Control Of Distillation Column Research Articles

Decentralized control of ideal ternary reactive distillation column with inert

Decentralized control of an ideal hypothetical ternary reactive distillation column with an inert component is explored. Both composition measurement based and temperature inferential control structures are designed using simple heuristic approaches (two and three-point). The three-point composition control structure is proposed for the example RD column for the first time in this work. Although stable closed loop responses are seen for the throughput changes for all, the two-point structures have failed to achieve tight control of the product purity in the bottoms or purity of inert in the distillate for the inert composition changes due to fixed reflux ratio. The performances of the three-point control structures for the inert composition changes are quite satisfactory due to the indirect manipulation of the reflux ratio. The independent manipulation of the reflux rate and distillate (instead of fixed reflux ratio policy) is an important control decision for the successful regulation of the example RD column.

Design of an Advanced Optimal Fuzzy Controller For a Binary Distillation Column

The most common control philosophy followed in the chemical process industries is the SISO system using the conventional PID controller algorithms. One drawback is relying on models for both control and design work in Chemical process industries (CPI) is that many problems are very complex and accurate models are difficult, if not impossible to obtain. To overcome these problems, it will be helpful to apply techniques that use human judgment and experience rather than precise mathematical models, which in the major cases deduced from the linearization of the system and simplification hypothesis. The fuzzy logic systems are capable of handling complex, nonlinear systems using simple solutions. However, obtaining an optimal set of fuzzy membership functions is not an easy task. In this chapter a solution based on artificial intelligence is proposed to improve the control of a binary distillation column. The solution is based on the use of the advantages of both fuzzy logic and genetic algorithms. The fuzzy logic is used as a supervisory PI controller that is a simple PI controller that generally used in controlling distillation columns with parameters deduced from the fuzzy supervisor. The membership functions shape is deduced by using research algorithms based on hierarchical genetic algorithms. The results show that the Fuzzy supervisory PI controller provide an excellent tracking toward set point change.

Identification, Analysis and Implementation of Model Predictive Controller (MPC) for Binary Distillation Column

Identification, Analysis and Implementation of Model Predictive Controller (MPC) for Binary Distillation Column

Process intensification in bio-jet fuel production: Design and control of a catalytic reactive distillation column for oligomerization

Biojet-fuel represents a promising avenue for decreasing CO2 emissions within the aviation industry. Among different biojet-fuel production processes, the Alcohol-to-Jet (ATJ) process stands out as highly promising. Despite its potential, the ATJ process currently faces economic challenges due to low yields and high energy usage. This study introduces a strategy for enhancing the economic feasibility of the ATJ process through process intensification, specifically by incorporating a novel reactive distillation column in the oligomerization stage. This innovative process was evaluated and compared with its conventional counterpart. The total annual cost, eco-indicator 99, and individual risk were chosen as critical parameters for assessing the improvements in cost, environmental impact, and safety brought about by the reactive column. Additionally, control studies were conducted using both Proportional-Integral (PI) and Model Predictive Control (MPC) controllers to determine the feasibility of operating the reactive distillation column. The results suggest that the reactive distillation process offers 20 % cost savings, reduces environmental impact by 50 %, and lowers risk by 22 % when compared to the conventional process. In terms of control strategies, the study found that the catalytic column can be successfully operated using both traditional feedback control and more advanced techniques, such as model predictive control.

