© 2018 IEEE.Distillation column is very important equipment in the chemical industry which requires large thermal energy around 70% to 85% in ethanol production, it requires optimal control to obtain optimum cost and high product result. Model predictive control is an optimal control which can solve the operational problem. In this research, model predictive control is implemented in mini batch distillation column separating ethanol water mixture. The dynamic of ethanol concentration in the process is modeled as a linear system with time delay at certain operating point. Model predictive control with 5 horizon implemented in Arduino Mega 2560 with 1 minute’ sampling time. From several experiments, with 1400 W heating power, and 30 minute’ controlling time, the result of ethanol concentration is obtained with different tracking error. A higher tracking error can be found due to various things, such as a less accurate of online ethanol concentration sensors compare to offline sensors. To improve model prediction, the implementation with 3 minutes of sampling time and 5 horizons are also performed with unsatisfactory results due to the sensor’s inaccuracies. In general, the concentration of ethanol can be achieved by a linear model from water concentration using model predictive controller. It shows that the linear model used is quite representative. Improved performance of the control system requires a more accurate concentration sensor.

Batch distillation columns,Concentration sensors,Ethanol concentrations,Ethanol water mixtures,Linear,Model predictive controllers,Operational problems,Water concentrations

Batch distillation column,Concentration,Linear,MPC

https://doi.org/10.1109/IC3INA.2018.8629541