Coal Feed Research Articles

A novel PEMFC-GT-ST power generation system employing supercritical water gasification of coal for enhanced hydrogen production

This study presents an innovative coal-fired power generation system that integrates a Proton Exchange Membrane Fuel Cell (PEMFC), Gas Turbine (GT), and Steam Turbine (ST) with a supercritical water gasification (SCWG) process designed to efficiently convert coal into hydrogen. A pivotal feature of this system is the first-time integration of advanced two-step SCWG technology with PEMFC, which significantly boosts hydrogen yield, thereby elevating overall efficiency. By effectively addressing the thermodynamic limitations of the Carnot cycle, the proposed system achieves an impressive coal-to-electricity conversion efficiency of 51.61%. Economic viability is demonstrated through a competitive levelized cost of energy (LCOE) of $0.052 per kWh and a rapid payback period of approximately 2.87 years. Furthermore, the study analyzes the impact of various operational parameters—such as coal feed capacity, water-to-coal ratio, operating temperatures of the PEMFC, fuel utilization rates, airflow, and hydrogen bypass flow—on system performance. The findings not only advance the efficiency of coal-to-electricity conversion but also contribute valuable insights to the evolution of sustainable coal-fueled power generation technologies.

Simulation of Dynamic Characteristics of Supercritical Boiler Based on Coupling Model of Combustion and Hydrodynamics

To accommodate the integration of renewable energy, coal-fired power plants must take on the task of peak regulation, making the low-load operation of boilers increasingly routine. Under low-load conditions, the phase transition point (PTP) of the working fluid fluctuates, leading to potential flow instability, which can compromise boiler safety. In this paper, a one-dimensional coupled dynamic model of the combustion and hydrodynamics of a supercritical boiler is developed on the Modelica/Dymola 2022 platform. The spatial distribution of key thermal parameters in the furnace and the PTP position in the water-cooled wall (WCW) are analyzed in a 660 MW supercritical boiler when parameters on the combustion side change under full-load and low-load conditions. The dynamic response characteristics of the temperature, mass flow rate, and the PTP position are investigated. The results show that the over-fire air (OFA) ratio significantly influences the flue gas temperature distribution. A lower OFA ratio increases the flue gas temperature in the burner zone but reduces it at the furnace exit. The lower OFA ratio leads to a higher fluid temperature and shortens the length of the evaporation section. The temperature difference in the WCW outlet fluid between the 20% and 60% OFA ratios is 11.7 °C under BMCR conditions and 7.4 °C under 50% THA conditions. Under the BMCR and 50% THA conditions, a 5% increase in the coal caloric value raises the flue gas outlet temperature by 32.7 °C and 35.4 °C and the fluid outlet temperature by 6.5 °C and 9.9 °C, respectively. An increase in the coal calorific value reduces the length of the evaporation section. The changes in the length of the evaporation section are −2.95 m, 2.95 m, −2.62 m, and 0.54 m when the coal feeding rate, feedwater flow rate, feedwater temperature, and air supply rate are increased by 5%, respectively.

Optimization of Low-Calorific Coal Application at Different Loads in 600 MW Supercritical Thermal Power Plant with the PROMETHEE-GAIA Method

This study examined the 600 MW supercritical unit of a 1200 MW imported coal-fired thermal power plant in Çanakkale, Türkiye. Coal blends consisting of low-calorific domestic coal (4087 kcal/kg) and high-calorific imported coal (5954 kcal/kg) were combusted at the single mill and burner level to analyze unit parameters at different loads. Initially, input parameters, levels affecting unit parameters, and output parameters influenced by different coal types were identified and prioritized. Using criteria weights determined by the entropy method, the optimal load and domestic-imported coal blend ratio were determined using the Preference Ranking Organization Method for Enrichment Evaluation-Geometrical Analysis for Interactive Aid (PROMETHEE-GAIA) multicriteria decision-making method. The optimization study concluded that a 450 MW load with a 14.6% domestic coal feed rate is the most suitable alternative.

