Thermal Power Generation Research Articles

Multivariate Image Analysis for Core Monitoring in PWRs

In pressurized water reactor (PWR) it is crucial for the operator to monitor the reactor parameters at the same time, such as temperature, pressure, boron concentration, control rod position, coolant density, etc., in order to make proper decision. However, the huge size of data reading from the different instrumentations, in addition to the limited human ability to visually detect, interpret, assess add a lot of uncertainty to the operator qualitative and quantitative analysis of the reactor performance. Therefore, this paper proposes the utilization of radial thermal flux maps (Neutron Images) technique, positioned on the reactor core as a sensitive monitoring technique for all changes of the reactor parameters as a result of the position of the control rods changing. Hence, the features contained in these neutron images are extracted (Multivariate image analysis and regression) via Principal Component Analysis (PCA), and Cluster Analysis (Dendrogram). To determine the effectiveness of the suggested technique in determining the location of the control-rods, several simulations are run. The 3D TRITON FORTRAN-code was utilized to simulate the radial thermal neutron flux of the Westinghouse 2775-MWth PWR benchmark at 100% thermal power generation. The SIMCA software programme is used to develop, test, and generalise the PCA model. Additionally, clustering analysis (CA) is carried out using the statistics software programme Minitab in order to demonstrate the effectiveness of the suggested method.

A two-layer optimal scheduling method for multi-energy virtual power plant with source-load synergy

With the increasing penetration of clean energy sources such as wind power and photovoltaic in the grid, the volatility, intermittency, and randomness of their power output have impacted the safe and stable operation of the grid. To address the challenges posed by scheduling and the potential wastage of renewable energy due to these factors, a two-layer optimal scheduling model for a virtual power plant that takes into account source-load synergy is proposed in this paper. In the upper model, emphasis is placed on demand response strategies to optimize load-side dispatch. This includes encouraging customers to adjust their electricity consumption patterns through time-of-use pricing and effectively managing controllable loads for peak shaving and valley filling. These actions collectively aim to maximize the virtual power plant's overall performance. The upper-tier model then communicates the power output to the lower-tier model. In the lower model, we consider the costs associated with wind, photovoltaic, thermal, and energy storage power generation to optimize power-side scheduling. This approach ensures a comprehensive optimization process, addressing both demand and power generation aspects of the virtual power plant's operations. Compared to existing research, this paper takes into account the impact of various clean energy sources, meticulously categorizes controllable loads, and conducts in-depth modeling and analysis of demand response. Finally, the results of optimal scheduling of the virtual power plant under different scenes are compared and analyzed using examples, to validate the effectiveness of the proposed method.

Fault Diagnosis of Centrifugal Fan Based on Grid Search Optimized Voting Weighted KNN

Fan plays the roles of the forced draft fan, induced draft fan, primary air fan, seal fan, and powder exhauster. As an important auxiliary of the fossil-fuel power station, its working environment is harsh. Timely and efficient fault diagnosis can effectively reduce equipment failure and shutdown losses, and improve the efficiency of thermal power generation. K-Nearest Neighbour (KNN) has good classification ability for non-stationary data samples. In response to the shortcomings of the traditional KNN algorithm, this paper constructs a fault diagnosis model based on the voting weighted k-nearest neighbor algorithm. The model constructs a weight voting equation that is negatively correlated with the distance value based on the first k-nearest neighbors and then conducts fault diagnosis based on the voting score. We use grid search to optimize the model and select the k value in the model, and the relationship between the k value and accuracy was verified. The grid search optimization voting weighted k-nearest neighbor is used to diagnose the faults of nine common operating states of centrifugal fans, and the diagnostic accuracy can reach 100%.

