<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The paper reveals three applications of neural network predictive control (NNPC) to a system of four heat exchangers (HEs) in series with counterflow configuration to save energy expressed by cooling water in the system of HEs cooling the distillation product. Neural networks (NNs) are used at first in conventional NNPC and subsequently, neural network predictive controllers (NNPCLs) are employed as a master controller in a cascade control, and as a feedback controller in the control system with disturbance measurement. Neural-network-predictive-control-based (NNPC-based) feedback control systems are compared with PI controller based feedback control loop. Series of simulation experiments were done and the results showed that using NNPC-based cascade control reduced cooling water consumption. This control system also significantly reduced the settling time and overshoots in the control responses and provided the best assessed integral quality criteria compared to other control systems. NNPC-based cascade control can also be interesting for industrial use. Generally, simulation results proved that NNPC-based control systems are promising means for the improvement of HEs control and achievement of energy saving.</p></abstract>

The paper reveals three applications of neural network predictive control (NNPC) to a system of four heat exchangers (HEs) in series with counterflow configuration to save energy expressed by cooling water in the system of HEs cooling the distillation product. Neural networks (NNs) are used at first in conventional NNPC and subsequently, neural network predictive controllers (NNPCLs) are employed as a master controller in a cascade control, and as a feedback controller in the control system with disturbance measurement. Neural-network-predictive-control-based (NNPC-based) feedback control systems are compared with PI controller based feedback control loop. Series of simulation experiments were done and the results showed that using NNPC-based cascade control reduced cooling water consumption. This control system also significantly reduced the settling time and overshoots in the control responses and provided the best assessed integral quality criteria compared to other control systems. NNPC-based cascade control can also be interesting for industrial use. Generally, simulation results proved that NNPC-based control systems are promising means for the improvement of HEs control and achievement of energy saving.

Control of heat exchangers in series using neural network predictive controllers

Slovak University of Technology in Bratislava

Acta Chimica Slovaca

This work is licensed under the Creative Commons Attribution 4.0 Public License.

{"article-title":"Control of heat exchangers in series using neural network predictive controllers"}

The paper reveals three applications of neural network predictive control (NNPC) to a system of four heat exchangers (HEs) in series with counterflow...

Control of heat exchangers in series using neural network predictive controllers

Published Online: Jun 29, 2020

Page range: 41 - 48

DOI: https://doi.org/10.2478/acs-2020-0007

Keywords
heat exchanger, neural network predictive control, neural-network-predictive-control-based cascade control, neural-network-predictive-control-based control system with disturbance measurement

© 2020 Anna Vasičkaninová et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Control of heat exchangers in series using neural network predictive controllers

Published Online: Jun 29, 2020

Page range: 41 - 48

DOI: https://doi.org/10.2478/acs-2020-0007

Keywordsheat exchanger, neural network predictive control, neural-network-predictive-control-based cascade control, neural-network-predictive-control-based control system with disturbance measurement

© 2020 Anna Vasičkaninová et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Keywords
heat exchanger, neural network predictive control, neural-network-predictive-control-based cascade control, neural-network-predictive-control-based control system with disturbance measurement