A Review of Methane Gas Detection Sensors: Recent Developments and Future Perspectives

Research on high performance combustible gas concentration sensor based on pyramid beam splitter matrix

Combustible gas concentration detection faces challenges of increasing accuracy, and sensitivity, as well as high reliability in harsh using environments. The special design of the optical path structure of the sensitive element provides an opportunity to improve combustible gas concentration detection. In this study, the optical path structure of the sensitive element was newly designed based on the Pyramidal beam splitter matrix. The infrared light source was modulated by multi-frequency point signal superimposed modulation technology. At the same time, concentration detection results and confidence levels were calculated using the 4-channel combustible gas concentration detection algorithm based on spectral refinement. Through experiment, it is found that the sensor enables full-range measurement of CH4, at the lower explosive limit (LEL, CH4 LEL of 5%), the reliability level is 0.01 parts-per-million (PPM), and the sensor sensitivity is up to 0.5PPM. The sensor is still capable of achieving PPM-level detections, under extreme conditions in which the sensor's optical window is covered by 2/3, and humidity is 85% or dust concentration is 100mg/m3. Those improve the sensitivity, robustness, reliability, and accuracy of the sensor.

Point Sensor Networks Struggle to Detect and Quantify Short Controlled Releases at Oil and Gas Sites

This study evaluated multiple commercially available continuous monitoring (CM) point sensor network (PSN) solutions under single-blind controlled release testing conducted at operational upstream and midstream oil and natural gas (O&G) sites. During releases, PSNs reported site-level emission rate estimates of 0 kg/h between 38 and 86% of the time. When non-zero site-level emission rate estimates were provided, no linear correlation between the release rate and the reported emission rate estimate was observed. The average, aggregated across all PSN solutions during releases, shows 5% of the mixing ratio readings at downwind sensors were greater than the site's baseline plus two standard deviations. Four of seven total PSN solutions tested during this field campaign provided site-level emission rate estimates with the site average relative error ranging from -100% to 24% for solution D, -100% to -43% for solution E, -25% for solution F (solution F was only at one site), and -99% to 430% for solution G, with an overall average of -29% across all sites and solutions. Of all the individual site-level emission rate estimates, only 11% were within ±2.5 kg/h of the study team's best estimate of site-level emissions at the time of the releases.

Gas Imaging with Uncooled Thermal Imager

Gas imaging has become one of the research hotspots in the field of gas detection due to its significant advantages, such as high efficiency, large range, and dynamic visualization. It is widely used in industries such as natural gas transportation, chemical, and electric power industries. With the development of infrared detector technology, uncooled thermal imagers are undergoing a developmental stage of technological advancement and widespread application. This article introduces a gas imaging principle and radiation transfer model, focusing on passive imaging technology and active imaging technology. Combined with the actual analysis, the application scenarios using uncooled thermal imaging cameras for gas imaging measurement are analyzed. Finally, the limitations and challenges of the development of gas imaging technology are analyzed.

Calibration of a Low-Cost Methane Sensor Using Machine Learning

In order to combat greenhouse gas emissions, the sources of these emissions must be understood. Environmental monitoring using low-cost wireless devices is one method of measuring emissions in crucial but remote settings, such as peatlands. The Figaro NGM2611-E13 is a low-cost methane detection module based around the TGS2611-E00 sensor. The manufacturer provides sensitivity characteristics for methane concentrations above 300 ppm, but lower concentrations are typical in outdoor settings. This study investigates the potential to calibrate these sensors for lower methane concentrations using machine learning. Models of varying complexity, accounting for temperature and humidity variations, were trained on over 50,000 calibration datapoints, spanning 0-200 ppm methane, 5-30 °C and 40-80% relative humidity. Interaction terms were shown to improve model performance. The final selected model achieved a root-mean-square error of 5.1 ppm and an R2 of 0.997, demonstrating the potential for the NGM2611-E13 sensor to measure methane concentrations below 200 ppm.

Research on High Performance Methane Gas Concentration Sensor Based on Pyramid Beam Splitter Matrix

Methane gas concentration detection faces the challenges of increasing accuracy and sensitivity, as well as high reliability in harsh environments. The special design of the optical path structure of the sensitive element provides an opportunity to improve methane gas concentration detection. In this study, the optical path structure of the sensitive element was newly designed based on the Pyramidal beam splitter matrix. The infrared light source was modulated by multi-frequency point-signal superimposed modulation technology. At the same time, concentration detection results and confidence levels were calculated using the four-channel methane gas concentration detection algorithm based on spectral refinement. Through the experiment, it was found that the sensor enables the full-range measurement of CH4; at the lower explosive limit (LEL, CH4 LEL of 5%), the reliability level is 0.01 parts-per-million (PPM), and the limit of detection is 0.5 ppm. The sensor is still capable of achieving PPM-level detections under extreme conditions in which the sensor's optical window is covered by two-thirds and humidity is 85% or dust concentration is 100 mg/m3. Those improve the sensitivity, robustness, reliability, and accuracy of the sensor.

