Abstract:
This dissertation presents the development and implementation of a model predictive
control (MPC) system for a coffee roasting process, to optimise roasting quality while
minimising energy consumption. The study involved analysing historical temperature
profile data and roaster inputs to develop a hybrid model, combining empirical and first principles techniques, which predicts the measured bean temperature as a function of the
available roaster inputs. The combination of the first-principles model with empirical
modelling techniques reduced validation data error by increasing measured temperature
prediction accuracy. Subsequently, a nonlinear MPC was designed and tuned through
a series of simulations, adjusting prediction and control horizons while limiting input
changes relative to the real-time input value. The optimal configuration achieved a sig nificant reduction in the average usage of liquefied petroleum gas (LPG) while
maintaining a wide input range.
The impact of the intelligent modelling and control system on the reduction of raw
material waste, the improvement of the quality of the final product, and the overall
efficiency of the roasting process was evaluated, showing significant improvements in all
three areas. The proposed system enables operators to perform simulations of roasts
and reduce raw material wastage when developing roast profiles, providing a valuable
contribution to the coffee roasting industry. Future work includes further investigation
of hybrid modelling and nonlinear optimisation techniques.
