Combustion Efficiency Of Boiler Research Articles

Energy Saving Potential in Steam Systems: A Techno-Economic Analysis of a Recycling Pulp and Paper Mill Industry in Morocco

The industrial sector represents nearly 40% of global energy consumption, with steam systems accounting for 30% of energy use in manufacturing. As the need for cleaner processes intensifies within African and global energy transitions, adopting energy efficiency practices becomes imperative. Governments are focusing on reducing energy losses in fossil fuel-dominated manufacturing industries, which are major producers of greenhouse gas emissions. Despite their significant energy consumption, industrial heat processes are often overlooked in efforts to reduce costs and emissions. To address this, the study examines thermal energy processes and identifies energy-saving opportunities within steam systems through a techno-economic analysis of a recycling pulp and paper mill in Morocco. This is the first study focusing specifically on steam systems within a particular industry in the country. The research uses a system approach covering steam generation, distribution, end-use, and recovery. The current system is benchmarked through a rating questionnaire, and proposed energy-saving actions include leak repair, pipe insulation, blowdown management, and improved boiler combustion efficiency. An economic study evaluates the cost-effectiveness of these opportunities. Findings reveal a 6% reduction in thermal energy consumption, annual savings of $417 899 and nearly 482 TJ, a 6% decrease in GHG emissions (2158.5 tons of CO2 per year), and water savings of approximately 5300 m³ per year (13%). The total investment of $8750 has negligible payback periods. Despite diverse industrial activities, this work is useful for other industries since the fundamental components of the steam system are widely shared. Moreover, it supports the African Union's Agenda 2063 and United Nations Sustainable Development Goals by providing practical insights and recommendations for enhancing industrial energy efficiency.

Modeling on rapid prediction and cause diagnosis of boiler combustion efficiency

Presently, a method for determining pulverized coal burnout rate (PCBR) was developed on kinetics combustion model. Proposed method can promptly track influence of air coal ratio (ACR), boiler load, uniformity index (UI), coal powder fineness (CPF), particle size, and air powder mixing uniformity (PMUC) on PCBR. Moreover, developed method with fewer computations, time-saving, simplicity and low-cost can be exploited to diagnose decreasing causation of combustion efficiency.Results indicate, for anthracite and lean coal, ACR should be maintained near optimal value with 9.39–11.59 kg/kg to achieve consummate combustion efficiency. Correspondingly, higher and appropriate ACR with 10.87–12.0 kg/kg and 7.18–7.94 kg/kg should be maintained for bituminous coal and lignite. 0.1 UI improving achieves 0.18 % upgrading in PCBR. PCBR is more sensitive to UI variation above 75 % BMCR. More significant effect is presented for lean coal and lignite with changing PMUC especially below 50 % BMCR. Basically 0.1 PMUC improving achieves 0.55 enhancement in PCBR below 50 % BMCR. Concurrently 0.7 increment is delivered for anthracite and lean coal. Coal with high volatile content causes larger particle sizes of coal powder to burn out at furnace outlet. Investigation result can predict potential financial savings from unburnt carbon (UC) reduction.

Combustion efficiency control method of circulating fluidized bed boiler based on adaptive genetic algorithm

In order to improve the traditional boiler combustion efficiency control method, which has the problem of low efficiency after control, a new combustion efficiency control method of circulating fluidized bed boiler based on adaptive genetic algorithm is proposed in this paper. Firstly, the iterative principle of adaptive genetic algorithm is analyzed; Secondly, the combustion process model of circulating fluidized bed boiler is constructed by BP neural network method; Finally, based on the above model, the optimal control objective function of adaptive genetic algorithm is constructed, and the objective function is solved according to the iterative principle of adaptive genetic algorithm to complete the control of combustion efficiency of circulating fluidized bed boiler. The experimental results show that compared with the traditional control method, the combustion efficiency of the boiler controlled by this method is always more than 98%.

