Control-Scheduling Codesign for NCS based Fuzzy Systems

In the present paper, a fuzzy codesign approach is proposed to deal with the controller and scheduler design for a networked control system which is physically distributed with a shared communication network. The proposed fuzzy controller is applied to generate the control with different sampling-actuation periods, the configuration supposes a strict actuation period disappears the jitter. The proposed fuzzy scheduling is designed to select the sampling-actuation period. So, the fuzzy codesign reduces the rate of transmission when the system is stable through the scheduler while the controller adjusts the control signal. The fuzzy codesign guarantees the stability of all the system if the network uncertainties do not exceed an upper bound and is a low computational cost method implemented with an embedded system. An unstable, nonlinear system is used to evaluate the proposed approach and compared to a hybrid control, the results show greater robustness to multiple lost packets and time delays much larger than the sampling period. (This paper is an extension of [20]. Reprinted (partial) and extended, with permission based on License Number 4275590998661 IEEE, from "Electrical Engineering, Computing Science and Automatic Control, 2017 14th International Conference on"

SISTEMA DE MONITOREO INALÁMBRICO DE BAJO COSTO PARA MÓDULOS FOTOVOLTAICOS EMPLEANDO RASPBERRY PI

El presente trabajo muestra el desarrollo de una red de monitoreo para sistemas fotovoltaicos basado en el uso de la tecnología inalámbrica ZigBee,  microprocesadores ARM de 32 bits y una tarjeta de desarrollo Raspberry Pi. El sistema propuesto se encuentra formada por un módulo de sensado diseñados para la medición y transmisión de los parámetros de temperatura, voltaje y corriente de los paneles o arreglos fotovoltaicos. Dicho módulo se comunica con una tarjeta Raspberry Pi la cual realiza las funciones de sistema de coordinación central y servidor web. Así, el módulo ZigBee incorporado a la red es capaz de transmitir los parámetros a la tarjeta Raspberry Pi, la cual generará una base de datos con los valores recibidos, además de asignarles fecha y hora. Todos los datos registrados pueden ser visualizados desde una aplicación web desarrollada, la cual se actualiza constantemente, mediante cualquier computadora o dispositivo móvil

Viabilidad del modelo de confort térmico adaptativo bajo condiciones de clima cálido subhúmedo: Ahorro energético en refrigeración en Campeche, México

The conventional approach to achieve thermal comfort generally focuses on modifying the setpoint temperature in fully air-conditioned buildings. However, the adaptive thermal comfort approach is an alternative that considers the interaction between buildings, the local climate, and the users to allow significant improvements in energy savings. This paper analyzes the feasibility of implementing adaptive thermal comfort strategies, comparing adaptive models based on the ASHRAE 55-2020 Standard and a regional model for the tropical climate typology of Mexico, and contrasting the results with the static approach. Thirteen locations in the State of Campeche were thermally analyzed, seeing that the ventilation strategies are applicable throughout the State and that both models ensure improvements in energy consumption. In addition, the results suggest that it is necessary to develop more local adaptive models to propose strategies with better potential impact in the region.A abordagem convencional para alcançar o conforto térmico geralmente se concentra em modificar a temperatura de referência em edifícios totalmente climatizados. No entanto, a abordagem do conforto térmico adaptativo é uma alternativa que considera a interação entre os edifícios, o clima local e os usuários, a fim de permitir melhorias significativas na economia de energia. Este trabalho analisa a viabilidade de implementar estratégias de conforto térmico adaptativo, comparando modelos adaptativos baseados na norma ASHRAE 55-2020 e um modelo regional para a tipologia climática tropical do México, contrastando os resultados com a abordagem estática. Foram analisados termicamente 13 locais no estado de Campeche, concluindo-se que as estratégias de ventilação são aplicáveis em todo o estado e que ambos os modelos garantem melhorias no consumo de energia. Além disso, os resultados sugerem a necessidade de desenvolver mais modelos adaptativos locais para propor estratégias com maior potencial de impacto na região.El enfoque convencional para alcanzar el confort térmico generalmente se centra en modificar la temperatura de consigna en edificios totalmente climatizados. Sin embargo, el enfoque del confort térmico adaptativo es una alternativa que considera la interacción entre los edificios, el clima local y los usuarios para permitir mejoras significativas en el ahorro de energía. El trabajo analiza la viabilidad de implementar estrategias de confort térmico adaptativo, comparando modelos adaptativos basados en la norma ASHRAE 55-2020 y un modelo regional para la tipología climática tropical de México, contrastando los resultados respecto del enfoque estático. Se analizó térmicamente 13 locaciones del Estado de Campeche obteniendo que las estrategias de ventilación son aplicables en todo el Estado y que ambos modelos aseguran mejoras en el consumo de energía. Además, los resultaron siguieren que es necesario desarrollar más modelos adaptativos locales para proponer estrategias con mejor potencial de impacto en la región

