Assessment of carbon neutrality in waste water treatment systems through deep learning algorithm

Around the world, it is growing harder to provide clean and safe drinking water. In wastewater treatment, sensors are employed, and the Internet of Things (IoT) is used to transmit data. Chemical oxygen demand (COD), biochemical demand (BOD), total nitrogen (T-N), total suspended solids (TSS), and phosphorous (T-P) components all contribute to eutrophication, which must be avoided. The wastewater sector has lately made efforts to become carbon neutral; however, the environmental impact and the road to carbon neutrality have received very little attention. The challenges are caused by poor prediction. This research proposes deep learning modified neural networks (DLMNN) with Binary Spotted Hyena Optimizer (BSHO) for modeling and calculations to address this challenge. All efforts for resource recovery, water reuse, and energy recovery partially attain this objective. In contrast to previous modeling techniques, the DLMNN-training BSHOs and validation demonstrated outstanding accuracy shown by the model's high coefficient (R2) for both training and testing. Also covered are recent developments and problems with nanomaterials made from sustainable carbon and graphene quantum dots, as well as their uses in the treatment and purification of wastewater. The proposed model DLMNN-BSHO achieved 95.936% precision, 95.326% recall, 93.747% F-score, and 99.637% accuracy. HIGHLIGHTS The carbon neutrality has received much attention for water treatment.; The deep learning modified neural networks (DLMNN) with Binary Spotted Hyena Optimizer (BSHO) for modeling were used.; The proposed model DLMNN-BSHO achieved 95.936% precision, 95.326% recall, 93.747% F-score, and 99.637% accuracy.;