Bibliometric Analysis of the Application of Artificial Intelligence Techniques to the Management of Innovation Projects

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

Effecting Partial Elimination of Isocyanuric Acid from Swimming Pool Water Systems

It is essential to disinfect the water in swimming pools in order to deactivate pathogenic microorganisms. Chlorination of swimming pool water provides rapid and long-lasting disinfection, but leads to the formation of potentially toxic compounds, including isocyanuric acid, that are used to stabilize chlorine in pool water. Hygiene and health guidelines require an isocyanuric acid concentration in swimming pools of 25 to 75 ppm and that there be no level in excess of 100 ppm. This paper provides a new method to partially remove isocyanuric acid from the water of swimming pool systems with the use of melamine-based reagents. A melamine-photometry process stabilizes the isocyanuric acid. The melamine-based reagent that is added to the raw water reacts with the isocyanuric acid and forms a precipitated salt. The reaction also creates turbidity that is proportional to the isocyanuric acid concentration in the water. It was noted in this study that the optimum functioning range of melamine doses in the raw water was 0.04 to 0.06 g/L and that the reduction of isocyanuric acid in raw water increased as the dose of melamine was increased. Thus, it is necessary to obtain an estimate of the dose of melamine that is necessary to reduce the isocyanuric acid in the water without needing to add fresh water from the network to dilute it. Finally, it can be stated that eliminating isocyanuric acid that has accumulated in a pool’s water by treatment with melamine provides an efficient process, as it eliminates the amount of isocyanuric acid that is necessary to conform to the human health criteria of the European Union Directive 2006/7/EC. Treatment with melamine also reduces water network consumption and sewer discharge by successive purges that eventually will become unnecessary. Therefore, this proposed method is environmentally and economically beneficial

Development of a Steel Plant Rescheduling Algorithm Based on Batch Decisions

During the steelmaking and continuous casting process in the steel plant, it is common to encounter delays that affect initial planning. Furthermore, continuous casting machines themselves can lose much of their performance in the event of closure of one or more of their casting strands. The situation that is generated, far from being a planning problem, forces consideration of a vision of cost analysis when deciding changes in the planned sequences. This study presents a detailed analysis of the different circumstances that can cause strands closures or sequence breaks, their consequences and the different options available to minimize losses. Finally, an algorithm capable of analyzing the workshop situation and making the most favorable decision to optimize production is proposed, analyzed and compared with the efficiency of the original scheduling method in a real steel plant. The new algorithm proves its efficiency in all situations, with a time-saving average of 26.41 min per decision taken

Coagulation: Determination of Key Operating Parameters by Multi-Response Surface Methodology Using Desirability Functions

The clarification process removes colloidal particles that are suspended in waste water. The efficiency of this process is influenced by a series of inputs or parameters of the coagulation process, of which the most commonly used are initial turbidity, natural coagulant dosage, temperature, mixing speed and mixing time. The estimation of the natural coagulant dosage that is required to effectively remove these total suspended solids is usually determined by a jar test. This test seeks to achieve the highest efficiency of removal of the total suspended solids while reducing the final turbidity of waste water. This is often configured in iterative fashion, and requires significant experimentation and coagulant. This paper seeks to identify regression models that relate the clarification process parameters to the process outputs (final turbidity and total suspend solid) by the Response Surface Methodology (RSM) based on experiments of Central Composite Design (CCD) of experiments that involve three emerging natural coagulants. Several clarification process scenarios also were proposed and demonstrated using the Multi-Response Surface (MRS) with desirability functions. The experimental results were found to be in close agreement to what are provided by the regression models. This validates the use of the MRS-based methodology to achieve satisfactory predictions after minimal experimentation