Leaching characteristics and solidification mechanism of vanadium in preparation of titanium-containing pellets using spent SCR catalyst

The spent SCR catalysts produced from flue gas denitrification are hazardous to the environment and human health. This study proposed a harmless strategy for utilizing the spent catalysts for manufacturing magnetite pellets, in which the mechanical properties of the spent catalyst pellets and the distribution of vanadium during heating were studied. The leaching characteristics and solidification mechanism of vanadium in magnetite pellets were investigated. The results confirmed that the pellets with 10 wt% spent catalysts have mechanical properties satisfactory for application as an ironmaking feedstock. After preheating and roasting the pellets, 91.2 wt% vanadium in spent catalysts was solidified. The leaching ratio of vanadium in spent catalyst pellets was less than 1 % under different pH, liquid-to-solid ratio, and leaching time conditions. Raman, XPS, XRD, and SEM analyses confirmed that the V2O5 started to liquefy at 700 °C, and 8.8 wt% of V2O5 was volatilized into the flue gas at 700–950 °C. The remaining vanadium oxides reacted with iron oxides to form FeVO4, Fe2VO4, and FeV2O4 at 700–1250 °C, leading to its consolidation in the pellets. The treatment of spent catalysts by the pelletizing process is a new technology with the advantages of low investment cost and controllable environmental risk. This new technology could not only solve the problem of spent catalysts utilization in the iron and steel industry but also provide a reference for the treatment of spent catalysts generated from flue gas denitrification in thermal power generation, waste incineration, and other industries.

How do uncertain renewable energy induced risks evolve in a two-stage deregulated wholesale power market

Integrating renewable power into power market transactions is significant in terms of achieving carbon peaking and carbon neutrality. However, the integration of intermittent renewable energy sources (RES) introduces instability into power generation and brings certain risks to deregulated power market transactions. This study aims to identify the risks arising from RES power integration and analyze how these risks transmit and evolve in deregulated power markets. A Cournot game model is constructed to analyze the generation decisions of a thermal power generation company (GENCO) and a wind power GENCO in a two-stage power market. According to the results, in the mid-to-long-term power market, an increase in the uncertainty of wind power generation (WPG) could reduce wind power supply, increase the clearing price and decrease the power demand. Then, in the spot power market, the increased uncertainty related to WPG might decrease the amount of wind power traded, increase the marginal generation cost of the thermal power GENCO, raise the clearing price, and reduce the market demand for power. Finally, the uncertainty related to wind power generation could result in diminished revenue for the wind power GENCO and a loss in overall social welfare in the two-stage power market transaction.

Analysis of the Performance of a Hybrid Thermal Power Plant Using Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Approaches

The hybridization of conventional thermal power plants by the incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision making, and streamline power production planning. This paper explores the novel application of machine learning techniques to predict the performance of hybrid thermal power plants, specifically the integrated solar combined cycle power plant (ISCCPP). These plants combine conventional thermal power generation with renewable energy sources, making them crucial in the context of carbon reduction and enhanced efficiency. We employ three machine learning approaches: the adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized via particle swarm optimization (ANFIS-PSO), and ANFIS optimized through a genetic algorithm (ANFIS-GA). These methods are applied to the complex ISCCPP, comprising steam and gas turbine sections and a concentrated solar power system. The results highlight the accuracy of ANFIS-based models in evaluating and predicting plant performance, with an exceptional overall correlation coefficient of 0.9991. Importantly, integrating evolutionary algorithms (PSO and GA) into ANFIS significantly enhances performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA, with ANFIS-GA outperforming the others. This research provides a robust tool for designers, energy managers, and decision makers, offering valuable support in assessing the performance of hybrid thermal power plants. As the world transitions to cleaner energy sources, the insights gained here are poised to have a significant impact on the growing number of these thermal power plants globally.

Perspective of staged hydrogen economy in Japan: A case study based on the data-driven method

Green hydrogen is an important energy source for replacing fossil fuels for a cleaner transition. While small-scale applications of green hydrogen have proven successful, large-scale nationwide adoption is essential. However, such a change cannot be accomplished overnight. Therefore, this study proposes a staged hydrogen economy scenario for Japan based on the existing energy sources. In this scenario, thermal power generation will be gradually phased out, and the energy shortfall will be compensated by increasing the penetration of renewables and energy storage. Hydrogen and ammonia serve as short- and long-term energy storage solutions, respectively. A data-driven model combining heuristics and optimization methods was designed to address optimal planning and sizing problems. The results show that it is feasible to achieve a 20 % reduction in thermal power by replacing it with renewable energy and hydrogen or ammonia. The lowest levelized cost of electricity (LCOE) in Tokyo (Kanto district) is 0.072 USD·kWh−1, which is comparable to the baseline scenario (using fossil fuels). Hydrogen and ammonia are effective in reducing the LCOE and addressing the mismatch between supply and demand. A higher reduction in thermal power requires further technological and policy support. According to the sensitivity analysis, hydrogen prices and carbon taxes have a high potential to advance the hydrogen economy scenario in the future.

