Control Systems For Distillation Columns Research Articles

Saving the environment by automation of crude oil distillation in a rectification column

The article shows the results of the development of an automated control system for the distillation of oil in a distillation column, which makes it possible to save the environment. In the theoretical part, the process of distillation of crude oil in a distillation column is described and its technological scheme is given for atmospheric distillation with a single evaporation of oil. In the calculation part, the heat and material balance of mass transfer in the rectification column has been analyzed. In the practical part, the problems of installing process control instruments, improving the emergency shutdown system reliability, expanding the automatic and automated monitoring and control functions, and improving the process control and facility performance analysis quality have been solved. The authors used the Trace Mode 6 software package to develop the SCADA of the distillation column control system. In conclusion, it was noted that the development and implementation of an automated control system for the oil distillation process provides an increase in plant productivity, as well as the quality and reliability of the process, which in turn has a positive effect on environmental conservation.

Design of Robust Decentralized Control Systems for Distillation Columns

This paper is devoted to the development of a decentralized control system design method with improved robustness, applicable to distillation columns control problem. The aim is attained by solving the following tasks: selection of representative models of columns; development of an effective transfer function identification algorithm; development of a decentralized controller design algorithm, which takes into account the cross-links impact; verification of the developed method for the selected models of columns. The most important result is the developed design method, which includes two algorithms. The first algorithm can be used to identify simplified models of the effective transfer functions. The second algorithm makes it possible to design a decentralized control system based on the ratio between the diagonals of the original model and effective transfer functions. Modified PI controller is used. The achievement of the distillation column control system robustness to the synchronous technologically conditioned deviation of the model parameters by up to three hundred percent of the nominal value is the paper significant result. The digital implementation of the designed controllers is simple. The analysis of the literature shows that none of the previously proposed methods for designing decentralized automatic control systems solved such a problem. The efficiency of the developed algorithms is illustrated using the known models of dynamics of distillation columns. The analysis of limitations of the method as well as its significant differences to the Biggest Log Tuning and H∞ loop shaping methods is presented.

Development of Resilient Control Systems for Technological Plants

The aims of the research are increasing resiliency of industrial process control systems, the de-velopment of methods of creating resilient industrial process control systems and creating a resil-ient control system of the distillation column for the production of food alcohol. The set of ob-jectives were achieved without the use of hardware backup, but using the developed algorithms which were based on the use of the model of dynamics of technological plants in the software of the control system and modern theory of multivariable automatic control systems. The opti-mal linear-quadratic controller with disturbances model and a state observer was used as a full controller. The most important result was improvement of the survivability of industrial process control systems using the developed algorithm. Also, the resilient distillation column control sys-tem was developed. For the further researches significant results are formulas which allow de-fining new mode values of controls in a distillation column at failure event of corresponding control output channels and formulas of logic algebra for a choice of optimum configuration of control system of a distillation column of food spirit industry. The basis of the developed tech-nique was reconfiguration of the control system with inclusion, instead of the failed, additional devices, which were not being used in a current configuration, but were carried out the similar function taking into account dynamics coupling of a technological plant. The significance of these results is that they can be applied to the development of cyber-production systems that do not require permanent maintenance by personnel.

Design of multi-loop control systems for distillation columns: review of past and recent mathematical tools

Abstract Design of a control structure in distillation columns involves selecting proper sets of manipulated and controlled variables (often including tray temperatures for inferential control of product compositions) and one-to-one pairing between the two sets. In this paper, various mathematical tools for achieving this goal are reviewed. First, traditional methods such as Singular Value Decomposition (SVD) and Relative Gain Array (RGA) that build upon a simplified steady-state or dynamic model of the column are explored. The role of optimization in systematizing the control design procedures is also investigated. Then, more recent inferential control techniques that rely on statistical methods such as Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), and other machine learning techniques such as Artificial Neural Networks (ANN) and Support Vector Machine Regression (SVMR) are discussed extensively. The discussions include newer distillation technologies with complex configurations such as dividing-wall columns. Finally, the use of process simulators in aiding the control structure design of distillation columns is surveyed.

