The influence of the process characteristics on the role of the human operator in process control

Rational design of a process control system using an on-line computer requires a definition of the total control task and an allocation of function between the human operator and the machine. Both the nature of the total task and its subdivision depend very much on the process characteristics, which vary widely between different processes. The functions normally performed by a process control computer are described and those consequently left to the operator are defined. The wide variety of the operator's functions and the implications of this for studies of the operator's performance and for the design of the man-machine interface are emphasised. This article was originally presented as a paper at a symposium on Data Reduction, Communication and Presentation for Process Operation organised by the Institute of Measurement and Control at the University of Sussex, Brighton.     

The effects of heuristic rule training on operator performance in a simulated process control environment

In complex work environments, the occurrence of novel system states represents a particular challenge for the design of training. This article is concerned with the use of heuristic rules to prepare operators for the management of unfamiliar fault states. An experiment was carried out to examine the effects of heuristic rule training on operator performance and system management behaviour. Thirty-nine trainee operators from the chemical industry took part in the study. They were trained for 4 h on a PC-based simulation of a process control task. Operators in the experimental group received training on heuristic rules while operators in the control group did not. One week later the operators participated in a 70-min testing session. While the results showed that heuristic rules training led to better diagnostic performance, it was also associated with increased operator fatigue and impaired secondary task performance. The implications of the findings for using heuristic rule training are discussed.     

The effects of heuristic rule training on operator performance in a simulated process control environment

In complex work environments, the occurrence of novel system states represents a particular challenge for the design of training. This article is concerned with the use of heuristic rules to prepare operators for the management of unfamiliar fault states. An experiment was carried out to examine the effects of heuristic rule training on operator performance and system management behaviour. Thirty-nine trainee operators from the chemical industry took part in the study. They were trained for 4 h on a PC-based simulation of a process control task. Operators in the experimental group received training on heuristic rules while operators in the control group did not. One week later the operators participated in a 70-min testing session. While the results showed that heuristic rules training led to better diagnostic performance, it was also associated with increased operator fatigue and impaired secondary task performance. The implications of the findings for using heuristic rule training are discussed.     

Improving the performance of predictive process modeling for large datasets

Advances in Geographical Information Systems (GIS) and Global Positioning Systems (GPS) enable accurate geocoding of locations where scientific data are collected. This has encouraged collection of large spatial datasets in many fields and has generated considerable interest in statistical modeling for location-referenced spatial data. The setting where the number of locations yielding observations is too large to fit the desired hierarchical spatial random effects models using Markov chain Monte Carlo methods is considered. This problem is exacerbated in spatial-temporal and multivariate settings where many observations occur at each location. The recently proposed predictive process, motivated by kriging ideas, aims to maintain the richness of desired hierarchical spatial modeling specifications in the presence of large datasets. A shortcoming of the original formulation of the predictive process is that it induces a positive bias in the non-spatial error term of the models. A modified predictive process is proposed to address this problem. The predictive process approach is knot-based leading to questions regarding knot design. An algorithm is designed to achieve approximately optimal spatial placement of knots. Detailed illustrations of the modified predictive process using multivariate spatial regression with both a simulated and a real dataset are offered.     

Stochastic model predictive control: Insights and performance comparisons for linear systems

In this paper, we define several instances of model predictive control (MPC) for linear systems, including both deterministic and stochastic formulations. We show by explicit computation of the associated control laws that, under certain conditions, different formulations lead to identical results. This paper provides insights into the performance of stochastic MPC. Amongst other things, it shows that stochastic MPC and traditional MPC can give identical results in special cases. In cases where the solutions are different, we show that the explicit formulation of the problem can give insight into the performance gap.     

On the finite time performance of model predictive control

This paper addresses the finite time performance of model predictive control (MPC) for linear-time-invariant (LTI) systems without constraints. The performance of MPC is compared with that of finite horizon optimal control to find out how well model predictive control can perform relative to the optimal performance with the same or different horizons. By exploring the properties of the Riccati difference equation (RDE), an upper and a lower bound of the ratio between the finite time performance of MPC and finite horizon optimal cost are obtained. It is possible to extend the obtained results to more complicated systems such as nonlinear dynamic systems with constraints with appropriate generalizations. Simulation example supports our results.     

