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能源和环境问题是人类社会可持续发展所面临的最重要制约因素。我国碳交易市场于2017年正式启动,意味着能源发展必须走低碳清洁化道路。我国正处在统一碳交融交易市场关键时期,碳减排量与碳减排水平需要一个衡量标准及相应的函数关系,但目前缺乏相应的碳交易手段和政策制定依据。碳交易市场的启动会推动、倒逼发电企业进行发电结构调整,化解高碳落后产能,煤电低碳清洁转型升级、灵活性改造势在必行。把煤电和生物质能“撮合”在一起进行发电试点,将推动中国能源结构转型和煤电绿色发展,为煤电低碳清洁发展带来了新途径,燃煤耦合生物质发电的零碳排放优势将把握住电力产业低碳转型发展机遇[1-2]。
生物质能是一种清洁可再生能源,也是6种可再生能源中唯一可以转化为气、液、固体的零碳能源。近年来生物质发电逐渐兴起,利用生物质替代矿物燃料进行发电可以有效减少CO2和SO2排放。生物质与煤混合燃烧发电已被纳入国家产业规划,缺乏科学、公正的生物质应用量在线检测技术依旧是燃煤耦合生物质发电所面临的最大问题。目前较为先进的后端检测方法,主要包括SO2浓度检测法和14C含量检测法[3-6]。由于生物质含有一些碱金属,会与S发生反应,影响SO2浓度监测结果。生物质与大气中的14C活度相等,煤种14C活度几乎为零,因此14C含量检测法的准确性、可靠性仍然处于探索性阶段[7]。14C技术分析设备多为价格昂贵的核磁、加速器质谱(AMS)等精密仪器[8-14],影响了生物质应用量后端检测技术的发展。
目前,生物质应用量后端检测技术亟待解决实时检测、设备昂贵等问题。研究发现中红外激光同位素检测技术可实现快速实时检测稳定碳同位素比值δ13C (13CO2/12CO2)[15-23]。Castrillo等采用近红外激光设备测试CO2气体中的13C/12C同位素比值(δ),设备系统误差为0.5‰ [24]。传统光谱法相对质谱法测量灵敏度较低,但中红外量子级联激光器和腔衰荡光谱法(QCL-CRDS)相结合可以最大程度地提高气体光学检测的灵敏度,是目前最前沿的超低气体浓度检测技术,其具有体积小、操作简单、价格便宜、对环境敏感性低等优势,大大提高了现场布控和在线分析的能力,在生态、油气、环境监测等领域发挥着重要的角色。生物质与煤燃烧释放的CO2中均含有稳定碳同位素13C,生物质燃烧释放CO2气体中所含稳定碳同位素重,而煤燃烧过程中释放的碳同位素轻,可依据各自δ13C值的差异进行煤和生物质掺混比的判定[25-26]。现有碳同位素中红外激光检测设备检测误差仅为±0.025‰,可满足生物质应用量的精确评测需求,同时该检测仪造价较低,可实现对质谱等昂贵设备的替代,但与之相对应的评测方法国内外尚属空白[18, 27-28]。
本文研究建立基于稳定碳同位素中红外激光检测仪监测生物质掺烧比的实时在线检测分析方法十分重要,可为政府补贴政策的实施提供技术支撑,同时该分析方法可用于追踪“碳溯源”,推动碳排放监测和碳交易市场的快速发展,促进清洁能源的健康有序发展和碳交易市场建立建全定价机制。
稳定碳同位素激光检测仪监测生物质燃料掺烧比的研究
Monitoring biomass-coal fuel blending ratio by stable carbon-isotope laser detector
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摘要: 稳定碳同位素激光技术可用于检测煤和生物质的碳同位素比值δ13C,以确定燃煤耦合生物质的掺烧比,且不受含水量影响。本文通过对煤(山西煤、内蒙古煤、贵州煤)和生物质(玉米秸秆、棉花秸秆、木屑、稻壳)混燃烟气进行稳定碳同位素比值测定,在一定的生物质掺烧比范围(碳量基、热量基)内建立在线分析方法得到拟合线性方程y=ax+b,拟合优度R2>0.99。所得分析方法可用于在线监测生物质掺烧比变化情况,当生物质掺烧比为20%时,误差分析控制在0.5%—2.0%以内。此外,该检测-分析方法能够实时、准确地监测生物质掺烧比,可为国家针对生物质使用量补贴政策的制定和实施提供技术支撑;同时可用于追踪“碳溯源”,推动碳排放监测和碳交易市场的快速发展。Abstract: The stable carbon isotope laser technology can be used to detect the carbon isotope ratio of coal and biomass (δ13C) to determine the blending ratio of coal-fired coupled biomass, and is not affected by water content. In this paper, the stable carbon isotope ratio was measured in the flue gas of coal (Shanxi coal, Inner Mongolia coal or Guizhou coal) and biomass (cornstalks, cotton stalks, wood chips or rice husks). The online analysis method was established considering a certain range of biomass blend ratio (carbon-based and calorific value-based) to get the fitted linear equation y = ax + b, and the goodness-of-fit (R2) exceeded 0.99. The obtained analysis method can be used to on-line monitor the change of the biomass content in the blend. When the biomass blending ratio is 20%, the analysis error is controlled within 0.5%—2.0%. In addition, this detection-analysis method can real-time monitor the biomass blending ratio accurately, which provides technical support for the development and implementation of the national subsidy policy for biomass usage and promotes the rapid development of carbon emission monitoring and the carbon trading market.
