High-Precision Laboratory Dryer for Characterization of the Drying Behavior of Agricultural and Food Products

Reyer, Sebastian; Awiszus, Sebastian; Müller, Joachim

doi:10.3390/machines10050372

Cited by 4 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scheme effectively avoids the influence of temperature on the detection of the load sensor. Reyer et al. (2022) directly installed the load sensor in the drying chamber outside, more effectively eliminating the measurement error caused by temperature.…”

Section: Introductionmentioning

confidence: 99%

“…The scheme effectively avoids the influence of temperature on the detection of the load sensor. Reyer et al (2022) directly installed the load sensor in the drying chamber outside, more effectively eliminating the measurement error caused by temperature. However, this scheme destroyed the sealing of the drying chamber, which increased the difficulty of controlling the temperature and humidity in the drying chamber.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Yang,

Zheng,

Xiao

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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.

View full text Add to dashboard Cite

show abstract

“…In this context, the equipment-stop detection program seriously damages the continuity of the material drying process, and ensuring the quality of dry products becomes challenging. To address the nonlinear effect of drying temperature on the measurement results of the weighting sensor, Reyer et al (2022) installed the weighting sensor outside the drying chamber, thereby fundamentally solving the problem. However, this solution destroyed the drying chamber structure, affected the sealing of the drying chamber, and increased the difficulty of temperature and humidity control in the drying chamber.…”

mentioning

confidence: 99%

Online monitoring system of material moisture content based on the Kalman filter fusion algorithm in air-impingement dryer

Yang,

Zheng,

Xiao

et al. 2024

Front. Sustain. Food Syst.

View full text Add to dashboard Cite

A Kalman filter fusion algorithm was proposed, and an online monitoring system was developed for real-time monitoring of the moisture content of materials in an air-impingement dryer. The Kalman filter algorithm was used to estimate the optimal state of the original detection values of the weighting sensor and air velocity sensor. A backpropagation (BP) neural network fusion model was established, where the weight detection value, elastic substrate temperature, air velocity, and impingement distance were considered inputs and the real weight of the material was the output. The optimal topology of the BP neural network was selected, and the initial weights and thresholds of the BP neural network were optimized using a genetic algorithm. The coefficient of determination (R2) and root mean square error (RMSE) of the optimized BP neural network fusion model were 0.9995 and 4.9, respectively. The Kalman filter fusion algorithm, which can realize online monitoring of moisture content, was established using the Kalman filter algorithm and fusion model. Moreover, an online monitoring system for material moisture content was developed, validation experiments were carried out, and the R2 and RMSE of the nine sets of validation experiments were 0.9963 and 0.78, respectively. The monitoring system satisfied the requirements of material moisture content detection accuracy in the drying process. The developed monitoring system is greatly important for improving the automation level of the drying equipment for fruits and vegetables. The proposed Kalman filter fusion algorithm also provides a reference for other multifactor fusion detection.

show abstract

Technological Advancements for Processing and Preservation of Fruits and Vegetables

2024

Frontiers Research Topics

View full text Add to dashboard Cite

Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

show abstract

High-Precision Laboratory Dryer for Characterization of the Drying Behavior of Agricultural and Food Products

Cited by 4 publications

References 19 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

Online monitoring system of material moisture content based on the Kalman filter fusion algorithm in air-impingement dryer

Technological Advancements for Processing and Preservation of Fruits and Vegetables

Contact Info

Product

Resources

About