КОМПЛЕКС КОРМОВЫХ ДОБАВОК В РАЦИОНАХ ЦЫПЛЯТ-БРОЙЛЕРОВ

To this date to achieve high indicators in poultry farming, the use of feed additives is relevant. The purpose of the study is to study the influence of firming of the probiotic and sorbents on the organoleptic indicators of poultry meat. Studies were conducted in the conditions of the Mikhailovskaya poultry farms of the Republic of North Ossetia-Alania, subject to all requirements for the methodology of scientific and production research on poultry feeding. Cross-Broilers Cross «Ross-308» were divided into four groups. The first group received full -time farming feed. The second group received compound feed with the addition of a “Sporotermine” probiotic. The third group - compound feeds with 0.1 % of the “Sporotermine” and bentonite 3.6 % of the mass of feed and the fourth - compound feed with the addition of a “Sporotermine” probiotic and active coal feed additive (ACFA). The tasting of the broth and meat was carried out on a 5-ballast scale. The overall assessment of the quality of the chest muscles of experimental groups varied with the control group-by 0.10-0.36 points. The quality of the broth, according to organoleptic, in the 3rd experimental group exceeded analogues from the control group - by 0.22 points. According to the indicators of the elementary chemical muscles the meat of chickens of 3 experimental groups which received a comprehensively both probiotic and sorbent, there was an increase in the content of CB in the chest muscles - by 0.72 %, protein - by 1.04 % reliably and against this background there is a decrease in the amount of fat - by 0.21 %.

Reliability Analysis of Coal Feeder Instrumentation Using Failure Mode and Effect Analysis (FMEA) at PT. PLN Nusantara Power UP Tenayan

PLN Nusantara Power UP Tenayan is a coal-fired power plant supplying electricity to the Riau region with a capacity of 2 x 110 MW. Operating 24/7, the plant faces frequent failures, particularly in the instrumentation components of the coal feeder. This research employs the Failure Mode and Effect Analysis (FMEA) method to identify and analyze these failures. The Risk Priority Number (RPN), calculated by multiplying severity, occurrence, and detection ratings, helps determine the failure level. Research stages include a preliminary study, problem identification, observations and interviews, data collection and processing, FMEA analysis, and conclusion drawing. The results indicate that the instrumentation components are generally reliable, with RPN values below the critical threshold of 200. The speed sensor, with the highest RPN value of 160, is still categorized as reliable. Failures in these components can lead to operational failures, suboptimal coal feeder performance, and monitoring difficulties. The study recommends preventive maintenance every six months to enhance reliability and minimize disruptions.

Coal and Coal By-Products as Unconventional Lithium Sources: A Review of Occurrence Modes and Hydrometallurgical Strategies for Metal Recovery

Lithium, a critical material for the global development of green energy sources, is anomalously enriched in some coal deposits and coal by-products to levels that may be considered economically viable. Recovering lithium from coal, particularly from coal gangue or coal ashes, offers a promising alternative for extracting this element. This process could potentially lead to economic gains and positive environmental impacts by more efficiently utilizing coal-based waste materials. This review focuses on lithium concentrations in coal and coal by-products, modes of lithium occurrence, methods used to identify lithium-enriched phases, and currently available hydrometallurgical recovery methods, correlated with pretreatment procedures that enable lithium release from inert aluminosilicate minerals. Leaching of raw coal appears inefficient, whereas coal gangue and fly ash are more feasible due to their simpler composition and higher lithium contents. Lithium extraction can achieve recovery rates of over 90%, but low lithium concentrations and high impurity levels in the leachates require advanced selective separation techniques. Bottom ash has not yet been evaluated for lithium recovery, despite its higher lithium content compared to feed coal.

Modes of Occurrence, Migration, and Evolution Pathways of Lithium and Gallium during Combustion of an Al-Rich Coal, Inner Mongolia, China

The modes of occurrence, migration, and evolution pathways of lithium (Li) and gallium (Ga) during combustion of an Al-rich coal from Inner Mongolia, China, were investigated using methods of simulated combustion experiments, the sequential chemical extraction procedure (SCEP), and the thermodynamic equilibrium calculation. Mineralogical and chemical compositions of the feed coal and combustion ash were analyzed by X-ray fluorescence (XRF), X-ray diffraction (XRD), inductively coupled plasma mass spectrometry (ICP-MS), and scanning electron microscopy (SEM). The study reveals that Li and Ga are significantly enriched in the ash after combustion, with the contents reaching up to 1086 μg/g and 133 μg/g, respectively. The primary modes of occurrence of Li and Ga in the ash are quartz and aluminosilicates, and sulfides, respectively. Li, in the form of LiAlSi4O10 (s), primarily occurs in hematite, glass, and quartz below 800 °C. However, it migrates into the glass phase, mullite, and quartz above 1000 °C. On the other hand, Ga exists as Ga4S5 (s) and transforms into Ga2S (g) as the temperature rises from 800 °C to 1000 °C, maintaining this gaseous form until 1200 °C. Ga4S5 (s) predominantly occurs in the glass phase at 600 °C, whereas mullite and quartz become its dominant modes of occurrence in industrial combustion ashes and ashes obtained from simulated combustion above 600 °C.