Modeling for on-line monitoring of carbon burnout coefficient in boiler under partial load

Combustion stability of boiler deteriorates under low load, making carbon burnout coefficient decrease, further reducing boiler thermal efficiency. The online monitoring methods of carbon burnout coefficient and boiler thermal efficiency can track energy efficiency levels of boiler in real-time under low loads. It is of great practical significance to optimize and adjust combustion conditions of boilers and upgrade boiler real-time energy efficiency. Nevertheless, extremely difficult challenge for online monitoring technology of carbon burnout coefficient seriously restricts the online monitoring of boiler thermal efficiency.In this background, an innovative theoretical model between excess air coefficient and carbon burnout coefficient is derived according to incomplete combustion equation inside furnace. Calculation method of excess air coefficient is proposed with a brand new perspective on energy balance of economizer. Subsequently, on-line monitoring model of carbon burnout coefficient and boiler thermal efficiency is constructed and verified.The online monitoring results of a 600 MW unit indicate, lower carbon burnout coefficient magnifies the degree of virtual height of calculation results and calculation error from traditional methods of national standard for calculating excess air coefficient. This operating mode results in a significant shortage of actual air flow into boiler under partial load.Carbon burnout coefficient, showing an upward trend with unit load increasing, is as high as 0.99 with unit load of 489 MW. As unit load decreases to 161 MW, carbon burnout coefficient decreases to 0.80. Additionally, combustion conditions fluctuation is greatly influenced by changing in coal quality. Under partial load, volatility in carbon burnout coefficient increases with electrical load below 300 MW. Combustion stability and combustion efficiency of boiler under low load significantly decrease. The variation in exhaust flue gas heat loss of boiler with unit load changes is relatively small due to opposite effect by exhaust gas temperature and excess air coefficient. Actually, changing trends and fluctuation degree of boiler thermal efficiency are mainly influenced by carbon burnout coefficient with a variation in load. Overall, boiler thermal efficiency, affected by heat loss of incomplete solid on combustion, fluctuates greatly at low loads. The implementation of online monitoring for carbon burnout coefficient provides a quantitative basis for improving boiler energy efficiency under low load conditions.

Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)

The energy-intensive steel industry, which consumes substantial amounts of electricity, meets its power demands through external electricity purchases and self-generation through the operation of its own generators. This study aimed to optimize boiler combustion efficiency and increase power generation output by deriving optimal operational values for O2 and CO within the boiler flue gas using machine learning (ML) with the aim of achieving maximum boiler efficiency. This study focuses on the power-generation boilers at steel mill P in Korea. First, 361 types of operation data from power generation equipment were collected and preprocessed. Subsequently, a partial least squares regression (PLSR) algorithm was used to develop a prediction model for O2 and CO values, known as the Boiler Flue Gas Prediction Model (BFG-PM). The prediction accuracy for O2 was notably high (83.2%), whereas that for CO was lower (53.4%). Nonetheless, the model’s reliability was high because more than 90% of the predicted values were within a 10% error range. Finally, the correlation of the BFG-PM model was applied to the performance test code (PTC) 4.0 for the boiler efficiency calculations formula, deriving the optimal O2 and CO control points. Through a simulation, it was verified that the boiler efficiency was improved by controlling the combustion air. In addition, an average increase in boiler efficiency of 0.29% was confirmed by applying it directly to the generator operating on-site. The results of this study are expected to contribute to annual cost savings, with a reduction of USD 217,000 in electricity purchasing costs and USD 19,700 in greenhouse gas emissions trading expenses.

Kömür Yakıcılarında Yanma Verimi Tahmini için Gömülü Platformda Çalışabilen Evrişimsel Sinir Ağının Parametre Analizi

