Emission Monitoring System Research Articles

314 Steer Production Performance and Enteric gas Emissions Associated with Free-Choice Mineral Supplement Infused with Varying Levels of Garlic Powder

Abstract The addition of phytogenics such as garlic powder into beef mineral supplements is motivated by the potential for increased mineral intake and associated insectifugal outcomes. This 3-yr study compared feed and mineral intake and performance in crossbred beef steers that received free-choice mineral supplements infused with or without garlic powder (5% inclusion). The study design was a randomized complete block design where each steer (n=180) was randomly assigned to either a 5%GP or 0%GP treatment group, balancing for breed composition and initial body weight. Different garlic brands (5%GPLO, 5%GPSE, and 5%GPWL) were also assessed during the third year of study. Individual feed and mineral supplement intakes were monitored using automatic feeding units from different bunks ad libitum. Enteric gas fluxes (CH4, CO2, O2, and H2) were also measured with an automated head-chamber emission monitoring system. Over 3 yr, there was no difference (P > 0.05) in dry matter intake, average daily gain, and gain to feed ratio between 5%GP and 0%GP groups, but supplement intake was greater (P < 0.05) for 5%GP (206 gd-1) compared with 0%GP (152 gd-1) treatment. Comparing all brands in third year showed differences in mineral consumption where 5%GPLO (223 gd-1) had greatest (P < 0.0001) supplement intake, but there was no difference (P > 0.05) among 5%GPSE (133 gd-1), 5%GPWL (103 gd-1) and 0%GP (145 gd-1). Enteric gas fluxes did not differ between 5%GPLO and 0%GP for CH4 (129 gd-1, 119 gd-1; P = 0.69), CO2 (7912 gd-1, 7351 gd-1; P = 0.31), O2 (5394 gd-1, 4816 gd-1; P = 0.15) and H2 (0.84 gd-1, 0.67 gd-1; P = 0.42). Results suggest that infusing garlic at 5% increased supplement intake in feedlot cattle but had no effect on growth or enteric gas fluxes. Differences in the brands evaluated here may be related to the shelf/storage age of the products and the bioactive compound profiles.

Study of the mechanism of digitalization boosting urban low-carbon transformation

The climate problem is severe and many urban climates are poor. To deal with a series of climate problems, low-carbon measures are indispensable. Digital development is becoming a new driving force for social development. Therefore, the development of urban low-carbon transformation should be based on digital construction. This paper explores the significance and theoretical mechanism of the research of the mechanism of digitalization boosting urban low-carbon transition. Theoretically, digitalization can reduce carbon emissions through mechanisms such as optimizing industrial structure, adjusting energy structure, improving government governance efficiency, and changing people’s way of production and life. Finally, from the perspective of carbon neutrality, this paper puts forward relevant countermeasures, from the following six aspects: building a carbon emission measurement and monitoring system, promoting the digital transformation of enterprises, increasing the research and development of new energy technologies, improving the carbon emission market trading system, optimizing the carbon emission administrative management system, and practicing the low-carbon concept.

Risk Assessment of Delay for CEMS and WHRU Installation Project on Plant Shutdown at Central Processing Gas Gundih

This paper addresses a risk assessment of delay for installation of Continuous Emission Monitoring System and Waste Heat Recovery Unit as an effort to fulfil compliance with the Regulation of the State Minister of the Environment Number 13 of 2009 concerning Quality Standards for Emissions from Immovable Sources for Oil and Gas Businesses and Activities. The installation of the unit consists of three steps, pre-shutdown, window shutdown and post shutdown. In accordance with Gas Sale Agreement, that the entire period of normal maintenance work that causes gas to not flow, should not be more than 15 (fifteen) days every year. The planning and implementation of maintenance is very important by utilizing the condition of the window shutdown to avoid material and non-material losses. Reviewing the vulnerability of this project, it is necessary to conduct research that assesses the level of risk of project delays using the ISO 31000:2018 risk management concept approach, through identification of risk variables from several literature studies, risk analysis through Group Discussion Forums, and risk management efforts to reduce risk of delays in the installation project. From the risk identification, 30 risk variables that affect project delays consist of 2 High categories, 3 Medium to High categories, 15 Medium categories, 6 Low to Medium categories and 4 Low categories. The Risk Treatment carried out in the high and Medium to high categories resulted in 9 mandatory RCOs to reduce delays and 1 RCO to speed up the implementation schedule from 19 days to 13 days and earn a profit of $615,000 because the process of selling gas to consumers can be accelerated by 2 days.

