Comparative studies of conventional and AI methods for wastewater treatment in the sugar, food, textile, and pharmaceutical industries: a review

Abstract

The use of traditional methodologies in combination with artificial intelligence (AI) has attracted much interest for the treatment of industrial wastewater in different sectors, such as food, sugar, textiles, and pharmaceuticals. This combination of treatment technologies enhances operational and management efficiency by using conventional technologies and AI-enabled predictive capabilities, real-time optimization, decision-making, and management. This synergistic integration effectively addresses the complex environmental and operational challenges associated with industrial wastewater management. Herein, we review the characteristics of wastewater and the treatment technologies used, including conventional physicochemical and bioremediation methods, as well as AI techniques. These AI approaches have helped identify numerous pollutant types in industrial wastewater and support optimization modelling, while the integration of traditional techniques signifies cutting-edge innovation. Recent advancements in conventional and AI modelling tuned for sugar, food, textile, and pharmaceutical industrial wastewater are discussed, along with the exploration of AI methodologies for monitoring, prediction, and process optimization, offering valuable insights into the evolving scenario of wastewater treatment technologies.