Availability and cost-centered preventive maintenance scheduling of continuous operating series systems using multi-objective genetic algorithm: A case study

This article presents a multi-objective (maximization of availability and minimization of maintenance cost) preventive maintenance (PM) scheduling model for a continuous operating series system (COSS) which do not provide an off-working period for PM. The objective functions are optimized by using a Multi-Objective Genetic Algorithm (MOGA). The effectiveness of the model is demonstrated through a coal-fired boiler-tube. The case study shows that the model can improve the availability along with profound reduction of the maintenance cost, i.e., increases the profit of the plant.     

Modeling and analysis of renewable heat integration into non-domestic buildings - The case of biomass boilers: A whole life asset-supply chain management approach

This study proposes a whole life asset-supply chain optimization model for integration of biomass boilers into non-domestic (non-residential) buildings, under a renewable heat incentive scheme in the UK. The proposed model aims at identifying the optimal energy generation capacities and schedules for biomass and backup boilers, along with the optimal levels of biomass ordering and storage. The sensitivity of these decisions are then analyzed subject to changes in source, types and pricing of biomass materials as well as the choice of technologies and their cost and operational performance criteria. The proposed model is validated by applying it to a case study scenario in the UK. The results indicate that a Renewable Heat Incentive scheme could incentivize the adoption of biomass boilers, with a 3 to 1 ratio for biomass and backup boilers' utilization. As such, the findings from this study will be useful for industry managers, tasked with the decision of which biomass boiler system to utilize, considering the support from RHI. On the other hand, it is shown that RHI does not provide encouragement for efficiency when it comes to the choice of biomass technologies and fuels. This presents itself as a major implication for the success and sustainability of the UK government's renewable heat incentive scheme.     

Biomass boiler conversion potential in the eastern United States

The U.S. is the world's leading consumer of primary energy. A large fraction of this energy is used in boiler installations to generate steam and hot water for heating applications. It is estimated there are total 163,000 industrial and commercial boilers in use in the United States of all sizes.This paper characterizes the commercial and industrial boilers in the 37 states of the Midwest, Northeast, and Southern regions of the U.S. in term of number of units, unit capacity, aggregate capacity, and fuel type. A methodology is developed for evaluating and ranking the potential for converting from existing fossil-fuel boilers to biomass boilers in these states.In total, 3495 oil and coal boiler units in industrial and commercial buildings, and 1067 major wood energy facilities in the 37 eastern states were identified. These represent a subset of existing and potential conversions from fossil fuels to woody biomass. Based on this sample and energy consumption data from the Energy Information Administration (EIA), we estimate that there are currently 31,776 oil, coal, and propane boiler units over 0.5 MMBtus/hour capacity in these 37 states, representing a total energy consumption of 1.7 quadrillion Btus, or roughly the equivalent of 287 million barrels of oil. Were these units all converted to woody biomass fuel, they would consume a total of 121 million dry tons of wood per year, about three times the most recent US DOE estimates of woody biomass availability in those regions. Since only the most economical conversions typically occur, the reality of woody biomass market availability combined with thermal fossil-fuel consumption patterns suggests that roughly one-third of all potential projects could be achieved under sustainable utilization of existing biomass feedstocks in the three regions.Analysis of the results indicates that a targeted response to wood-conversion initiatives will yield the most successful program of fossil-fuel replacement in thermal applications. A ranking index developed in this study through analysis of existing boiler installations and availability of wood feedstocks suggests that the top ten states in the eastern United States on which to focus future messaging, feasibility studies, and policy development for potential woody biomass conversions are:1. Maine, 2. Texas, 3. New York, 4. Florida, 5. Georgia, 6. Alabama, 7. South Carolina, 8. North Carolina, 9. Arkansas, 10. Pennsylvania.     