Multi-objective nonlinear predictive control of process systems: A dual-mode tracking control approach

There typically exist different and often conflicting control objectives, e.g., reference tracking, robustness and economic performance, in many chemical processes. The current work considers the multi-objective control problems of continuous-time nonlinear systems subject to state and input constraints and multiple conflicting objectives. We propose a new multi-objective nonlinear model predictive control (NMPC) design within the dual-mode paradigm, which guarantees stability and constraint satisfaction. The notions of utopia point and compromise solution are used to reconcile the confliction of the multiple objectives. The designed controller minimizes the distance of its cost vector to a vector of independently minimized objectives, i.e., the steady-state utopia point. Recursive feasibility is established via a particular terminal region formulation while stabilizing the closed-loop system to the compromise solution via the dual-mode control principle. In order to derive the terminal region as large as possible, a terminal control law with free-parameters is constructed by using the control Lyapunov functions (CLFs) technique. Two examples of multi-objective control of a CSTR and a free-radical polymerization process are used to illustrate the effectiveness of the new multi-objective NMPC and to compare their performance.     

Efficient algorithms for trading off feasibility and performance in predictive control

This article introduces two simple modifications to conventional predictive control algorithms with the aim of enlarging the feasible regions without increasing computational complexity. It is shown that despite the relatively large feasibility gains, the loss in performance may be far smaller than expected and thus the algorithms give mechanisms for achieving low computational loads with good feasibility and good performance while using a simple algorithm set-up. Both algorithms have standard convergence and feasibility guarantees.     

积分过程预测控制的稳态分析及反馈校正

多变量积分过程的控制,一直是预测控制理论研究与应用过程中的难点问题.现有的研究成果更多的关注于算法的实现上,而很少关注理论依据.本文从积分过程的控制输入平衡关系出发,利用线性代数方程组解的相容性原理,得到了一个适用于判断多变量积分过程设定点是否可达的判据,可以作为算法能否实现多变量积分过程无静差控制的理论依据.同时分析了传统算法无法在存在模型失配情况下对积分过程进行优化与控制的原因,利用补偿因子重新设计反馈校正环节,使改进后的算法能够实现存在模型失配过程的优化与控制,并通过仿真验证了本文提出的结论.     

Modelling programming performance: Beyond the influence of learner characteristics

In the 21st century, the ubiquitous nature of technology today is evident and to a large extent, most of us benefit from the modern convenience brought about by technology. Yet to be technology literate, it is argued that learning to program still plays an important role. One area of research in programming concerns the identification of predictors of programming success. Previous studies have identified a number of predictors. This study examined the effect of a combination of predictors (gender, learning styles, mental models, prior composite academic ability, and medium of instruction) on programming performance. Data were collected anonymously through a website from 131 secondary school students in Hong Kong who opted for computer programming in the Computer and Information Technology curriculum. Partial Least Squares (PLS) modelling was used to test a hypothesized theoretical structural model. All of the five aforementioned variables were either direct or indirect predictors of programming performance and the antecedents accounted for 43.6% of the variance in programming performance. While this study shows the influence of learner characteristics such as gender, learning styles, and mental models on programming performance, it highlights the effect that prior composite academic ability and medium of instruction exert on learning outcomes, which is uncommon among studies of similar purpose. These findings have significant implications for policy makers and educators alike.     

A strategy for predictive control of a mixed continuous batch process

This paper deals with the on-line control of mixed processes composed of continuous and batch units in which some continuous variables must be kept as close as possible at prescribed values and the batch units must be scheduled in order to avoid bottlenecks in production. In particular, a pilot plant is considered with product recycle involving simultaneous continuous and scheduling decisions. The problem is of hybrid nature and can be formulated as a hybrid predictive control model in terms of integer and continuous variables. However, as the system must be controlled in real-time, an alternative formulation is proposed based on a hierarchical view of the problem and the use of flow patterns and time of occurrence of events in the batch units. The paper describes the process, the control problem formulation, and provides results of some test in the pilot plant.     

Grey box modelling for model predictive control of a heating process

This paper presents a rational modelling procedure of a pilot heating process by using the grey box modelling method. A simplified nonlinear continuous model, based on the conservation of energy, is formed and unknown parameters of the model are estimated by using measured data from an experiment with the process. The model is expanded with a wider model structure by testing formulated hypothesis about the process. The model is also expanded in an engineering way by considering the shape of the residuals. During the model expansion the Likelihood ratio test is applied for falsification tests. The study uses the continuous model for both estimating the states of the process and controlling the system. A continuous-discrete extended Kalman filter estimates the model states and time varying disturbances. The model predictive controller is based on the continuous process model, but the optimisation of the control performance index is made at discrete sampling instances. The control law compensates for changing temperature references and compensate for varying load situations. Besides using the model for control purposes the case demonstrate the possibility of using the grey box modelling technique to estimate physical process parameters such as the thermal diffusivity of the process.     

