Recycling Waste for Wastewater Treatment and Evaluation Using Artificial Intelligence for Irrigation Purposes

Abstract

Water resources are facing increasing pressures due to population and industrial expansion and climate change, calling for the development of innovative solutions for wastewater treatment and reuse. This study aims to prepare alum (aluminum sulfate) from industrial waste (aluminum waste and spent battery fluid) and evaluate its efficiency in removing pollutants from industrial wastewater, particularly turbidity and oily substances, and the potential for reusing the treated water for irrigation of non-food plants. Fifty water samples were collected from the Kawashi Industrial Area in northern Iraq, and multiple chemical analyses were conducted to assess water quality according to irrigation standards. The results showed that the prepared alum was highly effective in removing turbidity, with a removal rate of 98.97%, and oily substances, with a removal rate of 98.97% at an optimum pH (pH = 8.5) and a dosage of 200 mg/L. An adaptive neuro-fuzzy inference (ANFIS) model was also applied to evaluate the suitability of the treated water for irrigation purposes, based on indicators such as SAR, KR, and PI. The model results showed that the water ranged from good to excellent for irrigating non-food plants. The study suggests the potential for developing a sustainable environmental model that combines waste recycling, water treatment, and the use of artificial intelligence in assessment, supporting the circular economy and enhancing water and agricultural security in emerging industrial regions.