Gas Concentration Levels Research Articles

CMIP6 GCMs Projected Future Koppen‐Geiger Climate Zones on a Global Scale

AbstractThis study compared the future global climate zones based on four radiative forcings ranging from low‐end (SSP1‐2.6) to high‐end (SSP5‐8.5) using the Köppen‐Geiger climate classification. To reduce uncertainties in future projected precipitation and temperature, multimodel projections comprising 25 general circulation models (GCMs) were sourced from the recent Coupled Model Intercomparison Project phase six (CMIP6) and used to create a Multi‐Model Ensemble. The changes in historical climate zones on CMIP6 simulations were divided into six periods considering data availability (1954–1964; 1964–1974; 1974–1984; 1984–1994; 1994–2004; and 2004–2014). Furthermore, the climate zone reproducibility of 25 CMIP6 GCMs was compared with the reference data sourced from Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature. The future climate zones were projected into seven periods using monthly precipitation and surface temperature under four main SSP scenarios. Consequently, the climate variables from GCMs were overestimated compared to the reference data, and the composition of the climate zones was less complex. While temperature discrepancies of 1–2°C may not drastically alter the Köppen‐Geiger climate zone classifications, precipitation‐based classifications are significantly impacted by the observed errors. Thus, it is crucial to recognize that despite the advancements in GCMs, they still possess limitations in accurately predicting “real” future climate changes. The projected future climate zones are simpler compared to the historical periods across six continents, with tundra and ice caps expected to disappear. This study highlights potential risks by projecting future climate zones based on varying greenhouse gas concentration levels, stressing the importance of using these projections with caution given the inherent uncertainties and limitations of GCMs.

Data-Driven Modeling for the Prediction of Stack Gas Concentration in a Coal-Fired Power Plant in Türkiye

In this research, deep learning and machine learning methods were employed to forecast the levels of stack gas concentrations in a coal-fired power plant situated in Türkiye. Real-time data collected from continuous emission monitoring systems (CEMS) serves as the basis for the predictions. The dataset includes measurements of carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOx), oxygen (O2), and dust levels, along with temperatures recorded. For this analysis, deep learning methods such as multi-layer perceptron network (MLP) and long short-term memory (LSTM) models were used, while machine learning techniques included light gradient boosted machine (LightGBM) and stochastic gradient descent (SGD) models were applied. The accuracy of the models was determined by analysing their performance using mean absolute error (MAE), root means square error (RMSE), and R-squared values. Based on the results, LightGBM achieved the highest R-squared (0.85) for O2 predictions, highlighting its variance-capturing ability. LSTM excelled in NOx (R-squared 0.87) and SO2 (R-squared 0.85) prediction, while showing the top R-squared (0.67) for CO. Both LSTM and LGBM achieved R-squared values of 0.78 for dust levels, indicating strong variance explanation. Conclusively, our findings highlight LSTM as the most effective approach for stack gas concentration forecasting, closely followed by the good performance of LightGBM. The importance of these results lies in their potential to effectively manage emissions in coal-fired power plants, thereby improving both environmental and operational aspects.Graphical

High-resolution computation predicts that low dissolved CO concentrations and CO gradients promote ethanol production at industrial-scale gas fermentation

Gradients in dissolved gas concentrations are expected to affect the performance of large reactors for anaerobic gas (CO, H2, CO2) fermentation. To study how these gradients, and the dissolved gas concentration level itself, influence the productivity of the desired product ethanol and the product spectrum of C. autoethanogenum, we coupled a CFD model of an industrial-scale gas fermentor to a metabolic kinetic model for a wide range of metabolic regimes. Our model results, together with literature experimental data and a model with constant dissolved gas concentrations, indicate high ethanol specificity at low dissolved CO concentrations, with acetate reduction to ethanol at very low dissolved CO concentrations and combined ethanol and acetate production at higher CO concentrations. The gradient was predicted to increase both the biomass-specific ethanol production rate and the electron-to-ethanol yield by ∼25%. This might be due to intensified ferredoxin and NAD+ redox cycles, with the rate of the Rnf complex – a critical enzyme for energy conservation – as key driver towards ethanol production, all at the expense of a reduced flux to acetate. We present improved mechanistic understanding of the gas fermentation process, and novel leads for optimization and fundamental research, by coupling observations from various down-scaled lab experiments to expected microbial lifelines in an industrial-scale reactor.

