ECOLOGICALLY FRIENDLY OXIDATION PROCESSES DEEP LEARNING MODEL TAKING AIM AT ENVIRONMENTAL POLLUTANTS

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.