Accurately and effectively calculating combustion efficiency in coal burners is crucial for industrial boiler manufacturers. Two main approaches can be used to calculate boiler efficiency: 1) Analyzing the gas emitted from the flue; 2) Visualizing the combustion chamber in the boiler. Flue gas analyzers, which are not user-friendly, come with high costs. Additionally, the physical distance between the flue and the combustion chamber causes the measurement to be delayed. Methods based on visualizing the combustion chamber do not have these disadvantages. This study proposes a system based on visualizing the combustion chamber and has two contributions to the literature: 1) for the first time, the modern Convolutional Neural Networks (CNN) approach is used to estimate combustion efficiency; 2) the CNN architecture with optimal parameters can work on an embedded platform. When classical classification techniques and a CPU-supported processor card are used, efficiency can be calculated from one flame image in 1.7 seconds, while this number increases to approximately 20 frames per second (34 times faster) when the proposed CNN architecture and GPU-supported processor card are used. The results obtained demonstrate the superiority of the proposed CNN architecture and hardware over classical approaches in estimating coal boiler combustion efficiency.

Combustion Optimization Under Deep Peak Shaving Based on DYNA-A3C With Dynamic Weight

The combustion process of boilers under deep peak shaving is a multivariate process which has complex characteristics such as super multivariability, being nonlinear, and large delay. It is difficult to handle complex data and calculate appropriate distributed results. To this end, this study applies the A3C method based on the dynamic weight Dyna structure to the boiler combustion system. This method trains and optimizes the boiler combustion system by establishing a data center and designing appropriate states and reward values, and the simulation results show that this method can be used to optimize the boiler combustion system. It can effectively reduce NOX emissions and improve the boiler combustion efficiency.

Ultra-repeatability measurement of calorific value of coal by NIRS-XRF.

Calorific value is an important indicator to evaluate the comprehensive quality of coal, and its real-time and rapid analysis is of great significance for optimizing the coal blending process and improving boiler combustion efficiency. Traditional assays are time-consuming, and prompt gamma neutron activation analysis (PGNAA) and laser-induced breakdown spectroscopy (LIBS) have certain limitations. In this paper, a novel technique for ultra-repeatability measurement of coal calorific value by combining near-infrared spectroscopy (NIRS) and X-ray fluorescence (XRF) is proposed. In this NIRS-XRF technology, the former can stably measure organic components such as C-H and N-H that are positively correlated with the calorific value, while the latter can stably measure inorganic elements such as Na, Al, Si, Ca, Fe, and Mn that are negatively correlated with the calorific value. The combination of the two can greatly improve the measurement repeatability of coal calorific value. In the quantitative analysis algorithm, a holistic-segmented prediction model based on partial least squares (PLS) is proposed, that is, the holistic model is used to roughly predict the calorific value and determine the segment accordingly, and then the corresponding segmented model is used to accurately predict the calorific value. The experimental results show that the root mean square error of prediction (RMSEP), the average relative error (ARE), and the standard deviation (SD) of this method for predicting the calorific value of coal are 0.71 MJ kg-1, 1.18% and 0.07 MJ kg-1 respectively. The measurement repeatability meets the requirements of the Chinese national standard. This calorific value measurement technology based on NIRS-XRF is safe, fast, and stable, providing a new way to optimize and control the utilization process of coal in coal washing plants, power plants, coking, and other industries.

The prediction of the oxygen content of the flue gas in a gas-fired boiler system using neural networks and random forest

<p><span lang="EN-US">The oxygen content of the gas-fired boiler flue gas is used to monitor boiler combustion efficiency. Conventionally, this oxygen content is measured using an oxygen content sensor. However, because it operates in extreme conditions, this oxygen sensor tends to have the disadvantage of high maintenance costs. In addition, the absence of other sensors as an element of redundancy and when there is damage to the sensor causes manual handling by workers. It is dangerous for these workers, considering environmental conditions with high-risk hazards. We propose an artificial neural network (ANN) and random forest-based soft sensor to predict the oxygen content to overcome the problems. The prediction is made by utilizing measured data on the power plant’s boiler, consisting of 19 process variables from a distributed control system. The research has proved that the proposed soft sensor successfully predicts the oxygen content. Research using random forest shows better performance results than ANN. The random forest prediction errors are mean absolute error (MAE) of 0.0486, mean squared error (MSE) of 0.0052, root-mean-square error (RMSE) of 0.0718, and Std Error of 0.0719. While the errors using ANN are MAE of 0.0715, MSE of 0.0087, RMSE of 0.0935, and Std Error of 0.0935.</span></p>

