Long Short-Term Memory Networks for Automated Waste Treatment Augmented With IoT and Bioelectric Sensors

Abstract

In order to improve automated waste treatment system, this work investigates the combination of bioelectric sensors, internet of things (IoT), and long short-term memory (LSTM) networks. Using LSTM networks, the system gathers data from several sensors, including temperature, dissolved oxygen, pH, and microbial activity, and uses it to analyses data in real time. This makes it possible to identify anomalies, gain predictive insights, and optimize treatment parameters like chemical dosing and aeration rates. IoT and bioelectric sensor integration offers deeper insights into nutrient cycles and microbial dynamics, enabling more informed waste management decision-making. Compared to traditional procedures, this strategy seeks to minimize environmental effect, lower operating costs, and increase treatment efficiency. LSTM-based systems present promise advances in automating waste treatment processes for increased efficiency and sustainability through continuous learning and adaptation.