Improved the accuracy of IOT based LPG leakage detection system with early fire prediction and smart alert

Safety is of utmost importance in the realm of smart cities, smart homes, and industries. Among the various safety measures implemented in a smart city, the detection of gas leakage and the prevention of mass fires are crucial for safeguarding lives and valuable assets. This research paper introduces a novel system that not only detects gas leakage but also provides early predictions of fire outbreaks. Unlike the conventional gas leakage detectors available in the market, which only alert when the gas concentration reaches a high level, our system utilizes fuzzy logic, environmental temperature, and humidity to accurately detect gas concentration levels and predict early fire symptoms. By employing the Internet of Things (IoT) approach, this system promptly sends notifications and sensor readings to the relevant individuals, enabling them to take immediate action and prevent extensive damage.

Effect of Dissolved Carbon Dioxide on Cavitation in a Circular Orifice

In this work, we investigate the effect of dissolved gas concentration on cavitation inception and cavitation development in a transparent sharp-edged orifice, similar to that previously analyzed by Nurick in the context of liquid injectors. The working liquid is water, and carbon dioxide is employed as a non-condensable dissolved gas. Cavitation inception points are determined for different dissolved gas concentration levels by measuring wall-static pressures just downstream of the orifice contraction and visually observing the onset of a localized (vapor) bubble cloud formation and collapse. Cavitation onset correlates with a plateau in wall-static pressure measurements as a function of a cavitation number. An increase in the amount of dissolved carbon dioxide is found to increase the cavitation number at which the onset of cavitation occurs. The transition from cloud cavitation to extended-sheet or full cavitation along the entire orifice length occurs suddenly and is shifted to higher cavitation numbers with increasing dissolved gas content. Volume flow rate measurements are performed to determine the change in the discharge coefficient with the cavitation number and dissolved gas content for the investigated cases. CFD analyses are carried out based on the cavitation model by Zwart et al. and the model by Yang et al. to account for non-condensable gases. Discharge coefficients obtained from the numerical simulations are in good agreement with experimental values, although they are slightly higher in the cavitating case. The earlier onset of fluid cavitation (i.e., cavitation inception at higher cavitation numbers) with increasing dissolved carbon dioxide content is not predicted using the employed numerical model.

Design of an automated cyanide gas detection and control system for a gold mine

Cyanidation is an essential process that uses sodium cyanide in gold production. The use of sodium cyanide poses the risk of cyanide gas (HCN gas) poisoning, which has been a challenge in gold mines for many years. A gap exists in the literature regarding systems that simultaneously detect and control dangerous levels of HCN gas. Significant literature sources report HCN gas monitoring systems that detect and record gas concentration levels in the air at different intervals. Furthermore, most reported treatment methods focus on controlling free cyanide in processing effluent, not the HCN gas. Therefore, this paper presents the design of an automated, real-time HCN gas detection and control system for a gold mine using the Internet of Things (IoT). The designed system can detect any rise above the OSHA legal airborne HCN gas permissible exposure limit (PEL) of ten parts per million. In case of detection, the sensor sends a signal to the microcontroller, which triggers a warning alarm to alert workers to evacuate the area. Simultaneously, a signal is sent to the solenoid valve to close the cyanide tank supply. The DC fan immediately provides an air supply that neutralizes the toxic levels of HCN gas.

