Reimagining Wastewater

By 2050, the global population is expected to reach 9.7 billion, nearly 50% more than in 2019. With over two-thirds of people living in cities, urban areas will remain the major hubs of wastewater generation due to dense populations and concentrated water use.
However, farms will continue to be the largest hotspots for phosphate and nutrient losses, driven by fertilizer runoff, livestock waste, and inefficient nutrient cycling.
This creates a growing global challenge: cities generate wastewater, while farms lose nutrients, widening the disconnect between waste management and agricultural nutrient needs.

Optimization techniques and advanced control schemes are still not widely adopted in real-world wastewater treatment practice, largely because their complexity can be intimidating and the industry often operates under significant economic and operational constraints. Utilities typically prioritize reliability, simplicity, and cost-effectiveness, which can make highly sophisticated optimization tools seem impractical or risky to implement. However, a range of simulation and modeling approaches, such as process modeling, computational fluid dynamics (CFD), and reaction kinetics modeling offer powerful opportunities to enhance system performance. These tools can be used to identify bottlenecks, improve energy efficiency, refine chemical dosing, and strengthen overall process stability. As these methods become more accessible and user-friendly, they hold strong potential to support higher levels of optimization even as emerging wastewater challenges grow more complex and nonlinear.

To address the challenges of this urbanized future, smart technologies are essential. Artificial Neural Networks (ANN), the most widely used machine learning model, can play a crucial role in optimizing nutrient recovery and wastewater by-product management. By leveraging machine intelligence, wastewater treatment can achieve greater efficiency, accuracy, cost-effectiveness, and environmental sustainability.