Heat Transfer Efficiency Prediction of Coal-Fired Power Plant Boiler Based on CEEMDAN-NAR Considering Ash Fouling

Ash fouling has been an important factor in reducing the heat transfer efficiency and safety of the coal-fired power plant boilers. Scientific and accurate prediction of ash fouling of heat transfer surfaces is the basis of formulating a reasonable soot blowing strategy to improve energy efficiency. This study presented a comprehensive approach of dynamic prediction of the ash fouling of heat transfer surfaces in coal-fired power plant boilers. At first, the cleanliness factor is used to reflect the fouling level of the heat transfer surfaces. Then, a dynamic model is proposed to predict ash deposits in the coal-fired boilers by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and nonlinear autoregressive neural networks (NARNN). To construct a reasonable network model, the minimum information criterion and trial-and-error method are used to determine the delay orders and hidden layers. Finally, the experimental object is established on the 300 MV economizer clearness factor dataset of the power station, and the root mean square error and mean absolute percentage error of the proposed method are the smallest. In addition, the experimental results show that this multiscale prediction model is more competitive than the Elman model.

Investigation on ash fouling formation of induced fan blade and heat exchanger surface in a 1000MW coal-fired power plant

The control of fouling deposition on the main equipment has always been an im-portant issue concerned by scientific research and industrial application. How-ever, severe fouling deposits on the induced fan blade and the low temperature economiser were found in a 1000 MW coal-fired power plant with ultra-low emission. The deposit samples were collected and analysed through X-ray dif-fraction spectrometer, X-ray fluorescence, elemental analyser and SEM with en-ergy dispersive spectrometers. The result shows that the deposits are mainly composed of tschermigite (NH4)Al(SO4)2 ? 12H2O, letovicite (NH4)3H(SO4)2, cal-cium sulphate CaSO4, and quartz SiO2. The ammonium sulphate is the main component of the fouling deposits. It acts as an adhesive and makes an important contribution to the deposition. The analysis shows that the ammonia slip from denitrification system and the unreasonable temperature setting are the main reasons for fouling deposition. It is suggested that the high concentration of am-monium slip at denitrification system and the rapid condensation of the sulphuric acid mist at heat exchanger should be paid more attention in coal-fired power plants.

Online Ash Fouling Prediction for Boiler Heating Surfaces based on Wavelet Analysis and Support Vector Regression

Depending on its operating conditions, traditional soot blowing is activated for a fixed time. However, low-frequency soot blowing can cause heat transfer efficiency to decrease. High-frequency soot blowing not only wastes high-pressure steam, but also abrades surface pipes, reducing the working life of a heat exchange device. Therefore, it is necessary to design an online ash fouling monitoring system to perform soot blowing that is dependent on the status of ash accumulation. This study presents an online monitoring model of ash-layer thermal resistance that reflects the degree of ash fouling. A wavelet threshold denoising algorithm was applied to smooth the thermal resistance of the ash layer calculated by the heat balance mechanism model. Thus, the variation in thermal resistance becomes more visible, which is more conducive to optimizing the operation of soot blowing. The designed Support Vector Regression (SVR) model could achieve the online prediction of thermal resistance denoising for low-temperature superheaters. Experimental analysis indicates that the prediction accuracy was 98.5% during the testing phase. By using the method proposed in this study, online monitoring of heating surfaces during the ash fouling process can be realized without adding complicated and expensive equipment.

Numerical optimization of obstructed high temperature heat exchanger for recovery from the flue gases by considering ash fouling characteristics

Purpose The purpose of this paper is to recommend a validated numerical model for simulation the flue gases heat recovery recuperators. Due to fulfill of this demand, the influences of ash fouling characteristics during the transient/steady-state simulation and optimization of a 3D complex heat exchanger equipped with inner plain fins and side plate fins are studied. Design/methodology/approach For the particle dispersion modeling, the discrete phase model is applied and the flow field has been solved using SIMPLE algorithm. Findings According to obtained results, for the recuperator equipped with combine inner plain and side plate fins, determination of ash fouling characteristics is really important, effective and determinative. It is clear that by underestimating the ash fouling characteristics, the achieved results are wrong and different with reality. Originality/value Finally, the configuration with inner plain fins with characteristics of: di =5 mm, do = 6 mm, dg = 2 mm, dk = 3 mm and NIPFT = 9 and side plate fins with characteristics of: TF = 3 mm, PF = 19 mm, NSPF = 17·2 = 34, WF = 10 mm, HF = 25 mm, LF = 24 mm and ß = 0° is introduced as the optimum model with the best performance among all studied configurations.

Effects of the type of biomass and ashing temperature on the properties of solid fuel ashes

Ashes were prepared by annealing selected types of solid fuels (biomass: corn cobs, sunflower husks, olive pomace, hay pellets and rice husks; coal: lignite and bituminous; and alternative fuel: paper sludge) at different temperatures (550°C, 815°C and 975°C). Based on X-ray fluorescence spectra, the slagging/fouling indexes were used to study the effects of the type of ash and the ashing temperature on the ash fouling and slagging properties. Slagging indexes were compared with the ash fusion temperatures. Ash fusion temperatures were measured by a LECO AF-700. The lowest deformation temperature (below 1000°C) was seen for the ashes prepared from hay pellets and corn cobs. On the other hand, the deformation temperature exceeded 1500°C for ashes prepared from paper sludge, sunflower husks and rice husks. By calculating the different slagging/fouling indexes, all the ashes exhibited slagging/fouling problems of varying degrees.

An Optimization Study on Soot-Blowing of Air Preheaters in Coal-Fired Power Plant Boilers

This paper presents a comprehensive approach for optimization of soot-blowing of air preheaters in a coal-fired power plant boiler. In the method, modeling of the cleanliness factor is firstly proposed to monitor the ash deposition status of the air preheaters. Then, the statistical fitting of the ash fouling status is subsequently obtained to analyze the ash fouling dynamics and assessment of optimized soot-blowing strategies. Soot-blowing strategies are finally developed to optimize the steam consumption and heat transfer efficiency. Our methods can achieve the fouling monitoring and soot-blowing optimization of air preheater (APH) by using the existing monitoring data, not requiring additional special instruments and complex computing systems. The methodology is validated with the actual operating data of a 300 MW coal-fired power plant boiler. The results show the effectiveness of the proposed method. It can be used for the soot-blowing optimization in most coal-fired power plant boiler with air preheaters.