International Journal of Science, Engineering and TechnologyAuthors: Ranjan Kumar
Abstract: Pyrolysis reactors are difficult to control because thermochemical decomposition is nonlinear, the reaction rates are high, and feedstock variability causes disturbances. Traditional proportional-integral-derivative (PID) controllers often perform poorly, causing overshoot, sluggish response, and suboptimal energy efficiency. To address these challenges, this study proposes a soft-sensor-enabled nonlinear model predictive control (NMPC) strategy for temperature regulation in a pyrolysis reactor. This method combines an Extended Kalman Filter (EKF) soft sensor with predictive control to estimate unmeasured disturbances and states. A nonlinear energy balance model considers the heat input, heat loss, reaction dynamics, and disturbances. The NMPC controller solves an optimization problem over the prediction horizon while satisfying the constraints on the control inputs and reactor temperature. Simulations show that it outperforms PID and traditional MPC controllers, achieving a 499.75°C steady-state temperature, 0.247°C tracking error, 0.014°C overshoot, and minimized RMSE and IAE values of 129.53°C and 68060, respectively. The EKF soft sensor achieved a 0.40°C RMSE, enabling disturbance compensation. This method provides comparable energy efficiency while improving stability and robustness. In conclusion, this strategy offers an accurate and efficient solution for nonlinear pyrolysis reactor temperature control under disturbance conditions.