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表 1 煤和生物质工业分析、元素分析和热值测定
Table 1. Industrial analysis, element analysis, and calorific value determination of coal and biomass
样品/组分
Sample/component内蒙古煤
Inner Mongolia coal贵州煤
Guizhou coal山西煤
Shanxi coal玉米秸秆
Cornstalks稻壳
Rice husks棉花秸秆
Cotton stalks木屑
Wood chips水分Mad/% 6.83 4.77 5.58 7.59 6.54 7.77 7.01 灰分Aad/% 2.07 11.81 3.76 7.76 12.74 2.50 1.9 挥发分Vad/% 30.00 30.52 28.90 67.53 62.90 70.86 76.33 固定碳FCad/% 61.10 52.90 61.76 17.12 17.83 18.88 14.76 碳Cad/% 73.56 66.23 73.85 41.24 40.39 44.77 45.47 氢Ht,ad% 4.91 4.16 4.60 6.13 5.61 6.30 6.43 氮Nad/% 0.98 0.91 1.08 1.49 0.49 1.18 0.40 全硫St,ad/% 0.59 0.21 0.20 0.21 0.11 0.14 0.08 氧Oad/% 11.82 12.44 11.55 36.43 34.86 38.21 39.49 空干基低位发热量Qnet,ad/(J·g−1) 27060 24030 26400 15030 14660 16140 16580 空干基高位发热量Qgr,ad/(J·g−1) 28080 24890 27350 16290 15810 17440 17900 干基高位发热量Qgr,d/(J·g−1) 30130 26130 28960 17630 16920 18910 19250 注:本文发热量矫正使用的为空干基低位发热量.
Note: Low calorific value at air-dry basis was used in the calorific value correction in this study.表 2 纯燃料样品的碳同位素比值(δ13C)
Table 2. The 13C proportion (δ13C) in pure coal and biomass samples
样品
Sample山西煤
Shanxi coal贵州煤
Guizhou coal内蒙古煤
Inner Mongolia coal玉米秸秆
Cornstalks棉花秸秆
Cotton stalks木屑
Wood chips稻壳
Rice husksδ13C −20.61 −21.01 −21.91 −11.91 −26.09 −26.99 −27.02 表 3 燃料样品不同含水量的δ13C值
Table 3. The δ13C values of fuel samples with different water contents
样品
Sample煤量/g
Coal生物质量/g
Biomass加水量/%
Waterδ13C值
δ13C value山西煤 0.50 0 20 -20.60 山西煤 0.50 0 50 -20.61 贵州煤 0.50 0 20 -21.02 贵州煤 0.50 0 50 -21.01 内蒙古煤 0.50 0 20 -21.91 内蒙古煤 0.50 0 50 -21.92 玉米秸秆 0 0.50 20 -11.90 玉米秸秆 0 0.50 50 -11.92 木屑 0 0.50 20 -26.98 木屑 0 0.50 50 -26.97 棉花秸秆 0 0.50 20 -26.08 棉花秸秆 0 0.50 50 -26.09 稻壳 0 0.50 20 -27.01 稻壳 0 0.50 50 -27.02 -
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