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.
TopIntroduction
One of the most important components of contemporary environmental management is the treatment of waste, especially wastewater. Garbage treatment facilities face enormous challenges as a result of the growing global population and industrialization (Zhuang et al., 2022), which has led to a large increase in garbage creation. Conventional waste treatment techniques frequently depend on set control tactics and physical intervention, which results in inefficiencies (Pramanik, Pijush Kanti Dutta, Bijoy Kumar Upadhyaya et al., 2019), expensive operating expenses, and less than ideal treatment results. Utilizing cutting-edge technologies like bioelectric sensors and the Internet of Things (IoT) to improve the efficacy and efficiency of waste treatment procedures has gained popularity in recent years (Sun et al., 2019). Real-time monitoring and management of several parameters, including pH, temperature, dissolved oxygen, microbial activity, and chemical concentrations, are made possible by the integration of IoT devices into waste treatment systems. An abundance of data from these Internet of Things sensors can be utilized to optimize waste treatment procedures and obtain insights into their effectiveness. Furthermore, improvements in bioelectric sensors (Indrakumari et al., 2020) have made it possible to identify and measure the breakdown of organic matter and microbial activity in waste streams, which provides important data for process monitoring and optimization.
Using Long Short-Term Memory (LSTM) networks is one promising way to realize the potential of IoT and bioelectric sensors in waste treatment. Recurrent neural networks (RNNs) of the LSTM network type (Mangalampalli et al., 2023) are particularly good at identifying patterns and temporal connections in sequential data. Predictive models that can foresee changes in waste composition, spot anomalies, and optimize treatment procedures in real-time can be created by combining LSTM networks with data gathered from IoT and bioelectric sensors. Manual intervention, rigid control systems, and poor flexibility to changing conditions are common characteristics of traditional waste treatment operations (Pokkuluri and Nedunuri, 2020; Wang et al., 2021). These elements may result in ineffectiveness, less than ideal treatment results, and higher operating expenses. Furthermore, there are many obstacles to effective treatment due to the complexity and diversity of waste streams.
There are tremendous prospects to transform waste treatment processes with the introduction of bioelectric and Internet of Things (IoT) sensors (Wang et al., 2022). IoT sensors may give real-time information on important factors like pH, temperature, dissolved oxygen, and chemical concentrations, which makes treatment operations easier to regulate and monitor. Understanding the dynamics of waste and the needs for treatment is made easier by the insights that bioelectric sensors provide into microbial activity, the breakdown of organic matter, and the cycling of nutrients (Jawad et al., 2023). Long-term dependencies can be captured and temporal data sequences analysed with great effectiveness using LSTM networks. Predictive models that can foresee changes in waste composition, spot trends, and improve treatment procedures in real-time can be created by combining LSTM networks with data gathered from IoT and bioelectric sensors (Sharmila et al., 2023).
This study's main goal is to find out how well LSTM networks operate to optimize waste treatment procedures that are enhanced by bioelectric and Internet of Things sensors. In particular, we want to: Create predictive models based on LSTMs to analyze data obtained from bioelectric and IoT sensors in waste treatment plants. Analyze how well LSTM networks perform in spotting abnormalities, forecasting changes in waste composition, and streamlining treatment procedures. Examine how well LSTM networks perform in comparison to other machine learning techniques like Decision Trees (Zhang et al., 2023) and Support Vector Machines (SVM) (Erkoyuncu and Khan, 2023) as well as conventional control schemes.