Assessment of Indoor Radon Gas Concentration in National Open University of Nigeria: A Case Study of Calabar Study Centre

The current work deals with indoor radon (222Rn) concentrations measurements in the Calabar Study Centre of the National Open University of Nigeria using a Corentium Arthings digital radon detector meter for seven days representing a short–term average measurement of indoor radon gas concentration level. The geographical coordinates were recorded using a hand-held geographical positioning system for the sample point. Measurement were taken for seven days and the following data where obtained 83±2.19 Bq/m3,80±3.69 Bq/m3,86±5.57 Bq/m3,84±1.59 Bq/m3,82±3.59 Bq/m3,81±4.89 Bq/m3 and 85 ±5.59 Bq/m3.The average radon(222Rn) concentration level was found to be 83 ± 3.87 Bq/m3 with a geometric mean of 82 ± 3.54 Bq/m3. It was observed that the radon concentration was below the reference level of 100 Bq/m3 recommended by the World Health Organization (WHO). Although the current exposure of members of the public to natural radiation is not critical, the situation could change abruptly when other activities commenced. The excess life time cancer risk calculated for 70 years, 60 years, 50 years, 40 years and 30 years were 1.72 × 10−3,1.65× 10−3,1.39× 10−3,1.44× 10−3 and 0.69× 10−3 respectively. The calculated values of the excess life time cancer risk are all higher than the set limit of 0.029 × 10−3 by International Commission on Radiological Protection. However, there are no observed cases of lung cancer epidemic in this Centre. Therefore, it is advised to use fans and effective ventilation techniques to reduce radon levels. Identifying the regions of the country where people are most at risk from radon exposure should be the main goal of any national radon policy.

Experimental and numerical analysis of low-density gas dispersion characteristics in semi-confined environments

Hydrogen, as a clean fuel, offers a practical pathway to achieve net-zero targets. However, due to its physical and chemical characteristics, there are some safety concerns for large-scale hydrogen utilisation, particularly in process safety management. Leakage of gaseous hydrogen, especially in semi-confined spaces such as tunnels, can lead to catastrophic outcomes including uncontrolled fire and explosion. The current paper describes the outcome of an experimental and numerical study that aims to understand the dispersion of leaked light gas in a semi-confined space to support the adoption of hydrogen. A dispersion chamber with dimensions of 4m × 0.3m × 0.3m was constructed to investigate a baseline gas leakage scenario. To reduce the risk of the experiment in the laboratory, helium is utilised as a surrogate for hydrogen. Computational fluid dynamics simulations are conducted using FLACS-CFD to model the dispersion of leaked gas in different scenarios focusing on the impact of the ventilation velocity, leakage rate, and slope. The results from comprehensive numerical simulations show that ventilation is a critical safety management measure that can significantly reduce the growth of flammable clouds and mitigate the fire and explosion risk. Even with the lowest ventilation velocity of 0.25 m/s, an improvement in the gas concentration level of 29.34% can be achieved in the downstream chamber. The current results will help to further enhance the understanding of hydrogen safety aspects.

A study of radon, thoron and annual effective dose in some Egyptian dwellings by twin-cup dosimeter technique with LR-115 detectors

In the present study, Solid State Nuclear Track Detectors (LR-115–type II) based twin-cup dosimeters were used for estimating radon (222Rn) and thoron (220Rn) gas concentration levels in the environmental air of thirty dwellings in ten cities in Egypt. In the studied dwellings radon concentration levels were found to vary from 24.80 ± 3.98 to 39.71 ± 8.71 Bq m−3 with an average of 32.64 ± 4.34 Bq m−3 whereas thoron concentration is found to vary from 10.46 ± 2.87 to 15.41 ± 2.72 Bq m−3 with an average of 12.48 ± 1.58 Bq m−3. The total annual effective dose by the inhabitants of these dwellings due to radon and thoron was found below the recommended limit by the international agencies. The present study concluded that the dwellings are safe without posing significant radiological threats to human beings.