Recent Patents on Closed Coal Storage Systems and Research of Similar Experimental

Background:: Coal storage and conveying are key aspects of the coal industry, including coal stacking management systems and conveying systems. The traditional method of coal storage will easily lead to coal’s spontaneous combustion, which decreases the safety and reliability of storage. Meanwhile, problems are waiting to be solved with current conveying technologies, such as low levels of conveying efficiency. Objective:: To enhance the efficiency of coal conveying while improving safety and reducing environmental pollution, and promote the application of closed coal storage systems and pneumatic conveying technology. Method:: The patents and research proposed by many scholars analyzed the problems in coal transportation. Adopting pneumatic conveying technology and utilizing a similar experimental bench, by controlling the angle of the suction feeder and the height from the bottom, getting the optimal suction angle and position via experiment, which improves the efficiency of coal pneumatic feeding and discharging. Results:: A pneumatic feeding and discharging system and method are proposed for a coal storage silo with a high degree of integration and safety. Through similar experiments, it is concluded that the conveying efficiency is highest when the suction feeder is 30 mm high from the bottom, and the angle of the suction feeder is 45°. This provides theoretical support for the specific application of the device. Conclusion:: By analyzing patents and researching similar experiments, the proposed closed storage system for coal can improve coal conveying efficiency, reduce environmental pollution caused by coal particles during conveying, and decrease the possibility of spontaneous combustion and explosion of coal dust. The system is highly automated, easy to operate, and widely applicable.

Operation optimization of cement clinker production line based on neural network and genetic algorithm

The operation control plays a great role in the running performance of an industrial process. To achieve a successful control, the key point is searching for the target value of the control parameters. The relationship between operation parameters and performance parameters is usually nonlinear and complex, so it is hard to control an industrial process excellently with workers' experience. Based on a large amount of actual operation data, a bridge between control parameters and performance parameters might be built by the neural network. Prediction model is established by using neural network, genetic algorithm was then used to refine these parameters, the optimal condition can be further achieved by combining the relation bridge and genetic algorithm (GA). Paying attention to a cement clinker production process, this article established an optimizing approach based on the neural network and genetic algorithm and one-year operation data (615 sets). The feeding mass rate of raw meal and coal at the precalciner and the feeding coal mass rate at the rotary kiln are selected as the main independent variable and control parameters, and the specific standard coal consumption is determined as the key performance parameter and the optimization objective function. The mean square error and the correlation coefficient of the established neural network model are 12.84 and 0.89, respectively. The relative errors of approximately all prediction data (92.52 %) are within ±5 %. The optimal values for the raw meal feeding rate, the coal feeding rate into precalciner, and the coal feeding rate into rotary kiln are 259.57 t/h, 7.84 t/h, and 7.40 t/h, respectively. In that optimal condition, the specific standard coal consumption reaches 80.00 kgstandard coal/tclinker (This is a relative value, as there is a slight drift in zero point of the factory's instruments). A highly accurate neural network model is developed, which can significantly reduce standard coal consumption and improve industrial energy efficiency.

Trace Elements in Pernik Sub-Bituminous Coals and Their Combustion Products Derived from the Republika Thermal Power Station, Bulgaria

The contents of 49 trace elements in sub-bituminous Pernik coals and their waste products from preparation and combustion processes were investigated. The studied coals have trace element contents higher than the respective Clarke values for brown coals and some of them may pose environmental concerns. The elements Li, Rb, Cs, Ba, Sc, Y, La, Ce, Nd, Sm, Eu, Er, Ga, Zr, Sn, V, Nb, Ta, W, F, Cu, Zn, In, Pb, Cr, Co, Ni, and Th in the feed coals have concentrations that exceed twice the Clarke values. Most element contents in bottom ash are enriched compared with those in feed coal. Some of the volatile elements are equal or significantly depleted including Sn, Mo, Sb, F, Bi, Cd, Ge, and Pb. Fly ash has higher contents of Ga, Zr, Hf, Sn, V, Nb, Mo, and F in comparison with bottom ash. Most elements have a significant positive correlation with ash yield, indicating their inorganic association. The mixed wastes (coal slurry, bottom ash, and fly ash) in the disposal pond are slightly depleted of most of the elements studied with the exclusion of Cl, Ba, and Br. The Pernik coals and their waste products are unpromising for the extraction of REY due to their low element contents.