Utilization of a Low-Cost Sensor Array for Mobile Methane Monitoring

The use of low-cost sensors (LCSs) for the mobile monitoring of oil and gas emissions is an understudied application of low-cost air quality monitoring devices. To assess the efficacy of low-cost sensors as a screening tool for the mobile monitoring of fugitive methane emissions stemming from well sites in eastern Colorado, we colocated an array of low-cost sensors (XPOD) with a reference grade methane monitor (Aeris Ultra) on a mobile monitoring vehicle from 15 August through 27 September 2023. Fitting our low-cost sensor data with a bootstrap and aggregated random forest model, we found a high correlation between the reference and XPOD CH4 concentrations (r = 0.719) and a low experimental error (RMSD = 0.3673 ppm). Other calibration models, including multilinear regression and artificial neural networks (ANN), were either unable to distinguish individual methane spikes above baseline or had a significantly elevated error (RMSDANN = 0.4669 ppm) when compared to the random forest model. Using out-of-bag predictor permutations, we found that sensors that showed the highest correlation with methane displayed the greatest significance in our random forest model. As we reduced the percentage of colocation data employed in the random forest model, errors did not significantly increase until a specific threshold (50 percent of total calibration data). Using a peakfinding algorithm, we found that our model was able to predict 80 percent of methane spikes above 2.5 ppm throughout the duration of our field campaign, with a false response rate of 35 percent.

Temperature Compensation of Laser Methane Sensor Based on a Large-Scale Dataset and the ISSA-BP Neural Network

We aimed to improve the detection accuracy of laser methane sensors in expansive temperature application environments. In this paper, a large-scale dataset of the measured concentration of the sensor at different temperatures is established, and a temperature compensation model based on the ISSA-BP neural network is proposed. On the data side, a large-scale dataset of 15,810 sets of laser methane sensors with different temperatures and concentrations was established, and an Improved Isolation Forest algorithm was used to clean the large-scale data and remove the outliers in the dataset. On the modeling framework, a temperature compensation model based on the ISSA-BP neural network is proposed. The quasi-reflective learning, chameleon swarm algorithm, Lévy flight, and artificial rabbits optimization are utilized to improve the initialization of the sparrow population, explorer position, anti-predator position, and position of individual sparrows in each generation, respectively, to improve the global optimization seeking ability of the standard sparrow search algorithm. The ISSA-BP temperature compensation model far outperforms the four models, SVM, RF, BP, and PSO-BP, in model evaluation metrics such as MAE, MAPE, RMSE, and R-square for both the training and test sets. The results show that the algorithm in this paper can significantly improve the detection accuracy of the laser methane sensor under the wide temperature application environment.

Mid-Infrared Gas Sensing Based on Electromagnetically Induced Transparency in Coupled Plasmonic Resonators

The existence of surface plasmon polaritons in doped silicon micro-scale structures has opened up new and innovative possibilities for applications, such as sensing, imaging, and photonics. A CMOS-compatible doped Si plasmonic sensor is proposed and investigated. The plasmon resonance can be tuned by controlling the carrier density and dopant concentration. In this paper, we demonstrate that using silicon doped with phosphorus at a concentration of 5 × 1020 cm-3 can induce surface plasmon resonance in the mid-infrared region. Two ring resonators of two different radii based on metal-insulator-metal waveguide structures are studied individually. Then, the two ring resonators are integrated in the same device. When the two ring resonators are coupled and resonate at the same frequency; two distinct resonance spectral lines are generated with striking features that improve its potential use for sensing and modulation applications. The propagating plasmonic mode is studied, including its mode profile and bend loss. We evaluate the effectiveness of a microstructure gas sensor with dimensions of 15 μm × 15 μm by measuring its sensitivity and selectivity towards methane and ethane gases. Small alterations in the surrounding refractive index led to noticeable shifts in the resonance peak. The sensor achieved a sensitivity of 7539.9 nm/RIU at the mid-infrared spectral range around the 7.7 μm wavelength. Furthermore, by combining the resonators, we can achieve a smaller full width at half maximum (FWHM), which will ultimately result in greater sensitivity than using a single-ring resonator or other plasmonic resonator configurations. Once the sensitivity and selectivity of the sensor are measured, the FOM can be calculated by dividing the sensitivity by the selectivity of the sensor, resulting in an FOM of 6732.

