Continuous Emission Monitoring System Research Articles

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.

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.

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

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.

Estimation of power plant SO2 emissions using the HYSPLIT dispersion model and airborne observations with plume rise ensemble runs

Abstract. The SO2 emission rates from three power plants in North Carolina are estimated using the HYSPLIT Lagrangian dispersion model and aircraft measurements made on 26 March 2019. To quantify the underlying modeling uncertainties in the plume rise calculation, dispersion simulations are carried out in an ensemble using a total of 15 heat release parameters. For each heat release, the SO2 emission rates are estimated using a transfer coefficient matrix (TCM) approach and compared with the Continuous Emissions Monitoring Systems (CEMS) data. An “optimal” member is first selected based on the correlation coefficient calculated for each of the six segments that delineate the plumes from the three power plants during the morning and afternoon flights. The segment influenced by the afternoon operations of Belews Creek power plant has negative correlation coefficients for all the plume rise options and is first excluded from the emission estimate here. Overestimations are found for all the segments before considering the background SO2 mixing ratios. Both constant background mixing ratios and several segment-specific background values are tested in the HYSPLIT inverse modeling. The estimation results by assuming the 25th percentile observed SO2 mixing ratios inside each of the five segments agree well with the CEMS data, with relative errors of 18 %, −12 %, 3 %, 93.5 %, and −4 %. After emission estimations are performed for all the plume rise runs, the lowest root mean square errors (RMSEs) between the predicted and observed mixing ratios are calculated to select a different set of optimal plume rise runs which have the lowest RMSEs. Identical plume rise runs are chosen as the optimal members for Roxboro and Belews Creek morning segments, but different members for the other segments yield smaller RMSEs than the previous correlation-based optimal members. It is also no longer necessary to exclude the Belews Creek afternoon segment that has a negative correlation between predictions and observations. The RMSE-based optimal runs result in much better agreement with the CEMS data for the previously severely overestimated segment and do not deteriorate much for the other segments, with relative errors of 18 %, −18 %, 3 %, −9 %, and 27 % for the five segments and 2 % for the Belews Creek afternoon segment. In addition, the RMSE-based optimal heat emissions appear to be more reasonable than the correlation-based values when they are significantly different for CPI Roxboro power plant.

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.

Machine learning for accurate methane concentration predictions: short-term training, long-term results

Abstract Although methane emissions from Alberta’s oil and gas sector have decreased in recent years, monitoring these emissions using Continuous Emission Monitoring Systems (CEMS) can be costly. Predictive Emissions Monitoring Systems (PEMS), powered by machine learning, offer an alternative to or can supplement CEMS. However, effective machine learning models for methane emissions prediction rely heavily on the amount of training data. To address this, we compare the prediction performance of different neural network models, including Long Short-Term Memory (LSTM), Stacked LSTM, Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), using varying time intervals for training of methane concentration data from Alberta airshed stations. The results showed that the GRU model performed better with shorter datasets, whereas the LSTM and Stacked LSTM models outperformed the GRU and BiLSTM models when trained with more historical data. However, the study found that more training data did not necessarily result in significantly better prediction models.

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.

Non-nulling protocols for fast, accurate, 3-D velocity measurements in stacks

ABSTRACT The authors present protocols for making fast, accurate, 3D velocity measurements in the stacks of coal-fired power plants. The measurements are traceable to internationally-recognized standards; therefore, they provide a rigorous basis for measuring and/or regulating the emissions from stacks. The authors used novel, five-hole, hemispherical, differential-pressure probes optimized for non-nulling (no-probe rotation) measurements. The probes resist plugging from ash and water droplets. Integrating the differential pressures for only 5 seconds determined the axial velocity V a with an expanded relative uncertainty U r(V a) ≤ 2% of the axial velocity at the probe’s location, the flow’s pitch (α) and yaw (β) angles with expanded uncertainties U(α) = U(β) = 1 °, and the static pressure p s with U r(p s) = 0.1% of the static pressure. This accuracy was achieved 1) by calibrating each probe in a wind tunnel at 130, strategically-chosen values of (V a, α, β) spanning the conditions found in the majority of stacks (|α| ≤ 20 °; |β| ≤ 40 °; 4.5 m/s ≤ V a ≤27 m/s), and 2) by using a long-forgotten definition of the pseudo-dynamic pressure that scales with the dynamic pressure. The resulting calibration functions span the probe-diameter Reynolds number range from 7,600 to 45,000. Implications: The continuous emissions monitoring systems (CEMS) that measure the flue gas flow rate in coal-fired power plant smokestacks are calibrated (at least) annually by a velocity profiling method. The stack axial velocity profile is measured by traversing S-type pitot probes (or one of the other EPA-sanctioned pitot probes) across two orthogonal, diametric chords in the stack cross-section. The average area-weighted axial velocity calculated from the pitot traverse quantifies the accuracy of the CEMS flow monitor. Therefore, the flow measurement accuracy of coal-fired power plants greenhouse gas (GHG) emissions depends on the accuracy of pitot probe velocity measurements. Coal-fired power plants overwhelmingly calibrate CEMS flow monitors using S-type pitot probes. Almost always, stack testers measure the velocity without rotating or nulling the probe (i.e., the non-nulling method). These 1D non-nulling velocity measurements take significantly less time than the corresponding 2D nulling measurements (or 3D nulling measurements for other probe types). However, the accuracy of the 1D non-nulling velocity measurements made using S-type probes depends on the pitch and yaw angles of the flow. Measured axial velocities are accurate at pitch and yaw angles near zero, but the accuracy degrades at larger pitch and yaw angles. The authors developed a 5-hole hemispherical pitot probe that accurately measures the velocity vector in coal-fired smokestacks without needing to rotate or null the probe. This non-nulling, 3D probe is designed with large diameter pressure ports to prevent water droplets (or particulates) from obstructing its pressure ports when applied in stack flow measurement applications. This manuscript presents a wind tunnel calibration procedure to determine the non-nulling calibration curves for 1) dynamic pressure; 2) pitch angle; 3) yaw angle; and 4) static pressure. These calibration curves are used to determine axial velocities from 6 m/s to 27 m/s, yaw angles between ±40°, and pitch angles between ±20°. The uncertainties at the 95% confidence limit for axial velocity, yaw angle, and pitch angle are 2% (or less), 1°, and 1°, respectively. Therefore, in contrast to existing EPA-sanctioned probes, the non-nulling hemispherical probe provides fast, low uncertainty velocity measurements independent of the pitch and yaw angles of the stack flow.

