Control Of Batch Distillation Research Articles

Operation parameters of a small scale batch distillation column for hydrous ethanol fuel (HEF) production

Batch distillation applied to hydrous ethanol fuel (HEF) production on a small scale still requires operating conditions that ensure optimal top product quality and productivity. The aim of this study is to statistically validate a batch still through the employment of response surface methodology (RSM). Operational and productivity parameters were formulated in order to guarantee quality compliance with the legal requirements for the top product concentration, besides providing support information to control the production of HEF on a small scale. The reboiler control and dephlegmator temperatures maintained within the range of 97.5 to 99.5°C and 60 to 70°C, respectively, combined with a variable reflux ratio, was satisfactory in obtaining a top product concentration, in accordance with legal regulations, as well as high productivity. The results of this study may contribute to the assembly of a simple and low-cost batch distillation control system.

Batch distillation control improvement by novel model predictive control

Novel model predictive control (MPC) based on a sequence of reduced-order models is developed for a ternary batch distillation operated in an optimal reflux policy. A Kalman filter (KF) estimator is employed for estimating process outputs in the presence of disturbance and measurement noise. To handle highly nonlinearity and non-stationary by nature of the batch, a series of local models are developed around different parts of the reference profiles, and further reduced their orders individually to attain only observable and controllable state-contributions. The control performance of MPC based on reduced-order models incorporating with KF has been compared with conventional MPC (based on simple model) incorporating with an extended Kalman filter (EKF). Simulation results demonstrate that the proposed control strategy gives good control performance even in a presence of external disturbance and measurement noise. The main advantage of using the reduced-order models is small computation effort and sampling frequency requirement. In addition, the knowledge of thermodynamic is not necessarily required and augmented states can be easily initialized which is applicable in real situation.

Optimal rectification column, reboiler vessel, connection pipe selection and optimal control of batch distillation considering hydraulic limitations

In this article the optimal selection of the reboiler vessels, rectification columns and connection pipes with regard to reboiler swelling, and tray and pipe flooding is investigated. Since the reboilers are constrained by liquid swelling, while pipes and columns are constrained by flooding it is important to pair the equipment so that the vapor rate limitations are as close as possible, thus ensuring maximal equipment capacity usage. Calculations at 1 and 0.25 bar pressure are performed for the standardized 1.6, 2.5, 4 and 6.3 m 3 reboiler vessels [Rührbehälter Basler Norm, N 253, 1977] and sieve tray rectification columns with nominal diameters ranging from 0.2 up to 0.6 m. The dynamic operation of the batch distillation process having the vapor load and reflux ratio as input profiles is evaluated by formulating and solving an optimal control problem constrained by the hydraulic limitations. The open-loop optimization problem is solved using a dedicated Matlab tool – OptCon – which implements the multiple shooting strategy and the large-scale non-linear programming problem is handled by the HQP engine.

Profile Position Control of Batch Distillation Based on a Nonlinear Wave Model

Batch distillation poses challenging operation and control problems caused by a great change in process gain and a very nonlinear response in distillate composition during the operation. A nonlinear wave model captures an essential dynamic characteristic of the batch distillation column. A profile position control strategy based on the nonlinear wave model is proposed for the control of batch distillation columns. The profile position and distillate composition are estimated from selected tray temperature measurements by observers, which are based on the combination of the nonlinear wave propagation model and the quasi-dynamic model by Quintero-Marmol and Luyben (Chem. Eng. Sci. 1992, 47, 887). This control strategy has been applied to binary and multicomponent separation of ideal and nonideal mixtures. The proposed control scheme is shown to deal with severe nonlinear and varying process gains of batch operation and to be robust to model−plant mismatches.

