The cooling zone of an induration furnace is a highly interactive multivariable process with strong nonlinearities and dissimilar dynamics. Linear controllers, implemented on a first-principles process model, are unable to properly control the unit in a wide operating range. This paper proposes a design procedure which considers relevant process characteristics, such as nonlinearity, interaction, directionality, and dynamics, for the synthesis of decentralised extended PIDs and model-based predictive controllers (MPCs). Linear controllers with variable transformations are used since the process model shows that a Hammerstein model can approximate the process nonlinear behaviour. The decentralised PIDs are tuned using efficient rules that take into account the process interaction. The performance of both control strategies is evaluated for set-point tracking, disturbance rejection, and robustness to modelling errors. Similar results are obtained for the gas temperature control, which is the most important process variable. Slightly better results are obtained with the MPC for the gas pressure, the fastest dynamic variable.