CFB锅炉安装防磨梁效果及热流密度测试分析

针对引进型CFB锅炉存在水冷壁磨损严重的问题,提出在锅炉基建期间安装水冷壁多阶防磨梁装置,装置运行1a后,测定防磨梁的防磨效果,同时利用热流计对安装防磨梁后热流密度的变化进行了测试分析。结果表明防磨梁具有良好的防磨效果,延长了水冷壁的使用寿命。     

蛇形管车间的工艺设计

以锅炉生产厂蛇形管车间为例,介绍工业锅炉蛇形管的结构特点和制造流程,初步探讨工厂设计中的工艺设计一些问题,并结合实际工程进行了分析,可供设计同类工厂时参考.     

燃煤锅炉SCR脱硝过程对颗粒物和痕量元素的影响

选择性催化还原（SCR）技术广泛被应用于燃煤电厂NOx减排,而SCR脱硝技术对颗粒物（PM）的影响尚不清楚。在两台300 MW煤粉锅炉机组SCR反应器的入口和出口进行了颗粒物现场采样,分析了SCR脱硝过程对PM的质量粒径分布和痕量元素含量的影响。研究结果显示,SCR脱硝过程产生NH+4,SO2+4等组分并部分转化为颗粒物,促使0.1 μm以下颗粒物团聚、长大,导致0.3 μm以下颗粒物的峰值粒径由0.1 μm增大到0.3 μm,同时使S含量显著升高,最终导致0.3 μm以下颗粒物浓度升高约30.67%。烟气中部分0.3 μm以上颗粒物在SCR反应器中受惯性碰撞作用而分离,使其浓度降低0.06%~33.11%,但峰值粒径和组成成分不变。SCR反应器入口烟气中的气态痕量元素在脱硝过程中向颗粒物转移,引起PM中痕量元素含量变化。实验条件下,经过SCR反应器后,0.3 μm以下颗粒物中Cd,As,Pb含量分别升高104.15%,12.26%和12.18%,而Cr含量降低23.38%;0.3 μm以上颗粒物中各痕量元素含量均略有升高。     

燃煤锅炉的冷态性能调整措施探讨

通过对改造后的低氮燃烧锅炉的冷态性能进行分析,对锅炉一次风、二次风、三次风、燃尽风的喷口角度、标高和轴线位置进行了调整,并对风速进行调平试验,确定了符合锅炉燃烧的空气动力场条件,满足了锅炉燃烧的热效率要求。     

工业锅炉烟气多污染物排放协同净化技术综述

介绍了国内外工业锅炉烟气多污染物协同净化技术现状,包括基于液相催化氧化法等传统协同技术、LJD循环流化床法等新协同技术的原理、特征和改进方向,并对国内外发展的烟气多污染物净化技术的原理及研究现状进行评述,对比了各种技术的性能,展望了工业锅炉多污染物净化技术的发展前景与技术方向。     

我国煤电大气污染物控制现状及展望

"十二五"以来,我国煤电大气污染物的控制取得了很大的成就,烟尘、二氧化硫、氮氧化物排放总量和强度快速下降,控制水平达到世界先进水平。"十三五"乃至更长时期,煤电仍将承担国家大气污染物减排的重任。重点研发高性能、高可靠性、高适用性、高经济性污染物的控制技术、资源化技术、多污染物协同控制技术是未来我国煤电大气污染物控制的主要技术方向。预计到2020年,烟尘、二氧化硫、氮氧化物年排放量分别降至2×10~5~3×10~5 t、1×10~6~1.5×10~6 t、1×10~6~1.5×10~6 t。     

基于PCAGSVR的燃煤锅炉N Ox 排放预测

以某超超临界700 MW 机组锅炉为对象,建立了基于主成分分析(PCA)和支持向量回归(SVR)的氮氧化物(NOx )排放预测模型(PCA-SVR 模型)。运用 PCA 方法对包含有2000条锅炉运行记录的数据集进行分析,降低数据集维数,提取有效信息(主成分);以得到的主成分为输入变量,锅炉NOx 排放值为输出变量,利用SVR建立NOx 预测模型。与传统SVR模型相比,PCA-SVR模型的计算时间更短,并且能获得较高的 NOx 排放预测精度,其预测N Ox 排放浓度与实际排放浓度相比平均误差在1％以内。