Profile position control of distillation columns based on a nonlinear wave model

A nonlinear wave model captures an essential dynamic chanctcristic of a variety of distillation columns. A profile position control strategy based on the non1inear wave model is proposed for the control of a variety of distillation colunms such as high purity distillation column, complex distillation configurations, batch distillation configurations and for the startup of the columns. In this paper, we are mainly concerned with the control of batch distillation without and with chemical reaction among them. The controller has been developed by incOIpOrating nonlinear wave model into the generic model control framework. An observer based on the nonlinear wave model is also proposed to determine the profile position. Tight control of profile position, which leads to fast stabilization of product compositions. has been achieved. Since the profile position control alone produces a slight offset from the desired product concentration, the composition/profile position cascade system has been used to remove the offset.

Tracking the predefined optimal policies for multiple-fraction batch distillation by using adaptive control

Predefined optimal policies will be tracked with control systems to realize the optimum of multiple-fraction batch distillation. Adaptive control is proposed to carry out this task. Characteristics of batch distillation control are analyzed and a proper system is designed for controlling such processes. Besides tracking the optimal reflux ratio profile, the maximum vapor load will be maintained during the batch. In addition, a changing temperature profile of the condenser should be followed to reduce the operating energy with a possibly minimum subcooling. Recursive least square estimation (RLSE) with a variable forgetting factor is applied to the on-line identification of the plant to follow the changing dynamics of the process. Generalized predictive control (GPC) is used to track the predefined policies. The effectiveness of the control strategy is verified with a pilot batch column and the tracking performance is compared with that of PID controllers.

Sensor locations and noise reduction in high-purity batch distillation control loops

The influence of the sensor locations on the composition control of high-purity batch distillation columns has been investigated. Using concepts of the nonlinear control theory, an input-output linearizing controller was implemented to keep the distillate composition constant at a desired value by varying the reflux ratio. An Extended Kalman Filter was developed to estimate the compositions required in the control algorithm using temperature measurements. In the presence of measurement noise, the control performance depended greatly on the sensor locations. Placing the sensors further from the top stages reduced the detrimental effects of noise but increased the inference error. To achieve accurate composition control, both noise reduction and composition estimate accuracy should be considered in the selection of the sensor locations.

A Novel Proportional−Integral-Derivative Control Configuration with Application to the Control of Batch Distillation

The aim of this paper is to propose a novel proportional-integral-derivative (PID) control configuration based on an observer structure. Batch distillation is used as the base case study where the regulated output is the distillate composition. The proposed PID control law is derived in the framework of robust nonlinear control with modeling error compensation techniques. A reduced-order observer is proposed to estimate both the derivative of the regulated output and the underlying modeling error. These observations are subsequently used in a control loop to feedback variations of distillate composition (derivative feedback) and to counteract the effects of modeling errors. It is shown that, under certain conditions, the resulting control law is equivalent to a classical PID controller with an antireset windup scheme. Moreover, the tuning of the controller is performed very easily in terms of a prescribed closed-loop time constant and an estimation time constant. Numerical results are provided for binary and multicomponent separations. Sampled/delayed measurements and several sources of uncertainties are considered in order to provide a realistic test scenario for the proposed control design procedure.

Tracking the predefined optimal policies for multiple-fraction batch distillation by using adaptive control

Predefined optimal policies will be tracked with control systems to realize the optimum of multiple-fraction batch distillation. Adaptive control is proposed to carry out this task. Characteristics of batch distillation control are analyzed and a proper system is designed for controlling such processes. Besides tracking the optimal reflux ratio profile, the maximum vapor load will be maintained during the batch. In addition, a changing temperature profile of the condenser should be followed to reduce the operating energy with a possibly minimum subcooling. Recursive least square estimation (RLSE) with a variable forgetting factor is applied to the on-line identification of the plant to follow the changing dynamics of the process. Generalized predictive control (GPC) is used to track the predefined policies. The effectiveness of the control strategy is verified with a pilot batch column and the tracking performance is compared with that of PID controllers.

