Predictive functional control applied to multicomponent batch distillation column (original) (raw)
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Chemical Product and Process Modeling, 2014
The objective of present research work is to develop a neural network–based model predictive control scheme (NN-MPC) for distillation column. To fulfill this objective, an existing laboratory setup of continuous binary-type distillation column (BDC) is used. An equation-based model that uses the fundamental physical and chemical laws along with valid normal assumptions is validated for this experimental setup. Model predictive control (MPC) is one of the main process control techniques explored in the recent past for various chemical engineering applications; therefore, the conventional MPC scheme and the proposed NN-MPC scheme are applied on the equation-based model to control the methanol composition. In NN-MPC scheme, a three-layer feedforward neural network model has been developed and is used to predict the methanol composition over a prediction horizon using the MPC algorithm for searching the optimal control moves. The training data is acquired by the simulation of the equati...
Multivariable predictive control with constraint handling for distillation columns
In this paper the conceptual basis for the design of a discrete-time constrained multivariable predictive controller for a nonlinear process are exposed. The controller uses a standard predictive algorithm, whose solution is obtained by solving a well-known convex Quadratic Problem (QP). This controller combines the simplicity of the linear models with the essential nonlinearities of the process using an online linearized model of the process for the output prediction. The controller has been developed for a pilot binary distillation column, presenting as main advantages the facility of tuning and its adaptation to the different operating points of the plant with no need of readjusting the controller parameters. The results obtained in simulation for diverse studied cases are exposed. Between these cases, the compensation of measurable disturbances is included, this allows to eliminate the steady state errors.
Batch distillation control improvement by novel model predictive control
Journal of Industrial and Engineering Chemistry, 2010
Ternary batch distillation Model reduction Optimal policy Model predictive control Kalman filter A B S T R A C T 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 reducedorder 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. Crown
Model identification and predictive control of a laboratory binary distillation column
2015 20th International Conference on Process Control (PC), 2015
This paper deals with identification and control of a laboratory binary distillation column used for separation of methanol from water. Main goals of this work are to identify a state-space model of the column, to design a suitable controller using model predictive control (MPC) concepts, and to implement on-line predictive controller on the laboratory device in real time framework.
Generalized predictive control to a packed distillation column for regulatory problems
Computers & Chemical Engineering, 1998
In this work, adaptive Gcncralizcd Prcdictivc Control (GPC) was invcsligatcd al 11~: optimal opcraling conditions for a pilot plant binary packed distillation column. The studies were made experimentally and theoretically. The dynamic behavior of lhc distillation column has been simulated using backmixing model and solved by utilizing Hcmli1c,Polynomials within the tinitc clement procedure. The control of the ovcrhcad product kmpcralure was exam&d for both experimental and theoretical works. Perturbations in feed composition wcrc u&cd as the distmtxmce and the rcboilcr heat duty was sckcted as the manipuhucd variabk. Pseudo Random Binary Scqucncc (PRBS) signal and Bicrman algorithm wcrc used to estimate the rclcvant paramctcrs of the system model for GPC. Gcncrally theoretical and cxpcrimental control rcsuhs were in good agrccmcd with each other.
reactive distillation is aimed at achieving a high purity product, therefore, there is a great deal to find an optimal operating condition and effective control strategy to obtain maximum of the high purity product. An off-line dynamic optimization is first performed with an objective function to provide optimal product composition for the batch reactive distillation: maximum productivity. An inferential state estimator (an extended Kalman filter, EKF) based on simplified mathematical models and on-line temperature measurements, is incorporated to estimate the compositions in the reflux drum and the reboiler. Model Predictive Control (MPC) has been implemented to provide tracking of the desired product compositions subject to simplified model equations. Simulation results demonstrate that the inferential state estimation can provide good estimates of compositions. Therefore, the control performance of the MPC with the inferential state is better than that of PID. In addition, in the...
Temperature-temperature cascade control of binary batch distillation columns
In this paper it is addressed the joint operation and control design for binary batch distillation columns, based on temperature measurements. The combination of nonlinear constructive control theory with passivation and observability notions and existing batch distillation concepts yields: (a) a simple methodology for temperature sensor locations, (b) the on-line generation of temperature policies that ensure constant distillate purity, and (c) a temperature-to-temperature cascade tracking controller to force the prescribed constant distillate product purity policy. The methodology is tested with representative binary systems.
2007
A model predictive control strategy is proposed for multivariable nonlinear control problem in a distillation column. The aim is to provide a solution to nonlinear control problem that is favorable in terms of industrial implementation. The scheme utilizes multiple linear models to cover wider range of operating conditions. Depending on the operating conditions, suitable model is used in control computations. Servo and regulatory controls of the system are examined. Comparisons are made to conventional controllers. The results confirmed the potentials of the proposed strategy.