Controlling a thermal power plant optimally during load-cycling operation is a very challenging control problem. The control complexity is enhanced further by the possibility of simultaneous occurrence of sensor malfunctions and a plethora of system disturbances. This paper proposes and evaluates the effectiveness of a sensor validation and reconstruction approach using principal component analysis (PCA) in conjunction with a physical plant model. For optimal control under severe operating conditions in the presence of possible sensor malfunctions, a predictive control strategy is devised by appropriate fusion of the PCA-based sensor validation and reconstruction approach and a constrained model predictive control (MPC) technique. As a case study, the control strategy is applied for thermal power plant control in the presence of a single sensor malfunction. In particular, it is applied to investigate the effectiveness and relative advantage of applying rate constraints on main steam temperature and heat-exchanger tube-wall temperature, so that faster load cycling operation is achieved without causing excessive thermal stresses in heat-exchanger tubes. In order to account for unstable and non-minimum phase boiler-turbine dynamics, the MPC technique applied is an infinite horizon non-linear physical model-based state-space MPC strategy, which guarantees asymptotic stability and feasibility in the presence of output and state constraints.