Industrial Internet of things-based solar photo voltaic cell waste management in next generation industries

Muthusamy, Parimala Devi; Velusamy, Gowrishankar; Thandavan, Sathya; Govindasamy, Boopathi Raja; Savarimuthu, Nithya

Industrial Internet of things-based solar photo voltaic cell waste management in next generation industries

2022

Muthusamy, Parimala Devi | Velusamy, Gowrishankar | Thandavan, Sathya | Govindasamy, Boopathi Raja | Savarimuthu, Nithya

Nowadays, modern industries generate their energy by using renewable solar. The rapid increase in photovoltaic (PV) module installations provides a better energy conversion, but their life cycle is a major concern. This research paper focuses on the recycling process for solar PV modules using the Internet of Things in industries. The smart bin with the Internet of Things (IoT) utilizes a machine learning approach to collect solar waste. The proposed smart bin uses k-Nearest Neighbor’s algorithm (k-NN) and Long Short-Term Memory (LSTM), a network-based learning algorithm. These algorithms are useful in updating the level of the bin via alert messages. It also helps in identifying the type of waste material. The k-NN algorithm provides 83% accuracy in predicting the bin level in a real-time testing environment. The smart dust bin classifies the waste materials, and notifies its level to the collection center through the IoT platform when the level reaches a prescribed threshold, the signal corresponding to the level is passed to the common waste collection unit. IoT is connected to Cloud Server. It helps to predict the level of the smart bin. Delay is introduced in the order of 3–8 s while the alert message is sent to the common waste collection unit. The system monitors the smart bin levels and sends the notifications to alert and initiate the collection unit. Real-time mobile app monitors the bin’s level and location. The cloud IoT analytics analyze the solar e-waste in a different locations in industries.The proposed system works better and provides accurate results by using machine learning approach.

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