Effects of diet on feed intake, weight change, and gas emissions in beef cows.

The objective of this study was to examine the effects of diet energy density on ranking for dry matter intake (DMI), residual feed intake (RFI), and greenhouse gas emissions. Forty-two mature, gestating Angus cows (600 ± 69 kg body weight [BW]; body condition score [BCS] 5.3 ± 1.1) with a wide range in DMI expected progeny difference (-1.38 to 2.91) were randomly assigned to two diet sequences; forage then concentrate (FC) or concentrate then forage (CF). The forage diet consisted of long-stem native grass hay plus protein supplement (HAY; 1.96 Mcal ME/kg DM). The concentrate diet consisted of 35% chopped grass hay and 65% concentrate feeds on a dry matter basis (MIX; 2.5 Mcal ME/kg DM). The GreenFeed Emission Monitoring system was used to determine carbon dioxide (CO2), oxygen (O2), and methane (CH4) flux. Cow performance traits, ultrasound back fat and rump fat, feed DMI, and gas flux data were analyzed in a crossover design using a mixed model including diet, period, and sequence as fixed effects and pen and cow within sequence as random effects. For all measured traits excluding DMI, there was a diet × sequence interaction (P < 0.05). The correlation between MIX and HAY DMI was 0.41 (P = 0.067) and 0.47 (P = 0.03) for FC and CF sequences, respectively. There was no relationship (P > 0.66) between HAY and MIX average daily gain (ADG), regardless of sequence. Fifty-seven percent of the variation in DMI was explained by metabolic BW, ADG, and BCS for both diets during the first period. During the second period, the same three explanatory variables accounted for 38% and 37% of the variation in DMI for MIX and HAY diets, respectively. The negative relationship between BCS and DMI was more pronounced when cows consumed the MIX diet. There was no relationship between MIX and HAY RFI, regardless of sequence (P > 0.18). During the first period, correlations for CO2, CH4, and O2 with MIX DMI were 0.69, 0.81, and 0.56 (P ≤ 0.015), respectively, and 0.76, 0.74, and 0.64 (P < 0.01) with HAY DMI. During the second period, correlations for CO2, CH4, and O2 with MIX DMI were 0.62, 0.47, and 0.56 (P ≤ 0.11), respectively. However, HAY DMI during the second period was not related to gas flux (P > 0.47). Results from this experiment indicate that feed intake of two energy-diverse diets is moderately correlated while ADG while consuming the two diets is not related. Further experimentation is necessary to determine if gas flux data can be used to predict feed intake in beef cows.

Strain rate effects on characteristic stresses and acoustic emission properties of granite under quasi-static compression

In order to further investigate the strain rate effects on characteristic stresses and acoustic emission parameters of rock under quasi-static compression, uniaxial compressive tests were conducted on cylindrical specimens measuring 50 mm in diameter and 100 mm in height using a rock material testing machine and a multi-channel acoustic emission monitoring system at strain rates ranging from 10–6 s−1 to 10–2 s−1. The stress-strain curves of rock samples, characteristic stresses, energy data, and temporal and spatial distribution of acoustic emission signals were obtained and analyzed. The experimental results certified a linearly positive correlation between characteristic stresses and the logarithm of strain rates, despite the fact that the linear correlation varies for different characteristic stresses, whereas the ratios of characteristic stresses essentially do not change with increasing strain rates. The input energy and elastic strain energy at the damage point, UCS point and failure stress point exhibit a linearly positive correlation with the logarithm of strain rates when the strain rate exceeds 10–5 s−1. Meanwhile, the characteristics of energy conversion between input energy and elastic strain energy or the dissipated energy at different characteristic stresses points were explored. Based on this, the energy conversion process of rock under quasi-static compression can be divided into three stages: energy accumulation, energy dissipation, and energy release, respectively. Besides, it is noted that the total number of the located AE events decreases as strain rates increase when the strain rate exceeds 10–5 s−1, and the majority of located AE events occur during the crack closure stage and unstable crack growth stage. Finally, based on the perspective of energy conversion and the structural properties of multi-scale defects in rock, the mechanism of the increase of characteristic stresses with the increase of strain rates was proposed: that is, when rock is subjected to quasi-static compression, the higher strain rates can activate the small-scale defects, which necessitates more input energy from the external load via continuous work and causes an increase in the associated characteristic stresses.

