Pilot-scale Binary Distillation Column Research Articles

Teaching Classical and Advanced Control of Binary Distillation Column

This paper deals with education of graduate and undergraduate students in the field of classical and advanced controllers. A pilot scale binary distillation column is utilized to serve the teaching purposes. The emphasis of the paper is to practically teach identification, and widely used classical and advanced control schemes, such as Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC), over a plant. MATLAB/Simulink-based Human Machine Interface (HMI) is used by students to implement these control strategies. Performance results of both controllers subject to reference tracking, constraints and disturbance handling are presented. Developed directives and control schemes can be used for effective process control education of chemical/process engineers.

Neural Network based Soft Sensor for Inferential Control of a Binary Distillation Column

Distillation column is one of the most widely used unit operations in process industries, and the operation and control of this unit was always a challenging task due to the complexity of operational parameters interactions and the lack of a real time measurement of the products composition. Time delay involved with GC measurement of the product composition prevented the utilization of effective closed-loop control. To overcome this problem an inferential control scheme based on soft sensor estimation of the products composition is now widely adopted. In this work, a neural network based soft sensor is developed to be used in an inferential control scheme of a pilot-scale binary distillation column. Data were collected from the distillation column under different operating conditions with forced disturbances in number of operation variables. The collected data is pre-processed for the removal of outliers, normalized and segmented into training and test data subsets. The most important process variables in the model and their lags have been chosen systematically. Different neural networks have been trained using the preprocessed data. Trial and error method is used to find the optimum number of neuron in the hidden layer for each network. The performance of different networks is discussed. The developed soft sensor can be utilized in an inferential control scheme on the distillation column.

Supervisory multiple regime control

Abstract The objective of this work is to identify a control algorithm that is capable of handling nonlinear behaviour (operating point dependent) witnessed in most industrial processes. To this end, the proposed solution is that of a supervisory multiple model control scheme, SMMC. This work demonstrates that the multiple model methodology can be recast into a Supervisory approach, whereby the supervisor is employed as a selector. This selector (supervisor) identifies the appropriate local-controller from a fixed family set. Unlike other supervisory techniques a multiple model observer (MMO) is proposed for the selection mechanism. Switching between local-controllers is accomplished bumplessly through a multiple model bumpless transfer scheme. Consequently, producing a continuous control signal as the process transverses between different operating regimes. The key issue in this application is the unique interaction between the local-controllers and the supervisor. This interaction is necessary to ensure global stability is maintained at all times, especially during switching. In short, the SMMC scheme enables the implementation of linear control theory, which is well accepted in industry, to standard nonlinear processes. The SMMC approach warrants the control design to extend beyond normal operating conditions that breakdown when standard linear control techniques are applied. The above notion is applied to a pilot-scale binary distillation column. In this example the column's distinct operating points describe the nonlinear behaviour. The results illustrate that as the distillation column shifted between different operating points the SMMC self-regulates accordingly. This self-regulation ensures that global stability and performance are maintained at an optimum. The entire SMMC design was implemented within a PC Windows-NT environment that was interfaced to an industrial DCS system.

Minimum Deviation Control of a Binary Distillation Column Using Reduced Order Dynamic Matrix.

Minimum deviation control, an advanced technique using a reduced-order dynamic matrix, has been proposed and its performance is investigated through simulation and experiment. Performances of the existing dynamic matrix control technique and the proposed technique are compared in a simulation study by changing set points of top and bottom temperatures and feed flow rate. A control experiment is also conducted with a pilot-scale binary distillation column using the proposed control method.From the simulation result, it is found that the new technique is better than dynamic matrix control in overall performance. The experimental test also shows that the proposed control strategy is suitable for practical application and is better than the existing technique.

Globally convergent multivariable adaptive decoupling controller and its application to a binary distillation column

This paper describes a new multivariable adaptive decoupling controller combining a decoupling compensator with a generalized minimum variance technique. The controller can completely decouple closed-loop systems both dynamically and in the steady state. It can control an unstable and/or non-minimum phase system and it can control the process with an arbitrary time delay structure. The proof of global convergence for the algorithm is also given. Successful experimental results of the adaptive decoupling control for a pilot-scale binary distillation column demonstrate the effectiveness of the proposed adaptive decoupling algorithm.

Multivariable and multirate self-tuning control: a distillation column case study

This paper is concerned with the evaluation of self-tuning control algorithms for the multivariable control of distillation columns. Effective distillation control remains a key concern of industry because of the high energy consumption associated with distillation processes. The results presented here are taken from a continuing programme of investigations aimed at improving distillation column control performance. The performance of several multi-variable self-tuning strategies is evaluated by online application to a pilot-scale binary distillation column which is subjected to disturbances of ±25% step changes in feed flow rate. The performance of the multivariable algorithms is compared with that achieved using conventional digital multiloop PI/PID control. The results have demonstrated that use of multivariable self-tuning control, in particular multirate-multivariable self-tuning control, can provide better regulatory performance for feed flow rate disturbances than can be achieved using a multiloop PI/PID strategy.

Multivariable adaptive predictive control of a binary distillation column

This paper presents a tutorial review of an adaptive predictive control system (APCS). Special emphasis is given to the key issues involved in the practical application of APCS to real processes. These practical issues are illustrated by actual application of SISO and MIMO control of a pilot scale binary distillation column. The experimental evaluation of this method reveals the simplicity of the adaptive algorithm and its excellent performance in an industrial type environment. The experimental results easily outperformed well-tuned classical PID controllers. A brief review of other applications of adaptive control to chemical processes is also included in this paper.

Robust multivariable control of a binary distillation column

Davison's robust, multivariable, feedback-feedforward controller (DAVISON, E.J.: IEEE Trans., 1976, AC-21, pp. 35–47) is applied to the control of a pilot-scale binary distillation column, as a master loop in a cascaded configuration. The inner or slave loop consists of the original plant with multiloop proportional control. This cascaded configuration is shown to have a number of advantages for practical implementation. The performance of the controller is evaluated by experimental application to a computer-controlled distillation column. The robust controller gives improved control performance compared to that achieved using conventional multiloop proportional plus integral plus derivative (PID) control.

Terminal composition control of a binary distillation column

Terminal composition control of a pilot scale binary distillation column operated under the control of an IBM 1800 digital computer has been studied for disturbances in feed flow rate. Conventional two point control, whereby the overhead composition is controlled by reflux flow rate and bottom composition by means of steam rate, was demonstrated to be unsatisfactory. Two alternate control systems, namely a noninteracting control system and a ratio control system were evaluated. The noninteracting control system was constructed from a transfer function representation of the distillation column dynamics, determined from a series of on-line pulse tests. The results show that a very significant improvement in the control of both compositions is achieved by using noninteracting control or the ratio control scheme suggested by Rijnsdorp compared to the behavior that results using conventional two point feedback control. However, the control performance obtained using noninteracting control was only marginally better than that using the ratio control scheme.