Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption

Zhang, Xingyu; Li, Hua

doi:10.1016/j.camwa.2020.07.002

Cited by 2 publications

(3 citation statements)

References 40 publications

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“…Based on this, high-performance devices could be thereby devised for significant drag reduction. Recently, Zhang and Li 256 reported an ANN-assisted method for control of the local nonfluidic solid phase flow pattern by learning the relationship between the inlet for optimization of the process flow rate in order to reach the maximum yield for continuous processing of biopharmaceuticals. Overall, most of the above studies mainly applied the pure ML models to optimize a single parameter of flow and transport processes while most often the maximization of multiphase flow and device performance needs to optimize multiple parameters simultaneously.…”

Section: Flow and Transportmentioning

confidence: 99%

“…Based on this, high-performance devices could be thereby devised for significant drag reduction. Recently, Zhang and Li reported an ANN-assisted method for control of the local nonfluidic solid phase flow pattern by learning the relationship between the inlet flow rate and recirculation zone. The trained ANN was then used to optimize the input energy consumption to fit a continuous multiphase flow process over the long-term.…”

Section: Current Status and Challengesmentioning

confidence: 99%

See 1 more Smart Citation

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

118

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Artificial intelligence (AI), machine learning (ML), and data science are leading to a promising transformative paradigm. ML, especially deep learning and physics-informed ML, is a valuable toolkit that complements incomplete domain-specific knowledge in conventional experimental and computational methods. ML can provide flexible techniques to facilitate the conceptual development of new robust predictive models for multiphase flows and reactors by finding hidden pattern/information/mechanism in a data set. Due to such emergence, we thereby comprehensively survey, explore, analyze, and discuss key advancements of recent ML applications to hydrodynamics, heat and mass transfer, and reactions in single-phase and multiphase flow systems from different aspects: (1) development of multiphase closure models of drag force, turbulence stresses and heat/mass transfer to improve the accuracy and efficiency of typical CFD simulations; (2) image reconstruction, regime identification, key parameter predictions, and optimization of multiphase flow and transport fields; (3) reaction kinetics modeling (e.g., predictions of reaction networks, kinetic parameters, and species production) and reaction condition optimization. These sections also discuss and analyze the key advantages and weakness of ML for solving the problems in the domain of multiphase flows and reactors. Finally, we summarize the under-solving challenges and opportunities in order to identify future directions that would be useful for the research community. Future development and study of multiphase flows and reactors are envisaged to be accelerated by ML and data science.

show abstract

Section: Flow and Transportmentioning

confidence: 99%

Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

118

View full text Add to dashboard Cite

show abstract

“…Oguz et al used the mode of alternating control of fuzzy controller and recurrent neural network for nonlinear model prediction, which made the control system have the advantages of selfadaptability, small overshoot and reduction, and shortened adjustment time [15]. Zhang and Li put forward the formal mathematical description of neurons and the method of network structure and proved that a single neuron can perform logical functions, thus initiating the era of artificial neural network research [16]. Tavakoli and Assadian proposed a neural network predictive control algorithm to solve the time-varying and large time-delay problems in the aluminum electrolysis process and realized the optimal control of aluminum electrolysis process [17].…”

Section: Research On Neural Network Predictive Controlmentioning

confidence: 99%

Prediction Method of Graphene Defect Modification Based on Neural Network

Feng

Yao

et al. 2022

Mobile Information Systems

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Because of its excellent thermal, mechanical, and electrical properties, graphene has been used in a variety of functional coatings. Noncovalent bond functionalization and covalent bond functionalization are the most common graphene surface functionalization methods. Polymer modification, for example, can be used to give graphene and its derivatives new structure, morphology, and properties. The basic structure and predictive control principle of neural networks are discussed in this study, and a high thermal resistance porous graphene structure is reversely designed using machine learning. The effect of a graphene defect modification prediction model based on a GA (genetic algorithm) and improved BPNN (BP neural network) algorithm is investigated. The RMSE predicted by submodels 1–4 decreases by 13.26%, 3.86%, 11.71%, and 19.63%, respectively, according to the simulation results. The BPNN graphene defect modification prediction model optimized by GA has a better training and prediction effect than before optimization.

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Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption

Cited by 2 publications

References 40 publications

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Prediction Method of Graphene Defect Modification Based on Neural Network

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