Cd/Pb behavior during combustion in a coal-fired power plant and their spatiotemporal impacts on soils: New insights from Cd/Pb isotopes

Coal power plants annually generate quantities of byproducts that release environmentally hazardous heavy metals like Cd and Pb. Understanding the behavior and spatiotemporal impacts on soils of these releases is crucial for pollution control. This study investigated the concentrations and isotope ratios of Cd/Pb in combustion byproducts, depositions and soils collected from a coal-fired power plant or its surrounding area. The pulverized fuel ash (PFA) and desulfurized gypsum (DG) exhibited heavier Cd isotopes with Δ114Cd values of 0.304‰ and 0.269‰, respectively, while bottom ash (BA) showed lighter Cd isotopes (Δ114CdBA-coal = –0.078‰), compared to feed coal. We proposed a two-stage condensation process that governs the distribution of Cd/Pb, including accumulation on PFA and DG within electrostatic precipitators and desulfurization unit, as well as condensation onto fine particles upon release from the stack. Emissions from combustion and large-scale transport make a significant contribution to deposition, while the dispersion of Cd/Pb in deposition is primarily influenced by the prevailing wind patterns. However, the distribution of Cd/Pb in soils not only exhibit predominant wind control but is also potentially influenced by the resuspension of long-term storage byproducts. The power plant significantly contributes to soil in the NW–N–NE directions, even at a considerable distance (66%–79%), demonstrating its pervasive impact on remote regions along these orientations. Additionally, based on the vertical behavior in the profile, we have identified that Cd tends to migrate downward through leaching, while variations in Pb respond to the historical progression of dust removal.

A study on nitrogen oxide emission prediction in Taichung thermal power plant using artificial intelligence (AI) model

In recent studies, artificial intelligence models have been developed for the prediction of nitrogen oxide emissions from thermal power plants. This research utilizes an artificial intelligence prediction model with more coal input features than boiler features, including volatility, ash content, sulfur content, fixed carbon, total moisture, calorific value, grinding rate, fuel ratio, coal feeding rate, boiler efficiency, total air volume, and excess air volume. The paper delves into the importance analysis of input features for artificial intelligence models. Moreover, feature importance analysis is not only a prerequisite for predicting nitrogen oxide emissions but also a study providing insights into model performance. An artificial neural networks (ANN) regression model is employed to predict nitrogen oxide emissions, and the results demonstrate that the number of feature importance significantly impacts model performance. The best model performance is achieved with eight specific input features. Finally, after the training and validation processes, the ANN model yields the optimal coefficient of determination (R2) value.

Pyrolysis characteristics and product distribution of low-rank coal with heat-carrying particles adopting TG-FTIR and a novel self-mixing down tube reactor

A novel self-mixing down tube (SDT) pyrolyzer has been developed, which can separate the heat carrying particles preheated in the fluidized bed from the coal, and the two can be mixed through a specific structure to instantly heat the coal particles to a predetermined temperature. The present study investigated the pyrolysis behavior of low-rank coal with hot silica sand heat-carrying particles adopting TG-FTIR. Identifies optimal conditions for maximum tar production in a laboratory-scale SDT using a significantly larger range of coal feed rates (4 kg/h). An increase in the degree of coal metamorphism increased the initial temperature and the yield of char. The tar yield from Huolinhe lignite coal (6.56 %) and Daliuta bituminous coal (7.48 %) showing a trend of first increased and then decreased with elevating the operating temperature and optimal temperature at 520 °C, respectively. The effect of coal particle size on tar yield mainly comes from the different degrees of secondary cracking caused by contact with solid heat-carrying particles as well as the differences in the content of macerals. GC‒MS spectra of tar and deposited carbon on silica sand indicated that heavy tar can easily deposit on the surface of sand and form char through polymerization reactions.

