Continuous Emission Monitoring Research Articles

Soft sensing of NOx emission from waste incineration process based on data de-noising and bidirectional long short-term memory neural networks.

Continuous emission monitoring system is commonly employed to monitor NOx emissions in municipal solid waste incineration (MSWI) processes. However, it still encounters the challenges of regular maintenance and measurement lag. These issues significantly impact the accurate and stable control of NOx emissions. Therefore, developing a soft NOx emission sensor to complement hardware monitoring becomes imperative. Considering data noise, dynamic nonlinearity, time series characteristics and volatility in the MSWI process, this article introduces a soft sensor model for NOx emission prediction utilizing the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN)-wavelet threshold (WT) method and bidirectional long short-term memory (Bi-LSTM). Firstly, the original data signal is decomposed into a group of intrinsic mode functions (IMFs) using the CEEMDAN. Subsequently, the WT processes the high-frequency IMFs that are noise-dominant. Then, all IMFs are reconstructed to obtain the denoized signal. Finally, the Bi-LSTM model is employed to predict NOx emissions. Compared to conventional modelling approaches, the model proposed in this article demonstrates the best predictive performance. The mean absolute percentage error, root-mean-squared error and average absolute error on the test set of the proposed model are 3.75%, 5.34 mg m-3 and 4.34 mg m-3, respectively. The proposed model provides a new method to soft sensing NOx emissions. It holds significant practical value for precise and stable monitoring of NOx emissions in MSWI processes and provides a reference for research on modelling key process parameters.

Using metal oxide gas sensors to estimate the emission rates and locations of methane leaks in an industrial site: assessment with controlled methane releases

Abstract. Fugitive methane (CH4) emissions occur in the whole chain of oil and gas production, including from extraction, transportation, storage, and distribution. Such emissions are usually detected and quantified by conducting surveys as close as possible to the source location. However, these surveys are labour-intensive, are costly, and fail to not provide continuous emissions monitoring. The deployment of permanent sensor networks in the vicinity of industrial CH4 emitting facilities would overcome the limitations of surveys by providing accurate emission estimates, thanks to continuous sampling of emission plumes. Yet high-precision instruments are too costly to deploy in such networks. Low-cost sensors using a metal oxide semiconductor (MOS) are presented as a cheap alternative for such deployments due to their compact dimensions and to their sensitivity to CH4. In this study, we demonstrate the ability of two types of MOS sensors (TGS 2611-C00 and TGS 2611-E00) manufactured by Figaro® to reconstruct a CH4 signal, as measured by a high-precision reference gas analyser, during a 7 d controlled release campaign conducted by TotalEnergies® in autumn 2019 near Pau, France. We propose a baseline voltage correction linked to atmospheric CH4 background variations per instrument based on an iterative comparison of neighbouring observations, i.e. data points. Two CH4 mole fraction reconstruction models were compared: multilayer perceptron (MLP) and second-degree polynomial. Emission estimates were then computed using an inversion approach based on the adjoint of a Gaussian dispersion model. Despite obtaining emission estimates comparable with those obtained using high-precision instruments (average emission rate error of 25 % and average location error of 9.5 m), the application of these emission estimates is limited to adequate environmental conditions. Emission estimates are also influenced by model errors in the inversion process.

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

Investigating the impact of control strategies on the sulfur dioxide emissions of South Korean industrial facilities using an aircraft mass balance approach

We present top-down SO2 emission estimates from four large point sources, including two power plants, a steel mill, and a petrochemical industrial facility in Taean, South Korea. The airborne observations were conducted over two years, with three intensive observations in Fall 2019, Fall 2020, and Spring 2021. During this period, an active policy implementation to reduce air pollutant emissions from large industrial point sources in the region had been exercised, in addition to activity disruptions from the global COVID-19 pandemic. We quantify the observed SO2 emission reduction over the time period resulting from the policy implementation. The Continuous Emission Monitoring System (CEMS) datasets from the coal power plants were 1) validated by comparing them with emissions calculated using a top-down airborne mass balance method and 2) compared with the annual bottom-up national SO2 emission inventory. The comparisons illustrate that the policy implementation resulted in a 35% reduction in SO2 emissions from increased implementation of SO2 reduction technologies such as scrubbers. Finally, the COVID-19 pandemic did not cause a meaningful disruption in SO2 emissions from industrial sectors, such as steel mills and petrochemical manufacturing facilities.