Wavelet Energy Evolution Characteristics of Acoustic Emission Signal under True-Triaxial Loading during the Rockburst Test

The rockburst simulation test is conducted by utilizing a mineral-containing marble specimen. The loading condition is set to the three directions, each loading on five surfaces except for a single free surface. The whole test procedure is monitored in real time by using a PCI-II acoustic emission monitoring system and a high-speed camera. According to the test outcomes, rockburst is a process in which energy is rapidly released from the free surface. Rock block and rock plate are buckled and ejected from the free surface and a severe rockburst process is accompanied by spray rock powder. An explosion sound can be heard during the process, which can be analyzed by signal processing techniques. The failure mode of the specimen is a splitting-shearing composite failure, and the free surface becomes a rockburst destruction surface. A V-type rockburst pit is formed in the ejection area. The effective acoustic emission signal of the whole test process is decomposed and reconstructed using five-layer wavelets to produce six frequency band sub-signals. In addition, the wavelet energy and its energy distribution coefficients are assessed for various frequency bands, and the proportion of each dominant frequency band within each period is computed. Finally, it was found that the dominant frequency band is 125~250 kHz, while the suboptimal frequency band is 250~500 kHz. The succeeding features are noticed to be used as predicted features for the rockburst disaster. Namely, acoustic emission signals arise in large numbers and the energy distribution coefficient of the dominant frequency band concentrates above 0.4. The proportion of dominant frequency band appears in continuous valley type and keeps below 80%, while the proportion of suboptimal frequency band appears in continuous peak type and keeps above 20%.

An improved hourly-resolved NOx emission inventory for power plants based on continuous emission monitoring system (CEMS) database: A case in Jiangsu, China

High-resolved emission inventories are essential to evaluate air quality and health effects. However, for one of the most polluting industries in China – power plants, emission uncertainties remain high due to the limitation of point measurements. In this study, we established an hourly NOx emission inventory based on the continuous emission monitoring system (CEMS) network of Jiangsu, China, with an integrated new method that coupled unit-level fuel consumption and emission factors. To evaluate the accuracy of our method, we compared the accuracies of our method to three other bottom-up inventories and determined that, our results (CEMS(Improved) for which the normalized mean bias (NMB) was 3.24%) exhibited higher accuracies compared to the other two methods: BASE EIs and CEMS(Traditional), for which the NMB was −38.53% and 294.1%, respectively. The total NOx emissions of power plants in Jiangsu, China, were 43701 tons in 2018, equivalent to 42.2% of that in 2017. 53% of total emissions were contributed by the southern Jiangsu area and super units (>1000 MW) exhibited the largest emissions shares (70%). The daily, monthly, and hourly average emissions demonstrated a “high in winter and summer” pattern. The ratio of the highest daily emissions in the June-July-August period (JJA) to the lowest in the September–November period (SON) was 1.20, which is relatively higher than that of fuel consumption (1.18) and power generation (1.15). The weekly and monthly emissions on hourly trends varied consistently, with peaks at 12:00,16:00,19:00, and 21:00 (except for high-discharge months). The hourly emissions on holidays and weekends were 6.4% and 1.6%, lower than those of workdays and weekends, with emission disparities being the largest in the period of 8:00–10:00. Our new hourly CEMS emission inventory can significantly reduce the uncertainties of emission estimation and better serve policy-making for mitigating power plant emissions in China.

Design of a low-cost wireless emission monitoring system for solid-fuel heat sources

INTRODUCTION: The study presents a conceptual and detailed design methodology overview of a wireless sensor network (WSN) aimed to evaluate the SmartCity air quality in real-time. The aim of the article is to provide low-cost, reliable, and accurate emission data at remote locations, assisting the 2050 Climate-Neutral target set by the European Commission. The sensor devices shall monitor emission parameters such as ozone, carbon dioxide concentration and particle concentrations, specifically concentrations PM2,5 and PM10.OBJECTIVES: The overall objective of the article is to demonstrate the use of a modern home-made air quality device within the market of boilers, fireplaces or other heating devices.METHODS: The methodology consists of reverse engineering of used professional portable emission analyzers, determining the detailed parts, building a model of how the device's internal systems operate. The paper later explains a series of future experiments including the setup, the necessary components and specific aim. Two numerical models shall be constructed in MATLAB Simscape and Simulink, specifically a telecommunication model, assisting the design of the telecommunication module, and thermal numerical model, that aids in verifying the system temperature and cooling.RESULTS: The study presents a large potential in increasing the accuracy and spectrum of modern air quality data and decreasing emission levels.CONCLUSION: In the conclusion of the paper, the functional requirements of the data processing are stated, along with schematic illustration of the user interface of measured emissions.

