Abstract:
Pollution by industrial effluent, chemical fertilisers, and drugs has turned into a very
relevant problem in the world today that is posing a very great danger to water sources
and its users. Effective and popular methods of oxidation processes include advanced
oxidation processes (AOP) to remove pollutant concentrations but their executions
are methodically power intensive and are generally known to produce secondary
toxic products. To overcome these limitations, this work proposes a green oxidation
process accompanied by a deep learning (DL) model for the successful identification
of pollutants and their efficient degradation. This oxidation technique uses green oxidising agents such as hydrogen peroxide, and plant based catalysts, which lead to
minimal production of secondary hazardous pollutants. The DL model acquires es
sential data including diverse pollutants, reaction conditions, and water chemistries
to forecast optimum sets of the operating parameters such as oxidant concentration,
pH, temperature, reaction time. This helps in achieving the degradation of pollutants
in a more enhanced way depending on the environmental conditions. The deep learn
ing incorporated into the model enables it to learn from the previous performance
and provides optimal pollution removal efficiency with low energy complicity and
environmental impact as well. Furthermore, the DL model involves a prediction of
the best routes by which the production of undesirable by-products can be minimised
making the whole process more sustainable. There is experimental confirmation
that the proposed method enhances the efficiency of pollutant elimination rates in
contrast to the conventional oxidation approaches while requiring less energy and
having lower costs of operation. It is evidenced by the model’s ability to function
under varying environmental conditions, mainly due to its sustainable design, which
makes the presented model suitable for large scale application in the remediation of
the environment. This work lays the foundation for combining green chemistry and
artificial intelligence in developing technologies for reducing the amount of hazardous
pollutants in the environment that are eco-friendly and highly efficient.
