Sistema de control automático para la detección de fuga de gas natural

The object of study of this research was to evaluate and propose a control system capable of efficiently detecting gas leaks for domestic and commercial use. Thus, different control techniques were analyzed through the review of scientific literature and experimental observation. The resulting control proposal was through a fuzzy neuro control algorithm, which classifies gases by detecting the input signal to the system, through MQ sensors, and using the Arduino Mega open source electronics creation platform, as well as the Neuro Fuzzy program. MatLab designer. The neuro-fuzzy network helps to obtain better equations, "biases" and "weights" of the neural network, consequently, the classification and detection of the gas is in constant evaluation to better interpret the data obtained by the sensors. As a result, an acceptable detection of natural gas was obtained, as well as having a system that remains in constant evaluation of the input variable, that is, the data obtained by the sensors, which allows optimization of the detection.El objeto de estudio de esta investigación fue evaluar y proponer un sistema de control capaz de detectar eficientemente las fugas de gas de uso doméstico y comercial. Es así que se analizaron diferentes técnicas de control mediante la revisión de literatura científica y la observación experimental. La propuesta de control resultante fue mediante un algoritmo de control neuro difuso, el cual clasifica los gases detectando la señal de entrada al sistema, mediante sensores MQ, y empleando plataforma de creación de electrónica de código abierto Arduino Mega, así como el programa Neuro Fuzzy Designer de MatLab. La red neuro- difusa ayuda a obtener mejores ecuaciones, “bias” y “pesos” de la red neuronal, en consecuencia, la clasificación y detección del gas está en constante evaluación para interpretar mejor los datos obtenidos por los sensores. Como resultados se obtuvo una detección aceptable del gas natural y contar con un sistema que permanece en constante evaluación de la variable de entrada, es decir, los datos obtenidos por los sensores, lo cual permite optimizar la detección

Synthesis and Characterization of Polymeric Gas Sensing Materials for Detection of Agriculture Lagoon Off-Gas

With increasing food demand, agriculture and farming industry have grown. This led to an increase in production of agricultural waste. The waste is converted into manure via anaerobic fermentation which continuously produces biogas. Every year, a large volume of biogas-containing pollutants, including greenhouse gases (GHG), are released from manure fermentation. To monitor the progress of manure fermentation and control gas pollutants released into the atmosphere, a sensor that can detect components of biogas in a continuous and reliable manner is necessary. For a more economic system, the sensor should be able to operate at room temperature (roughly, 22-25℃ range) and specifically detect certain gas analytes. Methane and ammonia are selected as the target gases for detection because of their significance. Methane is a small molecule hydrocarbon and very potent greenhouse gas. At high concentrations, there is a possibility of combustion and asphyxiation, thus monitoring its presence and concentration is essential. Ammonia is the main source of odor from manure fermentation and can be toxic at a low concentration. As both target gases are toxic and reactive, formaldehyde was used as a “simulant” or “surrogate” gas (a less hazardous gas) for preliminary in-lab testing of sensing material sensitivity with the gas chromatography (GC) set-up. Any sensor requires a sensing material that responds to one specific gas analyte. Polyaniline (PANI), polypyrrole (PPy), and polydimethylsiloxane (PDMS) have been indicated in the literature to exhibit affinity for ammonia and/or methane. They were tested first with gas chromatography (GC) using formaldehyde. Out of the three, PANI showed better sensing capabilities. Tin (IV) oxide, zinc oxide, sodium dodecyl sulphate, titanium (IV) oxide and hydrochloric acid were selected as the top 5 dopants for polyaniline. PANI was synthesized in the lab with varying dopant levels for GC testing. Scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and ultraviolet-visible (UV-Vis) spectroscopy were used to further characterize the synthesized materials for their physical and chemical properties. PANI doped with 2.5% ZnO-sodium dodecyl sulfate (SDS) showed higher sensitivity for sensing formaldehyde as it had the greatest response (sorption) at a low concentration. ZnO incorporation into PANI was poor. When SDS was added, ZnO incorporation improved, which led to higher gas sorption. The notable interaction of SDS and ZnO could be scrutinized further if the formulation needs to be optimized for best ZnO incorporation without sacrificing the PANI structure. When tested with the actual micro-electrical-mechanical system (MEMS) sensor at 50 ppm methane source (test chamber in System Design Engineering), both PANI with 5% SnO2 and 5% ZnO sorbed methane at room temperature. PANI-5% ZnO was proven to be the most suitable sensing material for methane detection, showing the highest signal at 50 ppm methane levels

A Sensor Array for the Detection and Discrimination of Methane and Other Environmental Pollutant Gases

We address the sensitive detection and discrimination of gases impacting the environment, such as CH4, NH3, SO2, and CO, using a sensor array and aided by principal component analysis (PCA). A 32-element chemiresistive sensor array consisting of nine different sensor materials including seven types of modified single-walled carbon nanotubes and two types of polymers has been constructed. PCA results demonstrate excellent discriminating ability of the chemiresistor sensor chip in the 1–30 ppm concentration range. The accuracy of the sensor was verified against data collected using cavity ring down spectroscopy. The sensor chip has also been integrated with a smartphone and has been shown to reproduce the sensing performance obtained with the laboratory measurement system