Effects of combination mode on mechanical properties of bi-material samples consisting of rock and coal

We performed uniaxial compression tests on coal–rock, rock–coal, and rock–coal–rock bi-material samples. We studied their mechanical behavior using an acoustic emission monitoring and digital speckle strain measurement systems. The average uniaxial compressive strengths of the rock–coal–rock, coal–rock, and rock–coal bi-material samples were 20.54 MPa, 14.28 MPa, and 14.03 MPa, respectively. The strength of the coal determines the overall strength of the bi-material sample. However, the overall strengths of the coal–rock, rock–coal, and rock–coal–rock bi-material samples were greater than that of coal by 11.76%, 10.19%, and 31.84%, respectively. A crack in the coal part of a bi-material sample can develop and spread to the rock part, thus resulting in its splitting failure. The rock–coal–rock bi-material samples exhibited plastic failure, while the coal–rock and rock–coal bi-material samples exhibited brittle failure. Progressive failure of the coal sample induced rebound deformation of the rock sample. The rebound deformation rates of the rock–coal–rock, coal–rock, and rock–coal samples were 0.57%, 0.09%, and 0.12%, respectively. In the pre-peak stage, the average elastic energy of the rock–coal–rock bi-material sample was 92.57%, indicating a strong ability to store external energy. In the post-peak stage, the average post-peak release energy of the rock–coal–rock bi-material sample was the largest, but the average surplus energy was the smallest. Therefore, the rock–coal–rock bi-material exhibited the highest macro-cracking but the dynamic behavior was the least prominent. The coal–rock and rock–coal bi-material samples exhibited less prominent macro-cracking after failure but a more pronounced dynamic behavior.

Carbon Emission and Battery Monitoring System

Abstract: Automobile exhaust gases are one of the most significant sources of carbon dioxide. Carbon monoxide from the exhaust system can seep into the vehicle through holes. Carbon dioxide poisoning can be lethal if inhaled at a specific dosage and for a long time. To ensure that the level of carbon dioxide in the vehicle is safe, the air quality must be monitored. Our goal is to create a project that focuses on vehicle carbon monoxide monitoring, alerting, and control. The carbon monoxide level in the vehicle is detected by the system. The collected sensor data will be stored in a database. The technology allows the user to keep track of carbon dioxide levels and vehicle battery discharge. The user can also be notified when carbon dioxide levels are dangerously high. When the battery level drops due to infrequent use of the vehicle, the system can provide effective notification about the battery discharge levels. The device is affordable and simple to install in the vehicle. Keywords: Monitoring, Emission, Carbon, Battery, IOT, EV, Hybrid vehicle

In-situ manifestation of lapping mechanisms by rapid intelligent pattern recognition analysis (RIPRA) of acoustic emission via a point density fuzzy C-means (PD-FCM) method

Rapid pattern-recognition capabilities are very challenging tasks to fulfill while essential steps for the ultimate mission of achieving in-time security response, hazardous disaster prevention, structural failure prediction, and intelligent manufacturing applications. Mechanistic manifestation of a machining process through pattern recognition is even more daunting since one has to gain full understanding at the very fundamental level of the manufacturing processes, but it would be the most beneficial should it be accomplished. We designed and built an in-situ Acoustic Emission (AE) monitoring system, and successfully applied this newly developed AE system to manifest the lapping removal mechanisms of a novel fixed abrasive pad embedded with agglomerated diamond (AD) abrasives. To successfully extract valuable mechanistic information, the time-frequency spectrum of collected AE signals in a sapphire lapping process was decomposed by Wavelet Packet Transform (WPT) into three characteristically patterned bands, each representing rubbing, ploughing and cutting action in the lapping process correspondingly. We creatively applied Point Density Fuzzy C-Means (PD-FCM) algorithm for self-identifying clustering centers to classify contributions from rubbing, ploughing and cutting actions in the lapping process. By rapid intelligent pattern recognition analysis (RIPRA) of AE signals, the lapping removal mechanisms using fixed abrasive pads were clearly identified and quantitatively monitored in-situ throughout the whole lapping processes. Such a capable approach implicates a great potential in a wide range of ultra-precision manufacturing applications.