Optimal batch distillation control based on specific measures

Two independent measures characterise a single batch distillation run: the degree of separation difficulty, which indicates the difficulty at the start and the degree of exhaustion, which indicates bottom exhaustion at the end of the run. If one of both measures remains within bounds, then constant quality control appears to be the best control policy. It has been proven in the literature that the application of slop recycling increases the production rate. One of the goals of this study is to derive simple scheduling models based on specific measures for the optimal operation of batch distillation with slop recycling. Simulation studies for a cyclic pseudo-steady state operation over a broad range of degrees of difficulty for binary and ternary distillations are performed. Also the influence of the tray hold-up has been studied. All simulations show that at maximum production rate the degree of separation difficulty balances the degree of exhaustion (dependent variable in the optimum). The slop recycling strategy keeps the degree of exhaustion during the production phase and the degree of difficulty during the slop phase within bounds. As a result, the improvement of slop recycling at constant quality control compared to constant reflux control is 15–20% for difficult separation and can amount to more than 35% for relatively easy separations. The resulting production time appears to have a linear relation with the average separation difficulty. This relation has been experimentally verified.

Hierarchical Sub-Optimal Control of Batch Distillation with Slop Recycling

Slop recycle increases the production capacity of batch distillation. It avoids exhaustion of the mixture or decreases the difficulty of separation. A hierarchical control approach is proposed. At scheduling level predictions of the required component production times and the ratio between product and slop cut quantities are made. These predictions are based on the degree of difficulty of separation and the mean feed quality and quantity. At conditioning level sub-optimal operation under constant quality control is applied. The production capacity approximates the capacity under dynamic optimal control, and is up to 20% higher than under piecewise constant reflux control.

Application of Nonlinear Model Predictive Control to Optimal Batch Distillation

The problem of properly determining and implementing the optimal operational trajectory for batch distillation is discussed. Modeling, control, and optimization aspects are considered. A common modeling approach is shown to lead to significant errors in the solution trajectory. Errors arising from the equilibrium stage assumption show the need for rigorous models with efficient solution methods. Several industrially important control problems are discussed and a control pairing which eliminates some interactions is suggested. Optimization results are shown to be quite sensitive to model parametric (e.g. Murphree efficiency) and product specification changes. Parameter sensitivity studies indicate that a priori parameter estimates must be applicable over the range of operation, or that on-line parameter estimation with reoptimization is required. Nonlinear Model Predictive Control (NMPC) offers an efficient, straightforward method of addressing difficulties in modeling, optimization, and control of batch distillation.

Inferential model-based control of multicomponent batch distillation

Industrial interest in batch distillation has increased significantly in recent years as more batch processing is being used for low-volume high-value specialty chemicals. Most studies have concentrated on the determination of optimum operating strategies: optimum trajectories of reflux ratio and pressure during the batch and processing of the slop cuts that are produced. Little attention has been paid to the problems of control. If instantaneous and perfect analyzers were available, the control of batch distillation would be straightforward once the optimum policies are available. Switching from total-reflux operation to producing product and from this to slop-cut production could be performed when compositions in the reflux drum or in product tankage reached the desired values. However, in most industrial applications of batch distillation, perfect composition measurements are not available. This means that an inferential control system that relies on temperature and flow measurements must be used. Models of the batch distillation process can be used to provide estimates of the required compositions. This paper presents the results of a study of two types of model-based inferential control systems for multicomponent batch distillation. The results showed that both steady-state and dynamic estimators provide good estimation of the distillate compositions using only one temperature measurement.

Optimal control of batch distillation via nonlinear programming

Up to now, batch distillation units have been controlled by means of dynamic programming or variational calculus, neither of which is easily implementable, particularly when multicomponent mixtures are considered. A quite different approach based on an augmented Lagrangian nonlinear programming procedure is presented in this paper. For the maximum distillate problem, an optimal policy for linear time-dependent reflux is determined for each distillation period. From three examples involving binary and ternary mixtures, it appears that the Lagrangian procedure is an efficient tool for solving batch distillation problems.