Real Process Control Research Articles

Execution Monitoring of Industrial Process Controllers: An Application of Ontological Control

In previous work we have introduced and discussed the execution monitoring paradigm Ontological Control (OC). The fundamental idea of OC is that the execution of a controller for a hybrid system should be monitored and that discrepancies between expected behavior (according to some model) and real behavior should be detected and classified as being either due to external actions (disturbances) or due to violations of ontological assumptions (faulty expectations or inadequate model).In this paper we report a first set of experimental results where the OC theory has been implemented and tested on data from a real process control system: The ABB STRESSOMETER rolling mill flatness control system.

Generalized nets in neurology: an example of mathematical modelling

The purpose of this paper is to explain the basic theory of generalized nets (GNs) and their applications in the context of the differential diagnosis of neurological diseases. We define formally the concepts of a GN and transitions of a GN and also outline some remarks on their theory. The work here constructs an example which aims to trace the process of diagnosing different signs and symptoms in neurology. This will enable the interested reader to see the scope of nets in general as tools for the modelling, simulation, optimization and control of real processes.

Factors affecting the preparation of permanently end-grafted polystyrene layers

Deuterated and protonated end-functionalized polystyrenes of low and high molecular weight were grafted onto silicon substrates by solution-spincasting, followed by annealing and removal of ungrafted chains. The remaining layers were characterised by spectroscopic ellipsometry and atomic force microscopy. The effect of a variation of the initial film thickness, the annealing temperature and time on the resulting grafted layers was investigated. It was found that only the initial film thickness and deuteration had a marked effect on the layer morphology and thickness. Further improvement in reproducibility is needed to achieve real process control.

Conceptual model for process control software specification

The paper presents a method for software specification in the domain of industrial process control. As a result of ever more demanding applications, particularly in the area of PLC based control, there is an emerging need for introducing methods that include an appropriate conceptual model and define the specification process. An approach to the method development is presented, based on an architectural extension of the Ward–Mellor structured real time software engineering method. The core of the method is a conceptual model of the equipment control specification, which consists of two essential parts: the first is that of process equipment entities, and the second is the functional part. The former is further decomposed in relation to two aspects of the process equipment entities: the structural aspect and the classification aspect. In order to make the conceptual model more clear some segments of the real process control application dealing with the hydrolysis process in the production of TiO 2 are given.

On-Line Process Identification Using the Laguerre Series for Automatic Tuning of the Proportional-Integral-Derivative Controller

A new on-line identification method based on the approximation of closed-loop response is proposed to tackle the shortcomings of previous autotuning methods using a proportional controller as a test signal generator. That is, for both the overdamped and the underdamped closed-loop response in the identification step, the proposed method can easily estimate the second-order plus time delay model only with the measurements of the transient. From the orthonormal property of the Laguerre series, the closed-loop response in the identification step is approximated by a simple least-squares technique effectively, and then a high-order process transfer function is estimated from it. Finally, a simple model reduction method is used to reduce the high-order process transfer function to a second-order plus time delay model via the least-squares technique in the frequency domain. The proposed method does not need any numerical technique such as root-finding or iterative optimization. Since it uses all the measurements of transient response instead of several dominant data points such as peak or valley values, the robustness to the measurement noise is greatly enhanced. From simulation results and comparisons with previous works, we can recognize that it provides more accurate models and better control performances for various process dynamics. In experimental study, it can be noted that it is also very effective in identification and control of real processes.

Get the most from real statistical process control

Thanks to the development of the small, inexpensive, but powerful computer and a wide range of inspection and control equipment which can be linked to it, it is very easy to collect a mass of information about the end results of any production cycle. All of this information can be printed out numerically or graphically and, in itself, is quite impressive.

A Data Acquisition and Control System Framework

A software package for data acquisition and control of real time processes ts described. The system is designed as an open system built as a number of separate tasks, and with emphasis on expansion possibilities. Utility tasks (like e.g. control tasks) may be added and deleted online. Operator communication, file access and process interface is handled by special “manager” tasks, thus making utility tasks independent of their current environment. Common variables are globally accessible, and tasks may request “software interrupts” whenever a common variable has been updated The system is process independent except for the task which interfaces directly with the actual process; process configuration information is supplied by means of commands to the process manager.