Air pollutant emission and reduction potentials from the sintering process of the iron and steel industry in China in 2017

The iron and steel industry (ISI) is one of the most energy-intensive industries in China, which makes a substantial contribution to the emissions of air pollutants. Among the various manufacturing processes, sintering is the major emitting process, which shares over half of the emissions of sulfur dioxide (SO2), nitrogen oxide (NOx) and particulate matter (PM) for the entire industry. In this study we made a comprehensive evaluation of the air pollutant emissions from the sintering process of China's ISI in 2017 based on the Continuous Emission Monitoring System (CEMS) database and estimated the future reduction potentials. We found that there was a general decreasing trend of emission concentrations in the sintering flue gas in response to the strengthened emission control policies, but the mild increase of the oxygen content in the second half of the year flattened the decreasing trend, indicating the necessity for simultaneous control of the oxygen content in the flue gas. Despite the relative high standard-reaching rates of 90% to the emission concentration limits in GB 28662-2012, the standard-reaching rates to the ultra-low emission standards were only 12%, 40% and 27% for NOx, SO2 and PM respectively, with the lowest value mostly occurred in the western provinces. In 2017, the NOx, SO2 and PM emissions from the sintering process were 378.6 kt, 169.0 kt and 51.9 kt, respectively. If the ultra-low emission standards were met, the corresponding NOx, SO2 and PM emissions would decrease by 69.9%, 52.9%, and 56.4% respectively, illustrating large emission reducing potentials by achieving the ultra-low emission standards.

Daily Emission Patterns of Coal-Fired Power Plants in China Based on Multisource Data Fusion.

Daily emission estimates are essential for tracking the dynamic changes in emission sources. In this work, we estimate daily emissions of coal-fired power plants in China during 2017-2020 by combining information from the unit-based China coal-fired Power plant Emissions Database (CPED) and real-time measurements from continuous emission monitoring systems (CEMS). We develop a step-by-step method to screen outliers and impute missing values for data from CEMS. Then, plant-level daily profiles of flue gas volume and emissions obtained from CEMS are coupled with annual emissions from CPED to derive daily emissions. Reasonable agreement is found between emission variations and available statistics (i.e., monthly power generation and daily coal consumption). Daily power emissions are in the range of 6267-12,994, 0.4-1.3, 6.5-12.0, and 2.5-6.8 Gg for CO2, PM2.5, NO x , and SO2, respectively, with high emissions in winter and summer caused by heating and cooling demand. Our estimates can capture sudden decreases (e.g., those associated with COVID-19 lockdowns and short-term emission controls) or increases (e.g., those related to a drought) in daily power emissions during typical socioeconomic events. We also find that weekly patterns from CEMS exhibit no obvious weekend effect compared to those in previous studies. The daily power emissions will help to improve chemical transport modeling and facilitate policy formulation.

Experimental simulation study of rockburst characteristics of Sichuan–Tibet granite: A case study of the Zheduoshan tunnel

Rockburst poses a great threat to the construction safety of deep tunnels under high geostress conditions. Recently, numerous deeply buried tunnels have been constructed in China, especially in the Sichuan–Tibet railway area. In this study, two rockburst experiments (strainbursts and impact-induced rockbursts) were conducted under different initial geostresses using the strainburst experimental apparatus and impact-induced rockburst experimental apparatus equipped with an acoustic emission (AE) monitoring system. A video acquisition system was used to capture the rockburst process. The results demonstrated that the rockburst process is similar under different initial geostresses, including grains ejection, rock plates spalling/buckling, and fragments ejecting violently. Based on the result of the AE characteristics, the large- amplitude AE signals were concentrated in the lower frequency band and the rockburst intensity was increased gradually with increase in the initial geostress. The b-value and βt-value fluctuated slightly at first, subsequently increased steadily, and then decreased abruptly when rockburst occurred. Furthermore, the displacement and divergence fields on the specimen surface were calculated using digital image correlation (DIC) technology. The result shows that the zone of large deformation and high divergence is consistent with the rockburst zone. Moreover, 80%σpeak is a critical stress state where rocks begin experiencing slight damage.

