Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Effect of the extrusion process on the production of a precooked adjunct for American-Lager beer, with the aim of reducing energy and water consumption(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-03) Vázquez del Mercado Pardiño, Jorge Arturo; Heredia Olea, Erick; mtyahinojosa, emipsanchez; Rosa Millán, Julían de la; Pino Espinosa Ramírez, Johanan del; School of Engineering and Sciences; Campus Monterrey; Pérez Carrillo, EstherLager beer style is the most consumed beer in North America, and Mexico is the 4th biggest exporter of Lager in the world. Among the Lager styles, the American-Lager beer is one of the most consumed due to its crispness and light mouthfeel, mainly caused using adjuncts. Brewing adjuncts represent another source of fermentable sugars (FS) for the fermentation. The main sources of adjuncts are corn and rice, but their use during mashing is limited by the necessity of cooking them in a different tun to gelatinise them and allow barley malt enzyme to hydrolyse them. Consequently, in this research, extrusion cooking parameters were evaluated on white degermed corn grits to gelatinise and obtain the highest FS conversion yield. 2 corn grits (MS: Vixim MS-60 and VX: Vixim Cereal) were extruded to produce 9 different treatments at different screws speed (200 rpm, 300 rpm and 500 rpm) and moisture (15%, 20% and 25%). Brewer's worts were produced with each extruded adjunct, and with non-extruded corn starch and the two raw corn grits. It was found that extrusion cooking is capable not only to equalise the FS yield of the wort produced with corn starch but also to produce, in 10 out of 18 treatments, an average increase of 32.01% in FS yield at different extrusion conditions. Condition at 300 rpm and 20% moisture resulted in the treatment with the highest FS yield with 46.72%. Free Amino Nitrogen and protein content were quantified, and in VX treatments, a significant decrease of 37.4% and 59.3% respectively, was observed, mostly due to a higher presence of Maillard reaction during extrusion. Through an Artificial Neural Network (ANN) and the FS yields produced, the extrusion parameters were analysed and optimised to produce a condition (MS10) where the FS yield was maximised at 64% (16.2% moisture, 233 rpm, 159.6°C product temperature, 234.6°C extrusion barrel temperature and 341.8 SME). The wort produced with MS10 adjunct presented a 28.29% FS yield, 4.22 SRM colour, 1.25 mL/min filtration speed, parameters not statistically different to the wort produced with corn starch, while FAN concentrations were reduced by 12.4%. The use of MS10 as a brewing adjunct produced a 33.54% and 15.13% reduction in mashing energy and water usage, respectively. The water usage was also reduced by 35.78% by using MS10 instead of corn starch as an adjunct.
- Neural network circuit implementation using operational amplifiers and digital potentiometers(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-09) Posada Hoyos, Jacobo; GOMEZ ESPINOSA, ALFONSO; 57957; Gómez Espinosa, Alfonso; puemcuervo; Escobedo Cabello, Jesus Arturo; Domínguez Oviedo, Agustín; González García, Josué; School of Engineering and Sciences; Campus Monterrey; Valdés Aguirre, BenjamínImplementations of Artificial Neural Networks (ANN) have been advancing for almost three decades and their importance has been marked by the different methods used in their construction, their applications, and comparisons in terms of speed, costs, and performance between implementations made by software and hardware. As analog implementations of ANN have been shown to have good levels of performance, high processing speed, low power consumption, small size, and low cost, they have played an important role in the development of new designs. This work presents a proposal to design a circuit implementation of an ANN by using Operational Amplifiers (Opamps) and digital potentiometers to create a network that can be trained by using an external training system. This, based on circuit analysis and training algorithm by the back propagation (BP) approach. The proposed design will be simulated in the circuit simulator Proteus. The circuit is tested using the logical gates benchmark problem to verify its performance with the BP learning algorithm. The results of this work demonstrate that it is possible to create a neural network using analogous components. Furthermore, it shows good performance when implementing the training algorithm using digital potentiometers. As future work is expected to improve the performance of training to create a controller based on neural networks and thus, perform the control of a dynamic system.
- Sensor data fusion for a mobile robot using a neural network algorithm(Instituto Tecnológico y de Estudios Superiores de Monterrey) Barreto Cubero, Andrés Javier; GOMEZ ESPINOSA, ALFONSO; 57957; Gómez Espinosa, Alfonso; puelquio, emipsanchez; Cuan Urquizo, Enrique; Cruz Ramírez, Sergio Rolando; Escuela de Ingeniería y Ciencias; Campus Monterrey; Escobedo Cabello, Jesús ArturoMobile robots must be capable to obtain an accurate map of their surroundings and move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to have at least two sensors, with this is possible to generate a 2D occupancy map that is as close to reality as possible. In this thesis, an artificial neural network is used to fuse data from a tri-sensor (Intel RealSense Stereo Camera, 2D 360° LiDAR-Light Detection and Ranging Sensor and an HC-SR04 Ultrasonic Sensor) setup capable of detecting glass, polished metals, brick walls, wooden panels and other materials typically found in indoor environments. When a map is to be compiled out of different sensor’s data, it is necessary to implement a preprocessing scheme to filter all the outliers in the data for each sensor. Then, run a data fusion algorithm to integrate all the information into a single, more accurate 2D map that considers all sensor’s information. The Robotis Turtlebot 3 Waffle Pi robot is used as an experimental platform along with Robotic Operating System as the main Human Machine Interface to implement the algorithms. Test results show that with the fusion algorithm implemented, it is possible to detect glass and other obstacles invisible to the LiDAR with an estimated root-mean-square error of 4 cm with multiple sensor configurations.