Automatic Control on Batch and Continuous Distillation Columns

Distillation is fundamental in Chemical Engineering. It is a highly complex and non-linear process. Therefore, developing intelligent control systems for distillation columns is challenging. These control techniques are based on previous knowledge and intuitive rules. In this work, several control strategies, such as IMC, Gain Scheduling, Expert, Fuzzy (Mamdani and Sugeno) and Neural-Network Control are applied to control a simulated distillation column for batch and continuous processes, and their performance is compared with a traditional PI controller. The controlled variable was the distillate molar fraction using as manipulated variable the reflux ratio. All control strategies were tested with respect set-point changes in two scenarios: without and with disturbances. The best control strategy was the Neural-Network, using a NARMA-L2 controller. This control has a good disturbance rejection and a fast set-point tracking with a smooth control action.

Controlling the Oil Rectification Process in a Primary Oil Refining Unit Using a Dynamic Model

The composition of Devonian oil has been simulated using an experimental true-boiling-point (TBP) curve, and the physicochemical properties (density, molecular weight, enthalpy, etc.) of the feedstock arriving at the plant have been calculated. The optimization criterion proposed is the calculation of the yield of light fractions. The optimization problem with constraints on variables (initial and final boiling points of light fractions) and constraints in the form of inequalities (overlapping neighboring fractions) using the function fmincon has been solved and implemented in the programming environment Matlab. A nonlinear dynamic model of the oil rectification process has been developed and implemented. The Matlab ode 15s solver and the explicit second-order Euler method have been used to develop the model. The model in the form of a system of algebraic–differential equations has been applied to the distillation column control system of an atmospheric pipe still at a small oil refinery. In the case of controlling according to the model, the column profile temperatures are maintained at optimal (preset) values and the quality of the petroleum products is also maintained. A comparison has been made between two operating modes of the distillation column in the processing unit. The results of the study suggest the following. The method of controlling in accordance with the model provides an increase in the recovery of light products through the use of optimization and the dynamic model, shortening of the overlap intervals of the temperatures of the gasoline and diesel fractions, and a decrease in deviations of the boiling points of petroleum products.

The Dos and Don’ts of Distillation Column Control

The paper discusses distillation column control within the general framework of plantwide control. In addition, it aims at providing simple recommendations to assist the engineer in designing control systems for distillation columns. The standard LV-configuration for level control combined with a fast temperature loop is recommended for most columns.

An economic measure for comparing dynamic robustness

Abstract This paper develops a procedure that enables an engineer to present a more compelling argument for process control investment. The procedure calculates an economic measure of the dynamic robustness of plant control systems in the face of plant-based and external uncertainties. It involves: (a) characterising each type of plant-based or external uncertainty that affects operations and profitability; (b) computer-simulating a plant-control system model to show how it responds to different combinations of uncertainties; (c) calculating an economic index of control quality for each combination of uncertainties; and (d) plotting a frequency distribution of the economic indices. The tighter the distribution, the more robust the plant-control system. The procedure is demonstrated by comparing the robustness of a distillation column control system, with and without decoupling. This procedure has three advantages. Firstly, it presents a more realistic picture of robustness because it characterises uncertainty by a range of probable values, rather than a single value. Secondly, the procedure can be applied to SISO, MIMO, linear and nonlinear systems alike without requiring model simplification. Thirdly, it presents results in economic terms using a graphical format that is more meaningful to business managers than an abstract mathematical quantity, such as a structured singular value.

Robust control design using eigenstructure assignment and multi-objective optimization

This paper develops a new approach to multiple objective optimization design for robust multivariable control systems, based on eigenstructure assignment and genetic algorithms. It considers various performance indices (or cost functions) in the objectives, which are individual eigenvalue sensitivity functions, and the sensitivity and the complementary sensitivity functions in the frequency domain, instead of a single performance index for a control system. Based on these performance indices, the robustness criteria are expressed by a set of inequalities. The paper will make full use of the freedom provided by eigenstructure assignment to find a controller to satisfy the robustness criteria. A numerical algorithm for multi-objective optimization using genetic algorithm approaches is developed and applied to the simulation of a distillation column control system design

Nonlinear Observers for Distillation Columns

A nonlinear observer, capable of accurate on-line-calculation of the product and feed composition of a binary distillation column is presented. The observer is based on a nonlinear reduced-order process model. In simulation studies the observer shows better performance, better robustness and easier adaption to real plant dynamics than an observer of conventional design. Experiments prove the high accuracy and flexibilty of the new observer under real-life conditions. The observer was used to test advanced feedforward/feedback control systems for distillation columns in experiments.

