Precise temperature control in high-temperature furnaces is challenged by nonlinearities, parameter drift, and high thermal inertia. This study proposes an adaptive control strategy combining a classical PID loop with real-time gain updates from a feed-forward artificial neural network (ANN). Implemented on an 18 kW retrofitted Blue-M furnace, the system was characterized by second-order transfer functions for heating and forced convection cooling. A dataset of 9702 samples was built from eight fixed PID configurations tested under a multi-ramp thermal profile. The selected 3-64-64-32-2 ANN, executed in Python and interfaced with LabVIEW, computes optimal gains in 0.054 ms while preserving real-time monitoring capabilities. Experimental results show that the ANN-assisted PID reduces the mean absolute error to 5.08 °C, limits overshoot to 41% (from 53%), and shortens settling time by 20% compared to the best fixed-gain loop. It also outperforms a fuzzy controller and remains stable under ±5% signal noise. Notably, gain reversals during cooling prevent temperature spikes, improving transient response. Relying on commodity hardware and open-source tools, this approach offers a cost-effective solution for legacy furnace upgrades and provides a replicable model for adaptive control in high-temperature, safety-critical environments like metal processing, battery cycling, and nuclear systems.

A representative urban driving cycle was developed for Quito, Ecuador, using the K-Means clustering method. From 64 samples and 188,713 geospatial and speed data points, a 2870 s driving cycle was constructed to capture real-world traffic characteristics. Key parameters include an average speed of 22.68 km/h, acceleration and deceleration rates of 0.55 m/s² and −0.57 m/s², and a dwell time of 9.66%. Due to Quito’s linear urban development, where mobility is limited to north–south/south–north corridors, the driving cycle reflects frequent accelerations and decelerations along congested arterial roads. A comparative analysis with international driving cycles revealed that Quito’s traffic follows a unique pattern shaped by its geographic constraints. The HK cycle in China showed the greatest similarities, although differences in instantaneous speeds highlight the need for localized models. While this study primarily focuses on methodological robustness, the developed driving cycle provides a foundational dataset for future research on traffic flow optimization, emissions estimation, and sustainable urban mobility strategies. These insights contribute to data-driven decision-making for improving transportation efficiency and environmental impact assessment in cities with similar urban structures.

Se ha determinado la relación entre la calidad de la gasolina que se expende en el Ecuador (determinada por su octanaje) y el grado de las emisiones contaminantes producidas por el mismo vehículo de prueba, pero a presión atmosférica distinta en las distintas regiones del país, ubicadas desde nivel del mar hasta sobre los 2500 metros de altura. Para lograr dicho objetivo, se realizó una serie de pruebas y se ha considerado cinco medidas. Se realizó una medición estática de gases, en la que se consideró cuatro parámetros que son: hidrocarburos no combustionados HC (ppm), monóxido de carbono CO (%V), dióxido de carbono CO2 (%V) y oxígeno O2 (%V). Una vez procesados los datos se determinan los efectos de variables tales como revoluciones por minuto del motor (rpm), octanaje del combustible y altura del lugar de las mediciones, sobre las emisiones de CO, CO2, HC y O2.

Biomass gasification, a thermochemical conversion process that turns organic feedstocks like wood, agricultural residues, and solid waste into a combustible gas known as synthesis gas (syngas), is the focus of this study. In this study, Aspen Plus^® as a process simulation platform to optimize key operational parameters for the gasification of sugarcane bagasse was employed. The results are promising, with an equivalence ratio (ER) of 0.25 and a carbon conversion efficiency (XC) of 62.44% achieved, indicating the potential for the produced syngas to be compatible with injection into natural gas distribution networks. The lower heating value (LHV) of the syngas was determined to be 3.93 MJ·kg⁻¹, with an overall gasification efficiency of 49.85%. The simulation results showed strong agreement with experimental data, validating the modeling approach as a reliable predictive tool for biomass gasification systems and reducing unnecessary resource consumption. This validation instills trust and confidence in the reliability of our findings.

In the following project, the theory of constraints was applied in order to implement a manufacture CAD-CAM system into the metal mechanic industry processes of polymers injection and blown of polymers. The research showed that the manufacture of the mold with the engraving took 223,17 hours. In the workflow for the manufacture of the mold, a restriction was found in the outsource service of CNC. It took 120 hours of the whole process and represent the 51,47 % the total time of tooling manufacturing. There is also a constraint found in the design time. It was 60 hours that corresponds to 26,88 % of the overall time. In order to reduce the time, a modern system of design in 3D and CAM was established to improve the model process of design and manufacture. A simulation by computational resource was applied to the plastic. The design was changed from 2D to 3D. The implementation was focused in the design. A software was installed to improve the speed of modeling methods with reliable information. In the manufacture of molds, a new CNC machine was acquired with three simultaneous axes to eliminate the outsource service. By acquiring the design system, the working time was diminished in 79% and regarding to the CNC process, the working time was improved in 88%. En el presente proyecto se aplica la teoría de restricciones para implementar un sistema de manufactura CAD-CAM en la industria del sector metalmecánico, en productos plásticos, mediante procesos de inyección y soplado de polímeros. En la investigación se determinó que la fabricación del molde para el producto a evaluar tuvo una duración de 223,17 horas y para el procesamiento de 240 kg de materia prima fue de 25 horas. En el flujo de operaciones para la fabricación del molde se encontró restricción en el servicio contratado de manufactura CNC con 120 horas de duración que corresponde al 51,47% del tiempo total de elaboración. La fase de diseño, tomó 60 horas que corresponde al 26,88% del tiempo total del proyecto. Para reducir los tiempos se mejoró el proceso de diseño y manufactura, mediante la implementación de un sistema moderno de diseño CAD y un proceso de fabricación CAM. Para elevar la restricción se cambia de un entorno 2D de trabajo a un sistema de diseño 3D, y se invirtió en una máquina con tecnología CNC. Con la implementación del sistema el tiempo se redujo en un 79% en el diseño, con respecto al proceso manufactura CNC el tiempo se redujo en un 88%.