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A formação do Engenheiro Químico no contexto da Indústria 4.0
(Universidade Federal de São Carlos, 2020-10-13) Pinto, Regina Carneiro; Lopes, Gabriela Cantarelli; https://lattes.cnpq.br/5680967191791061
In 2018, the Federação das Indústrias do Estado de São Paulo (Fiesp) carried out a survey to identify the degree of knowledge of Brazilian companies related to the concept of industry 4.0 and the challenges to be faced for its adoption. Two hundred and twenty-seven companies were interviewed, 55% of which were small, 30% medium and 15% large. Initially, 32% of companies said they did not know the terms "fourth industrial revolution", "industry 4.0" or "advanced manufacturing". For the remaining 68%, some other results were obtained: 90% agree that the fourth industrial revolution will have positive impacts on productivity, 67% expect to feel a medium impact with the implementation of advanced manufacturing, but only 5% feel very prepared for the transformations to be faced. Asked about the biggest challenges, que workforce qualification was one of the mentioned points. Since chemical engineering is so close to industry, reflection on the consequences of the fourth industrial revolution in the graduation is necessary. A demonstration of the importance of the theme is that updating regarding innovation, science and technology are topics directly cited in Resolução CNE / CES nº2, of April 24, 2019, which establishes the national curriculum guidelines for the undergraduate engineering course. As a study tool, a research was carried out with graduates of the chemical engineering course at Universidade Federal de São Carlos (UFSCar). All respondents are inserted in the job market in functions directly related to the area of performance of the chemical engineer and answered questions related to the tools used in their daily work and the challenges faced in their professional performance. In addition to the research, the undergraduate curriculum offered by the Department of Chemical Engineering at UFSCar was studied in order to find options for the insertion of new themes in the course structure, using as a resource also the comparison with curricular grades of other universities in the country. The modernization proposals were developed following two paths: the adaptation of existing subjects and the addition of new subjects, helping with ideas to restructure the curriculum.
Análise elementar de carnes: desenvolvimento de métodos analíticos e estratégias para técnicas com fonte de plasma
(Universidade Federal de São Carlos, 2020-10-13) Silva, Ana Beatriz Santos da; Nogueira, Ana Rita de Araújo; https://lattes.cnpq.br/7034773971317045; https://lattes.cnpq.br/5816161183502861
Meat is widely consumed around the world. Besides, this food has a significant influence on the Brazilian economy. Regarding the nutritional terms, it is considered as a source of essential elements (e.g., molybdenum, copper, and manganese) that are important to the human organism. In this context, this thesis's goal is the development of analytical methods and the investigation of strategies for the determination of essential and toxic elements in meat by microwave-induced plasma optical emission spectrometry (MIP OES) and inductively coupled plasma – Tandem mass spectrometry (ICP-MS/MS). The use of MIP OES was investigated for the determination of phosphorus from phospholipids in meat. Due to the introduction of organic solvent, some strategies were adopted, such as the employment of an external gas control module (EGCM) and the signal correction applying internal standardization (Te) and molecular species (OH). The accuracy was estimated by comparing the results obtained with ion chromatography. The recovery found using the internal standardization was 95%, while molecular specie was 94%. These results indicated that these signal corrections were effective, and their employment significantly improved accuracy. The dispersive liquid-liquid microextraction (DLLME) was used to enhance the procedure’s sensitivity for the determination of molybdenum in meat by MIP OES. In this study, the selection of extracting and dispersing solvents was based on the mixture design. Additionally, the DLLME parameters were optimized by applying a Box-Behnken design. A bovine liver certified reference material (1577c, NIST) was employed to check the accuracy and a recovery of 92% was obtained. The enhancement factor and the limit of detection estimated were 4.5 and 0.20 µg g-1, respectively. In another work, a critical evaluation of nebulizer effects in a nitrogen plasma was performed. Four nebulizers (concentric, MiraMist, OneNeb series 1 and 2) were investigated concerning the plasma fundamental parameters (e.g., temperature and number of electron density) and solvent transport efficiency. Besides, the reproducibility was evaluated in different matrices, and the accuracy was investigated by analyzing four CRMs and s spiked sample from sugarcane spirit due to its alcoholic composition. The results demonstrated that the plasma fundamental parameters were considerably affected by the nebulizer type. Besides, the OneNeb series 2 showed the best performance considering the sensitivity, solvent transport efficiency and precision. The determination of nine essential and toxic elements (As, Cd, Co, Cr, Mn, Mo, Pb, Sr, and V) in meat samples was performed by ICP-MS/MS. Initially, the use of gases (O2, H2, and He) in the reaction octopolar system (ORS) was explored to minimize the spectral interferences. The best operating mode was selected evaluating the recoveries obtained during the CRM analysis (1577b, NIST). For Cd, Co, Mo, Pb, and V, the ORS's gas use was not required. On the other hand, for chromium and arsenic, He was more suitable, and for Sr, H2 was the best. The limit of detection varied from 0.08 µg kg-1 (Cd) to 16 µg kg-1 (Mo). Then, meat samples were digested and analyzed by ICP-MS/MS using the optimal conditions. A correlation graphic showed the occurrence of a positive correlation between Cd – Pd and Sr – Pb.
Algoritmos de aprendizado de máquinas aplicados no dimensionamento e controle de estoque na indústria de bebidas
(Universidade Federal de São Carlos, 2020-10-13) Ganem, Alan Motta; Horta, Antonio Carlos Luperni; https://lattes.cnpq.br/5923938048634505; https://lattes.cnpq.br/0826732869672295
Inventory planning is an extremely important task within any industry. This importance becomes even more remarkable for industries that deals with complex and intricate supply chains, with plants spread around many regions of the country, varying lead times and different suppliers. In this work, we will use data science and machine learning techniques to carry out forecasting and inventory control of various supplies in different plants, in a large beer industry. For the consumption forecast, Holt-Winters, Gradient Boosting (LGBM implementation), Dense Neural Networks and a simple persistence model (predicting the future as being exactly the past values) were used. The predictive models generated were validated using the mean absolute error (MAE) metric and the residuals were tested for normality (Shapiro-Wilk), zero mean (t-test) and autocorrelation (Ljung-Box). A Python software was developed in order to simulate a predictive inventory control system using the prediction of each of these models alongside with a heuristic inventory policy provided by the company. The resizing of the inventory policy was also tested (lower bound threshold), taking into account the predictive performance of the models for each time series. Finally, using inventory metrics from simulation and the Pareto front technique for multiobjective optimization, the best candidates were selected for further validation in production stage.