Decision supporting system for the design of control systems for distillation columns

Distillation is regarded as the most important thermal separation process in chemical industry. The adjustment of desired product qualities lays special emphasis on the control system, especially in view of minimum specific energy consumption. The standard separation process does not exist. For this reason there is no general solution for the automation of distillation columns at the moment. The great variety of distillation columns and operating conditions requires the design of new, and the review of existing control systems. The presented decision supporting system shall help process and control engineers in screening alternative control systems at an early stage of the design process. The system is part of a cooperation project of the companies Henkel KGaA, Düsseldorf, Schmidding Werke GmbH&Co, Köln, and the University of Siegen.

Distillation column control design using steady state models: Usefulness and limitations

Steady-state models continue to be powerful and efficient tools for designing control systems for distillation columns. This paper presents a control design procedure and three example applications of this technique to actual columns. One of the columns is now operating and the control performance is presented.

A Neural Net to Learn the Design of Distillation Controls

This paper presents the application of a neural net to learn the design of control configurations for distillation columns by example. In earlier papers, the authors presented an expert system to perform the design of control systems for distillation columns. During construction of the expert system, knowledge extraction from the expert proved to be the most time consuming and difficult part of the system development. The application of neural nets to the problem as presented in this paper is shown to avoid the difficulties associated with knowledge acquisition.

Analysis of control‐output interactions in dynamic systems

AbstractA general theory for examining the dynamic as well as static interactions among inputs and outputs in a multivariable control system has been developed. Both state space and frequency domain approaches are used, and at steady state the interaction equations assume the same form as the relative gain array developed by Bristol. Implications of selecting input‐output pairings with regard to steady state vs. dynamic consideration are discussed. The application of the method of analysis to a distillation column control system is presented.

Steady-State Energy Conservation Aspects of Distillation Column Control System Design

Steady-State Energy Conservation Aspects of Distillation Column Control System Design

Distillation, feedforward control with intermediate feedback control trays

Feedback control systems for distillation columns are often based on controlling the temperatures of intermediate trays near the top and/or bottom of the column. This paper studies the application of feedforward control to this kind of column. In addition to adjusting manipulative variables like reflux and/or vapor boilup, the feedforward controller must also change feedback controller set points. Nonlinear steadystate and linear dynamic synthesis techniques are used to design the six feedforward controllers required. Effectiveness when applied to a nonlinear column is illustrated by digital simulation of a 20-tray binary column. Combined feedback/feedforward control performance is compared with feedback control alone.

Interaction in two-variable control systems for distillation columns—I: Theory

In general, multivariable control systems are too complex for even a qualitative description of their behaviour. However, for linear two-variable control systems with two control loops, the situation is more favourable. In this article, the influence of one loop on the other is deduced from a polar diagram. The interaction quotient of the process κ= (G 12 G 21) (G 11 G 22) in which the G's represent response functions of the process is used as a convenient measure for the degree of mutual influence. This study is focussed on control systems for continuous distillation columns and gives the theoretical background. The application to distillation columns will be treated in Part II.

Interaction in two-variable control systems for distillation columns—II: Application of theory

The theory developed in part I is applied to the following two-variable control systems for distillation columns: 1. (a) pressure control by condenser cooling, and temperature control by reboiler heating; 2. (b) overhead quality control by reflux, and bottom quality control by reboiler heating. Approximation formulae are derived for the interaction quotient κ. These are compared to results from a simulation on an analogue computer. Moreover, attention is paid to the effect of vapour flow variations on the liquid overflow, and its influence on the interaction. Finally, various ways of avoiding unfavourable interaction are discussed and compared.