Computational fluid dynamics modeling of biomass co-firing in a 300 MW pulverized coal furnace

Biomass energy is one of the most accessible and readily available carbon-neutral energy options as a RES. It is regarded as a viable alternative fuel for coal combustion, particularly for biomass co-firing with pulverized coal, with numerous applications. The CFD can provide reasonably accurate solutions to complex thermo-chemical-fluid interactions, which is useful for understanding the design or retrofit of boilers and can save time, money, and effort. In this study, a CFD simulation of a 300 MW pulverized coal boiler with biomass co-firing was performed to investigate the impact of biomass co-firing with coal, considering the biomass co-firing ratio, mixing effect, and feeding temperature. The results show that the flow field in the furnace does not change significantly under different bio-mass blending ratio. Biomass co-firing can reduce peak temperatures in the furnace and make the temperature distribution more uniform. The concentration of unburned carbon in the furnace decreases as the biomass blending ratio increases. Furthermore, biomass blending has a significant impact on nitrogen oxide reduction, with NOx emissions reduced by 20% and 28%, respectively, when the biomass blending ratio is 15% and 30%. The change of parameters inside the furnace caused by the reduction of biomass powder feeding temperature about 80 K is not significant. On the other hand, co-firing biomass with coal, reduces the risk of biomass spontaneous combustion while maintaining the furnace combustion stability and boiler combustion efficiency. The optimum ratio of biomass co-firing ration is deduced in this study is up to 20%.

Quantitative analysis of carbon content in fly ash using LIBS based on support vector regression

Laser-induced breakdown spectroscopy (LIBS) has been proved as an on-line detection technology to measure the carbon content in fly ash, which is beneficial for immediate assessment of the boiler combustion efficiency. Support vector regression (SVR) was adopted as the quantitative model for the carbon content measurement in fly ash in this study. Ash species was one of the key factors affecting quantitative accuracy. Experiments have proven that, the index of plasma temperature and the electron density among different species could be similar, while the partition function ratios and the temperature correction factor showed obvious differences among different ash species. Based on the partition function ratios, the Matrix Effect Correction Factor (MECF) was defined. SVR model was optimized by MECF and the analysis results showed that the correlation coefficient of calibration (R2) increased from 0.989 to 0.991, the root-mean-square error of calibration (RMSEC) decreased from 2.02% to 0.850%, the root-mean-square error of prediction (RMSEP) decreased from 2.13% to 1.07%, and averaged relative standard deviation (ARSD) decreased from 8.62% to 1.89%. The results showed that SVR combined with MECF was an effective method to improve the accuracy of LIBS quantitative analysis of the carbon content in fly ash.

Experimental study on on-line monitoring pulverized coal fineness of 300 MW coal-fired plant by digital holography

Pulverized coal fineness is one of the key parameters in the operation of coal-fired power plant, which is closely related to the combustion efficiency of boiler, power consumption of pulverizing system and equipment wear. In this paper, an experimental study on on-line monitoring of pulverized coal fineness by digital holography was carried out. An on-line measurement system based on pulse digital in-line holography was established. The system included four main parts: sampling, measuring, collecting and data processing. The measurement system was applied to the pulverized coal fineness monitoring in two primary air pipes of a 300 MW unit. The pulverized coal flow in the two pipes were from two different mills. The self-developed reconstruction software was used to process the holograms in real time and output the fineness results. The pulverized coal samples collected by the system were sieved in the laboratory, and the sieving results were compared with the holographic results to verify the feasibility and accuracy of the measurement system. Results show that the holographic results of area distribution are basically consistent with the sieving results, and the average absolute deviation is less than 4%. The test also diagnose that the coal fineness of one mill deviates greatly from the designed value, proving that the measurement system has great application potential for on-line monitoring of pulverized coal fineness in coal-fired power plants.

