DLUP: A Deep Learning Utility Prediction Scheme for Solid-State Fermentation Services in IIoT

Wang, Min; Pang, Shan chen; Ding, Tonghai; Qiao, Sibo; Zhai, Xue; Wang, Shuo; Xiong, Naixue; Huang, Zheng

doi:10.1109/tii.2021.3106590

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…For solid-state fermentation (SSF) using IoT, Wang et al (2022) suggested a Deep Learning Utility Prediction (DLUP) scheme. The objectives of the study were to create a systematic framework for collecting SSF process parameters and predicting utility, propose an edge-rewriteable Petri net to model and evaluate the soundness of the system frameworks, and create a combined prediction model by integrating "LSGAN's generating and "FCNN's predicting abilities.…”

Section: Survey Of Iot Architecture and Iot Application In Food Ferme...mentioning

confidence: 99%

Internet of Things (IoT) in the food fermentation process: A bibliometric review

Adeleke

Nwulu

Adebo

2023

J Food Process Engineering

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This study investigated available literature on leveraging the Internet of Things (IoT) to optimize the food fermentation process. Utilizing a subjective examination strategy through bibliometric investigation, this paper assessed 44 articles between 2013 and 2022. The bibliometric study focused on publications per author, publications per country, and a research center based on co-occurrence keywords, and research patterns. While available publications in this domain show IoT can be used to optimize the fermentation process, uptake has been slow and has been in an infant stage in the past 8 years. The scholarly articles, using the co-occurrence of keywords, showed five (5) clusters, which are (i) implementing IoT in forecasting the fermentation process to achieve effective quality control, (ii) remotely monitoring and automating the production process to achieve effective process control, (iii) monitoring temperature in real-time during the fermentation process, (iv) digitally storing parameters during the fermentation process using WLAN, and (v) applying middleware in the processing properties of agricultural produce. These cluster areas revealed how IoT can be used to optimize the food fermentation process. Given the growth and interest in IoT technologies in the food fermentation process, this study explored available literature to highlight available IoT technologies in developing strategies for the food fermentation process and predictive analysis of fermented food products. Practical applicationsUsing IoT technologies, manufacturers may optimize their regulatory procedures and product quality by monitoring food fermentation process parameters such as temperature, carbon dioxide, humidity, viscosity, and so on in real-time. Food manufacturers may also obtain and use these real-time data for food safety. Some cutting-edge smart sensors and cloud-based predictive analytics systems can equally predict stages of the fermentation process and possibly detect challenges along the process.

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Section: Survey Of Iot Architecture and Iot Application In Food Ferme...mentioning

confidence: 99%

Internet of Things (IoT) in the food fermentation process: A bibliometric review

Adeleke

Nwulu

Adebo

2023

J Food Process Engineering

View full text Add to dashboard Cite

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Temperature control of beer fermentation based on variable domain fuzzy PID and neural network technology and its application analysis

2024

Adv Control Appl

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Temperature control in the beer manufacturing process is crucial for product quality. Given the gap between China's automation in beer production and the international level, improving the technology in this area has gradually become a core issue in optimizing domestic beer production. This study combines a proportional integral derivative controller with a fuzzy modeling strategy and incorporates a variable‐domain structure to propose a variable‐domain fuzzy proportional integral derivative controller control method. To cope with the challenges of production interaction, the study also introduces neural network technology. The experimental data indicated that the variable‐domain fuzzy proportional integral derivative controller outperforms the conventional proportional integral derivative controller and the fuzzy proportional integral differential controller in terms of overshooting, with a maximum overshoot of only 1.0, compared with 0.50 and 0.70, respectively. The variable‐domain fuzzy proportional integral differential controller exhibited a minimal overshoot of only 0.01 when the model parameter is increased by 20%. In comparison, the other methods reach overshoot values of 0.92 and 1.0. The proposed method maintained superior stability even under the influence of impulse disturbance, step disturbance, and modeling variations. These results demonstrated that the research method is significantly more stable than both the proportion integration differentiation (PID) controller and fuzzy PID controller in complex dynamic parameter environments. The proposed method involved 60 rounds of neural network control, which was successfully implemented. The temperature readings T1 and T2 remained stable within the range of 1.0%–1.02% throughout the experiments. The study demonstrates that the proposed methods have higher accuracy and less fluctuation in actual application, making them more available. Taken together, the above results show that the combination of variable‐domain fuzzy PID controller and neural network technology in beer production has achieved excellent control results. This study not only provides a strong technical support for the progress of beer production technology in China, but also has important industrial application value and wide promotion prospects.

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DLUP: A Deep Learning Utility Prediction Scheme for Solid-State Fermentation Services in IIoT

Cited by 2 publications

References 30 publications

Internet of Things (IoT) in the food fermentation process: A bibliometric review

Internet of Things (IoT) in the food fermentation process: A bibliometric review

Temperature control of beer fermentation based on variable domain fuzzy PID and neural network technology and its application analysis

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