Multi-open-path laser dispersion spectroscopy combined with Bayesian state estimation for localizing and quantifying methane emissions

A global effort towards improved quantitative understanding of greenhouse gas emissions is taking pace. This includes developing source identification, quantification, and apportionment in an attempt to understand global budget and trends, but also developing monitoring systems making emission reduction commitment verifiable. In this context, we demonstrate a novel approach to continuous methane emission monitoring at the spatial scale of an industrial facility. By combining multi-directional measurements of path-integrated methane concentrations with Bayesian state estimation, we show a realistic tomographic gas plume reconstruction, its evolution in time, and the associated estimation of the source map. The method is validated using measurements from controlled methane releases over a domain of area 120×40 m2. For the first demonstration, a two dimensional geometry has been used in the gas flow model; nevertheless, sources are located within 3–12 meters, and mass emission rates are estimated within <30% for 80% of the cases.

Full life cycle emission reduction potential of ultra-low emission transformation in China's cement industry

China accounts for more than 50% of the global total cement production, the emission of pollutants in the cement industry occupies a large proportion of the industries. Some provinces have first carried out in-depth treatment of the cement industry, and the national level will also introduce an implementation plan for ultra-low emission transformation of the cement industry to promote continuous improvement of China's air quality. In this work, a pollutant emission inventory covering the full life cycle of the cement industry, including organized emission, unorganized emission and transportation, was established based on pollutant discharge permit, Continuous Emission Monitoring System (CEMS) database and field investigation. And, the emission reduction potential of particulate matter (PM), sulfur dioxide (SO2) and nitrogen oxide (NOx) after the implementation of ultra-low emission transformation in cement industry was evaluated. The results show that the existing dedusting, desulphurization and denitration technologies can control the concentrations of PM, SO2 and NOx below 10 mg/m3, 35 mg/m3 and 50 mg/m3, respectively. The emission reduction potential of PM, SO2 and NOx in cement industry is 259.54 kt, 14.98 kt and 607.04 kt, corresponding emission reduction ratio is 37%, 24% and 64%, respectively. In addition, this work reveals the spatial characteristics of pollutant emission level and emission reduction potential of the cement industry, which is conducive to the rational layout of ultra-low emission transformation, and push forward the transformation task by area and stage.

Predicting air pollutant emissions of the foundry industry: Based on the electricity big data

Industrial enterprises are one of the largest sources of air pollution. However, the existing means of monitoring air pollutant emissions are narrow in coverage, high in cost, and low in accuracy. To bridge these gaps, this study explored a predicting model for air pollutant emissions from foundry industries based on high-accuracy electricity consumption data and continuous emission monitoring system (CEMS). The model has then been applied to the calculation of air pollutant emissions from foundries without CEMS and the optimization of air pollutant emission temporal allocation factors. The results reveal that electricity consumption and PM emissions during the 2022 Beijing Winter Olympics have the same ascending and descending relationship. Furthermore, a cubic polynomial model between electricity consumption and flue gas flow is established based on the whole year data of 2021 (R2 = 0.85). The relative errors between the PM emissions calculated by the model and the emission factor method are small (−17.09–24.12 %), and the results from the two methods revealed a strong correlation (r = 0.93, p < 0.01). In addition, the monthly PM emissions from foundries are mainly concentrated in spring and winter, and the daily emissions on weekends are significantly lower than those on workdays. These results can be useful for environmental regulation and optimization of air pollutant emission inventories of foundry industry.

Perhitungan Beban Emisi Particulate Matter berdasarkan Data CEMS dari PLTU Batu Bara Milik PT PLN (Persero)

In the Coal-Fired Power Plant (CFPP) operation, various pollutants will be generated, including Particulate Matter (PM), which can be monitored with the Continuous Emission Monitoring System (CEMS). The parameters monitored include PM concentration, volumetric flow rate, % O2, and the operation hours and/or CEMS monitoring hours. Emission load calculation by utilizing CEMS data is a Tier 3 calculation method that uses real operation data from a CFPP unit. Information related to PM emission load from CFPP can be considered for corporate decision making in developing emission control strategies. This research utilizes secondary data from January 2021 to July 2023 period, from 52 PT PLN (Persero)’s CFPP units with various boiler, installed capacity, and types of Air Pollution Control Devices specifications. The largest average/month emission load value is 139,632 kg PM/month for CFPP unit that uses PC boiler, have a capacity of &gt; 600 MW, and use low NOx burners and ESP as its APCD; while the smallest average/month emission load value is 525 kg PM/month for CFPP unit that uses PC boiler, have a capacity of 101-300 MW, and use ESP as its APCD. If the average emission load/month from January 2021 to July 2023 is compared with the maximum emission load/month with a PM quality standard of 100 mg/Nm3, all CFPP units do not exceed it and are generally ready to face the tightening of PM quality standard up to 75 mg/Nm3.