Fuzzy K-Means Cluster Based Generalized Predictive Control of Ultra Supercritical Power Plant

This article proposes a fuzzy k-means cluster based generalized predictive control (GPC) method for a 1000 MW ultra supercritical power plant to improve of the boiler combustion efficiency. First, to fully use the statistic characteristic of the historical data, a fuzzy k-mean cluster network (FKN) is well constructed to derive the local linear models, and the nonlinear dynamic process of studied system is elaborately approximated by the fuzzy combination of the local linear models. Then, a global GPC method is proposed to improve the control performance by using the membership of the current FKN. Different from the traditional GPC, the advantage of proposed GPC is that local GPC is fuzzily combined together to achieve the purpose of global GPC by a scheduling algorithm. Finally, an example illustrates that the proposed control strategy can achieve the satisfactory performance.

Feasibility Study of Co-Firing of Torrefied Empty Fruit Bunch and Coal through Boiler Simulation

Torrefied empty fruit bunch (EFB) co-firing is a promising technology to reduce emissions from coal-fired power plants. However, co-firing can influence the combustion and heat transfer characteristics in a coal boiler. In order to study the feasibility of co-firing application of torrefied EFB (T-EFB) in boilers, the combustion characteristics, gas emissions and heat flux distribution were analyzed, respectively. First, the kinetic parameters of T-EFB devolatilization and char oxidation were obtained by experimental analysis. Second, the computational fluid dynamics (CFD) analysis was applied to the actual 500 MWe boiler simulation to further evaluate the differences in the co-firing performance parameters (combustion characteristics and emissions) of the T-EFB and the heat transfer characteristics within the boiler. Numerical results show that T-EFB co-firing can improve the ignition characteristics of pulverized coal, reduce the formation of unburned particles. When the blending ratio was increased from 10% to 50%, significantly NOx (oxides of nitrogen) reduction (levels from 170 to 98 ppm at 6% O2) was achieved. At a blending ratio above 40%, boiler combustion efficiency decreases as the total heat flux of the boiler decreases due to an increase in the amount of unburned carbon. In addition, T-EFB co-firing can affect the heat transfer characteristics of the boiler.

Improving the LIBS Quantitative Analysis of Unburned Carbon in Fly Ash Based on the Optimization of Reference Value

The unburned carbon in fly ash is an important indicator of the coal-fired boiler combustion efficiency. Laser-induced breakdown spectroscopy (LIBS) has been applied for measurement of unburned carbon for its fast response. Traditionally, the unburned carbon content determined by the loss-on-ignition (LOI) method was used as the reference value of the LIBS calibration model. However, the reference value of unburned carbon content would not correspond to the LIBS spectral information because the result measured by the LOI method represents not only unburned carbon but also decomposition of some mineral phases in fly ash during ignition. In this paper, different values of unburned carbon content determined by the loss-on-ignition method and elemental analysis technique, respectively, were compared as the reference of LIBS calibration model. The analysis results showed that there is a better correspondence between the total carbon content determined by elemental analysis technique and the LIBS spectral information on fly ash. Compared with the reference value determined by the LOI method, the regression coefficient (R2) of the calibration model by the reference value determined by elemental analysis technique improved from 0.980 to 0.992, and the root-mean-square error of prediction decreased from 0.702% to 0.111%. This research demonstrates that the total carbon content is more suitable to be used as reference value to establish a calibration model of unburned carbon content in fly ash by LIBS analysis.

Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints

A data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal component analysis method is employed to reconstruct new variables as the input of the predictive model, reduce the over-fitting of data and improve modeling accuracy. Then, a k-nearest neighbors algorithm is used to classify the samples to distinguish the data by the different operating conditions. Based on the classified data, a least square support vector machine optimized by the differential evolution algorithm is established. Based on the boiler key parameter model, the proposed model attempts to maximize the combustion efficiency under the boiler load constraints, the nitrogen oxide (NOx) emissions constraints and the boundary constraints. The experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) The model predictive control algorithm can effectively control the boiler combustion efficiency, the average errors of simulation are less than 5%. The proposed model predictive control method can improve the quality of production, reduce energy consumption, and lay the foundation for enterprises to achieve high efficiency and low emission.

