Dinie Muhammad | Universiti Sains Malaysia (original) (raw)
Uploads
Thesis Chapters by Dinie Muhammad
One of the main constraints in industrial distillation control is the time delay in the compositi... more One of the main constraints in industrial distillation control is the time delay in the composition measurement and analysis. This delay can introduce dead time in the control loop and deteriorate the controller performance. In addition, the occurrence of disturbances in a distillation process can severely affect the product quality. One of the promising control schemes that can handle all the problems mentioned earlier is the combined internal model and inferential control (CIMIC) as proposed by Häggblom (1996). However, to date, the CIMIC implementation is based on a linear model which can degrade its performance when dealing with processes that are nonlinear such as distillation. Therefore, in this work the nonlinear based CIMIC (NLCIMIC) is proposed to control an industrial n-butane/i-butane separation process. The multicomponent distillation process is developed using Aspen software and successfully validated based on actual plant data available in the literature. In addition, a degree of the nonlinearity study of the distillation process is also evaluated which then classifies that the distillation process under consideration has a strong nonlinearity characteristic. Prior to the NLCIMIC development, the performance of a linear CIMIC (LCIMIC) has been compared to 2DOF IMC and IMC and the results achieved proved the advantage of the inferential element embedded in the LCIMIC. Then, the linear based CIMIC integrated with a nonlinear based model developed in Aspen (LCIMIC-AS) is implemented and the results obtained justify the need of the NLCIMIC to be implemented. In all the control implementation, the performance is evaluated based on the setpoint tracking and disturbances rejection capability. In the NLCIMIC development, the neural network (NN) model is used as a model and inverse model. The best NN model chosen is based on the MSE value obtained. In order to evaluate the NLCIMIC performance, the control scheme is compared with the LCIMIC-AS in controlling the n-butane/i-butane separation process. Based on the setpoint tracking test, it is found that the NLCIMIC performed better in tracking the step up setpoint (IAE = 0.0349) if compared to the LCIMIC-AS (IAE = 0.636). Furthermore, the NLCIMIC profound ability in rejecting disturbance is also observed in the disturbance rejection test (IAE = 0.0107) as it outperforms the LCIMIC-AS (IAE = 0.0154) significantly. As a conclusion, the NLCIMIC overall performance is better than the LCIMIC-AS in controlling a nonlinear distillation process
Papers by Dinie Muhammad
Distillation columns have been widely used in chemical plants for separation process. The high no... more Distillation columns have been widely used in chemical plants for separation process. The high nonlinearity and dynamic behavior of the column make them hard to control. Internal Model Control (IMC) is one of the model based control strategy that have become the central of research to control such column. This paper will review the implementation of IMC in controlling continuous distillation column for the past 28 years. The type of models used as internal models in the IMC is reviewed and highlighted. In addition, the past implementation of IMC weather in simulation based or real application is also reviewed. Based on the review, it is found that many implementations of IMC in the continuous distillation column were based on linear model, using binary components distillation system and tested in the simulation environment. Thus, the implementation of nonlinear based IMC using multi components distillation system is still open for research. Furthermore, the successful of the real time application for such control algorithm also need to be proved.
One of the main constraints in industrial distillation control is the time delay in the compositi... more One of the main constraints in industrial distillation control is the time delay in the composition measurement and analysis. This delay can introduce dead time in the control loop and deteriorate the controller performance. In addition, the occurrence of disturbances in a distillation process can severely affect the product quality. One of the promising control schemes that can handle all the problems mentioned earlier is the combined internal model and inferential control (CIMIC) as proposed by Häggblom (1996). However, to date, the CIMIC implementation is based on a linear model which can degrade its performance when dealing with processes that are nonlinear such as distillation. Therefore, in this work the nonlinear based CIMIC (NLCIMIC) is proposed to control an industrial n-butane/i-butane separation process. The multicomponent distillation process is developed using Aspen software and successfully validated based on actual plant data available in the literature. In addition, a degree of the nonlinearity study of the distillation process is also evaluated which then classifies that the distillation process under consideration has a strong nonlinearity characteristic. Prior to the NLCIMIC development, the performance of a linear CIMIC (LCIMIC) has been compared to 2DOF IMC and IMC and the results achieved proved the advantage of the inferential element embedded in the LCIMIC. Then, the linear based CIMIC integrated with a nonlinear based model developed in Aspen (LCIMIC-AS) is implemented and the results obtained justify the need of the NLCIMIC to be implemented. In all the control implementation, the performance is evaluated based on the setpoint tracking and disturbances rejection capability. In the NLCIMIC development, the neural network (NN) model is used as a model and inverse model. The best NN model chosen is based on the MSE value obtained. In order to evaluate the NLCIMIC performance, the control scheme is compared with the LCIMIC-AS in controlling the n-butane/i-butane separation process. Based on the setpoint tracking test, it is found that the NLCIMIC performed better in tracking the step up setpoint (IAE = 0.0349) if compared to the LCIMIC-AS (IAE = 0.636). Furthermore, the NLCIMIC profound ability in rejecting disturbance is also observed in the disturbance rejection test (IAE = 0.0107) as it outperforms the LCIMIC-AS (IAE = 0.0154) significantly. As a conclusion, the NLCIMIC overall performance is better than the LCIMIC-AS in controlling a nonlinear distillation process
Distillation columns have been widely used in chemical plants for separation process. The high no... more Distillation columns have been widely used in chemical plants for separation process. The high nonlinearity and dynamic behavior of the column make them hard to control. Internal Model Control (IMC) is one of the model based control strategy that have become the central of research to control such column. This paper will review the implementation of IMC in controlling continuous distillation column for the past 28 years. The type of models used as internal models in the IMC is reviewed and highlighted. In addition, the past implementation of IMC weather in simulation based or real application is also reviewed. Based on the review, it is found that many implementations of IMC in the continuous distillation column were based on linear model, using binary components distillation system and tested in the simulation environment. Thus, the implementation of nonlinear based IMC using multi components distillation system is still open for research. Furthermore, the successful of the real time application for such control algorithm also need to be proved.