Prediction and balanced allocation of thermal power carbon emissions from a provincial perspective of China.

Carbon control in the thermal power generation industry is crucial for achieving the overall carbon peak target. How to predict, evaluate, and balance the allocation of inter provincial carbon emissions has a significant impact on the decision-making of reasonable allocation of inter provincial carbon emissions in the target year. Therefore, this paper uses Monte Carlo-ARIMA-BP neural network and ZSG-DEA model to conduct temporal trend prediction and carbon emission quota allocation research. We propose the "intra provincial and inter provincial" framework for carbon emissions trading in thermal power plants, which aims to break through the barriers in carbon emission rights exchange among provinces. The conclusions are as follows: (1) the growth trend of carbon emissions from thermal power is gradually slowing down and is expected to peak before 2030. (2) Inner Mongolia, Jiangsu, and Shandong have high input-output efficiency, and are all the main output provinces for carbon emission quota allocation. After being adjusted using the ZSG-DEA model, they can still be at the forefront of efficiency. (3) The "intra provincial and inter provincial" framework for carbon emissions trading can effectively predict and allocate the carbon emission demand of each province from time and space dimensions, balance the carbon emission rights and interests of each province, and provide forward-looking planning suggestions for inter provincial carbon emission rights exchange.

Current density and nonlinear radiation absorber of electromagnetic Williamson thermal migration of [formula omitted] and [formula omitted] nanoparticles in [formula omitted]: A concentrated thermal power

Most concentrated thermal solar power uses convectional liquids as its working fluid for power generation. These fluids have a low heat conduction strength that cannot keep up with the present industrial and thermal technology demands. Meanwhile, nanomaterials have been established as good heat conductors and propagators for an enhanced thermal system. As such, this study considers electromagnetic Williamson fluid combined with water+ethylene-glycol (H2O+C2H6O2) solvent for thermal migration of Fe3O4 and SiO2 metallic oxide nanoparticles and a nonlinear radiation absorber for concentrated thermal power maximization. A transformed invariant model from the developed nonlinear boundary value partial derivative equations governing the thermofluid is obtained via similarity variables. This model is solved by Chebyshev spectral collocation method to determine the sensitivity of the flow dimensions. The investigations revealed that a decreased in the current density magnified the heat transfer, and electromagnetic field enhanced the hybrid nanofluids thermal power generation. Hence, the terms that enhanced heat transfer should be monitored to present thermal system blowup.

Optimization of the Parameters for Gravity Heat Pipes in Coal Gangue Hills by Measuring Thermal Power Generation

In order to effectively control high temperatures inside coal gangue hills, gravity heat pipes with specific spacings are vertically installed in coal gangue hills. Heat extracted from these heat pipes can be utilized for power generation through energy conversion. In this study, an equivalent model of gravity heat pipes in coal gangue hills was established and, in a laboratory setting, experimental research and optimization were conducted on power generation per unit area using the temperature difference of gravity heat pipes for electricity generation. To facilitate real-time testing of different heat pipe parameters and to display the experimental results, a multi-parameter measurement system was designed and constructed. This study systematically investigated the effects of various structural parameters such as inclination angle, heating temperature, initial absolute pressure, and working fluid height. Through single-factor experiments, it was determined that the inclination angle had no significant impact. The range of values for heating temperature, initial absolute pressure, and working fluid height were confirmed based on six sets of experiments. To maximize the performance of the thermoelectric generator, a response surface analysis experiment was conducted using the Design-Expert software. The optimal conditions were determined to be a working fluid height of 200.001 mm, an initial absolute pressure of 0.002 MPa, and a heating temperature of 413.15 K. Under these conditions, the power generation per unit area of the thermoelectric generator reached 0.122981 W/(m2·K). The accuracy of the theoretical experiments was verified through on-site industrial experiments. By calculations, it was determined that the maximum temperature difference power generation capacity per gravity heat pipe was 42.39 W. This provides a new solution for the management of coal mine gangue hills and the secondary utilization of waste energy.

