In the heart of South Africa, a pressing environmental challenge is unfolding—one that threatens both public health and the delicate balance of ecosystems. Antibiotic residues, seeping into water systems from pharmaceutical discharge, agricultural runoff, and poor waste management, are proving to be a formidable adversary. Conventional water treatment methods are falling short, leaving researchers scrambling for innovative solutions. Enter Molly Katlo Keitemoge, a researcher from the Department of Chemical Engineering at the University of Johannesburg, who is pioneering the use of artificial intelligence to tackle this pressing issue.
Keitemoge’s groundbreaking review, published in the South African Journal of Chemical Engineering—translated to English as the “Journal of Chemical Engineering in South Africa”—delves into the application of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models to predict and optimize antibiotic removal from South African water bodies. This research is not just a scientific endeavor; it’s a beacon of hope for a future where technology and environmental stewardship go hand in hand.
The growing occurrence of antibiotic residues in South African water systems poses serious environmental and public health risks. “Conventional water treatment procedures frequently fail to properly remove these micropollutants,” Keitemoge explains. “This necessitates new predictive and analytical approaches.” Her review critically investigates the implementation of ANN and ANFIS models, which have shown promising results in forecasting and optimizing antibiotic removal.
The review highlights the challenges associated with removing pharmaceuticals like diclofenac sodium and tetracycline, emphasizing the need for advanced modeling techniques. Keitemoge’s work compares the performance of ANN and ANFIS models in predicting the removal of emerging contaminants, revealing that AI models achieved higher R² and lower error metrics compared to classical statistical or isotherm models.
The implications of this research extend far beyond the laboratory. In the energy sector, where water is a critical resource, the ability to predict and optimize antibiotic removal could revolutionize water treatment processes. “This research could shape future developments in the field by providing a robust framework for predicting and optimizing the removal of emerging contaminants,” Keitemoge notes. “It offers a promising avenue for enhancing water treatment technologies, ensuring safer and more sustainable water resources.”
As we stand on the brink of a technological revolution, Keitemoge’s work serves as a reminder that the solutions to our most pressing environmental challenges may lie in the intersection of science, technology, and innovation. Her research not only sheds light on the potential of AI in environmental engineering but also paves the way for a future where technology and sustainability coexist harmoniously. In the words of Keitemoge, “The journey towards sustainable water management is a collective effort, and every step counts.”