MODELADO DE PARTÍCULAS PM10 Y PM2.5 MEDIANTE REDES NEURONALES ARTIFICIALES SOBRE CLIMA TROPICAL DE SAN FRANCISCO DE CAMPECHE, MÉXICO

In this paper, a computational methodology based on Artificial Neural Networks (ANN) was developed to estimate the index of PM10 and PM2.5 concentration in air of San Francisco de Campeche city. A three layer ANN architecture was trained using an experimental database composed by days of the week, time of day, ambient temperature, atmospheric pressure, wind speed, wind direction, relative humidity, and solar radiation. The best ANN architecture, composed by 30 neurons in hidden layer, was obtained using the Levenberg-Marquardt (LM) optimization algorithm, logarithmic sigmoid and linear transfer functions. Model results generate predictions with a determination coefficient of 93.01% and 90.10% for PM2.5 and PM10, respectively. The proposed methodology can be implemented in several studies as public health, environmental studies, urban development, and degradation of historical monuments

ARTIFICIAL NEURAL NETWORK MODELING OF PM10 AND PM2.5 IN A TROPICAL CLIMATE REGION: SAN FRANCISCO DE CAMPECHE, MEXICO

<p></p><p>In this paper, a computational methodology based on Artificial Neural Networks (ANN) was developed to estimate the index of PM10 and PM2.5 concentration in air of San Francisco de Campeche city. A three layer ANN architecture was trained using an experimental database composed by days of the week, time of day, ambient temperature, atmospheric pressure, wind speed, wind direction, relative humidity, and solar radiation. The best ANN architecture, composed by 30 neurons in hidden layer, was obtained using the Levenberg-Marquardt (LM) optimization algorithm, logarithmic sigmoid and linear transfer functions. Model results generate predictions with a determination coefficient of 93.01% and 90.10% for PM2.5 and PM10, respectively. The proposed methodology can be implemented in several studies as public health, environmental studies, urban development, and degradation of historical monuments.</p><p></p

Esterification Optimization of Crude African Palm Olein Using Response Surface Methodology and Heterogeneous Acid Catalysis

In this work, the effect of zeolite montmorillonite KSF in the esterification of free fatty acids (FFAs) of crude African palm olein (Eleaias guinnesis Jacq) was studied. To optimize the esterification of FFAs of the crude African palm olein (CAPO), the response surface methodology (RSM) that was based on a central composite rotatable design (CCRD) was used. The effects of three parameters were investigated: (a) catalyst loading (2.6–9.4 wt %), (b) reaction temperature (133.2–166.2 °C), and (c) reaction time (0.32–3.68 h). The Analysis of variance (ANOVA) indicated that linear terms of catalyst loading (X1), reaction temperature (X2), the quadratic term of catalyst loading ( X 1 2 ), temperature reaction ( X 2 2 ), reaction time ( X 3 2 ), the interaction catalyst loading with reaction time ( X 1 * X3), and the interaction reaction temperature with reaction time ( X 2 * X3) have a significant effect (p &lt; 0.05 with a 95% confidence level) on Fatty Methyl Ester (FAME) yield. The result indicated that the optimum reaction conditions to esterification of FFAs were: catalyst loading 9.4 wt %, reaction temperature 155.5 °C, and 3.3 h for reaction time, respectively. Under these conditions, the numerical estimation of FAME yield was 91.81 wt %. This result was experimentally validated obtaining a difference of 1.7% FAME yield, with respect to simulated values