Corn ethanol production with thin stillage anaerobic digestion for bioenergy recovery: A technical and economic evaluation

The generation of bioenergy from thin stillage in a corn ethanol mill provides a new perspective for the use of by-products in a circular economy concept. Alternative technological routes for the corn ethanol by-products energy and nutrients recovery must be investigated to assess present and future decision-making toward economic and environmental indicators. The theoretical biogas production from an up-flow anaerobic sludge blanket (UASB) reactor in a corn-ethanol mill and the potential thermal and electrical power generation along with the avoided greenhouse gas (GHG) emissions were estimated for a large-scale industrial facility. This work aimed to assess the anaerobic digestion (AD) contribution to techno-economic indicators of a corn ethanol mill in three scenarios: 1) Standard corn-to-ethanol mill; 2) AD-CHP - thin stillage AD to produce biogas and its burning in a combined heat and power (CHP) system; 3) AD-BU - biogas purification to obtain and commercialize biomethane. The existent Brazilian market and fuels and biofuel policies place the system without AD in a slightly better position when analyzing financial indicators related to investment return. In contrast, the global energy and mass balances considering thin stillage AD showed that in the AD-CHP scenario, processing 1 ton of thin stillage would produce 10.37 m³ of biogas, which could generate 119.9 MJ of heat and 24.7 kWh of electricity in a CHP system, favoring a greener energy scenario. The energy recovered from biogas returns to the industry, supplying 7.15% of the heat required for steam production and 52.74% of the electricity requirement for industrial processing. As a renewable energy source, biogas would replace fossil sources for energy generation avoiding GHG emissions. Extrapolating the results of the present work to the Brazilian national scale, the biogas yield from the corn-to-ethanol industry would be 179.68 million m³ , the thermal energy generation from biogas could mitigate 125,574.42 ton CO2eq, and the electricity replacement could mitigate 58,193.02 ton CO2eq. Besides the mentioned energy generation and environmental benefits, the financial indicators for the examined scenarios showed that the Brazilian market and public policies related to biogas/biomethane are constraints to overcoming to larger adoption of AD by the corn-to-ethanol industry.

Competitive Equilibrium Analysis of Power Generation Transaction Subjects Considering Tradable Green Certificates

The implementation of renewable portfolio standards (RPS) and tradable green certificate schemes will exert significant influences on the market equilibrium outcomes and generation firms’ strategic behaviors. To quantitatively investigate these influences, firstly, considering the difference in power generation cost and the uncertainty of renewable energy power generation, the equilibrium model for various trade subjects in the electricity market is established. Secondly, the nondominated sorting genetic algorithm II is adopted for solving the equilibrium model to find well-distributed Pareto-optimal solutions. Finally, the grey relational projection method is used to calculate the priority membership of each decision-making scheme so as to determine the optimal compromise solution. The simulation focuses on analyzing the impact of RPS on the equilibrium results and market behavior of power generators and introduces the Lerner index to quantify the market power of generators. The results show that: (1) An increase in the quota ratio can effectively increase power generation in renewable energy generators. The game between thermal power generators and renewable energy generators raises the prices of both markets at the same time. (2) Improving the forecasting accuracy is conducive to alleviating the market power behavior of various power generators, thereby ensuring the healthy operation of the power market.

Statistical data-based prediction of carbon dioxide emission factors of China's power generation at carbon peak in 2030

China has promised to reach its peak carbon dioxide emissions by 2030. Power generation emits the most carbon dioxide in China. Fossil fuels are currently still the largest sources of energy for electricity generation in China. As the most commonly used method for estimating emissions from thermal power generation, emission factor data has a statistical lag, which brings inconvenience to carbon emission accounting and the formulation of energy conservation and emission reduction policies. It is of great significance to predict the emission factors of fossil fuel-fired power generation in China. In this paper, the correlations between the data of coal-fired, gas-fired, and oil-fired power generation from 2012 to 2019 and the emission factors of China's power generation and the correlations between the data of fossil fuel-fired power generation from 2006 to 2019 and the emission factors of China's power generation are evaluated using SPSS. On this basis, two models for predicting emission factors of China's power generation from 2020 to 2030 are established and the relevant data are obtained. The calculation results show that when the carbon peak reaches in 2030, the emission factor of China's power generation may be 0.6290 and 0.7363 corresponding to the two prediction models.

