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• W4387666779 abstract "The energy sources of the universe are limited and draining with increasing speed. One of the biggest consumption of energy is in the manufacturing industry with 35% of the overall consumption. Collecting and analyzing data is becoming a crucial topic with the Internet of Things (IOT). Using the science of data is becoming a hotter topic in manufacturing industries as well. The study aimed to apply theoretical analytical methods in real-life cases and demonstrate potential returns on IOT investments for fully automated systems. This study not only analyzed and predicted the energy consumption but also provided a prescriptive receipt to minimize energy spending while keeping high-quality production standards. For the use case, the data gathered from real-production of a multinational food manufacturer and machine learning algorithms were applied. The study provides an energy efficiency of 5–6% for a single machine which consumes 35–45% of the total energy spending of the entire factory." @default.
• W4387666779 created "2023-10-17" @default.
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• W4387666779 date "2023-01-01" @default.
• W4387666779 modified "2023-10-17" @default.
• W4387666779 title "Energy Efficiency Optimization Application in Food Production Using IIOT Based Machine Learning" @default.
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• W4387666779 doi "https://doi.org/10.1007/978-3-031-38387-8_11" @default.
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