Development and Validation of a Mathematical Model of Human Decisionmaking for Human-Computer Communication

As the sophistication of computer technology increases, the number of systems in which both a human and a computer take controlling roles will increase. Effective communication between the human and computer is essential to prevent redundant or interfering actions. If a method for accurately predicting the human's actions can be determined, the computer can use the predictions to complement the human's actions rather than interfere with them. This correspondence develops and empirically validates a two-stage mathematical model of human performance in a situation with real time, multiple tasks, and process control. The model is a consistent and accurate predictor of human performance, correctly predicting over 80 percent of the subjects' actions over a range of situations. A subsequent study is suggested to assess the value of this model when the computer is implemented as a parallel decisionmaker, selecting its own actions to complement rather than conflict with the human decisionmaker.

Tracking Multimodel Control of Distillation Processes

This paper presents a multimodel control technique to overcome some of the problems of control of real processes. It depends mainly on the multiple representation of the process by linear models in different operating conditions. The multimodel control consists of two parts : location, which means locate the different models with respect to the process evolution and the control synthesis which is a function of the different models. The application of this technique to a binary distillation column is given.

Adaptive Inferential Control for Chemical Processes

A SISO adaptive inferential control system is proposed to deal effectively with many real process control problems characterized by unmeasured disturbances, nonlinearities and/or time-varying process parameters, the proposed control system couples the basic structure of an inferential control (internal model control) with an on-line system identification method for estimation of the process model parameters and optimal adjustment of the controller. To obtain a stable approximation to the inverse of the process model as the controller, the use of adaptive inverse modeling based on a discrete convolution model (adaptive deconvolution model) is examined with two different process models, transfer function and convolution models. Simulation results of two numerical examples are presented to illustrate and compare the performance of the proposed adaptive inferential control system with those of conventional PID control and inferential control. The proposed control system is shown to perform well with both minimum and nonminimum phase systems in the presence of unmeasured load changes and dead time variations. In addition, Vogel-Edgar's adaptive dead time compensator and its modified controller (pole-zero placement adaptive controller) for discrete convolution models are analyzed from the viewpoint of adaptive inferential control.

An adaptive control algorithm for fed‐batch culture

The implementation of adaptive control for a fed-batch culture in order to maximize the output of product based on a self-adjusting model is discussed in the present work. Optimization methods were applied to the generalized mathematical model of a fed-batch fermentation process to determine control algorithms that could be used for on-line process control. The efficiency of the proposed adaptive algorithms was investigated by simulating a model system. The model of amylotytic enzyme fermentation that was proposed by the authors was taken from a real process. Dynamic modelling has shown that the main problem of realization is connected with the on-line identification of the adaptive model's parameters. To avoid this problem, we have introduced special limitations on the parameters' time variations that increased the convergence of the identification algorithm. The results of the investigation have shown the efficiency of the proposed adaptive algorithms, and the results of this work should be investigated for real process control.

Experience with a High Order Programming Language on the Development of the Nova Distributed Control System

Interest in the impact of high order languages (HOL) on real time and process control applications development has intensified recently, due in part to the Ada language development effort sponsored by the U.S. Department of Defense. High order languages, such as Ada, incorporate state-of-the-art features in the areas of modularity, data abstraction, separate module compilation, strong type checking of data, multi-tasking, distributed processing and exception handling. The intent of such languages is to improve programmer productivity, software maintainability, and the effective management of software development in large real time systems.This paper explores the impact of an HOL on the development of the distributed computer control system for a large laser fusion facility called Nova. Nova utilizes fifty microcomputers and four VAX-11/780’s in its distributed process control computer system.Praxis, a high level real time computer language, was designed and implemented for Nova to support the control system development activity. Praxis is similar to Ada in its design goals, has many features comparable to those found in Ada, and has had operational compilers for over two years.The purpose of this paper is to assist readers who are beginning to evaluate the importance of HOL’s in small to medium scale process control applications. It includes a summary of the major design objectives and features of these HOL’s which is followed by a description of our experiences with Praxis. The article concludes with our preliminary expectations and concerns for the use of Ada within process control applications, based upon Praxis experience.

Software for process control—A survey

A survey is presented on applications which have appeared in the open literature since 1970 of linear-quadratic-Gaussian (LQG) control theory to the control of real chemical or physical processes in industry. The shortcomings of the LQG approach are discussed together with recent advances in LQG theory and its implementation which justify renewed interest in its application to process control. Competing process control strategies are also considered and compared with LQG control.