Development of a real-time drying control system for a pneumatic conveying dryer for sawdust in pellet production

Euh, Seung Hee; Choi, Yun Sung; Nam, Yun Sung; Lee, Chung Gun; Lee, Sang Yeol; Oh, Kwang Cheol; Jeong-hak, Oh，; Kim, Dae Hyun

doi:10.1016/j.energy.2018.07.056

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…Another study conducted by Li et al [14] proposed an NARX-based PSO-MPC controller for counter-current flow grain dryers, achieving an acceptable control accuracy of 0.52%, but this approach also faces challenges related to high data dependency. Similar results have been reported in MPC drying experiments with agricultural products like sawdust [41], corn [42], and red maple [43].…”

Section: Discussionsupporting

confidence: 87%

Deep-Learning-Based Model Predictive Control of an Industrial-Scale Multistate Counter-Flow Paddy Drying Process

Zhang,

Fang,

et al. 2023

Foods

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In practical industrial-scale paddy drying production, manual empirical operation is still widely used for process control. This often leads to poor uniformity in the moisture content distribution of discharged grains, affecting product quality. Model Predictive Control (MPC) is considered the most effective control method for paddy drying, but its implementation in industrial-scale drying is hindered by its high computational cost. This study aims to address this challenge by proposing a deep-learning-based model predictive control (DL-MPC) strategy for paddy drying. By establishing a mapping relation between the inlet and outlet paddy moisture content and paddy flow velocity, a DL-MPC strategy suitable for multistage counter-flow paddy drying systems is proposed. DL-MPC systems are developed using long short-term memory (LSTM) neural networks and trained using datasets from single-drying-stage and multistage drying systems. Simulation and analysis are conducted, followed by verification experiments on a 5HNH-15 multistage counter-flow paddy dryer. The results show that the DL-MPC system significantly improves computational speed while achieving satisfactory control performance. The predicted paddy flow velocity exhibits a smooth variation and matches field data obtained from multiple transition points, confirming the effectiveness of the designed DL-MPC system. The mean absolute error between the predicted and actual paddy moisture content under the DL-MPC system is 0.190% d.b., further supporting the effectiveness of the control system.

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Section: Discussionsupporting

confidence: 87%

Deep-Learning-Based Model Predictive Control of an Industrial-Scale Multistate Counter-Flow Paddy Drying Process

Zhang,

Fang,

et al. 2023

Foods

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“…Systems for real-time monitoring can offer insightful information about the operating circumstances, performance metrics, and potential problems in the pneumatic conveying system. There are few control system studies, the most notable being developed by Euh et al [15], wherein the researchers made a control system for a sawdust pneumatic conveyor dryer in pellet production.…”

Section: Research Gapsmentioning

confidence: 99%

Pneumatic Conveying Technology: Recent Advances and Future Outlook

Abe,

Gelladuga,

Mendoza

et al. 2023

The 4th International Electronic Conference on Applied Sciences

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“…Mathematical models based on energy and mass balances are capable of describing drying processes including convective dryers, see, for example, (Gaillemard, 2006;Smit et al, 2007;Thant et al, 2009;Rouch, 2010;Laurijssen et al, 2010;Jamaleddine & Ray, 2010;Sandoval Torres et al, 2011;Baxi et al, 2015;Di Marco et al, 2016;Gluesenkamp et al, 2019). In particular, Euh et al (2018) developed a real-time drying control that was capable to improve the drying efficiency of a pneumatic conveying dryer. The veneer drying process is based on the physical principles of moist air thermodynamics and moisture mass transfer.…”

Section: Introductionmentioning

confidence: 99%

Modelling of a Continuous Veneer Drying Unit of Industrial Scale and Model-Based Anova of the Energy Efficiency

Gradov

Oluwatoki

Ohjainen³

et al. 2021

SSRN Journal

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Development of a real-time drying control system for a pneumatic conveying dryer for sawdust in pellet production

Cited by 8 publications

References 16 publications

Deep-Learning-Based Model Predictive Control of an Industrial-Scale Multistate Counter-Flow Paddy Drying Process

Deep-Learning-Based Model Predictive Control of an Industrial-Scale Multistate Counter-Flow Paddy Drying Process

Pneumatic Conveying Technology: Recent Advances and Future Outlook

Modelling of a Continuous Veneer Drying Unit of Industrial Scale and Model-Based Anova of the Energy Efficiency

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