Gas nanosensors for health and safety applications in mining

The ever-increasing demand for accurate, miniaturized, and cost-effective gas sensing systems has eclipsed basic research across many disciplines. Along with the rapid progress in nanotechnology, the latest development in gas sensing technology is dominated by the incorporation of nanomaterials with different properties and structures. Such nanomaterials provide a variety of sensing interfaces operating on different principles ranging from chemiresistive and electrochemical to optical modules. Compared to thick film and bulk structures currently used for gas sensing, nanomaterials are advantageous in terms of surface-to-volume ratio, response time, and power consumption. However, designing nanostructured gas sensors for the marketplace requires understanding of key mechanisms in detecting certain gaseous analytes. Herein, we provide an overview of different sensing modules and nanomaterials under development for sensing critical gases in the mining industry, specifically for health and safety monitoring of mining workers. The interactions between target gas molecules and the sensing interface and strategies to tailor the gas sensing interfacial properties are highlighted throughout the review. Finally, challenges of existing nanomaterial-based sensing systems, directions for future studies, and conclusions are discussed.

Biosensing systems for the detection and quantification of methane gas

Climate change due to the continuous increase in the release of green-house gasses associated with anthropogenic activity has made a significant impact on the sustainability of life on our planet. Methane (CH4) is a green-house gas whose concentrations in the atmosphere are on the rise. CH4 measurement is important for both the environment and the safety at the industrial and household level. Methanotrophs are distinguished for their unique characteristic of using CH4 as the sole source of carbon and energy, due to the presence of the methane monooxygenases that oxidize CH4 under ambient temperature conditions. This has attracted interest in the use of methanotrophs in biotechnological applications as well as in the development of biosensing systems for CH4 quantification and monitoring. Biosensing systems using methanotrophs rely on the use of whole microbial cells that oxidize CH4 in presence of O2, so that the CH4 concentration is determined in an indirect manner by measuring the decrease of O2 level in the system. Although several biological properties of methanotrophic microorganisms still need to be characterized, different studies have demonstrated the feasibility of the use of methanotrophs in CH4 measurement. This review summarizes the contributions in methane biosensing systems and presents a prospective of the valid use of methanotrophs in this field. KEY POINTS: • Methanotroph environmental relevance in methane oxidation • Methanotroph biotechnological application in the field of biosensing • Methane monooxygenase as a feasible biorecognition element in biosensors.

Integrated Edge Deployable Fault Diagnostic Algorithm for the Internet of Things (IoT): A Methane Sensing Application

The Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud. Towards this, an integrated algorithm is formulated to detect fault at source and to identify the root cause element(s), based on Random Forest (RF) and Fault Tree Analysis (FTA). The RF classifier is employed to detect the fault, while the FTA is utilized to identify the source. A Methane (CH4) sensing application is used as a case-study to test the proposed system in practice. We used data from a healthy CH4 sensing node, which was injected with different forms of faults, such as sensor module faults, processor module faults and communication module faults, to assess the proposed model's performance. The proposed integrated algorithm provides better algorithm-complexity, execution time and accuracy when compared to FTA or standalone classifiers such as RF, Support Vector Machine (SVM) or K-nearest Neighbor (KNN). Metrics such as Accuracy, True Positive Rate (TPR), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), Precision and F1-score are used to rank the proposed methodology. From the field experiment, RF produced 97.27% accuracy and outperformed both SVM and KNN. Also, the suggested integrated methodology's experimental findings demonstrated a 27.73% reduced execution time with correct fault-source and less computational resource, compared to traditional FTA-detection methodology.

Highly sensitive and reliable optical fiber TDLAS gas detection system for methane in situ monitoring in near space

A highly sensitive and reliable tunable diode laser absorption spectroscopy gas detection system with a temperature-pressure compensation algorithm is demonstrated for detecting C H 4 concentrations in near space. Near space generally refers to the airspace 20-100 km away from the ground, where temperature and pressure changes are complex. Since the gas absorption spectrum is easily affected by temperature and pressure, a temperature-pressure compensation algorithm is proposed and used in the C H 4 sensor to improve the detection accuracy of the sensor. First, we measured the basic characteristics of the sensor in the laboratory, such as linearity and long-term stability. Experimental results showed that the linear correlation coefficient R-square can reach 0.999, and the concentration fluctuation of C H 4 is less than 0.17 ppm within 3.5 h. Then the sensor was applied to a research activity in Qinghai Province, China, in September, and the results show that the sensor can effectively monitor the C H 4 concentration in near space.