Introduction to Advancement in Thermal Power Generation and Propulsion with Low/Zero CO2 Emission Virtual Collection

Introduction to Advancement in Thermal Power Generation and Propulsion with Low/Zero CO2 Emission Virtual Collection

Evaluation and spatial convergence of carbon emission reduction efficiency in China's power industry: Based on a three-stage DEA model with game cross-efficiency

Reducing carbon emissions is essential for achieving sustainable development in China. In this study, we developed a framework for measuring carbon emission reduction considering both thermal power and clean energy generation perspectives. Subsequently, we constructed a three-stage data envelopment analysis (DEA) model with game cross-efficiency to eliminate the influence of external environmental factors, random factors, and regional competition. Thereafter, we calculated the carbon emission reduction efficiencies (CEREs) of the power industries of 30 provinces in China from 2010 to 2020. Based on this, we conducted temporal and spatial analyses of the carbon emission reduction amount (CERA), evaluated CERE, compared different DEA models, and assessed spatial convergence effects. The evaluation results of CERE across 30 provinces, cities, and autonomous regions in China showed that: (1) although China's CERA increased, substantial regional differences exist in the carbon emission reduction structure. (2) The overall average CERE ranged from 0.485 to 0.737. Since 2016, the highest CERE values have been observed in southwest China, followed by the eastern coastal and central regions, while the northwestern region experienced notable fluctuations. (3) The three-stage DEA model with game cross-efficiency, which eliminates the influence of competition and external environmental factors, can be used to accurately measure CERE. (4) CERE has a spatial convergence effect on China's power industry and is promoted by energy structure, upgrading of industrial structure and government interventions. These findings provide important insights for optimizing the carbon emissions reduction structure and improving CERE.

Efficient energy generation and thermal storage in a photovoltaic thermal system partially covered by solar cells and integrated with organic phase change materials

To address the limitations of conventional photovoltaic thermal systems (i.e., low thermal power, thermal exergy, and heat transfer fluid outlet temperature), this study proposes a photovoltaic thermal system with a solar thermal collector enhancer (PVT-STE), incorporating phase change materials for simultaneous electricity and thermal power generation and thermal energy storage. The system consists of a water based solar collector with a partially covered absorber plate utilizing photovoltaic cells, while each component is equipped with a thermal storage enclosure filled with phase change materials. The primary objectives of this research encompass the evaluation of the proposed system's performance and the identification of appropriate phase change materials for each component. To gain comprehensive insight into the effects of employing and selecting appropriate phase change materials, the study investigates various configurations under both hot and cold weather conditions of Shanghai, China. Water serves as the heat transfer fluid under real-world operational conditions. The numerical results indicate that, for the given conditions, optimal phase change material selection entails using higher melting temperatures (e.g., RT42) in the STE component and lower melting temperatures (e.g., RT31) in the PVT component. Employing the optimal phase change materials for each component, the system can store 3234 and 1802 kJ/m2 of thermal energy during the charging process in July and November, respectively. Moreover, the proposed system generates almost 1.8 and 2 times more thermal energy per square meter in July and November, respectively, compared to a standalone photovoltaic thermal system coupled with a phase change material.

Distributed security state estimation-based carbon emissions and economic cost analysis for cyber–physical power systems under hybrid attacks

Sustainable cyber–physical power systems (CPPSs) significantly reduce carbon emissions due to climate change. However, when the data exchange in CPPSs suffers from hybrid attacks, the distributed state estimation and optimal power flow (OPF) analysis will inevitably be compromised, leading to inadequate or faulty scheduling of clean energy and thermal power generations and further affecting the total carbon emissions and economic cost. To address these problems, this paper proposes a novel consensus-based distributed security state estimation (DSSE) method for CPPSs, which is used to analyze the impact of hybrid attacks on carbon emissions and economic cost. Firstly, the incomplete and non-authentic data features caused by hybrid attacks are described, and their influence on distributed state estimation model is analyzed. A new residual-based attack detection method is then constructed in each subregion, where secure and non-secure sets are employed to describe whether the subregion is attacked and the compensation mechanism is designed for the non-secure set. Secondly, considering data compensation, distributed state estimation model is reconstructed, and a distributed security state estimation method under hybrid attacks is proposed while its convergence condition is derived. Thirdly, the impacts of hybrid attacks on carbon emissions and economic costs are analyzed based on the proposed DSSE method. Finally, experimental results confirm the validity of the theoretic analysis.

