Internal Thermally Coupled Distillation Columns (ITCDIC) are the frontier of energy saving distillation research. In this paper, the ideal ITCDIC is considered. A novel mathematical model and a related simulation algorithm are proposed. The dynamic responses of open-loop, PID controllers and the responses of closed-loops are carried out. The results show that the ITCDIC is a self-balance process and could be operated smoothly with two PID controllers; the steady-state optimization met the need of ITCDIC optimization. Furthermore, a steady-state optimization model of the operation parameters is presented, which can be used to directly obtain the optimal operation parameters simultaneously guaranteeing not only the product quality and the maximum energy savings but also the dynamic operability and controllability. The benzene-toluene system is studied as an illustrative example.

No abstract

No abstract

No abstract

No abstract

A method of nonlinear model predictive control based on an identified LPV model is proposed. In process identification, a linear parameter varying (LPV) model approach is used. First, typical working-points are selected and linear models are identified using data sets at various working-points; then the LPV model is identified by interpolating the linear models using total data that include transition test data. Further, nonlinear model predictive control based on the LPV model is proposed. The control action is computed via a multistep linearization method of nonlinear optimization problem. The method uses low cost tests and can reach higher control performance than linear MPC. Simulation studies are used to verify the effectiveness of the method. IntroductionModel predictive control (MPC) is one of the most successful controllers in process industries. 1,2 MPC refers to a class of control algorithms that control the future behavior of a plant through the use of an explicit process model. Until recently, most industrial applications of MPC have relied on linear dynamic models. 3 However, MPC based on linear models often results in poor control performance for highly nonlinear processes because of the inadequacy of a linear model to predict dynamic behavior of a nonlinear processes. This has led to the development of nonlinear model predictive control in which a more accurate nonlinear model is used for process prediction and optimization. 4 While nonlinear MPC offers the potential for improved process operation, it offers theoretical and practical problems that are considerably more challenging than those associated with linear MPC. The main obstacle is the high cost of modeling and identification of nonlinear processes. There are several approaches to nonlinear process modeling. One approach is to develop a first principle model of a given industrial process. [5][6][7] Developing a first principle model of a given industrial process costs a lot in terms of manpower; the accuracy of first principle models is often not high enough for dynamic control. Another approach is to use theoretically sound nonlinear functions and to develop identification schemes for these models. Identification using nonlinear autoregressive with exogenous inputs (NARX) models, 8 Volterra series expansion models, 9 block oriented models (Hammerstein and Wiener structures) 10,11 and artificial neural networks (ANN) 12,13 belong to this methodology. When using these models in system identification, it is difficult to perform detailed plant tests in the whole operation range because of large disturbances and too much production loss. Moreover, computation of the model parameters will be too costly and often not convergent because nonlinear optimizations are often needed. Therefore, finding a low cost method in nonlinear process identification is very important for industrial applications of nonlinear MPC.Although most industrial processes are nonlinear in their operation ranges, no single process will run chaotically in its

No abstract

No abstract