Nonlinear MPC for fed-batch multiple stages sugar crystallization

Suarez, Luis Alberto Paz; Georgieva, Pétia; Azevedo, S. Feyo de

doi:10.1016/j.cherd.2010.10.010

Cited by 30 publications

(22 citation statements)

References 21 publications

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“…Model predictive control (MPC) has received great attention of both academia and industries due to its critical potential to provide tighter control with lower control update rates while dealing effectively with nonlinear variable problems, actuator dynamics, and future disruptions . It has been widely adapted in petroleum refineries, food processing, chemicals, automotive sector, and aerospace applications and can also provide efficient control of unconstrained nonlinear time‐variant plants .…”

Section: Introductionmentioning

confidence: 99%

System Behavior and Predictive Controller Performance Near the Azeotropic Region

et al. 2018

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High‐purity distillation columns present challenges of nonlinearity, interactions, directionality, and ill‐conditioning for the control systems and thus exhibit poor controller performance. Effects of these issues on the performance of model predictive control in operations in near‐azeotropic regions are investigated. A dynamic model of a pilot‐scale distillation column is simulated and controlled by a linear model predictive controller. As the system moves towards the azeotropic region, the nonlinearity, directionality, and interactions of the system increase. A significant decline is observed in the system control performance since the controller incurs overshoots and offsets for negative and positive setpoint changes. The integral time absolute errors are substantially higher near the azeotropic region. Only a small operating range is able to ensure sufficient controllability near the azeotropic region.

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Section: Introductionmentioning

confidence: 99%

System Behavior and Predictive Controller Performance Near the Azeotropic Region

et al. 2018

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“…The basis of this application is the stabilization and tracking problems, that became the basis of model predictive control [5]. The steps in nonlinear Step of NMPC predictive control models are given in Fig.…”

Section: B Nonlinear Model Predictive Controlmentioning

confidence: 99%

Auto Floodgate Control Using EnKf-NMPC Method

Purnaningrum¹,

Apriliani

2016

IJCSAM

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Abstract-One of the flood controls, especially in the downstream areas are barrage. Those are optimized using Ensemble Kalman filter based non linear predictive control. Ensemble Kalman Filter is used to predict water levels and flow of waters when it reaches the barrage. The results obtained from this method is then used as input for controlling the floodgates. Simulations are performed in three circumstances, namely the normal flow, flooding and drought. For normal flow, using optimum quantities are obtained from NMPC by opening the floodgates. Simulations were performed for 100 hours, with a gap of 5 per hour of observation. EnKf fulfilled with RMSE yields accuracy of the system and estimates of less than 1, RMSE debit is 0.5346 and RMSE water level is 0.2716. Furthermore the operation of the opening gate achieves optimum value, with the movement of between 40 − 65 per cent, with an average difference of movement is 0.10065 percent. Flood conditions, the water flow 2, 000 m 3 /s and the water level 10 m operation of opening gate ranging between 98 − 100 per cent and the amount of the difference opening gate is 0.028835. RMSE to estimate the flow rate of 1.5835, while for the water level of 0.3145. While the flow conditions dry, with water flow 10 m 3 /s and the water level 1 m operation of opening gate ranging between 0 − 1 per cent and the amount of the difference opening gate is 0.41289 percent. RMSE to estimate the flow rate of 0.0826, while for the water level of 0.0677.

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“…Therefore, a lot of researchers had dedicated their efforts in putting forward various advanced control methods for automation in the cane sugar process field. Nevertheless, even if advanced control methods had been proposed (nonlinear model based predictive control; genetic model control; HN control etc) for both continuous and batch crystallization processes [2][3][4][5][6][7], the lack of advanced sensors remain a major difficulty to the implementation of relevant control strategies [8].…”

Section: Introductionmentioning

confidence: 99%