Multi-criteria decision analysis for the planning of island microgrid system: A case study of Yongxing island, China

Remote island communities often struggle to meet energy needs affordably, sustainably, and reliably. Island microgrid (IM) systems offer a promising solution; however, optimal planning considering diverse components and alternatives remains challenging. Using China's Yongxing Island as a case study, we propose a novel indicator system integrating economic, resilience, energy, and environmental dimensions. The indicator system is integrated with the HOMER Pro software and the reference point method to identify the optimal energy configuration of Yongxing Island's IM system. Moreover, a comprehensive uncertainty analysis is conducted on the optimal configuration to reveal how optimal configuration results change relative to changes in energy demand, energy prices, and investment costs of technologies and to demonstrate how the system would cope with disturbance. To meet Yongxing Island's 2030 energy demand (including electricity, thermal, and hydrogen), the best energy configuration scheme for the microgrid is the combination of photovoltaic panels, wind turbines, diesel generators, energy storage batteries, external grid, electrolyzers, diesel reformers, hydrogen tanks, thermal load controllers, boilers, and power converters. Moreover, wind turbines dominate power supply and would change the sizing and hourly power generation under uncertainties. Thermal demand relies on thermal load controllers, while electrolyzers and diesel reformers address hydrogen demand.

Influence of cellulose nanofiber fluid on flow instability and heat transfer of two-phase closed thermosyphon

In this research, the geyser phenomenon occurring in a small-diameter two-phase closed thermosyphon (TPCT) was observed and the instability of the device was discussed. Geyser phenomena interfere with the natural circulation of internal working fluids, increasing the thermal resistance of the system and contributing to the instability of the device. This study attempts to improve the thermal performance and stability of the system using cellulose nanofiber (CNF) fluid as the working fluid. The use of CNF fluid was observed to reduce the magnitude of temperature change inside the evaporator of the TPCT significantly. Moreover, it improved the local boiling heat transfer coefficient by 3.1 %, 87.3 %, and 181.2 % on average when the filling ratios are 0.25, 0.5, and 0.75, respectively. Studying the local heat transfer performance and instability will be helpful in designing a more stable TPCT efficiently. Additionally, the findings of this study can be applied to solar thermal power generation or heat pipe research for cooling strategies in computing servers, depending on the input heat load.

The impacts of clean energy policies on air pollutants and CO2 emission reduction in Shaanxi, China

In response to environmental problems, China has implemented many policies to control its emission of air pollutants and CO2. As a significant source of pollutant emissions, energy use has received special attention. Shaanxi Province is a region of China with major thermal power generation, which consumes a large amount of fossil fuel and causes severe deterioration in ambient air quality. However, it is rich in natural resources that could support the development of solar energy, wind energy, and other renewable clean energies, making it a typical example of a region suitable for energy structure transformation. On the basis of environmental statistical data, field surveys, and data from the Shaanxi Provincial Bureau of Statistics, this study developed an inventory of anthropogenic source emissions of seven pollutants in 2017 in Shaanxi Province. In accordance with the established inventory, various scenarios were considered to analyze the impact of energy structure transformation on air pollutant emissions. In comparison with the Business-as-Usual scenario, the comprehensive clean energy use scenario reduced SO2, NOx, CO, PM10, PM2.5, VOCs, NH3 and CO2 emissions by 32.50%, 33.10%, 14.56%, 26.64%, 16.72%, 2.88%, 4.36% and 36.85%, respectively. Ambitious development of clean energy, such as wind and solar, could greatly reduce the emission of air pollutants, especially CO2, SO2 and NOx, that are derived mainly from fossil fuel combustion, showing that strict control of the energy structure could reduce pollutant emissions. This work is important for quantifying the impact of energy restructuring on regional air quality and providing pollution control policies for environmental managers.