Reduction of HgCl2 to Hg0 in flue gas at high temperature. Part Ⅰ: Influences of oxidative species

The reduction of gaseous oxidized mercury Hg2+(g) to elemental mercury Hg0(g) is a key technology to obtain the total flue gas mercury in the mercury continuous emission monitoring system (Hg-CEMS). Investigation of the reduction of HgCl2(g) to Hg0(g) in the high-temperature decomposition (HTD) was conducted with/without oxidative components in a fixed bed reactor. The chemical reaction path was discussed in-depth by thermodynamic calculation and theoretical analysis. The results show that the higher the temperature, the higher the efficiency of HgCl2(g) reduction. The chlorine species produced in the HTD process inhibit the conversion rate because of re-oxidation on Hg0(g). For eliminating the negative effect of Cl radicals and promoting the reactive sites, a quartz filler with appropriate size demonstrates a good performance for enhancing the decomposition of HgCl2(g). However, the presence of O2 and HCl leads to the complete failure of HgCl2(g) HTD because O2 greatly promotes the combination of Hg0(g) with chlorine. Compared with HCl molecules, Cl atoms are more active in thermodynamic favor in speeding up re-oxidation of Hg0(g). Active Cl atoms are the most crucial factor hindering HTD reaction due to their high reaction initiative and low activation energy.

Differences in the Composition of the Rumen Microbiota of Finishing Beef Cattle Divergently Ranked for Residual Methane Emissions.

With the advent of high throughput technology, it is now feasible to study the complex relationship of the rumen microbiota with methanogenesis in large populations of ruminant livestock divergently ranked for enteric emissions. Recently, the residual methane emissions (RME) concept has been identified as the optimal phenotype for assessing the methanogenic potential of ruminant livestock due to the trait’s independence from animal productivity but strong correlation with daily methane emissions. However, there is currently a dearth of data available on the bacterial and archaeal microbial communities residing in the rumens of animals divergently ranked for RME. Therefore, the objective of this study was to investigate the relationship between the rumen microbiota and RME in a population of finishing beef cattle. Methane emissions were estimated from individual animals using the GreenFeed Emissions Monitoring system for 21 days over a mean feed intake measurement period of 91 days. Residual methane emissions were calculated for 282 crossbred finishing beef cattle, following which a ∼30% difference in all expressions of methane emissions was observed between high and low RME ranked animals. Rumen fluid samples were successfully obtained from 268 animals during the final week of the methane measurement period using a trans-oesophageal sampling device. Rumen microbial DNA was extracted and subjected to 16S rRNA amplicon sequencing. Animals ranked as low RME had the highest relative abundances (P < 0.05) of lactic-acid-producing bacteria (Intestinibaculum, Sharpea, and Olsenella) and Selenomonas, and the lowest (P < 0.05) proportions of Pseudobutyrivibrio, Butyrivibrio, and Mogibacterium. Within the rumen methanogen community, an increased abundance (P < 0.05) of the genus Methanosphaera and Methanobrevibacter RO clade was observed in low RME animals. The relative abundances of both Intestinibaculum and Olsenella were negatively correlated (P < 0.05) with RME and positively correlated with ruminal propionate. A similar relationship was observed for the abundance of Methanosphaera and the Methanobrevibacter RO clade. Findings from this study highlight the ruminal abundance of bacterial genera associated with the synthesis of propionate via the acrylate pathway, as well as the methanogens Methanosphaera and members of the Methanobrevibacter RO clade as potential microbial biomarkers of the methanogenic potential of beef cattle.

Nonlinear modeling of industrial boiler NOx emissions

ABSTRACT Pollutant emissions into the atmosphere are recognized as a significant problem in fossil fuel combustion. The pollution emission measurement in industrial boilers is difficult and expensive but fundamental for monitoring and controlling. Frequently continuous emissions monitoring (CEM) system is out of service or useless due to obsolescence, high maintenance cost, and so on or simply is not installed. When a system for measuring pollutant emissions is not available, an alternative method must be employed to get the pollutant emission value. According to the black-box model approach, this article describes the nonlinear modeling of NOx emissions from a utility boiler. Bayesian-Gaussian (BG), multilayer perceptron (MLP), and Volterra polynomial basis functions (VPBF) neural networks are developed for model benchmarking. Experimental data from a utility boiler was acquired in order to model definition and evaluation. The models process three boiler variables oxygen excess, fuel mass flow and flue gas recirculation gates for NOx emission estimation. Models with BG show better performance than models with MLP and VPBF for NOx prediction. Implications: The technology to control NOx emissions generated by combustion operates under strict regulations. In order to reduce NOx emissions, theoretical models of NOx generation have been studied extensively, including nitrogen chemistry and the dynamic flow of gas particles which is very complex. The new technology trends would require the continuous measurement of high precision NOx emissions to achieve further reductions in NOx emissions. Currently, NOx emissions are measured by a Continuous Emission Monitoring system, which turns out to be extremely expensive and difficult to maintain, so alternative low-cost solutions are desirable. Our contribution shows how algorithms based on different artificial intelligence techniques are viable and quality alternatives for the measurement of continuous NOx emissions. The NOx emissions models based on IA algorithms are viable alternatives that have versatility and self-tuning capacity due to the fact that they are based on boiler operation parameters which have valuable information few explored nowadays.