Optical Methods of Methane Detection

Methane is the most frequently analyzed gas with different concentrations ranging from single ppm or ppb to 100%. There are a wide range of applications for gas sensors including urban uses, industrial uses, rural measurements, and environment monitoring. The most important applications include the measurement of anthropogenic greenhouse gases in the atmosphere and methane leak detection. In this review, we discuss common optical methods used for detecting methane such as non-dispersive infrared (NIR) technology, direct tunable diode spectroscopy (TDLS), cavity ring-down spectroscopy (CRDS), cavity-enhanced absorption spectroscopy (CEAS), lidar techniques, and laser photoacoustic spectroscopy. We also present our own designs of laser methane analyzers for various applications (DIAL, TDLS, NIR).

A selective laser-based sensor for fugitive methane emissions

A mid-infrared laser-based sensor is reported for the quantification of fugitive methane emissions. The sensor is based on a distributed feedback inter-band cascade laser operating near 3.3 μm. Wavelength tuning with cepstral analysis is employed to isolate methane absorbance from (1) fluctuations in the baseline laser intensity, and (2) interfering species. Cepstral analysis creates a modified form of the time-domain molecular free-induction-decay (m-FID) signal to temporally separate optical and molecular responses. The developed sensor is insensitive to baseline laser intensity imperfections and spectral interference from other species. Accurate measurements of methane in the presence of a representative interfering species, benzene, are performed by careful selection of the scan index (ratio of laser tuning range to spectral linewidth) and initial and final time of m-FID signal fitting. The minimum detection limit of the sensor is ~ 110 ppm which can be enhanced with an optical cavity. The proposed sensing strategy can be utilized to measure methane leaks in harsh environments and in the presence of interfering species in environment-monitoring applications.

Accurate Concentration Measurement Model of Multicomponent Mixed Gases during a Mine Disaster Period

A variety of gaseous products are formed when mine fires and coal and gas outbursts occur in mines. On the one hand, these gas products affect the normal production of mines and the occupational health of miners; on the other hand, the gaseous products can also provide much important information to prevent mine disasters. Thus, the rapid and accurate determination of the component content of multicomponent mixed gases is of great significance. However, the distortion of gas chromatography measurement results, which deviate from the true values, has a serious impact on gas composition determination in mines. To reduce the influence of distortion, an Agilent 490 portable gas chromatograph is used to measure the component content of 11 groups of standard multicomponent mixed gases. It is found that the error rate of the measured result is highly related to the concentration of the selected reference component and the component to be measured. Besides, the key point of each gas concentration is determined according to the scatter diagram of the error rate. Each gas is divided into a high and a low concentration group by the key points, and each gas is selected as the reference component to measure the corresponding component concentration in other gases with multiple-point external standards. Researchers have used the least-squares method to fit univariate linear regression analysis between the measured values and true values of mixed gases. Then, the optimal analysis function and the optimal reference component concentration of each gas can be determined by comparing the regression analysis parameters. Finally, it is found that the error rate of measured values corrected by the optimal analysis function is significantly reduced. It is proved that this method can effectively alleviate the measurement results' distortion, which solves the problem of gas composition determination in underground areas.

An Operando X-ray Absorption Spectroscopy Study on Sensing Characteristics of Vertically Aligned ZnO Thin Film for Methane Gas Sensors

In this work, a simple, facile growth approach for a vertically aligned ZnO thin film is fabricated and its application towards methane gas sensors is demonstrated. ZnO thin film was prepared by a combination of hydrothermal and sputtering methods. First, a ZnO seed layer was prepared on the substrate through a sputtering technique, then a ZnO nanorod was fabricated using a hydrothermal method. The surface morphology of the ZnO film was observed by scanning electron microscopy (SEM). A ZnO nanorod coated on the dense seed layer is clearly visible in the SEM image. The average size of the hexagonal-shaped ZnO rod was around 50 nm in diameter, with a thickness of about 1 mm. X-ray absorption near-edge structures (XANES) were recorded to characterize the structural properties of the prepared film. The obtained normalized Zn K-edge XANES of the film showed the characteristic features of ZnO, which agreed well with the standard ZnO sample. The measurement of Zn K-edge XANES was performed simultaneously with the sensing response. The results showed a good correlation between sensor response and ZnO structure under optimal conditions.

