Stabilization ponds are easy to operate and their maintenance is simple. Treatment is carried out naturally and they are recommended in developing countries. The main disadvantage of these systems is the large land area they occupy. The aim of this study was to perform an optimization in the design and cost of a facultative pond, considering a mathematical analysis of the traditional methodology to determine the model constraints (fecal coliforms and organic matter). Matlab optimization toolbox was used for nonlinear programming. A facultative pond with the traditional method was designed and then the optimization system was applied. Both analyses meet the treated water quality requirements for the discharge to the receiving bodies. The results show a reduction of hydraulic retention time by 4.82 days, and a decrease in the area of 17.9 percent over the traditional method. A sensitivity analysis of the mathematical model is included. It is recommended to realize a full-scale study in order to verify the results of the optimization.

Lately, the development of green chemistry methods with high efficiency for metal nanoparticle synthesis has become a primary focus among researchers. The main goal is to find an eco-friendly technique for the production of nanoparticles. Ferro- and ferrimagnetic materials such as magnetite (Fe3O4) exhibit superparamagnetic behavior at a nanometric scale. Magnetic nanoparticles have been gaining increasing interest in nanoscience and nanotechnology. This interest is attributed to their physicochemical properties, particle size, and low toxicity. The present work aims to synthesize magnetite nanoparticles in a single step using extracts of green lemon Citrus Aurantifolia residues. The results produced nanoparticles of smaller size using a method that is friendlier to health and the environment, is more profitable, and can be applied in anticorrosive coatings. The green synthesis was carried out by a co-precipitation method under variable temperature conditions. The X-ray Diffraction (XRD) and Transmission Electron Microscopy (TEM) characterization showed that magnetite nanoparticles were successfully obtained with a very narrow particle size distribution between 3 and 10 nm. A composite was produced with the nanoparticles and graphene to be used as a surface coating on steel. In addition, the coating’s anticorrosive behavior was evaluated through electrochemical techniques. The surface coating obtained showed good anticorrosive properties and resistance to abrasion.

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A problem faced by the water operators is the compliance with the regulations on the quality of the treated wastewater. The most important thing is to implement strategies that favor compliance with the regulations. Data mining is a tool that allows the prediction of the water quality in the effluent of water treatment systems. In this research job, a criterion for nominal variables and data preprocessing is proposed. Subsequently, the data mining system (classification) was applied to define the prediction of water quality. The classificatory methodologies applied were: OneR, Decision Table, J48, single level decision tree, PART y LMT. Results show that, the best algorithm was the Decision Table with 85.45% of the instances correctly classified. The algorithm determined two rules for the regulation's achievement. It is important to mention that currently there are data mining procedures to predict water quality in the effluent of a treatment system, although, these procedures use strictly numeric variables; while in the current research, nominal variables were considered. Finally, results are discussed and industrial processes that generate organic waste and other pollutants are indicated. Resumen Un problema que enfrentan los organismos operadores de agua, es el cumplimiento de la normatividad en la calidad del agua residual tratada. Por lo que es recomendable implementar estrategias que favorezcan el cumplimiento de las regulaciones. La minería de datos es una herramienta que permite predecir la calidad del agua en el efluente de los sistemas de tratamiento. En el presente estudio se propone un criterio para el pre procesado de datos donde se consideraron variables nominales. Posteriormente se aplicó el sistema de minería de datos (clasificación) para definir la predicción de la calidad del agua. Se utilizaron los siguientes clasificadores: OneR; DecisiónTable, J48, árbol de decisión de un solo nivel, PART y LMT. Los resultados muestran que el mejor algoritmo fue el DecisiónTable con el 85.45 % de instancias correctamente clasificadas. El algoritmo determinó dos reglas para el cumplimiento de la normatividad. Es importante indicar que a la fecha existen procedimientos con minería de datos para predecir la calidad del efluente de un sistema de tratamiento, pero utilizan estrictamente variables numéricas; mientras que en el presente trabajo se utilizaron variables nominales, finalmente se discuten los resultados y se indican los procesos industriales que generan materia orgánica y otros contaminantes.