MODELING OF MIMO SYSTEMS WITH UNIVERSAL CONTROLLER

Abstract. The control of multi-input multi-output (MIMO) systems poses significant challenges due to the intricate dynamics and interactions between multiple input and output channels. This paper presents a simulation study on MIMO systems control using the universal control technique. The proposed method offers a versatile framework for regulating MIMO systems with varying dynamics, utilizing a single control algorithm. The underlying concept of the universal control law is to devise a set of principles or strategies that can be universally applied to any system, regardless of its specific characteristics or parameters. To achieve this, the control algorithm is designed based on the derivatives balance of the system's input-output dynamics, granting the controller adaptability to both single-input single-output (SISO) and MIMO systems. Numerical examples are provided to demonstrate the effectiveness of the proposed method, involving a MIMO system comprising four inputs in a two-coupled distillation column process and two inputs in a twin rotor setup. The challenges associated with distillation column control, such as nonlinearity, disturbances, and interaction between multiple inputs and outputs, are considered. Twin rotor systems, consisting of two interconnected rotors, pose unique control difficulties, primarily arising from the coupling between the two rotors and their nonlinear behavior. The simulation results show the capability of the proposed universal controller to achieve precise and robust control over the MIMO system. Keywords: MIMO, Control, Simulation, Distillation Column Process, Twin Rotor.

Control of an Industrial Distillation Column Using a Hybrid Model with Adaptation of the Range of Validity and an ANN‐based Soft Sensor

AbstractAdvanced control schemes such as model predictive control can be used to minimize the use of resources while guaranteeing the specified product quality. In this paper, we consider an industrial mother liquor distillation column varying flow rate and composition of the feed. There are specifications of the composition for all product streams. To address this challenging control problem, we employ a nonlinear model‐predictive controller using a hybrid model, which consists of a simple phenomenological model augmented by a data‐based component to compensate the plant‐model mismatch. The trustworthiness of the data‐based model is addressed using a domain of validity of the data‐based model, which is estimated using a one‐class support vector machine. During operation, it may turn out that the model is also reliable in a wider range, therefore, data of recently visited operating points is recorded and the domain of validity is extended if the model is sufficiently accurate. To improve the performance of the controller, an artificial neural network model is used to estimate the product composition from available measurements.

Data-driven worst case model predictive control algorithm for propylene distillation column under uncertainty of top composition

Composition soft sensors have wide application in the distillation process. In this study, considering the limitation of the prediction ability of the composition soft sensor, a data-driven worst case model predictive control of propylene distillation column is proposed to hedge against the uncertainty of top composition. Firstly, based on the compartmental method and the dynamic mechanism model, a linear state space model of the distillation column is constructed. Aiming at dealing with the uncertainty of top propylene content caused by composition soft sensor, the data-driven uncertainty set is constructed by the combination of principal component analysis and kernel density estimation based on the historical data. Then, the certainty equivalent, traditional worst case, data-driven worst case, set-point tracking and offset-free model predictive control algorithm are designed. Finally, a case study of composition control in a propylene distillation column is carried out. Compared with other strategies, the proposed algorithm ensures the quality of the product while achieving small quality surplus and low operating cost.

Reinforced temperature control of a reactive double dividing-wall distillation column

The mass and thermal coupling makes the control of the reactive double dividing-wall distillation column (R-DDWDC) an especially challenging issue with a highly interactive nature. With reference to the separation of an ideal endothermic quaternary reversible reaction with the most unfavorable ranking of relative volatilities (A + B ↔ C + D with α A > α C > α D > α B ), the operation rationality of the R-DDWDC is studied in this contribution. The four-point single temperature control system leads to great steady-state discrepancies in the compositions of products C and D and the reason stems essentially from the failure in keeping strictly the stoichiometric ratio between reactants A and B. A temperature plus temperature cascade control scheme is then employed to reinforce the stoichiometric ratio control and helps to secure a substantial abatement in the steady-state discrepancies. A temperature difference plus temperature cascade control scheme is finally synthesized and leads even to better performance than the most effective double temperature difference control scheme. These outcomes reveal not only the operation feasibility of the R-DDWDC but also the general significance of the proposed temperature difference plus temperature cascade control scheme to the inferential control of any other complicated distillation columns.

Model Predictive Control of an Industrial Distillation Column Based on a Hybrid Model: Adapting the Domain of Validity

In any model predictive control (MPC) implementation, the key factor is the quality of the employed system model in order to guarantee constraint satisfaction and to optimize the performance of the controlled system. However, it is difficult in practice to model all the phenomena of the true system which leads to plant-model mismatch. We propose to augment a simple first principles based model with a machine learning model to capture the plant dynamics which are not represented in the semi-rigorous model. The use of data-based models however can be problematic outside the region where the model was trained. In this work, we estimate the domain of validity of the data-based model using a one-class support vector machine (SVM) which is trained on a low-dimensional projection of the available training data. During the application of the controller, data of newly visited regions is collected and the domain of validity is adapted based on checking a performance criterion. The hybrid model incorporates a weighted contribution of the data-based model based on its predicted validity. We demonstrate the versatility and the benefits of the proposed adaptive MPC based on a hybrid model (AHMPC) approach via an industrial distillation column case study.

