dedicated and flexible processes under demand and supply uncertainty is addressed. To integrate the stochastic inventory control decisions with the production planning and scheduling, a mixed‐integer nonlinear programming (MINLP) model is proposed that captures the stochastic nature of the demand variations and supply delays using the guaranteed‐service approach. The model takes into account multiple tradeoffs and simultaneously determines the optimal selection of production schemes, purchase amounts of raw materials, sales of final products, production levels of processes, detailed cyclic production schedules for flexible processes, and working inventory and safety stock levels of all chemicals involved in the process network. To globally optimize the resulting nonconvex MINLP problems with modest computational times, the model properties are exploited and a tailored branch‐and‐refine algorithm based on the successive piecewise linear approximation is proposed. To handle the degeneracy of alternative optima in assignment configurations of production scheduling, three symmetry breaking cuts are further developed to accelerate the solution process. The application of the model and the performance of the proposed algorithm are illustrated through three examples with up to 25 chemicals and 16 processes including at most 8 production schemes for each flexible process. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1511–1532, 2013