ResumenA pesar de los esfuerzos derivados de la legislación española y la cada vez mayor implantación de los sistemas de gestión de calidad en las empresas del sector de la construcción, siguen apareciendo defectos de distinta naturaleza y gravedad, lo que genera las consiguientes quejas y reclamaciones por parte del usuario final. Distintos factores de la planificación del control de calidad en los proyectos de edificación residencial en España son analizados en este trabajo. Para ello, después de identificar y clasificar los defectos más habituales, el trabajo se centra en analizar la etapa inicial de planificación del control de calidad. Con ese objetivo inicialmente se estudia el contenido de los modelos proporcionados por los Colegios Profesionales de Arquitectos a sus asociados. Posteriormente, a partir de una muestra representativa de proyectos de edificación residencial, se analiza y discute su aplicación en la práctica.Palabras clave: edificación; residencial; control; calidad; planificación Abstract Despite the legal efforts and the major increase in the implementation of quality management control systems in construction companies in Spain, serious deficiencies of different nature keep appearing, with the subsequent claims and complaints reported by the final user. This study analyses different factors in the planning of quality control in residential construction projects in Spain. After identifying and classifying the most common deficiencies, the study is focused on analyzing the first stage of the quality control planning. First, the content of models provided by the Professional Associations of Architects to their associates are studied. Afterwards, based on a representative sample of residential construction projects, its practical application is analyzed and discussed.
The content of fats, oils, and greases (FOG) in wastewater, as a result of food preparation, both in homes and in different commercial and industrial activities, is a growing problem. In addition to the blockages generated in the sanitary networks, it also represents a difficulty for the performance of wastewater treatment plants (WWTP), increasing energy and maintenance costs and worsening the performance of downstream treatment processes. The pretreatment stage of these facilities is responsible for removing most of the FOG to avoid these problems. However, so far, optimization has been limited to the correct design and initial installation dimensioning. Proper management of this initial stage is left to the experience of the operators to adjust the process when changes occur in the characteristics of the wastewater inlet. The main difficulty is the large number of factors influencing these changes. In this work, a prediction model of the FOG content in the inlet water is presented. The model is capable of correctly predicting 98.45% of the cases in training and 72.73% in testing, with a relative error of 10%. It was developed using random forest (RF) and the good results obtained (R2 = 0.9348 and RMSE = 0.089 in test) will make it possible to improve operations in this initial stage. The good features of this machine learning algorithm had not been used, so far, in the modeling of pretreatment parameters. This novel approach will result in a global improvement in the performance of this type of facility allowing early adoption of adjustments to the pretreatment process to remove the maximum amount of FOG.
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