Penentuan Beban Emisi Karbon Dioksida PLTU Batubara Pulau Jawa dari Hasil Pengukuran CEMS

Climate change is a real phenomenon that occurs and is felt by all living things that live on earth. The increase in earth's surface temperature is one of the impacts of climate change that continues to occur and has the potential to threaten the sustainability of human life. Carbon dioxide is the main greenhouse gas that exacerbates this condition. One of the largest sources of carbon dioxide emissions comes from the energy sector, namely coal-fired power plants (CFPP). Java Island has CFPP’s with the largest total installed capacity in Indonesia, even the capacity will continue to be added by 8,5 GW or 39,4% until 2030. In its operation, the CFPPs have an air emission measuring device before being discharged into the atmosphere which works continuously called the Continuous Emission Monitoring System (CEMS). To be able to take appropriate climate change mitigation and adaptation steps, CO2 emission load data that has high accuracy and is analyzed directly is needed. The CO2 emission load generated from 20 CFPP units as the object of research is 88,56 million tons of CO2/year. The greater the generating capacity, the greater the CO2 emissions produced. The higher the quality of coal used, the lower the CO2 emissions tend to be. To support global efforts to combat climate change, mitigation actions are needed to reduce CO2 emissions into the atmosphere.

A carbon calculator: the development of a user-friendly greenhouse gas measuring tool for general dental practice (Part 2).

The need to reduce carbon emissions and limit global warming to 1.5 °C has spurred various sectors towards net-zero emission goals. This paper introduces a specialised carbon calculator for dental practices to compute and monitor their carbon footprints (CFPs). The carbon calculator is developed using recent carbon modelling, utilising methodologies and data from estimating the average NHS dental practice's CFP. It employs both spend-based and activity-based carbon accounting methods, simplifying carbon emission estimation. It offers dental practices a user-friendly, rudimentary, cost-free tool to determine their baseline CFP and track sustainability progress. It includes conversion factors for patient travel, procurement and waste management, enabling practices to input data and generate personalised CFP charts. It also acknowledges assumptions and uncertainties related to procurement and waste management, emphasising the availability of personalised consultancy services for more precise carbon footprinting. This carbon calculator supports environmental sustainability in dental practices as an accessible starting point. By raising awareness of their CFP, it encourages progress in 'green dentistry' and promotes environmental responsibility in oral healthcare. The calculator is freely downloadable and part of a broader 'green dentistry' initiative. Continuous carbon emission measurement and monitoring are crucial for a sustainable future, with this tool aiding dental practitioners in their environmental contributions.

Development of Emission Factors from Indonesian Coal-Fired Power Plant Using Continuous Emission Monitoring Data

Continuous Emission Monitoring System (CEMS) is generally used for monitoring compliance with emission standards set by the government regulations and has not been optimally used for other additional purposes yet. If operated CEMS can produce reliable and accurate data, they can develop further specific data such as emission factors. These emission factors can be used for estimating pollutant emission loads from coal combustion activity in Coal-Fired Power Plants (CFPPs) without conducting direct source measurements. In this study, hourly 1 yr CEMS data from several units of CFPPs were processed to develop specific emission factors for principal air pollutants (SO2, NOx, particulates) and greenhouse gases (represented by CO2). Emission factors were determined by dividing the emission load of each pollutant by the amount of combusted coal during 1 yr. The results showed that emission factor ratings for this study could not be classified as A ratings due to the limited number of investigated CEMS facilities. According to the variability of the derived emission factor values, SO2 and CO2 emission factors can be rated as B or above average (with fewer variability values). In comparison, NOx and particulate emission factors can be placed as C or average (with more variability values).

Selective adsorption characteristics of g-C3N4 for Hg0 and HgCl2: A DFT study and experimental verification

The partitioning of Hg0 and HgCl2 with selective adsorbent is an essential technology for the mercury continuous emission monitoring system (Hg-CEMS), but the existing adsorbents fail to satisfy the prolonged operational demands of Hg-CEMS due to the irreversible deactivation. This work proposed a novel regenerable selective adsorbent material, g-C3N4, and delved into the selective adsorption behaviors of Hg0 and HgCl2 on the g-C3N4 using DFT calculations and experiments. The results show that g-C3N4 has a strong affinity for HgCl2, with the binding energy of −0.843 eV, whereas Hg0 is only weakly adsorbed. The electronic structure and weak interaction analysis reveal both Hg0 and HgCl2 are coupled to g-C3N4 via van der Waals. Further, COHP calculation demonstrates that the adsorption of Hg0 and HgCl2 on g-C3N4 is essentially the consequence of polyatomic co-binding and the parallel triatomic structure of HgCl2, which binds to more C/N atoms, thereby leading to the greater binding energy. The experiments confirmed the effective capability of g-C3N4 in separating Hg0 and HgCl2, along with its thermal regeneration potential. It maintains a breakthrough rate of over 98 % for Hg0 and less than 1.6 % for HgCl2. This work updates the conventional chemical adsorption-based methods for the separation of Hg0 and HgCl2, enhances the comprehension of separating both Hg0 and HgCl2 while concurrently broadening the understanding of the interaction between g-C3N4 and small molecules.

