Neural network based nonlinear model predictive control for an intensified continuous reactor

Shi, Li; Li, Yueyang

doi:10.1016/j.cep.2015.07.024

Cited by 17 publications

(9 citation statements)

References 27 publications

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“…However, these papers only use simulation environments or non-production plants (only Conradie and Aldrich [7] applied their approach to a pilot plant) to verify the approach instead of testing under production conditions. Seeing the above-mentioned advantages of the model-based RL approach, it will be further derived for use in the described air separation control task [18].…”

Section: Reinforcement Learning As a Markov Decision Processmentioning

confidence: 99%

“…It is worth mentioning that the first paper proposing the use of (deep) reinforcement learning for process control came already up in 1992. Examples for model-based reinforcement learning applied to process engineering problems can be found in the publication of Li and Li [18]. However, these papers only use simulation environments or non-production plants (only Conradie and Aldrich [7] applied their approach to a pilot plant) to verify the approach instead of testing under production conditions.…”

Section: Reinforcement Learning As a Markov Decision Processmentioning

confidence: 99%

See 1 more Smart Citation

Investigation of a Model‐Based Deep Reinforcement Learning Controller Applied to an Air Separation Unit in a Production Environment

Blum

Krespach

Zapp

et al. 2021

Chemie Ingenieur Technik

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The need for load flexibility and increased efficiency of energy-intensive processes has become more and more important in recent years. Control of the process variables plays a decisive role in maximizing the efficiency of a plant. The widely used control models of linear model predictive controllers (LMPC) are only partly suitable for nonlinear processes. One possibility for improvement is machine learning. In this work, one approach for a purely data-driven controller based on reinforcement learning is explored at an air separation plant (ASU) in productive use. The approach combines the model predictive controller with a data-generated nonlinear control model. The resulting controller and its control performance are examined in more detail on an ASU in real operation and compared with the previous LMPC solution. During the tests, stable behavior of the new control concept could be observed for several weeks in productive operation.

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Section: Reinforcement Learning As a Markov Decision Processmentioning

confidence: 99%

Section: Reinforcement Learning As a Markov Decision Processmentioning

confidence: 99%

Investigation of a Model‐Based Deep Reinforcement Learning Controller Applied to an Air Separation Unit in a Production Environment

Blum

Krespach

Zapp

et al. 2021

Chemie Ingenieur Technik

View full text Add to dashboard Cite

show abstract

“…Because of their high potential for handling nonlinear relationships and self-learning capabilities, there has been considerable interest in the use of neural networks for the control in different fields of chemical processes such as thermal processes [24], reaction processes [25] and separation and purification [26,27].…”

Section: Application Of Neural Network In Chemical Process Controlmentioning

confidence: 99%

Application of AI in Chemical Engineering

Hajjar¹,

Tayyebi²,

EghbalAhmadi³

2018

Artificial Intelligence - Emerging Trends and Applications

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A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields.

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“…Bahroun et al [20] proposed a two-layer hierarchical control approach for an intensified three-phase catalytic slurry reactor. In [21], nonlinear model predictive control (NMPC) approaches were applied for an intensified continuous hydrogenation reactor.…”

Section: Accepted Manuscriptmentioning

confidence: 99%