Wastewater Management Using a Neural Network-Assisted Novel Paradigm for Waste Prediction from Vermicomposting

Vermicomposting is one of the most important waste management techniques in the process of vermiculture. In this study, a neural network-assisted novel paradigm is proposed to predict waste from vermicomposting. The proposed neural network skeleton is based on a gallium arsenide processing schema, which is used to separate wastes. By comparing the proposed system with existing methods, it was found that the proposed approach had the highest average prediction ratio of 91.32%, outperforming other techniques like the encoder-recurrent decoder (ERD) network, recurrent neural network (RNN), and deep long short-term memory (deep LSTM) network. The separation ratio analysis also demonstrated the effectiveness of the proposed method, with a range of 45–94%. Furthermore, the study emphasizes the importance of chemical equilibrium and the effectiveness of our proposed gallium arsenide processing schema in achieving high prediction and separation accuracies, showcasing its potential for practical application in waste management processes. Lastly, the prediction of the process evolution stages is detailed, indicating the efficiency of the proposed system in achieving various levels of waste separation. Overall, the study provides valuable insights into the potential of the proposed methods in optimizing wastewater management processes, paving the way for more effective and sustainable vermicomposting practices.