Neural Wiener Based MPC(NWMPC) for MTBE Catalytic Distillation (Creators without family name connot save)

Reactive distillation of MTBE has strong interaction between the variables and is highly nonlinear process. Here, nonlinear MPC was proposed to tackle the nonlinearity and the interaction in controlling tray temperature of MTBE reactive distillation. To improve the performance of the MPC, advanced nonlinear block oriented model known as Neural Wiener

Neural-Wiener-based Model Predictive Control (NWMPC) for Methyl Tert-butyl Ether Catalytic Distillation

The reactive distillation of methyl tert-butyl ether (MTBE) involves strong interactions between variables and is a highly nonlinear process. Here, a nonlinear model predictive control (MPC) was proposed to tackle the nonlinearity and the interaction involved in controlling the tray temperature in MTBE reactive distillation. To improve the performance of the MPC, an advanced nonlinear block-oriented model known as the neural Wiener model was employed. The control study was successfully simulated using Simulink (Matlab), which is integrated with the Aspen dynamic model. Set-point tracking, disturbance rejection and robustness tests were conducted to evaluate the neural-Wiener-based MPC (NWMPC) performance. The results achieved show that the NWMPC is able to maintain the product purity at its set-point of 99%, with isobutene conversion exceeding 99.98%. NWMPC is also able to reject disturbances, as shown in disturbance rejection study performed by changing the feed flowrate to 30% of the nominal value. This controller is also very robust and thus able to control the MTBE reactive distillation, even when the column efficiency was reduced to 80%

Comparative Study on modeling Efficiency Between Support Vector Machines (SVMs) model and Parallel OBF-NN model

This project is about the comparative study between model efficiency between support vector machine (SVM) and parallel OBF-NN model. To demonstrate the concept, basic support vector regression (SVR) model is developed as nonlinear model identification. Best parameter and option for SVR model is selected in order to construct optimum model performance. The study is developed using selected case study, which is using van de vusse reactor datasets. The data consist of input and output than applicable to perform simulation as training and validation data. Lastly, an OBF-SVR model is developed that use OBF model as linear part and SVR model as nonlinear part align in parallel. The performance of each developed model is tested in their performance in validation to approach real system value. The developed OBF-SVR model is compared with OBF-NN model and the deviation between each model is investigated

Comparative Study on modeling Efficiency Between Support Vector Machines (SVMs) model and Parallel OBF-NN model

This project is about the comparative study between model efficiency between support vector machine (SVM) and parallel OBF-NN model. To demonstrate the concept, basic support vector regression (SVR) model is developed as nonlinear model identification. Best parameter and option for SVR model is selected in order to construct optimum model performance. The study is developed using selected case study, which is using van de vusse reactor datasets. The data consist of input and output than applicable to perform simulation as training and validation data. Lastly, an OBF-SVR model is developed that use OBF model as linear part and SVR model as nonlinear part align in parallel. The performance of each developed model is tested in their performance in validation to approach real system value. The developed OBF-SVR model is compared with OBF-NN model and the deviation between each model is investigated