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 and Control Applied to an Extractive Distillation Column with Salt for the Production of Bioethanol

Extractive distillation with salts, unlike other dehydration technologies, is better due to the null toxicity that exists in the distillate, since salt cannot be evaporated. With this distillation technology, it is possible to obtain a high concentration of ethanol, however, there are still problems in the control of the distillation columns in the presence of disturbances. The present work deals with the simulation and control of an extractive distillation column using CaCl2 as a separating agent, for which the Aspen Dynamics® simulator is used. The measurement and control of the ethanol composition are carried out by means of temperature, in addition, four control structures are evaluated and compared. These structures are L, D, LV, and DV, which are the most common in conventional distillation, and their performance is measured by means of deterministic indicators applying changes (disturbances) of composition and the flow rate in the main feed of the column. The most relevant results of this work lead to the fact that by applying a controller, it is possible to maintain the desired purity above the international purity standards (99% ethanol) that govern biofuels.

Control of distillation columns with dual steam and hot-oil reboilers

Many distillation columns achieve process intensification by utilizing hot streams from other units in the plant to substitute for some of the utility-supplied steam used in the reboiler. This thermal coupling requires adequate differential temperature driving forces so that heat-exchanger area is not excessive. It also requires that the units are reasonably close together so that piping cost and pressure drops are not too large. There are two reboilers at the base of the column that operate in parallel with the two independent heat sources providing vapor boilup. If the external hot stream is vapor that is condensed in the one reboiler, the heat transferred is latent heat. If the external hot stream is hot liquid, the heat transferred is sensible heat, which requires much larger flowrates. Since pumping liquid is relatively inexpensive, larger flowrates and larger pressure drops are less important than in vapor streams.This paper explores the dynamic control of dual-reboiler distillation columns in which a hot liquid stream from another unit is fed to one reboiler and steam is fed as needed to the other reboiler. The key simulation implementation issue is how to dynamically model the heat transfer in the hot-oil reboiler since it is governed by log-mean differential temperature driving forces. Several alternative control structures are evaluated.

An Effective Temperature Control Method for Dividing-Wall Distillation Columns

Temperature control is widely perceived to be superior to direct composition control for the control of dividing-wall distillation columns (DWDCs) due to its advantages in dynamic characteristics. However, because of the limited estimation accuracy to the controlled product purities, the former cannot eliminate the steady-state errors in the maintained product purities as completely as the latter. In order to reduce the steady-state deviations in the maintained product purities, an effective temperature control method is proposed in the current article by means of a kind of simple but effective product quality estimator (PQE). For the proposed PQE, temperatures of three stages located in the controlled column section (TI1, TI2, and TI3) are employed as inputs, and a linear sum of these three inputted stage temperatures (α × TI1 + β × TI2 + γ × TI3) is given as output. A genetic algorithm with an elitist preservation strategy is used to optimize the locations of the three stage temperatures and the values of α, β, and γ to ensure the estimation accuracy of the PQE. Concerning the controls of two DWDCs, i.e., one Petlyuk DWDC separating an ethanol/propanol/butanol ternary mixture and one Kaibel DWDC separating a methanol/ethanol/propanol/butanol quaternary mixture, the effectiveness of the PQE is assessed through comparing the performance of the temperature inferential control scheme using the PQE and the double temperature difference control scheme. According to the dynamic simulation results obtained, the former control scheme displays not only smaller steady-state deviations in the maintained product purities, but also better dynamic characteristics as compared with the latter control scheme. This result fully demonstrates that the proposed PQE can be a useful tool for the temperature inferential control of the DWDC.