Programming code using the Duval method for diagnosing power transformers and direct way

A transformer is a piece of equipment of great value in a public or private power substation in that it allows power to be supplied to the production processes. Thus, as it is used, the equipment is exposed to different environmental conditions such as level of contamination, salinity. , height, humidity, among others, as well as energy demand and quality, causing different types of electrical and thermal stress inside. That is why the objective of this study is to implement a programming code using the Duval method for the diagnosis of power transformers. For this, programming based on the supervised learning algorithm is used as a methodology to process the gas concentration levels contained in the dielectric oil obtained from the analysis of dissolved gases and for its interpretation the Duval method is used, which is presented in IEEE C57.104™-2019 standard. The designed code individually processes the dissolved gas analysis reports as well as large quantities of reports from the same transformer or from different ones, allowing the comparison of historical data of the equipment or equipment and the evaluation of its status according to the electrical stress to which it is subjected.

High performance triboelectric nanogenerator with needle tips discharge for gas detection applications

Nowadays, there is an increasing demand for carbon dioxide (CO2) gas detection in various domains, including industry, agriculture, and livestock, et. However, existing CO2 gas detection methods often suffer from issues such as bulkiness, heaviness, high power consumption. Thus, there is a pressing need to develop a compact, lightweight, and efficient CO2 gas detection approach. This paper introduces a self-powered CO2 gas detection method based on a rotating triboelectric nanogenerator (TENG) with a needle-like ionization structure, capable of inducing gas ionization. This paper presents a self-powered CO2 gas detection method based on a rotating triboelectric nanogenerator (TENG) with a needle-like ionisation structure that induces ionisation of the gas. After gas ionisation, the output performance of the overall TENG system is increased extremely substantially, and CO2 is characterised by its high output of distinct signals. By ionizing CO2 gas under different air pressure, concentration, and humidity conditions, the TENG generates distinct voltage outputs, enabling discrimination of CO2 gas pressure, concentration, and humidity levels. Experimental tests involving gas ionization are carried out at air pressures ranging from − 10 kPa to − 60 kPa, yielding a post-ionization voltage increase from 650 V to 1800 V, showing a linear increase. As the CO2 gas concentration in the gas mixture rises from 10% to 90%, the gas ionization gradually and linearly enhances the voltage performance of the TENG. Humidity tests demonstrate a negative correlation between voltage change and humidity when humidity is below 55 RH (%), while a positive correlation is observed when humidity exceeds 55 RH (%). Leveraging the correlation between CO2 gas pressure, concentration, humidity, and the TENG's output performance, a simple and efficient method of CO2 gas detection can be proposed. The proposed CO2 gas detection method exhibits simplicity, cost-effectiveness, and self-powered operation, offering potential applications in industrial and Internet of Things (IoT) domains.

Carbon dioxide sensing using a PEI-Cr2O3-rGO nanocomposite sensor with patterned copper clad as a substrate

ABSTRACT The focus of this work is on carbon dioxide sensing using a nanocomposite sensor composed of PEI, Cr2O3, and rGO, with a patterned copper-clad substrate. Study explores how sensors with varying weight percentages of Cr2O3 and rGO in PEI perform in terms of sensitivity when exposed to carbon dioxide gas. Furthermore, the study probed into exploring the correlation between the fluctuations in electrical resistance of the sensors and the levels of CO2 gas concentration. This examination delves into the sensitivity, repeatability, response time, and recovery time of the sensors. At 1000 ppm of CO2, the sensor with 0.25 wt% of Cr2O3-rGO in PEI demonstrated the lowest sensitivity percentage of 1.71, while the highest sensitivity of 2.13 was achieved by the sensor with 1.0 wt% of Cr2O3-rGO in PEI. The repeatability of the sensors containing 0.25, 0.50, 0.75, and 1.00 wt% of Cr2O3-rGO in PEI was found to be comparable at 8 minutes. However, the repeatability of the sensor with 1.00 wt% of Cr2O3-rGO in PEI was especially noteworthy. A PEI nanocomposite with 0.25 wt% Cr2O3-rGO demonstrated the quickest response time of 33 seconds and recovery time of 36 seconds compared to the other sensors. The sensing mechanism involves the physical absorption of CO2, the effective interactions of the amino groups with CO2 to form carbamates, and the electrical response changes of the PEI-Cr2O3-rGO nanocomposite at room temperature. This mechanism provides a reliable and efficient means of sensing CO2 gas for various applications at equal to or less than 1000 ppm.

Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration

Reliable projection of evapotranspiration (ET) is important for planning sustainable water management for the agriculture field in the context of climate change. A global dataset of monthly climate variables was generated to estimate potential ET (PET) using 14 General Circulation Models (GCMs) for four main shared socioeconomic pathways (SSPs). The generated dataset has a spatial resolution of 0.5° × 0.5° and a period ranging from 1950 to 2100 and can estimate historical and future PET using the Penman-Monteith method. Furthermore, this dataset can be applied to various PET estimation methods based on climate variables. This paper presents that the dataset generated to estimate future PET could reflect the greenhouse gas concentration level of the SSP scenarios in latitude bands. Therefore, this dataset can provide vital information for users to select appropriate GCMs for estimating reasonable PETs and help determine bias correction methods to reduce between observation and model based on the scale of climate variables in each GCM.

Mapping of Alpha Emitters for Soil Samples in Kufa Districts, Iraq

Abstract: In this study, radon (Rn) gas concentrations were measured in the soil samples taken at various locations in the Kufa district, Iraq. The study was conducted using a solid-state nuclear track detector (SSNTD), commercially known as CR-39, purchased from the TASTRAK Analysis System. Also, radium and uranium concentrations and annual effective dose, mass exhalation rate, surface exhalation rate, and excess lifetime cancer risk due to radon gas were calculated. The results show that the average values, with a standard deviation of radon concentrations, in the air space of a tube containing the sample and in the samples were 14±1 Bq/m3 and 895±74 Bq/m3, respectively. We also found that the average value of the annual effective dose was 0.36±0.08 mSv/y. We employed the GIS (ArcGIS 10.7.1.) technique to draw the main conclusions of the study. The study demonstrates that the measured levels of radon gas concentrations are within acceptable ranges in terms of potential health risks. Keywords: Alpha emitters, Radon gas, Soil, GIS, Kufa district, Radiation maps.

Response Characteristics of Gas Concentration Level in Mining Process and Intelligent Recognition Method Based on BI-LSTM

Response Characteristics of Gas Concentration Level in Mining Process and Intelligent Recognition Method Based on BI-LSTM

Development of Autonomous Driving Patrol Robot for Improving Underground Mine Safety

To improve the working conditions in underground mines and eliminate the risk of human casualties, patrol robots that can operate autonomously are necessary. This study developed an autonomous patrol robot for underground mines and conducted field experiments at underground mine sites. The driving robot estimated its own location and autonomously operated via encoders, IMUs, and LiDAR sensors; it measured hazards using gas sensors, dust particle sensors, and thermal imaging cameras. The developed autonomous driving robot can perform waypoint-based path planning. It can also automatically return to the starting point after driving along waypoints sequentially. In addition, the robot acquires the dust and gas concentration levels along with thermal images and then combines them with location data to create an environmental map. The results of the field experiment conducted in an underground limestone mine in Korea are as follows. The O2 concentration was maintained at a constant level of 15.7%; toxic gases such as H2S, CO, and LEL were not detected; and thermal imaging data showed that humans could be detected. The maximum dust concentration in the experimental area was measured to be about 0.01 mg/m3, and the dust concentration was highly distributed in the 25–35 m section on the environmental map. This study is expected to improve the safety of work by exploring areas that are dangerous for humans to access using autonomous patrol robots and to improve productivity by automating exploration tasks.

