Turbidity is a measure of water quality. Excessive turbidity poses a threat to health and causes pollution. Most of the available mathematical models of water treatment plants do not capture turbidity. A reliable model is essential for effective removal of turbidity in the water treatment plant. This paper presents a comparison of Hammerstein Wiener and neural network technique for estimating of turbidity in water treatment plant. The models were validated using an experimental data from Tamburawa water treatment plant in Kano, Nigeria. Simulation results demonstrated that the neural network model outperformed the Hammerstein-Wiener model in estimating the turbidity. The neural network model may serve as a valuable tool for predicting the turbidity in the plant.

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Clay minerals have been recognized as one of the cheap and effective materials for wastewater remediation. Among the various clay minerals, montmorillonite (MMT) has received much attention due to its wide availability, low-cost and promising properties such as high porosity, mechanical strength, and cation exchange capacity. Additionally, MMT has high swelling properties. These features make it an ideal material for wastewater remediation applications. In addition, it possessed good cationic exchange capacity, making it easier to interact with various molecules. MMT and its composites exhibited good selectivity and catalytic activity for contaminants elimination from wastewater. Surface modification and functionalization have been identified as a way to improve the MMT’s adsorptive performance and endow it with light and light-harnessing properties. Thus, MMT composites, especially metal and metal-oxide nanoparticles, have shown good adsorption and photocatalytic activity toward the elimination/mineralization of various contaminants such as dyes, pharmaceuticals, heavy metals, and other organic and inorganic species. As such, MMT and its composites can be adopted as potential materials for wastewater remediation.