Selectivity in trace gas sensing: recent developments, challenges, and future perspectives

Selectivity is one of the most crucial figures of merit in trace gas sensing, and thus a comprehensive assessment is necessary to have a clear picture of sensitivity, selectivity, and their interrelations in terms of quantitative and qualitative views.

Methods for quantifying methane emissions using unmanned aerial vehicles: a review

Methane is an important greenhouse gas, emissions of which have vital consequences for global climate change. Understanding and quantifying the sources (and sinks) of atmospheric methane is integral for climate change mitigation and emission reduction strategies, such as those outlined in the 2015 UN Paris Agreement on Climate Change. There are ongoing international efforts to constrain the global methane budget, using a wide variety of measurement platforms across a range of spatial and temporal scales. The advancements in unmanned aerial vehicle (UAV) technology over the past decade have opened up a new avenue for methane emission quantification. UAVs can be uniquely equipped to monitor natural and anthropogenic emissions at local scales, displaying clear advantages in versatility and manoeuvrability relative to other platforms. Their use is not without challenge, however: further miniaturization of high-performance methane instrumentation is needed to fully use the benefits UAVs afford. Developments in the models used to simulate atmospheric transport and dispersion across small, local scales are also crucial to improved flux accuracy and precision. This paper aims to provide an overview of currently available UAV-based technologies and sampling methodologies which can be used to quantify methane emission fluxes at local scales. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.

A Portable Device for Methane Measurement Using a Low-Cost Semiconductor Sensor: Development, Calibration and Environmental Applications

Methane is a major greenhouse gas and a precursor of tropospheric ozone, and most of its sources are linked to anthropogenic activities. The sources of methane are well known and its monitoring generally involves the use of expensive gas analyzers with high operating costs. Many studies have investigated the use of low-cost gas sensors as an alternative for measuring methane concentrations; however, it is still an area that needs further development to ensure reliable measurements. In this work a low-cost platform for measuring methane within a low concentration range was developed and used in two distinct environments to continuously assess and improve its performance. The methane sensor was the Figaro TGS2600, a metal oxide semiconductor (MOS) based on tin dioxide (SnO₂). In a first stage, the monitoring platform was applied in a small ruminant barn after undergoing a multi-point calibration. In a second stage, the system was used in a wastewater treatment plant together with a multi-gas analyzer (Gasera One Pulse). The calibration of low-cost sensor was based on the relation of the readings of the two devices. Temperature and relative humidity were also measured to perform corrections to minimize the effects of these variables on the sensor signal and an active ventilation system was used to improve the performance of the sensor. The system proved to be able to measure low methane concentrations following reliable spatial and temporal patterns in both places. A very similar behavior between both measuring systems was also well noticeable at WWTP. In general, the low-cost system presented good performance under several environmental conditions, showing itself to be a good alternative, at least as a screening monitoring system.

A Review: Application and Implementation of Optic Fibre Sensors for Gas Detection

At the present time, there are major concerns regarding global warming and the possible catastrophic influence of greenhouse gases on climate change has spurred the research community to investigate and develop new gas-sensing methods and devices for remote and continuous sensing. Furthermore, there are a myriad of workplaces, such as petrochemical and pharmacological industries, where reliable remote gas tests are needed so that operatives have a safe working environment. The authors have concentrated their efforts on optical fibre sensing of gases, as we became aware of their increasing range of applications. Optical fibre gas sensors are capable of remote sensing, working in various environments, and have the potential to outperform conventional metal oxide semiconductor (MOS) gas sensors. Researchers are studying a number of configurations and mechanisms to detect specific gases and ways to enhance their performances. Evidence is growing that optical fibre gas sensors are superior in a number of ways, and are likely to replace MOS gas sensors in some application areas. All sensors use a transducer to produce chemical selectivity by means of an overlay coating material that yields a binding reaction. A number of different structural designs have been, and are, under investigation. Examples include tilted Bragg gratings and long period gratings embedded in optical fibres, as well as surface plasmon resonance and intra-cavity absorption. The authors believe that a review of optical fibre gas sensing is now timely and appropriate, as it will assist current researchers and encourage research into new photonic methods and techniques.