Heat and mass transfer analysis of coal coking process in the coking chamber

ABSTRACT To study the coking heat mass transfer process and reveal the law of industrial-scale coal pyrolysis, the heat transfer characteristics of coal material in the coking process are analyzed based on coke oven temperature measurement experiments. A mathematical model of the coking process in the coking chamber was constructed based on the distributed activation energy model. The heat transfer process of the coal feed, generation of coke oven gas, and dynamic precipitation characteristics of each component during the coking cycle were further studied. The results show that tar and water are the earliest released and fastest precipitated pyrolysis products, respectively, which are completely precipitated at 800 K. The contents of the components of the inward and outward gases are obviously different, with CH4 and H2 accounting for 73.79% of the outward gas, while H2O and tar account for 77.80% of the inward gas. When the coal reaches 1200 K, all pyrolysis products except H2 are precipitated completely. The model can effectively predict the changes of raw coke oven gas composition during the coking process at different locations in the coking chamber.

Improving flexibility of thermal power plant through control strategy optimization based on orderly utilization of energy storage

Operational flexibility in thermal power plants has assumed a growing significance in maintaining power grid stability primarily driven by the increased penetration of intermittent renewable energy sources. Substantial energy resides within the deaerator and boiler subsystems of thermal power plants, and optimizing the utilization of the stored energy is crucial for enhancing the operational flexibility of these plants. A novel coordinated control strategy, informed by the characteristics of distributed energy storage and power ramping stages of thermal power plants, is proposed. This control strategy systematically activates energy reserves within the deaerator, regenerative heaters, and boiler subsystems through load decomposition, valve regulation, and revised coal feeding rate, respectively. Control performance is subsequently assessed within a reference scenario. The results demonstrate a notable improvement in the control performance. The variations in the reheat steam temperature, main steam pressure, and output power are reduced by 2.0 °C, 0.47 MPa, and 2.4 MW, respectively. Moreover, the maximum power ramp rate increases from 1.5 % to 5.5 % Pe0 min−1. Furthermore, a substantial reduction in coal consumption of up to 7.09 % is achieved through the orderly utilization of energy storage. This study provides a comprehensive reference for enhancing the operational flexibility of power plants.

AGC regulation capability prediction and optimization of coal-fired thermal power plants

The improvement of the AGC regulation capability of thermal power plants is very important for the secure and stable operation of the power grid, especially in the situation of large-scale renewable energy access to the power grid. In this study, the prediction and optimization for the AGC regulation capability of thermal power plants is proposed. Firstly, considering parameters related to the AGC regulation of the thermal power plant, the max-relevance and min-redundancy (mRMR) is used to extract features from historical sequences of the parameters. Next, a model with multi-long short-term neural networks (mLSTM) is constructed to predict the AGC regulation capability; that is, the obtained feature set is considered as the inputs of the first LSTM sub-model to predict future values of the main steam pressure and main steam temperature, which are then utilized as the inputs of the second LSTM sub-model to predict the actual power generation during AGC regulation operation. Then, the AGC regulation index is calculated according to the “management rules of grid-connected operation of power plant in Northern China” and “management rules of auxiliary service of the grid-connected power plant in Northern China” (i.e., “two rules”), and it is then considered as the objective function to be maximized by optimizing the coal feed rate, air supply rate, and feedwater flow rate. Finally, the actual AGC regulation process of a 300 MW coal-fired power plant is used as an application, and the results show that the proposed method can effectively predict and improve the regulation capability when the AGC instruction is received from the power grid.

Effect of chemical, mineralogical, and petrographic properties on the grindability of a South African Highveld coal

Coal properties that are associated with wear and damage of grinding equipment during pulverization prior to combustion were investigated. A South African Highveld feed coal and its beneficiated fractions were crushed to <1 >0.25 mm particles for the grindability tests. These particles were milled and screened into <75 µm and >75 um fractions, which were analysed by different techniques to establish a correlation with the conventional Hardgrove Grindability Index (HGI). The <1.5 g.cm-3 float fraction, which contained 93 vol.% (mineral matter free) total -macerals, 9% kaolinite, and had the lowest HGI (57) (hardest to grind) was pulverized for 15, 30, 60, and 120 minutes for particle size distribution analysis. This analysis indicated that the content of <75 um particles increases with increased grinding time. The >1.9 g.cm-3 sink fraction, with the highest HGI (76), 44% kaolinite, and 20 vol.% total macerals, was ground easily to generate the highest proportion of <75 um particles. Based on higher correlation coefficients (R2 = 0.77-0.91), the ash yield, mineral matter, petrographic composition, and fixed-carbon have significant effects on the HGI. Conversely, the inherent moisture and total sulphur (R2 = 0.48-0.62) have only minor effects on the HGI. Results from this investigation could be implemented in the preparation of a suitable pulverized fuel with little equipment wear, as well as utilizing mineralogical and petrographic compositions. Finally, the effect of microlithotypes and maceral types in South African feed coals on the HGI should be investigated in further pulverization and grindability studies.

