ResumenEn este trabajo, se formula un modelo de programación no lineal (PNL) para el diseño de sistemas de tratamiento selectivo de efluentes que contienen trazas de amoxicilina. La presencia de antibióticos en aguas superficiales o subterráneas representa un problema ambiental importante por su potencial bioacumulativo y el desarrollo de resistencia antibiótica. Los métodos para la eliminación de fármacos y antibióticos incluyen la oxidación química usando reactivos Fenton, floculación y fotoquímica. Sin embargo, sus eficiencias de remoción son limitadas y se requiere de estudios orientados a optimizar los costos de tratamiento. Para la degradación de la amoxicilina se incorpora en el sistema una tecnología electroquímica. El modelo de PNL desarrollado se construye con base en una superestructura básica de red que expande el espacio de alternativas posibles de tratamiento, minimizando el flujo total de efluente que es tratado. Se resuelven cuatro ejemplos para mostrar la versatilidad y la utilidad del modelo propuesto.
Palabras clave: degradación de amoxicilina; tratamiento de aguas residuales; coagulación electroquímica; diseño óptimo
Nonlinear Programming Model for the Design of Electrochemical Treatment Systems for Wastewater Streams Contaminated with Amoxicillin Traces AbstractIn this work, a nonlinear programming (NLP) model is formulated for the design of selective effluent treatment systems containing traces of amoxicillin. The presence of antibiotic in surface water bodies or groundwater generates an important environmental problem due to its bio-accumulative potential and the development of antibiotic resilient bacteria. Current methods employed in the removal of drugs and antibiotics include chemical oxidation by Fenton reagents, flocculation and photochemical treatments. However, their removal efficiencies are still limited and studies are required for the optimization of the treatment costs. An electrochemical technology is incorporated in the system for the degradation of amoxicillin. The NLP model developed is built from a basic network superstructure that expands the space of alternative treatment possibilities, minimizing the total flowrate that is sent to treatment. Four illustrative examples are solved to show the versatility and usefulness of the model proposed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.