Enhancing Hospital Wastewater Treatment and Environmental Safety with Kernel Neural Network Models

Micropollutants, including pharmaceutical
residues, are prevalent in hospital wastewater and pose a
significant risk to public and environmental health. HWWT is
gaining significance as traditional wastewater treatment
methods are insufficient for the effective removal of these
complex pollutants. Hospital effluents provide significant risks
to human and environmental health if not properly treated and
then released into global water systems. Conventional hospital
wastewater treatment plants struggle to adequately process
hospital effluents due to their intricate chemical composition.
This research use cutting-edge machine learning techniques to
explore a novel approach for detoxifying hospital wastewater.
They employed PCA to identify the key elements influencing
water quality. They built a graph-based neural network with two
convolutional layers to extract advanced features from the data.
To enhance the nonlinearity of capsule networks, a
CNNCapsNet model was developed, and wastewater
classification was achieved using dynamic routing. The proposed
model had an accuracy rate of 97.81%, demonstrating its
effectiveness in predicting and classifying wastewater quality.
This study presents a feasible alternative to existing approaches
for classifying and treating hospital wastewater, utilising
advanced machine learning techniques. The results may have
significant implications for improved wastewater management
and the mitigation of pharmaceutical contamination.