A new idea presented in this paper is implementation of the neural network (NN) predictive controllers in the complex control structures that are used in industrial applications. The conventional feedback PID control, simple neural network predictive control (NNPC) and two complex control structures with NN predictive controllers were studied using simulation experiments and compared. The NN predictive controllers in the role of the primary controllers were used in the cascade control and in the control system with the auxiliary control input. All mentioned control structures were used for control of five counter-current heat exchangers in series that were used for cooling of a product of distillation. The neural network (NN) plant model of the heat exchangers was obtained off-line. The simulation experiments showed that the NNPC-based control system with the main NN predictive controller and with the auxiliary control input significantly reduced both, the settling time and the overshoots. This control structure assured also the best integral quality criteria IAE and ISE as well as the smallest coolant consumption. The disadvantage and the reason for rare using of this control structure in practice is that two manipulated variables are necessary. The second best was the NNPC-based cascade control. The NNPC-based complex control structures are promising for assuring effective operation of HEs and decreasing energy consumption.