2020
DOI: 10.1007/s43153-020-00058-2
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Implementation of a neural network MPC for heat exchanger network temperature control

Abstract: Heat exchanger network (HEN) control is considered to be a difficult task due to its nonlinear behavior, complexity, presence of disturbances and noise. The optimal operation of HEN implies the implementation of a robust control system able to overcome all these issues. This work presents an advanced technique based on an artificial neural network model predictive control (NNMPC) and compares the controlling performance to three widely used controllers: two PID and a linear MPC. The goal is to control an outle… Show more

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Cited by 13 publications
(6 citation statements)
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References 49 publications
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“…Inspired by these human capacities, researchers have developed models akin to those of the nervous system for information processing. This approach led to the creation of artificial neural networks, which mimic the functioning of the human brain’s nervous system to process input data . Evidence suggests that both deterministic and stochastic contexts can benefit from utilizing multilayer neural networks with tunable parameters, as they serve as effective pattern recognizers.…”
Section: Dynamic Controlmentioning
confidence: 99%
“…Inspired by these human capacities, researchers have developed models akin to those of the nervous system for information processing. This approach led to the creation of artificial neural networks, which mimic the functioning of the human brain’s nervous system to process input data . Evidence suggests that both deterministic and stochastic contexts can benefit from utilizing multilayer neural networks with tunable parameters, as they serve as effective pattern recognizers.…”
Section: Dynamic Controlmentioning
confidence: 99%
“…Bakošová et al proposed a predictive control strategy based on a simplified model-based heat exchange network approach [20]. Carvalho et al proposed an advanced technique based on artificial neural network model predictive control (NNMPC) and compared it with two PIDs and a linear MPC controller in simulation [21]. Oravec et al took the time-varying parameters of the heat exchanger as parameter uncertainty and adopted robust MPC to deal with the process with uncertainty [22].…”
Section: Introductionmentioning
confidence: 99%
“…The work in [21] focused on a neural-network-based, model-predictive controller (MPC) for temperature control within a heat exchanger network (HEN). Parallel heat exchangers were observed within the HEN.…”
Section: Introductionmentioning
confidence: 99%