Fixed-bed columns are a well-established water purification technology. Several models have been constructed over the decades to scale up and predict the breakthrough curve of an adsorption column varying the flow rate, length, and initial concentration of solute. In this work, we proposed using an emerging computational approach of a physic-informed neural network (PINN) that uses artificial intelligence to solve the partial differential equation model of adsorption.
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