IOT Based Vehicle Emission Monitoring System and Pollution Detection

Degradation of air quality in cities is the result of a complex interaction between natural and anthropogenic environmental conditions. With the increase in urbanization and industrialization and due to poor control on emissions and little use of catalytic converters, a great amount of particulate and toxic gases are produced. The objective of this paper is to monitor air pollution on roads and track vehicles which cause pollution over a specified limit. Increasing number of automobiles is a serious problem that has been around for a very long time. This paper proposes use of Internet of Things(IoT) to address this problem. Here, combination of Wireless Sensor Network and Electrochemical Toxic Gas Sensors and the use of a Radio Frequency Identification (RFID) tagging system to monitor car pollution records anytime anywhere. An increase in automobile vehicle increase in air pollution since automobiles are the main source of environmental pollution. The smoke emitted from the vehicle consists of gases like nitrogen oxides (NOx), carbon monoxide(CO), and hydrocarbon (HC). approximately one-half of the nitrogen oxide gases, carbon monoxide and one-fourth of hydrocarbon gases in our environment are emitted from automobile vehicles, which leads to global warming. Due to poor vehicle maintenance and ignition defect. the gases emitted from the exhaust may increase. In order to reduce environmental pollution and to increase vehicles life, we can use this system.

Prediction of NOx emissions from gas turbines of a combined cycle power plant using an ANFIS model optimized by GA

Combined cycle power plants, which combine gas and steam turbines, have negative impacts on surrounding populations and structures. Control of NOx emissions is an important issue for these gas-fired power plants. Accurate estimation of NOx emissions is critical for developing incinerators and reducing the environmental impact of existing plants. The objective of this study is to model ANFISGA and estimate NOx emissions from a natural gas-fired combined cycle power plant using emission monitoring system (PEMS) data. First, Adaptive Neuro Fuzzy Inference System (ANFIS) models were developed using fuzzy C-Means (FCM). Then, the parameters were optimized using a genetic algorithm (GA) to reduce the error. The proposed ANFISGA system was created, trained, and tested with PEMS datasets. The developed models were compared using several statistical performance criteria, including correlation coefficient (R2), mean squared error (MSE), error mean (EM), root mean square error (RMSE), standard deviation of error (STD), and mean absolute percentage error (MAPE). The obtained results show that the coefficient of determination varies between 0.79933 and 0.90363 for the data separated into test and training data with different rates. The minimum values of the criteria MSE, RMSE, EM, STD, and MAPE were found to be 24.8379, 4.9838, 3.4625e-05, 4.9839, and 5.1660, respectively, for the training data. The minimum values of these criteria for the test data were 26.5961, 5.1571, 0.065696, 5.157, and 5.3695, respectively. The collected results show that the proposed ANFISGA models have high potential for NOx prediction. Thus, the results show that GA has a great impact on the performance of ANFIS training and significantly improves the predictive accuracy of the model.

Applying Continuous Self-monitoring Systems for Industrial Stack Gases: International and Russian Experience of Legal Regulation

The approaches to the control of pollutant emissions of industrial enterprises by means of automatic measuring systems are presented. The available international experience of normative regulation of continuous emissions control, including the regulatory policy of the United States of America, the European Union, the People's Republic of China, the Republic of India, the Republic of Korea, is summarized. It was found that almost all countries that regulate air quality on the basis of the concept of the best available techniques have formed lists of pollutants, threshold values of their emissions, lists of industries or types of units, stationary emissions sources which are to be equipped with automatic measuring systems. The Russian system regulation of the negative impact on the environment in terms of the application of continuous emission monitoring systems is considered. Technical features of the organization of automatic control of emissions at different types of stationary sources are analyzed. The cases of the absence of practical possibility of making instrumental measurements of emissions are discussed.