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.

Remotely sensed and surface measurement- derived mass-conserving inversion of daily NOx emissions and inferred combustion technologies in energy-rich northern China

Abstract. This work presents a new model-free inversion estimation framework (MFIEF) using daily TROPOspheric Monitoring Instrument (TROPOMI) NO2 columns and observed fluxes from the continuous emission monitoring system (CEMS) to quantify 3 years of daily scale emissions of NOx at 0.05∘×0.05∘ over Shanxi Province, a major world-wide energy-producing and energy-consuming region. The NOx emissions, day-to-day variability, and uncertainty on a climatological basis are computed to be 1.86, 1.03, and 1.05 Tg yr−1 respectively. The highest emissions are concentrated in the lower Fen River valley, which accounts for 25 % of the area, 53 % of the NOx emissions, and 72 % of CEMS sources. Two major forcing factors (10th to 90th percentile) are horizontal transport distance per day (63–508 km) and lifetime of NOx (7.1–18.1 h). Both of these values are consistent with NOx emissions to both the surface layer and the free troposphere. The third forcing factor, the ratio of NOx/NO2, on a pixel-to-pixel basis, is demonstrated to correlate with the combustion temperature and energy efficiency of large energy consuming sources. Specifically, thermal power plants, cement, and iron and steel companies have a relatively high NOx/NO2 ratio, while coking, industrial boilers, and aluminum oxide factories show a relatively lower ratio. Variance maximization is applied to daily TROPOMI NO2 columns, which facilitates identification of three orthogonal and statistically significant modes of variability, and successfully attributes them both spatially and temporally to (a) this work's computed emissions, (b) remotely sensed TROPOMI ultraviolet aerosol index (UVAI), and (c) computed transport based on TROPOMI NO2.

Emission characteristics of condensable particulate matter (CPM) from FCC flue gas

Particulate matter (PM) as a major air pollutant, generally includes filterable particulate matter (FPM) and condensable particulate matter (CPM). CPM has gradually attracted widespread attention recently, due to its increasing proportion in total PM emissions. Fluid catalytic cracking (FCC) units, the main emission source in refineries, mostly use wet flue gas desulfurization (WFGD), which will produce a large amount of CPM. However, CPM emission and composition of FCC units are actually unclear. In this work, we aimed to understand the emission characteristics of CPM in FCC flue gas and provide some potential control strategies. Here, the stack tests of three typical FCC units were conducted to monitor FPM and CPM, and the field monitoring FPM results are higher than the concentration provided by Continuous Emission Monitoring System (CEMS). The emission of CPM is at a high-level concentration from 28.88 to 86.17 mg/Nm3, divided into inorganic fraction and organic fraction. The inorganic fraction is mainly composed in CPM, where water-soluble ions including SO42−, Na+, NH4+, NO3−, CN−, Cl−, and F−, are the major contributors. Moreover, a variety of organic compounds are detected as qualitative analysis of organic fraction in CPM, which can be roughly classified into alkanes, esters, aromatics, and others. Finally, on the basis of the understanding of the characteristics of CPM, we have proposed two strategies for CPM control. This work is expected to advance CPM emission regulation and control in FCC units.

Pilot experimental study on pollutant emission characteristics from co-combustion of coal and spent cathode carbon block

Spent cathode carbon block (SCCB) is a hazardous waste produced by the electrolytic aluminum industry. Collaborative treatment of the SCCB in pulverized coal boilers is a potentially valuable technology. Pilot experiments were carried out on a 240 t/h pulverized coal boiler and its supporting flue gas treatment measures as a function of the mixing ratio of SCCB to verify the feasibility of coal collaborative disposal of SCCB. The thermal conversion characteristics of fluoride after mixing SCCB with coal were studied. A continuous emission monitoring system was used to monitor the impact on SO2, NOx and particulate matter in real time. The effectiveness of existing flue gas control measures was evaluated, and optimization suggestions of the flue gas control measures were postulated. The content of heavy metals (Hg, Cd, Ni, Pb, As, Se) in solid waste from co-combustion and the physical phase of solid waste were investigated. In addition, the transformation of heavy metals such as Hg, Cd, Ni, Pb, Cr, As and fluoride in defluorination wastewater during flue gas treatment were analyzed. The results show that it is feasible to use a pulverized coal boiler to dispose SCCB. The existing flue gas treatment measures proved to be appropriate, and desulfurization wastewater can achieve the discharge standards. Defluorination gypsum, fly ash and slag are not hazardous waste. When the mixing ratio of SCCB is increased, the existing neutralization and flocculation precipitation processes of the desulfurization wastewater are affected. It is suggested to add Al2O3 for the defluorination process before the desulfurization tower.