https://hdl.handle.net/20.500.12008/37692 Incluye artículos, objetos de conferencias, seminarios y jornadas, reportes técnicos, comunicaciones y otros. Sat, 06 Jun 2026 22:58:02 GMT 2026-06-06T22:58:02Z https://hdl.handle.net/20.500.12008/55262 Título: Dewatering process : mathematical and experimental approach for optimal sludge management in dairy industry Autor: Porley Santana, Agustin; Lacuesta Cabral, Jonathan; Gutiérrez Parodi, Soledad Resumen: The dairy industry is striving to reduce its environmental impact, with a particular focus on sustainable sludge management. A decision-making tool has been developed for Uruguayan dairy companies to optimize sludge management processes. This tool was created by proposing a superstructure and employing mathematical programming, allowing for a comprehensive evaluation of various operational alternatives. The tool evaluated various options, including dewatering, drying, and final disposal. The most cost-effective solution identified was the use of geotextile bags for dewatering, followed by drying and combustion. This alternative proved to be 20% cheaper than the baseline method and also provided a valorization alternative of the waste as a fuel. The tool’s utility was demonstrated through sensitivity analyses. To enhance the tool’s accuracy, an experimental study was conducted to assess sludge dewaterability under pressure. Samples of sludge with varying origins and compositions were analyzed, revealing significant differences in the minimum moisture percentage achieved, despite similarities in the general shape of the curves. These findings enable a better selection of dewatering technologies based on the specific characteristics of each sludge, contributing to more efficient and sustainable management of dairy industry waste. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/20.500.12008/55262 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12008/55261 Título: Electric arc furnace dust waste management : a process synthesis approach Autor: Porley Santana, Agustin; Doldan, Mayra; Duarte Guigou, Martin; Ohanian, Mauricio; Gutiérrez Parodi, Soledad Resumen: The electric arc furnace dust (EAFD) is the residue from the gas collection system of steel mills which contains significant amounts of iron, zinc, and lead. The EAFD management presents scientific and technical challenges, due to its classification as hazardous waste, which significantly impacts the economics of steelmaking. Normally, this kind of waste is treated by specialized waste management companies; however, valuable metals can be recovered. In collaboration with a steel manufacturer, this work studies the management of steel mill residue with a process synthesis approach using mathematical programming. The results show that savings of 23 USD per ton of dust can be achieved by recovering pure Zn and Pb through hydrometallurgical dissolution and electrodeposition. Finally, this work provides a decision-making management tool for toxic waste that combines data from both pilot-scale operations and metallurgical companies, and insights from expert judgment, thereby offering a comprehensive framework for informed decision-making. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/20.500.12008/55261 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12008/55260 Título: Solubility prediction of lipid compounds using machine learning Autor: Gutiérrez Álvarez, Gabriel; Porley Santana, Agustin; Gutiérrez Parodi, Soledad; Ferreira, Jimena Resumen: Lipid purification processes are essential in lipid biomass valorization. Solubility is a key property in the solvent selection and process design. This work focuses on developing a predictive solubility model using machine learning techniques to optimize the separation of valuable compounds from a natural matrix derived from lanolin fat. First, the database was created from a literature review, then a database pre-processing step was performed, and the final step was model validation. Random Forest regression was selected for its ability to handle complex nonlinear relationships, showing better performance than bibliography models. An accurate model for lipids solubility in solvents was developed using machine learning techniques and experimental data. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/20.500.12008/55260 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12008/55259 Título: Decision making tool for industrial dairy sludge management : a real case study Autor: Porley Santana, Agustin; Lacuesta Cabral, Jonathan; Gutiérrez Parodi, Soledad Resumen: Uruguay, a small country with a population of 3 million people, is the seventh largest exporter of dairy products in the world. Also, seventy percent of the milk produced in Uruguay is exported. Uruguay's commitment to sustainable practices can be reflected in the country brand “Uruguay Natural", which emphasize a genuine connection with nature. In this context, the dairy sector faces the challenge of properly managing its waste, from primary production to consumption, including industrial processes. An important type of high-organic-content waste generated is the sludge from the wastewater treatment systems of dairy processing plants, which produce liquid milk, powdered milk, and a significant range of mass- consumption products. Currently, the most common method of managing these sludges in Uruguay is their disposal in a landfill. However, this option is neither the most suitable for minimizing environmental impact nor the most cost-effective one. There are different alternatives for the disposal of these sludges, considering the sustainability of the entire value chain. Some of them include the distribution of digested sludges on dairy farms as soil amendment, composting, use as an energy source, or as a partial substitute for nutrients after drying and pelletization, among others. However, the environmentally proper management of these sludges often entails significant costs for companies, whose finances are strained by intense international competition. How to select the most suitable path to minimize economic impact? This question resumes the aim of this work. It is addressed within the context of a case study involving an industrial plant located in the central region of the country. Limited attention has been devoted to the application of mathematical optimization tools specifically for the management of sludge within the dairy industry (Perimal et al. (2017), Yapiciočlu and Yeşilnacar (2021), Olajire and Shah (2009)). This research aims to develop a decision-making framework tailored for the board within the dairy industry, facilitating comparative analyses across diverse scenarios, that is, to solve a process synthesis problem (PSP) of sludge management within the framework of Process Systems Engineering (PSE). Different management alternatives were considered, with the selection criterion being the possibility of obtaining experimental data on efficiencies and costs at a real scale: combinations of chemical conditioning and mechanical dewatering, drying, on-field distribution as a soil amendment in dairy farms, use as fuel and disposal in sanitary landfills as a base case for comparison. The problem is implemented for a specific case, but by adjusting parameters related to geographic location, site-specific costs, and characteristics of the sludge to be disposed of, the tool is applicable to other cases. Predicting the mechanical dewatering efficiency of a candidate technology is challenging, as this parameter depends on the type of sludge under consideration. This aspect is significant because the residual moisture content in sludge cakes impacts subsequent treatment costs due to increased volume. Thus, it is important to identify the primary variables within the dewatering process (Novak (2006), Richard I. Dick and Novak (1980), WAK (2006)). A second objective is to assess dewatering efficiencies for sludges of different origins, providing valuable information to extrapolate the results obtained in this case to other dairy plants. Mon, 01 Jan 2024 00:00:00 GMT https://hdl.handle.net/20.500.12008/55259 2024-01-01T00:00:00Z