In this paper, a novel formulation for nonlinear model predictive control (MPC) has been proposed incorporating the extended Kalman filter (EKF) control concept using a purely data-driven artificial neural network (ANN) model based on measurements for supervisory control. The proposed scheme consists of two modules focusing on online parameter estimation based on past measurements and control estimation over control horizon based on minimizing the deviation of model output predictions from set points along the prediction horizon. An industrial case study for temperature control of a multiproduct semibatch polymerization reactor posed as a challenge problem has been considered as a test bed to apply the proposed ANN-EKFMPC strategy at supervisory level as a cascade control configuration along with proportional integral controller [ANN-EKFMPC with PI (ANN-EKFMPC-PI)]. The proposed approach is formulated incorporating all aspects of MPC including move suppression factor for control effort minimization and constraint-handling capability including terminal constraints. The nominal stability analysis and offset-free tracking capabilities of the proposed controller are proved. Its performance is evaluated by comparison with a standard MPC-based cascade control approach using the same adaptive ANN model. The ANN-EKFMPC-PI control configuration has shown better controller performance in terms of temperature tracking, smoother input profiles, as well as constraint-handling ability compared with the ANN-MPC with PI approach for two products in summer and winter. The proposed scheme is found to be versatile although it is based on a purely data-driven model with online parameter estimation.
Liquid-phase esterification of acetic acid with n-butanol to n-butyl acetate is studied in the presence of a polymeric catalyst, that is, poly(o-methylene ptoluene sulfonic acid). The performance of the proposed catalyst is compared with the other commercially available homogeneous and heterogeneous catalysts in terms of its activity. Experiments are conducted in an isothermal stirred batch reactor to study the effects of speed of agitation, temperature, and catalyst loading on the rate of reaction. A concentration-based pseudohomogeneous (PH) kinetic model and activity-based kinetic models such as PH, Eley-Rideal (ER), and Langmuir-Hinselwood-Hougen-Watson (LHHW) models are developed. All the models considered in this study resulted in similar percentage deviation close to 4%. Further, kinetic models are validated through additional experiments, and it is observed that the simple concentration-based PH model is able to predict experimental data with least deviation compared to activity-based PH, ER, and LHHW models. The developed kinetic models are also tested using the Fisher-Snedecor test (F-test) and are found to be acceptable. By incorporating both modeling data and validation data, the overall absolute average deviations of different models are found to be concentration-based PH model 4.354%, activity-based PH model 5.006%, ER I model 5.189%, ER II model 5.403%, ER III model 5.437%, and LHHW model 6.104%, illustrating the superiority of the simple concentration-based PH model. K E Y W O R D S activity-based model, concentration-based model, kinetic modeling, n-butyl acetate, poly(omethylene p-toluene sulfonic acid) 822
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