The study attempts to show that using the neural network predictive control (NNPC) structure for control of thermal processes can lead to energy savings. The advantage of the NNPC is that it is not a linear-model-based strategy and the control input constraints are directly included into the synthesis. In the designed approach, the neural network is used as a nonlinear process model to predict the future behaviour of the controlled process with distributed parameters. The predictive control strategy is used to calculate optimal control inputs. The efficiency of the described control approach is verified by simulation experiments and a tubular heat exchanger is chosen as a controlled process. The control objective is to keep the temperature of the heated outlet stream at a desired value and minimize the energy consumption. The NNPC of the heat exchanger is compared with classical PID control. Comparison of the simulation results obtained using NNPC and those obtained by classical PID control demonstrates the effectiveness and superiority of the NNPC because of smaller consumption of heating medium.

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Possibilities of using robust controllers for a shell-and-tube heat exchanger control were studied, tested and compared by simulations and obtained results are presented in this paper. The heat exchanger was used to pre-heat petroleum by hot water; the controlled output was the measured output temperature of the heated fluid — petroleum, and the control input was the volumetric flow rate of the heating fluid — water. Robust controllers were designed using ℋ2, ℋ∞, ℋ2/ℋ∞ strategies and μ-synthesis. A comparison with the classical PID control demonstrated the superiority of the proposed robust control especially in case when the controlled process is affected by disturbances.

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