Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking

Yang, Taoqing; Zheng, Xiaotong; Vidyarthi, Sriram K.; Xiao, Hong‐Wei; Yao, Xuedong; Li, Yican; Zang, Yongzhen; Zhang, Jikai

doi:10.3390/foods12091897

Cited by 15 publications

(6 citation statements)

References 38 publications

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“…MATLAB software was used for data processing, model prediction, and real-time display of moisture content in the moisture content online detection system. First, the initial weight and the initial moisture content of the material were set, and the initial moisture content was measured by the oven method ( Yang et al., 2023b ). The sensor cannot detect the tray position and is a fixed value.…”

Section: Principles and Methodsmentioning

confidence: 99%

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

Yang,

Zheng,

Xiao

et al. 2024

Front. Plant Sci.

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To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model’s predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R2) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R2 and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products.

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Section: Principles and Methodsmentioning

confidence: 99%

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

Yang,

Zheng,

Xiao

et al. 2024

Front. Plant Sci.

Self Cite

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“…The temperature of the inner chamber is regulated by controlling the power of the six infrared heating tubes. An increase in air velocity can significantly increase the drying rate of the material [ 21 ]. As the fan power is a small proportion of the total power of the equipment, an increase in air velocity does not greatly increase the total power of the equipment.…”

Section: Methodsmentioning

confidence: 99%

“…Training a neural network means continuously optimising the weights [ 21 ]. The initial weights are randomly generated, and the training of neural networks is sensitive to the initial weights, which can easily fall into a local optimum if the initial weights are not chosen appropriately.…”

Section: Introductionmentioning

confidence: 99%

Drying Temperature Precision Control System Based on Improved Neural Network PID Controller and Variable-Temperature Drying Experiment of Cantaloupe Slices

Yang

Zheng

Xiao

et al. 2023

Plants

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A drying temperature precision control system was studied to provide technical support for developing and further proving the superiority of the variable-temperature drying process. In this study, an improved neural network (INN) proportional–integral–derivative (PID) controller (INN-PID) was designed. The dynamic performance of the PID, neural network PID (NN-PID) and INN-PID controllers was simulated with unit step signals as an input in MATLAB software. A drying temperature precision control system was set up in an air impingement dryer, and the drying temperature control experiment was carried out to verify the performance of the three controllers. Linear variable-temperature (LVT) and constant-temperature drying experiments of cantaloupe slices were carried out based on the system. Moreover, the experimental results were evaluated comprehensively with the brightness (L value), colour difference (ΔE), vitamin C content, chewiness, drying time and energy consumption (EC) as evaluation indexes. The simulation results show that the INN-PID controller outperforms the other two controllers in terms of control accuracy and regulation time. In the drying temperature control experiment at 50 °C–55 °C, the peak time of the INN-PID controller is 237.37 s, the regulation time is 134.91 s and the maximum overshoot is 4.74%. The INN-PID controller can quickly and effectively regulate the temperature of the inner chamber of the air impingement dryer. Compared with constant-temperature drying, LVT is a more effective drying mode as it ensures the quality of the material and reduces the drying time and EC. The drying temperature precision control system based on the INN-PID controller meets the temperature control requirements of the variable-temperature drying process. This system provides practical and effective technical support for the variable-temperature drying process and lays the foundation for further research. The LVT drying experiments of cantaloupe slices also show that variable-temperature drying is a better process than constant-temperature drying and is worthy of further study to be applied in production.

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“…Fabani et al (Fabani et al 2021) demonstrated that, in convective drying experiments, GA-BP can better represent the drying kinetics of watermelon peels compared to 11 different empirical models. Yang et al (Yang et al 2023) optimized an artificial neural network using GA to effectively Compared with traditional machine learning algorithms, the GA-BP algorithm has unique advantages. The global search capability of the GA can seek optimization of the initial weights and thresholds of the BP neural network, thus improving the prediction accuracy and learning speed of the network.…”

Section: Introductionmentioning

confidence: 99%

Moisture content prediction of municipal sludge drying process based on genetic algorithm optimized BP neural network

Zhang,

Wang

2024

Preprint

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Municipal sludge drying process of internal moisture detection difficulties, and in order to predict the change rule of the internal moisture in the hot-air drying process of municipal sludge, the moisture change of municipal sludge under different drying conditions was monitored through experiments. The moisture prediction model of municipal sludge drying process was established by optimizing the BP neural network using mathematical model, back-propagation (BP) neural network and genetic algorithm (GA), and the performance analysis of the model was carried out. In addition, a sensitivity analysis of the GA-BP model was performed. The results showed that the temperature had the most significant effect on the drying process of municipal sludge, reducing the thickness of the mud layer could improve the drying rate of sludge, and the moisture reduction rate of municipal sludge showed a fast and then a slow trend during the whole drying process. Through the performance analysis, the coefficient of determination of the test set of GA-BP model is 0.9999, and the root mean square error is only 0.0039, which means that the GA-BP model has a better prediction effect and can predict the dynamic change of moisture in the drying process of municipal sludge more accurately. The sensitivity analysis shows that the drying time and drying temperature have the greatest influence on moisture ratio(MR), and the results of the study can provide a theoretical basis for the optimization of the sludge drying process and procedure, and provide a powerful reference for guiding the resource utilization of sludge.

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Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking

Cited by 15 publications

References 38 publications

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

Drying Temperature Precision Control System Based on Improved Neural Network PID Controller and Variable-Temperature Drying Experiment of Cantaloupe Slices

Moisture content prediction of municipal sludge drying process based on genetic algorithm optimized BP neural network

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