PERFORMANCE OPTIMIZATION OF EXISTING BOILERS AT SHAKARGANJ LLIMITED, JHANG

There are numerous well-documented phenomena that plague the efficient operation of bagasse boilers. Key Parameters that influence boiler combustion and operation are studied with the aid of combustion and flue gas analyzer. Combustion stability and efficiency is linked to various parameters such as fuel moisture and air temperatures supplied to the boiler and are investigated in this paper as part of a case study. The paper highlights number of modern developments that have been implemented at Shakarganj Limited, Jhang to optimize the existing boiler design to enhance the Boiler capacity from 65 tph to 80 tph. Along with that impact of various devices on performance improvement of boiler has been shown with experimental data for comprehensive evaluation of boiler operation and combustion efficiency. Results of suitable measures after installation of low cost retrofits to reduce losses including combustion instabilities, unburnt fuel, moisture in fuel and deposition of ash on tubes are also part of the paper.

Combustion Optimization for Coal Fired Power Plant Boilers Based on Improved Distributed ELM and Distributed PSO

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NOx emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NOx emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NOx emissions by combining different weight coefficients as needed.

Numerical Investigation on Co-firing Characteristics of Semi-Coke and Lean Coal in a 600 MW Supercritical Wall-Fired Boiler

Semi-coke is one of the principal by-products of coal pyrolysis and gasification, which features the disadvantages of ignition difficulty, low burnout rate, and high nitrogen oxides (NOx) emission during combustion process. Co-firing semi-coke with coal is a potential approach to achieve clean and efficient utilization of such low-volatile fuel. In this paper, the co-firing performance of semi-coke and lean coal in a 600 MW supercritical wall-fired boiler was numerically investigated which has been seldom done previously. The influences of semi-coke blending ratio, injection position of semi-coke, excess air ratio in the main combustion zone, the co-firing method, and over fire air (OFA) arrangement on the combustion efficiency and NOx generation characteristics of the utility boiler were extensively analyzed. The simulation results indicated that as the blending ratio of semi-coke increased, the NOx emission at furnace outlet decreased. The blending methods (in-furnace versus out-furnace) had certain impacts on the NOx emission and carbon content in fly ash, while the in-furnace blending method showed more flexibility in co-firing adjustment. The injection of semi-coke from the upper burners could significantly abate NOx emission at the furnace outlet, but also brought about the rise of carbon content in fly ash and the increase of outlet temperature. Compared with the condition that semi-coke and lean coal were injected from different burners, the burnout ratio of the blend premixed outside the furnace was higher at the same blending ratio of semi-coke. With the excess air ratio in the main combustion zone increased, NOx concentration at the furnace outlet was increased. The excess air ratio of 0.75 in the main combustion zone was recommended for co-firing 45% semi-coke with lean coal. The operational performance of the boiler co-firing semi-coke was greatly affected by the arrangement of OFA as well. The amount of NOx generated from the supercritical wall-fired boiler could be reduced with an increase of the OFA height.

Optimized scheme in coal-fired boiler combustion based on information entropy and modified K-prototypes algorithm

Abstract An integrated combustion optimization scheme is proposed for the combined considering the restriction in coal-fired boiler combustion efficiency and outlet NOx emissions. Continuous attribute discretization and reduction techniques are handled as optimization preparation by E-Cluster and C_RED methods, in which the segmentation numbers don't need to be provided in advance and can be continuously adapted with data characters. In order to obtain results of multi-objections with clustering method for mixed data, a modified K-prototypes algorithm is then proposed. This algorithm can be divided into two stages as K-prototypes algorithm for clustering number self-adaptation and clustering for multi-objective optimization, respectively. Field tests were carried out at a 660 MW coal-fired boiler to provide real data as a case study for controllable attribute discretization and reduction in boiler system and obtaining optimization parameters considering [ max η b , min y NO x ] multi-objective rule.