A neural network model used in continuous grain dryer control system

Jin, Yi; Wong, Kok Wai; Yang, Dongya; Zhang, Zhongjie; Wu, Wenfu; Yin, Jun

doi:10.1080/07373937.2021.1891930

Cited by 14 publications

(7 citation statements)

References 46 publications

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“…The developed neural network model effectively regulated the grain dryer performance by considering the nonlinearity, robust coupling and hysteresis of the parameters utilised in defining the grain drying process, which makes it a precise management of the drying system challenges. The comparison of the experimental findings with the analysis from the BPNN intelligent control system demonstrated that the produced intelligence had the benefit of higher stability and noise management [70].…”

Section: Paddymentioning

confidence: 95%

“…The convective hot air dryer was the most commonly used. Three drying parameters, namely temperature, air velocity and relative humidity, were reported as the factors that influenced moisture removal in the convective dryer [38,51,70,74]. In 7 h, the industrial paddy dryer at Louisiana State University (LSU) reduced the moisture content of the paddy to 14%wb.…”

Section: Paddymentioning

confidence: 99%

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Recent Advances in the Drying Process of Grains

et al. 2023

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Grain drying is a vital operation in preparing finished grain products such as flour, drinks, confectioneries and infant food. The grain drying kinetics is governed by the heat and mass transfer process between the grain and the environment. Incomplete, improper and over-drying are crucial to the grain quality and negatively influence the acceptance of the grain by the consumers. Dried grain moisture content is a critical factor for developing grain drying systems and selecting optimal performance by researchers and the grain processing industry. Many grain drying technologies such as fluidised bed dryers, fixed bed dryers, infrared dryers, microwave dryers, vacuum dryers and freeze dryers have been used in recent years. To improve the drying process of grain, researchers have combined some drying technologies such as microwave + hot air, infrared + hot air and microwave + a fluidised bed dryer. Also, they introduce some treatments such as ultrasound dielectric and dehumidification. These methods enhance the dryer performance, such as higher moisture removal, reduced processing time, higher energy efficiency and nutrient retention. Therefore, this review focused on the drying conditions, time, energy consumption, nutrient retention and cost associated with the reduction of moisture content in grain to a suitable safe level for further processing and storage.

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Section: Paddymentioning

confidence: 95%

Section: Paddymentioning

confidence: 99%

Recent Advances in the Drying Process of Grains

et al. 2023

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“…In the literature, rice kernels have been reported to have successfully undergone the dehydration process using several techniques. These include the use of hot air, 2 fluidized bed, 4 continuous grain dryer, 5 rotary dryer 6 and microwave rotary drum 7 . Due to the grain's poor heat conductivity, convective hot‐air dehydration is commonly acknowledged as a process that requires a lot of energy and takes a longer time 8 .…”

Section: Introductionmentioning

confidence: 99%

Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

Jimoh,

Hashim,

Shamsudin

et al. 2024

J Sci Food Agric

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BackgroundFive computational intelligence approaches, namely: gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT), and support vector machine (SVM) were used to describe the evolution of moisture during the dehydration process of glutinous rice. The hyperparameters of the models were optimized with three strategies i.e., Bayesian optimization, grid search and random search. To understand the parameters that facilitate intelligence model adaptation to the dehydration process, global sensitivity analysis (GSA) was used to compute the impact of the input variables on the model output.ResultThe result shows that the optimum computational intelligence techniques include the 3–9‐1 topology trained with Bayesian regulation function for ANN, gaussian kernel function for SVM, matern covariance function combined with zero mean function for the GPR, boosting method for EoT, and 4 minimum leaf size for DT models. The GPR has the highest performance with R2 of 100% and 99.71% during calibration and testing of the model respectively. The GSA reveals that all the models significantly rely on the variation in time as the main factor that affects the model outputs.ConclusionTherefore, the computational intelligence models especially the GPR can be applied for an effective description of moisture evolution during small‐scale and industrial dehydration of glutinous rice.This article is protected by copyright. All rights reserved.

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“…A large number of control strategies designed for different types of dryers and products can be found in the literature [2]. The works include: i) feedback control [3] and feedforward-feedback control [4][5][6], and ii) advanced control techniques such as model-based control [7,8], neural net controllers [9] and fuzzy logic controllers [10,11]. For the main technological advances in dryers the reader is referred to the works [12,13] and for excellent reviews about drying control strategies to the works [1,2,14].…”

Section: Introductionmentioning

confidence: 99%

Optimal Control Structure Selection Based on Economics for Continuous Cross-Flow Grain Dryer

Bottari

Braccia

2023

Drying Technology

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In this work, we present the control structure selection problem based on steady-state economics applied to a continuous cross-flow grain dryer model. The optimal selection of the controlled and manipulated variables and their set-points can be obtained for various external disturbance scenarios if an appropriate mathematical programming problem is formulated. Simulation results show that the economics performance of classical open loop and closed loop structure used for dryer control can be improved by the use of linear combination of controlled variables and also by the use of a hierarchical control structure. In addition, we study several alternatives (sub-optimal) control structures that show an acceptable economic performance.

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A neural network model used in continuous grain dryer control system

Cited by 14 publications

References 46 publications

Recent Advances in the Drying Process of Grains

Recent Advances in the Drying Process of Grains

Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

Optimal Control Structure Selection Based on Economics for Continuous Cross-Flow Grain Dryer

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