| Title: | Temporal and statistical insights into multivariate time series forecasting of corn outlet moisture in industrial continuous-flow drying systems |
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| Authors: | ID Simonič, Marko (Author)
ID Klančnik, Simon (Author) |
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| Files: |  applsci-15-09187_(1).pdf (3,02 MB)
MD5: AB1A82E7E1EC12DC480574A136B701FA
 https://www.mdpi.com/2076-3417/15/16/9187
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | Corn drying is a critical post-harvest process to ensure product quality and compliance with moisture standards. Traditional optimization approaches often overlook dynamic interactions between operational parameters and environmental factors in industrial continuous flow drying systems. This study integrates statistical analysis and deep learning to predict outlet moisture content, leveraging a dataset of 3826 observations from an operational dryer. The effects of inlet moisture, target air temperature, and material discharge interval on thermal behavior of the system were evaluated through linear regression and t-test, which provided interpretable insights into process dependencies. Three neural network architectures (LSTM, GRU, and TCN) were benchmarked for multivariate time-series forecasting of outlet corn moisture, with hyperparameters optimized using grid search to ensure fair performance comparison. Results demonstrated GRU’s superior performance in the context of absolute deviations, achieving the lowest mean absolute error (MAE = 0.304%) and competitive mean squared error (MSE = 0.304%), compared to LSTM (MAE = 0.368%, MSE = 0.291%) and TCN (MAE = 0.397%, MSE = 0.315%). While GRU excelled in average prediction accuracy, LSTM’s lower MSE highlighted its robustness against extreme deviations. The hybrid methodology bridges statistical insights for interpretability with deep learning’s dynamic predictive capabilities, offering a scalable framework for real-time process optimization. By combining traditional analytical methods (e.g., regression and t-test) with deep learning-driven forecasting, this work advances intelligent monitoring and control of industrial drying systems, enhancing process stability, ensuring compliance with moisture standards, and indirectly supporting energy efficiency by reducing over drying and enabling more consistent operation. |
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| Keywords: | advanced drying technologies, continuous flow drying, time-series forecasting, LSTM, GRU, TCN, deep learning, statistical analysis, optimization of the drying process |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 01.07.2025 |
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| Article acceptance date: | 20.08.2025 |
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| Publication date: | 21.08.2025 |
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| Publisher: | MDPI |
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| Year of publishing: | 2025 |
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| Number of pages: | 19 str. |
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| Numbering: | Vol. 15, iss. 16, [article no.] 9187 |
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| PID: | 20.500.12556/DKUM-95872  |
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| UDC: | 536:004.8 |
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| ISSN on article: | 2076-3417 |
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| COBISS.SI-ID: | 246397187 |
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| DOI: | 10.3390/app15169187 |
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| Publication date in DKUM: | 03.11.2025 |
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| Views: | 206 |
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| Downloads: | 10 |
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| Metadata: |    |
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| Categories: | Misc.
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