LPG gas leakage detection system using IoT

The objective of this study is to achieve a completely functional prototype that can detect the existence of gas leakage; however, in this case, our intention was to use liquefied petroleum gas (LPG). The prototype should also act upon auto-response with the execution of an alarm system and activate the GSM modem, so that the exhaust fan will activate and try to reduce the gas level. The vital part of this study is to perceive the occurrence of leakage, which is achieved by the sensor detecting the gas concentration level. Once the gas level is true, a signal is sent to the alarm system for triggering. This also turns on the GSM modem and activates the exhaust fan, which is triggered by the microcontroller ATMEGA328p through a relay circuit. In this paper, the system uses a highly accurate, fast sensing sensor device that can give an edge to fast sensing of gas.

Development of Microcontroller Based Gas Leakage Detector with Dual SMS Alert

The prevailing treat to lives and property by the usage of gas for cooking, etc, and the rapidly increasing evolution in new technologies worldwide, a novel approach utilizing dual communication network providers is applied to alert users in the event of leakage of Liquefied Petroleum Gas (LPG). In this work, a prototype gas leakage detector, based on dedicated Peripheral Interface Controller (PIC), sensitive MQ-6 gas sensor, GSM/GPRS SIM900 and discrete components is developed and tested. Glo and MTN network service providers were selected for the design. Both indoor and laboratory tests were conducted where the results indicate gas concentration levels of 372 ppm to 255 ppm were measured to display a message on Liquid Crystal Display (LCD) and trigger audio alarm at instances of 2.42 secs and 8.35 secs in locations of 5 cm and 30 cm away from gas source respectively. Finally, the viability test proved satisfactory as it indicates that whenever a particular network fails, the second network was available to receive the SMS message adequately. This could be applied in homes, restaurants, etc where cooking gas is often used. Future enhancements should include the deployment of all available communication network service providers to augment for the limitation of the only two networks in this work.

Advantages and Challenges of Composting Reactors for Household Use: Smart Reactor Concept

In the European Union, 88 Mt of food waste is generated annually, accounting for 6% of total EU greenhouse gas emissions. To reduce the amount of bio-waste going into the landfills, the composting of bio-waste at a household level must be facilitated. Traditional composting devices for garden and household biological waste solely rely on natural processes and do not hold online process control features or require energy input. This study describes a design and construction of a smart composting reactor for improved composting process control and compares the developed system with other laboratory-scale reactors and commercial devices available for this purpose. The Alternative Hierarchy Process (AHP) method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria analysis method were used to assess the importance of various parameters and devices. The results showed good thermal insulation by reducing thermal transmittance from 1.87 W/m²K to 1.27 W/m²K, the effective sensor system performance of the constructed system, providing continuous data logging of temperature, moisture, and gas concentration levels. The system demonstrated 58% proximity to the ideal solution.

On Data-Driven Self-Calibration for IoT-Based Gas Concentration Monitoring Systems

In this article, data-driven self-calibration algorithms for the Internet of Things-based gas concentration monitoring systems embedded with low-cost gas sensors are designed. The measurement errors are assumed to be caused by imperfect compensation for the variation of sensor component behavior. Specifically, the calibration procedure for the nondispersive infrared <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CO_{2}$ </tex-math></inline-formula> sensors is developed, for which the temperature dependency is the most dominant drift source. For a single sensor, the hidden Markov model is used to characterize the statistical relationship between different quantities introduced by the physical model that builds on the Beer–Lambert law. For the calibration in the Internet of Things-based system, sensors first transmit their belief functions of the true gas concentration level to the cloud. Then, the cloud fusion center computes a fused belief function according to certain rules. This belief function is then used as reference for calibrating the sensors. To deal with the case where belief functions highly conflict with each other, a Wasserstein distance-based weighted average belief function fusion approach is first proposed as a networked calibration algorithm. To achieve more long-term stable calibration results, the networked calibration problem is further formulated as a partially observed Markov decision process (MDP) problem, and the calibration strategies are derived in a sequential manner. Correspondingly, the deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network approach is applied as a computationally efficient method to solve the proposed MDP problem. The performance of practical designs of the proposed self-calibration algorithms is finally illustrated in numerical experiments utilizing real data.