Health Cost Estimation of Traffic-Related Air Pollution and Assessing the Pollution Reduction Potential of Zero-Emission Vehicles in Toronto, Canada

Fossil fuel vehicles, emitting air toxics into the atmosphere, impose a heavy burden on the economy through additional health care expenses and ecological degradation. Air pollution is responsible for millions of deaths and chronic and acute health problems every year, such as asthma and chronic obstructive pulmonary disease. The fossil-fuel-based transportation system releases tons of toxic gases into the atmosphere putting human health at risk, especially in urban areas. This analysis aims to determine the economic burden of environmental and health impacts caused by Highway 401 traffic. Due to the high volume of vehicles driving on the Toronto Highway 401 corridor, there is an annual release of 3771 tonnes of carbon dioxide equivalent (CO2e). These emissions are mainly emitted onsite through the combustion of gasoline and diesel fuel. The integration of electric and hydrogen vehicles shows maximum reductions of 405–476 g CO2e per vehicle-kilometer. Besides these carbon dioxide emissions, there is also a large amount of hazardous air pollutants. To examine the impact of air pollution on human health, the mass and concentrations of criteria pollutants of PM2.5 and NOx emitted by passenger vehicles and commercial trucks on Highway 401 were determined using the MOVES2014b software. Then, an air dispersion model (AERMOD) was used to find the concentration of different pollutants at the receptor’s location. The increased risk of health issues was calculated using hazard ratios from literature. Finally, the health cost of air pollution from Highway 401 traffic was estimated to be CAD 416 million per year using the value of statistical life, which is significantly higher than the climate change costs of CAD 55 million per year due to air pollution.

A Review of Heavy-Duty Vehicle Powertrain Technologies: Diesel Engine Vehicles, Battery Electric Vehicles, and Hydrogen Fuel Cell Electric Vehicles

Greenhouse gas emissions from the freight transportation sector are a significant contributor to climate change, pollution, and negative health impacts because of the common use of heavy-duty diesel vehicles (HDVs). Governments around the world are working to transition away from diesel HDVs and to electric HDVs, to reduce emissions. Battery electric HDVs and hydrogen fuel cell HDVs are two available alternatives to diesel engines. Each diesel engine HDV, battery-electric HDV, and hydrogen fuel cell HDV powertrain has its own advantages and disadvantages. This work provides a comprehensive review to examine the working mechanism, performance metrics, and recent developments of the aforementioned HDV powertrain technologies. A detailed comparison between the three powertrain technologies, highlighting the advantages and disadvantages of each, is also presented, along with future perspectives of the HDV sector. Overall, diesel engine in HDVs will remain an important technology in the short-term future due to the existing infrastructure and lower costs, despite their high emissions, while battery-electric HDV technology and hydrogen fuel cell HDV technology will be slowly developed to eliminate their barriers, including costs, infrastructure, and performance limitations, to penetrate the HDV market.

Design of a Hybrid Electric Vehicle Powertrain for Performance Optimization Considering Various Powertrain Components and Configurations

Emissions from the transportation sector due to the consumption of fossil fuels by conventional vehicles have been a major cause of climate change. Hybrid electric vehicles (HEVs) are a cleaner solution to reduce the emissions caused by transportation, and well-designed HEVs can also outperform conventional vehicles. This study examines various powertrain configurations and components to design a hybrid powertrain that can satisfy the performance criteria given by the EcoCAR Mobility Challenge competition. These criteria include acceleration, braking, driving range, fuel economy, and emissions. A total of five different designs were investigated using MATLAB/Simulink simulations to obtain the necessary performance metrics. Only one powertrain design was found to satisfy all the performance targets. This design is a P4 hybrid powertrain consisting of a 2.5 L engine from General Motors, a 150 kW electric motor with an electronic drive unit (EDU) from American Axle Manufacturing, and a 133 kW battery pack from Hybrid Design Services.

Design, Development and Validation of a Portable Gas Sensor Module: A Facile Approach for Monitoring Greenhouse Gases

We report the unique design and prototype of a portable gas sensor module for monitoring greenhouse gases. The commercially available gas sensors (MQ-02, MQ-135, and TGS2602) were adopted in designing the module using Arduino Uno. Different locations in the city of Solapur, India (17.6599° N, 75.9064° E), were scanned for the usability of the developed prototype of the mobile gas sensor module. The choice of gas sensors in combination with Arduino Uno led to an excellent prototype for measuring the concentration of greenhouse gases, and therefore the wrong alarm for toxic gases. The prototype model and corresponding greenhouse gas concentrations (ppm) are described using an interplay of sensor design, software program, and greenhouse gases sites.