Control of heat exchangers in series using neural network predictive controllers

IF 0.9 Q4 CHEMISTRY, MULTIDISCIPLINARY
A. Vasickaninova, M. Bakosová, A. Mészáros
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引用次数: 0

Abstract

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.
基于神经网络预测控制器的换热器串联控制
摘要:本文介绍了神经网络预测控制(NNPC)在4台换热器串联逆流配置系统中的三种应用,以节省换热器冷却蒸馏产品系统中冷却水的能量。神经网络(Neural network, NNs)首先用于传统的NNPC中,随后,神经网络预测控制器(Neural network predictive controller, nnpcl)在串级控制中作为主控制器,在具有扰动测量的控制系统中作为反馈控制器。将基于神经网络预测控制的反馈控制系统与基于PI控制器的反馈控制回路进行了比较。进行了一系列的仿真实验,结果表明,采用基于nnpc的串级控制可以降低冷却水的消耗。与其他控制系统相比,该控制系统还显着减少了控制响应中的沉降时间和超调,并提供了最佳的评估整体质量标准。基于nnpc的串级控制在工业应用中也很有趣。总的来说,仿真结果证明了基于nnpc的控制系统是改善HEs控制和实现节能的有效手段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Acta Chimica Slovaca
Acta Chimica Slovaca CHEMISTRY, MULTIDISCIPLINARY-
自引率
12.50%
发文量
11
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