Due to their specific characteristics, innovation projects are developed in contexts with great volatility, uncertainty, complexity, and even ambiguity. Project management has needed to adopt changes to ensure success in this type of project. Artificial intelligence (AI) techniques are being used in these changing environments to increase productivity. This work collected and analyzed those areas of technological innovation project management, such as risk management, costs, and deadlines, in which the application of artificial-intelligence techniques is having the greatest impact. With this objective, a search was carried out in the Scopus database including the three areas involved, that is, artificial intelligence, project management, and research and innovation. The resulting document set was analyzed using the co-word bibliographic method. Then, the results obtained were analyzed first from a global point of view and then specifically for each of the domains that the Project Management Institute (PMI) defines in project management. Some of the findings obtained indicate that sectors such as construction, software and product development, and systems such as knowledge management or decision-support systems have studied and applied the possibilities of artificial intelligence more intensively.

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.

The preliminary treatment of wastewater at wastewater treatment plants (WWTPs) is of great importance for the performance and durability of these plants. One fraction that is removed at this initial stage is commonly called gross solids and can cause various operational, downstream performance, or maintenance problems. To avoid this, data from more than two operation years of the Villapérez Wastewater Treatment Plant, located in the northeast of the city of Oviedo (Asturias, Spain), were collected and used to develop a model that predicts the gross solids content that reaches the plant. The support vector machine (SVM) method was used for modelling. The achieved model precision ($R_{adj}^2$ = 0.7 and MSE = 0.43) allows early detection of trend changes in the arrival of gross solids and will improve plant operations by avoiding blockages and overflows. The results obtained indicate that it is possible to predict trend changes in gross solids content as a function of the selected input variables. This will prevent the plant from suffering possible operational problems or discharges of untreated wastewater as actions could be taken, such as starting up more pretreatment lines or emptying the containers.

A 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.