Rare Earth Element Characteristics in Coal Ash from the Jungar Energy Gangue Power Plant, Inner Mongolia, China

The coal and coal-bearing measures in the Jungar Coalfield in Inner Mongolia are characterized by rare earth element (REE) enrichment. Combustion in coal-fired power plants can lead to further enrichment of REEs in coal ash, which serves as a new potential source for REE extraction and smelting. Further, investigating the content, modes of occurrence, and transformation behavior of REEs during coal combustion may help in better understanding REE differentiation during coal combustion and facilitate the development of economically feasible REE recovery technologies. Therefore, in this study, we analyzed coal ash from the Jungar Energy Gangue Power Plant in Inner Mongolia via inductively coupled plasma mass spectrometry, X-ray diffraction, and scanning electron microscopy combined with energy-dispersive spectroscopy. Our results showed that the REE content of the feed coal was 220 μg/g, slightly higher than the average for global coal. Additionally, fly ash had a higher REE content (898 μg/g) than bottom ash, and its rare earth oxide content was approximately 1152 μg/g, which meets the industrial requirements. Bottom and fly ashes contained similar minerals; however, their relative abundances were different. Specifically, mullite, quartz, calcite, and gypsum were slightly more abundant in fly ash than in bottom ash, whereas amorphous solids were slightly more abundant in bottom ash than in fly ash. Furthermore, fly ash, dominated by Si- and Al-rich minerals, was composed of irregular particles of different shapes and sizes. It also contained monazite and REE fluoro-oxides, which possibly originated from the feed coal and had mineral structures that remained unchanged during coal combustion. Thus, the REE fluoro-oxides possibly resulted from the conversion of bastnaesite in the feed coal during combustion and thereafter became attached to the edge of the Si–Al minerals in the fly ash.

Assessing the efforts of coal phaseout for carbon neutrality in China

As the world's largest producer and consumer of coal, coal phaseout is critical for China to achieve carbon neutrality and contribute to global decarbonization. Here, utilizing the Global Change Assessment Model, this paper simulates China's coal phaseout pathways at the sectoral level between 2015 and 2060, considering the deployment of carbon capture and storage (CCS). The results show that China's coal consumption peaks at 96.4 Exajoules (EJ) in approximately 2020 and needs to decrease to 10.9 EJ by 2060 to achieve carbon neutrality. Specifically, the power sector bears the most significant proportion of responsibility, as its coal consumption must fall to 1.4 EJ by 2060, which is approximately 97.2% lower than the peak in 2020. The industrial sector is next and needs to decrease consumption to 9.2 EJ of coal in 2060, which is a reduction of 32.3 EJ below the 2020 level. Electricity will replace coal as the dominant energy source in the industrial sector after 2040. However, a portion of feed coal will remain in the industry for a long time. Among the industrial subsectors, the steel sector needs to put significant pressure on efforts to phase out coal, followed by the cement and nitrogen fertilizer sectors. In 2060, these three sectors will consume 5.7 EJ of coal, accounting for 64.1% of the total coal consumption in the industrial sector. The deployment of coal-CCS is one of the alternatives to achieve carbon neutrality and ensure smooth coal phaseout. Under the carbon neutrality scenario, the annual deployment of coal-CCS needs to exceed 0.13 EJ from 2025 to 2045, with a peak at 3.3 EJ in 2045.

Numerical simulation of pulverized coal combustion in a 660MW tangentially fired boiler with fuel variation

To study the effects of various coal properties on the combustion characteristics during actual operation, and on the high temperature corrosion of the water wall. The paper presents a numerical simulation of a 660MW boiler using ANSYS FLUENT software. Three cases were investigated with various fuel properties: (1) the designed coal NO.1, (2) the non-designed coal NO.2 with high ash, high sulphur content and low volatile matter, (3) the blended fuel of coal NO.3with similar coal quality to the coal NO.1 and NO.4 with high sulphur content. Simulation results indicate that blended coal feeding can improve the combustion characteristics of poor-quality coals. It provides a theoretical reference for the actual operation of power plants with non-designed coals.