Implementation of model predictive control in tracking dynamic optimal profiles of semi batch autocatalytic esterification reactor

Optimization of ester production that yields from sec‐butyl propionate in a batch operation mode need to be solved by dynamic–nonlinear programming‐based optimization because the dynamics of this autocatalytic esterification process can be described using the detailed first principle model. In order to maximize profit, the control vector parameterisation technique combined with a hybrid strategy, that is, a deterministic–stochastic nonlinear programming solver, is implemented by generating optimal temperature and feed flowrate trajectories. The final time and profit achieved is 60.0 min and RM 12.840 min−1, respectively. Model predictive control is then applied to track the optimal temperature trajectory obtained from the maximize profit study. The result reveals that the model predictive control is able to track the optimal temperature trajectory very well and consequently able to maintain the on‐spec product profit.