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.

Optimal control of a rate-based modelled batch distillation column: Initialization strategy

Batch distillation columns are dynamic processes represented by a system of differential and algebraic equations (DAE). Their optimal control problem can be solved through orthogonal collocation, which is an effective discretization method with accurate approximations to the behavior of state and control variables. Convergence of such technique is greatly improved by providing good initial values and bounds of the variables. Thus, this work proposes a strategy to estimate the initial values for a sieve trays batch distillation column modeled with a rate-based approach. During the initialization procedure, the modeling equations are solved under a steady state assumption by using the GAMS modeling environment. An experimental pilot scale batch distillation column was used as the case-study to evaluate the proposed initialization algorithm. This column was used to separate a mixture of methanol and ethanol. The initialization results for the state profiles show good agreement with the experimental results.

H<sub>∞</sub> Robust Control System of a High-Purity Distillation Column

This paper presents the design of a robust control system for a high-purity distillation column. It is concerned with the design of a two degree-of-freedom (2DOF) product-composition controller for a high-purity distillation column. The H∞ optimization problem is set up to ensure a guaranteed level of robust stability, robust disturbance attenuation and robust reference tracking performance.

Data-Driven Nonlinear Model Reduction Using Koopman Theory: Integrated Control Form and NMPC Case Study

We use Koopman theory for data-driven model reduction of nonlinear dynamical systems with controls. We propose generic model structures combining delay-coordinate encoding of measurements and full state decoding to integrate reduced Koopman modeling and state estimation. We present a deep-learning approach to train the proposed models. A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity cryogenic distillation column.

Experimental performances of nonlinear GPC and discrete–time PID in multivariate process: NARIMAX and ARX models in temperature control of reactive distillation column in synthesis of biodiesel from waste cooking oil

Experimental performances of nonlinear GPC and discrete–time PID in multivariate process: NARIMAX and ARX models in temperature control of reactive distillation column in synthesis of biodiesel from waste cooking oil

Control and dynamic optimization of middle vessel batch distillation column for the separation of ethanol/propanol/butanol mixture

The middle vessel batch distillation column is an alternative to the regular batch distillation column. This configuration allows simultaneous separation of the light fraction (accumulated at top), heavy fraction (accumulated at bottom) and the intermediate fraction (accumulated in the middle vessel) in a single column. The objective of this article is to extensively analyse and discuss different control systems for the middle vessel batch distillation column (MVBDC) and dynamically optimize the column. Three control structures such as composition level cascade, composition temperature cascade, and temperature control system, have been tested and evaluated. Further, a dynamic optimization study has been performed on the control system providing the fastest separation. The optimizer is set to minimize the total energy consumed during the process. This resulted in a decrease in batch time from 26.8 to 25.2h and a 13.5% decrease in overall energy usage. The presented dynamic optimization technique and control system design is useful for improving the performance of the MVBDC.

Application and Performance Evaluation of Model Predictive Controllers on Driving Force Based Reactive Distillation Processes

In this work, integrated design and control of Reactive Distillation (RD) column operating under Model Predictive Controller (MPC) is presented. At first, a design target is defined, and the RD column is designed based on the element concept at the maximum driving force. After that, the steady state simulation is carried out in ASPEN PLUS that satisfies the design target. Next, the flow driven dynamic system is linearized in ASPEN PLUS Dynamics and the state space matrices are obtained. Using the matrices, MPC closed-loop simulation is carried out in MATLAB. To quantify both the steady state and dynamic operation, a multi objective function is defined that takes into account both the design (total energy consumption, total carbon footprint), and the control performance (Integral Absolute Error, total variation of input, relative gain array, Niederlinski Index). For further verification, an alternative design based on non-maximal driving force is also obtained, and the corresponding steady state and dynamic simulation is performed. The closed-loop performance of MPC controller under feed disturbance shows that the process that is designed at the maximum driving force exhibit better control performance compared to the design alternative that does not take into account the maximum available driving force.
 Chemical Engineering Research Bulletin 21(2020) 49-53