Enhancing Environmental Sustainability: Extreme Learning Machine Approach to Industrial Waste Management
J. Environ. Nanotechnol., Volume 13, No 2 (2024) pp. 220-228
Abstract
Industrial activities pose significant challenges to environmental conservation due to the generation of large volumes of waste. Effectively managing industrial waste is essential for mitigating environmental impact and fostering sustainable development. This research proposes the utilization of Extreme Learning Machine (ELM) algorithms to optimize industrial waste management practices and enhance environmental conservation efforts. The study encompasses various aspects, including predictive modelling for waste generation, automated waste segregation and sorting, optimization of waste treatment processes, environmental impact assessment, resource recovery from waste streams, real-time monitoring and control systems, decision support systems for policy-making, data-driven compliance monitoring, risk assessment, and mitigation strategies. Overall, the advantages of ELM make it a powerful tool for various machine learning tasks, particularly in scenarios where efficiency, scalability, and simplicity are crucial. By integrating ELM algorithms with Internet of Things (IoT) devices and sensor networks, smart waste management systems can be developed for proactive intervention and pollution prevention. This research aims to contribute to the advancement of sustainable industrial practices and environmental conservation efforts through innovative applications of Extreme Learning Machine in waste management.
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