Optimal linear combination of Poisson variables for multivariate statistical process control

In this paper we analyze the monitoring of p Poisson quality characteristics simultaneously, developing a new multivariate control chart based on the linear combination of the Poisson variables, the LCP control chart. The optimization of the coefficients of this linear combination (and control limit) for minimizing the out-of-control ARL is constrained by the desired in-control ARL. In order to facilitate the use of this new control chart the optimization is carried out employing user-friendly Windows© software, which also makes a comparison of performance between this chart and other schemes based on monitoring a set of Poisson variables; namely a control chart on the sum of the variables (MP chart), a control chart on their maximum (MX chart) and an optimized set of univariate Poisson charts (Multiple scheme). The LCP control chart shows very good performance. First, the desired in-control ARL (ARL₀) is perfectly matched because the linear combination of Poisson variables is not constrained to integer values, which is an advantage over the rest of charts, which cannot in general match the required ARL₀ value. Second, in the vast majority of cases this scheme signals process shifts faster than the rest of the charts.     

Iterative learning control-based batch process control technique for integrated control of end product properties and transient profiles of process variables

Importance of batch processes has grown recently with the increasing economic competition that has pushed the manufacturing industries to pursue small quantity production of diverse high value-added products. Accordingly, systems engineering research on advanced control and optimization of batch processes has proliferated. In this paper, we examine the potentials of ‘iterative learning control (ILC)’ as a framework for industrial batch process control and optimization. First, various ILC rules are reviewed to provide a historical perspective. Next it is shown how the concept of ILC can be fused with model predictive control (MPC) to build an integrated end product and transient profile control technique for industrial chemical batch processes. Possible extensions and modifications of the technique are also presented along with some numerical illustrations. Finally, other related techniques are introduced to note the similarities and contemplate the opportunities for synergistic integration with the current ILC framework.     

提高分布式多预测控制器协调及稳定性的鲁棒区域控制

大型复杂化工程过程控制中,常规的集中式控制方式不利于实时性、灵活性和容错控制。而采用多预测控制器协调的分布式控制是解决这一问题的有效方法:。针对子系统间的动态耦合行为严重影响多预测控制器协调以及稳定性的问题,提出一种鲁棒区域控制策略。即在每个子系统的目标函数中加入松弛因子增加控制器间协调时的余量来达到分布式预测控制的稳定性。通过以反应器-存储器分馏器组成的过程为事例,仿真结果:表明该方法:的可行性和有效性。     

混合约束过程的多变量协调预测控制

研究了工业生产过程存在着输入、输出软硬混合约束的优化控制技术。给出了基于约束的预测控制算法，提出了用动态优先级对有硬约束的操作变量进行在线协调，当协调过程找不到满足所有约束条件的可行解时，对被控变量进行约束软化，本文采取的线性和二次型相结合的惩罚函数对预测时域上每个时刻的激活值进行惩罚，不仅可以保证可行解的存在，而且能使系统处于动态和稳态的优化性能。     

The use of operator factorization for linear control and estimation

The linear filtering, prediction and smoothing problems as well as the linear quadratic control problems can very generally be formulated as operator equations using basic linear algebra.The equations are of Fredholm type II, and they are difficult to solve directly.It is shown how the operator can be factorized into two Volterra operators using a matrix Riccati equation. Recursive solution of these triangular operator equations is then obtained by two initial value differential equations.The proofs of smoothing and optimal control under known disturbances are in this way especially clear and simple.     

Model predictive control of an activated sludge process: A case study

This paper details a case study application of model predictive control for a wastewater treatment process in Lancaster, North England. The control system was implemented in real time, together with a plant monitoring system for the purposes of process supervision. Following implementation, the model predictive control system provided significant benefits compared with the previously applied control system. These benefits included a reduction of over 25% in power usage and a similar increase in plant efficiency. The system therefore represents a useful tool in helping the water industry to reach its goal of significantly reducing its carbon footprint.     

The influence of local networking technology on process control interfaces

The rapidly evolving technology and standardization of local area data networks will have a significant influence on the interfaces adopted for process control applications. In this paper, some of the current standardization efforts in the local network field are examined and the possible impact on process control interfaces is considered.     

Designing of non-fragile robust model predictive control for constrained uncertain systems and its application in process control

A non-fragile robust model predictive control (RMPC) is designed in the uncertain systems under bounded control signals. To this aim, a class of the nonlinear systems with additive uncertainty is considered in its general form. The RMPC synthesis could lead to the proper selection of the controller’s gains. Thus, the non-fragile RMPC design is translated into a minimization problem subjected to some constraints in terms of linear matrix inequality (LMI). Hence, the controller’s gains are computed by solving such a minimization problem. In some numerical examples, the suggested non-fragile RMPC is compared with the other methods. The simulation results demonstrate the effectiveness of the proposed RMPC in comparison with similar techniques.