Particulate Matter Continuous Emission Monitoring System on Car Free Day Based on the Internet of Thing

Particulate Matter Continuous Emission Monitoring System on Car Free Day Based on the Internet of Thing

GIS-based analysis of the spatial distribution of CO2 emissions and slow steaming effectiveness in coastal shipping

The sustainability of maritime activities is increasingly gaining interest, with the shipping sector actively focusing on decarbonization efforts. Throughout the years, researchers have considered slow steaming for improving the environmental footprint of maritime networks. In order to assess such strategies’ effectiveness on existing emissions, research also focuses on the accurate estimation of emission inventories. However, there is a significant gap concerning both fields when considering short-sea shipping, especially passenger shipping. Furthermore, while emissions are characterized by spatial aspects in several studies, there is an apparent gap in considering such aspects for detailed analysis purposes rather than only for visualization purposes. In this study, the Greek Coastal Shipping Network (GCSN) is considered, with its emissions estimated using a top-down method, creating a spatial emission inventory used for further spatial analysis for accurate identification of highly polluted areas. Results indicate that ship emissions do not spread homogeneously throughout the GCSN and that targeted interventions are necessary in several areas of the network. The effectiveness of spatially related slow steaming implementations is evaluated and compared with their implementation on the whole network. The study highlights the need for additional future emission mitigation strategies, such as service optimization, network restructuring, continuous emission monitoring, and fleet renewal with more environmentally efficient ships. The study’s aim is to fill the research gap regarding the environmental assessment of passenger shipping and the effects of slow steaming on such networks while presenting an adaptable GIS-based decision support system for enhanced decision-making regarding the environmental efficiency of maritime networks.

Particulate emissions from biomass power plants: a practical review and measurement uncertainty issues

This paper follows a practical approach to evaluate particulate matter emissions from biomass-fuelled power plants and analysis measurement uncertainties. Sixty-seven measurement reports from 11 biomass power plants, all equipped with fabric filters as dust control device, were analysed. Particulate matter emission measurements by gravimetrical standard reference method were chosen to provide the most reliable values to investigate the influence of bag filter parameters, such as media age or bag filter length. 92.1% of total dust emission measurements were below lower BAT-associated emission levels (BAT-AELs) for waste incineration. Data also showed that bag filter media older than 6 years can still secure very low dust emission levels. This work also gives a summary of findings and problems with uncertainty of automated measuring systems for dust emissions in biomass combustion applications in practice and quality assurance level one (QAL1) process related to very low emission levels such as lower best available techniques reference document (BREF) values. Practical examples on issues of continuous dust emission monitoring devices are also given.

High temperature reduction of divalent mercury to elemental mercury for Hg-CEMS

The high temperature reduction (HTR) of flue gas divalent mercury to elemental mercury is the core technology for the mercury continuous emission monitoring system (Hg-CEMS). In this work, the high temperature reduction of HgCl2 to Hg0 was systematically investigated. The results show that HgCl2 can entirely decompose to Hg0 at 800 °C, but Cl, ClO, Cl2, and HCl can easily oxidize Hg0 at low temperature. A new efficient alkaline dechlorination agent for eliminating these Cl species was proposed firstly. The dechlorination agent can efficiently capture the HCl, Cl, and ClO but fail to completely adsorb Cl2. Consequently, a scheme was created that a pair of dechlorination agents were filled in inlet and outlet of the HTR furnace respectively. It features that the upper layer removes HCl and prevents the generation of Cl2 because HCl is the precursor of Cl2, and the lower layer adsorbs the Cl and ClO generated by the HTR process. The feasibility of the method was successfully verified in both a fixed-bed reactor and a Hg-CEMS pretreatment device. DFT calculation indicates that the adsorption of Cl, ClO, and HCl on CaO falls into the category of strong chemisorption while Cl2 is weakly chemisorbed.

Design of a continuous emission monitoring system (CEMS) for the NH3 analyzer at PT. Pusri Palembang

Design of a continuous emission monitoring system (CEMS) for the NH3 analyzer at PT. Pusri Palembang

Plants' emission behaviors under dual control of pollutant concentration and quantity

Quantity-based and concentration-based limits are two common environmental permitting approaches utilized by government worldwide in environmental management. While existing literature is still unambiguous about roles played by quantity-based versus concentration-based limits in environmental management, it becomes evident that relying exclusively on concentration-based or quantity-based limits to control plant emissions may not necessarily result in improved environmental quality. This paper leverages a unique opportunity arising from a recent reform in China's Pollutant Emission Permit System (PEPS) initiated in 2016 to analyze how the introduction of quantity-based limits in addition to concentration-based limits through the PEPS reform impact emissions at the plant level. Utilizing a unique plant-level continuous emission monitoring system data collected from Shaanxi Province (located in western China), the paper finds a significant reduction in air pollutant emissions as a result of the PEPS reform (nitrogen oxides (NOx) by 39%, sulfur dioxide (SO2) by 15% and particulate matter (PM) by 13%). The heterogeneity analyses show emission reductions in plants differ across those with varying quantity limits specified in their permits, distinct emission ratios and diverse ownership structures. Furthermore, plants that fall under the classified management system with more stringent regulations imposed, especially those operating in high-pollution sectors, situated within industrial parks, or classified as large-sized plants, attain higher pollutant quantity limits. Findings of the paper carry important implications for effective environmental management, particularly within developing countries, and shed some light on carbon emission reduction policies in China.

Development of a sampling assembly for PM2.5 CEMS

For an extractive type particulate matter continuous emission monitoring system (PM CEMS), the sampling assembly acts as an important interface between the exhaust gas and sensing unit. It serves to transport the aerosol sample and pre-treat it to meet the working conditions required by the sensing unit. To obtain representative samples, the sampling assembly should be designed to minimize the loss of particles. In this study, a conditioner based on a dilution approach was proposed and experimentally validated for use with pipelines for PM CEMS targeting particles less than 2.5 μm in diameter (PM2.5). The results showed that for 3.625 μm particles, the transport efficiency through the conditioner was 98.30% at a dilution ratio of 10. Meanwhile, the conditioner could reduce the sample air temperature to below 50 °C without condensation at flue gas temperatures up to 150 °C and water contents below 10%. Finally, a sampling assembly consisting of the conditioner, a 90° bend sampling probe, and a straight tube was designed to obtain transport efficiency higher than 95%, a stated requirement in this study for particles with a diameter less than 3.625 μm. Overall, this study contributed to designing and verifying a sampling assembly for PM CEMS and provided new strategies for improving monitoring data quality using PM CEMS.

An improved hourly-resolved atmospheric NOx emission inventory of industrial sources based on Continuous Emission Monitoring System data: Case of Jiangsu Province, China

Current emission inventories with low spatial and temporal resolutions, slow updates, and great uncertainty can no longer meet the new demands for the precise prevention and control of air pollution. This study considered industrial sources of Jiangsu province in 2018 as the research object and divided key industrial sources into 16 processes. Based on the continuous emission monitoring system (CEMS) data of key enterprises, millions of hourly scale monitoring data from 17842-point source enterprises at 169289 emission outlets were collected. An hourly scale high-resolution industrial source NOx emission inventory was constructed and compared with existing inventories. The total NOx emissions of power plants, industrial boilers, ferrous metal manufacturing, non-metallic mineral manufacturing, and chemical manufacturing industries in Jiangsu Province in 2018 were 55, 27, 64, 28, and 3 Gg, respectively. The total emissions from industrial sources were high in summer, reaching a peak of 17 Gg in July, and low in winter, reaching 11 Gg in February. The emission factors of the power plants, coking, and cement industries decreased by 15.95%, 29.03%, and 51.61%, respectively. Hourly scales showed that the power plants had the most considerable fluctuation in 24 h emissions at 5.94%, with high emissions occurring in the afternoon and at night. However, the 24 h emissions of ferrous metal manufacturing fluctuated slightly, at only 3.22%, and the high emissions mostly occurred at night. The WRF-Chem model was used for simulation validation. The NO2 simulation results based on this study's inventory were significantly better than those based on the Multi-resolution Emission Inventory for China (MEIC), with normalized mean biases (NMBs) of −7.1% and −10.7% for January and July, respectively, as opposed to 30.8% and 14.4% in the MEIC. The hourly scale high-precision emission inventory established in this study is significant for formulating real-time differentiated precise prevention and control policies and improving the accuracy of air quality models.