Optimization and attribution analysis of annual runoff simulation models in the upper reaches of the Heihe River, northwestern China
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摘要:目的
本研究旨在深入探究人类活动与气候变化对黑河上游年径流量的影响,为区域水资源保护与规划利用提供科学支持。
方法研究综合Mann-Kendall非参数统计检验、Pettitt检验和滑动t检验方法,对1954—2020年黑河上游年径流序列进行趋势检验,识别年径流序列趋势变化的突变点并划分基准期与分析期。在此基础上,采用BP神经网络模型、灰色时间序列模型和多元线性回归模型,模拟基准期年径流变化,优选模拟效果最佳模型,进而借助优选模型与径流变化归因方法,定量解析人类活动与气候变化要素对年径流变化的驱动规律。
结果趋势检验发现,年径流序列在1982年和2006年前后发生了突变,黑河上游年径流序列可划分为1954—1982年(基准期)、1982—2006年(分析期1)和2006—2020年(分析期2)3个阶段。在基准期年径流序列的模拟中,BP神经网络模型在验证期的相对误差(0.79%)、纳什效率系数(0.84)与拟合优度(0.84)3个参数上相较其他模型优势明显。借助神经网络模型进行年径流变化归因分析,发现人类活动导致年径流在1982—2020年间减少的平均值为7.56 × 108 m3。但2006—2020年间黑河上游人类活动对径流的负面贡献率较1982—2006年间减少约18.00%。详细解析气候变化对年径流量的影响,发现在2006—2020年间,降水量与蒸散发对年径流的贡献率较1954—1982年分别增加约11.00%和8.00%。
结论BP神经网络模型对于黑河上游年径流序列模拟有较好效果,模拟合格率达94.23%,最大误差仅为1.36%;黑河流域上游年径流量序列在1982年和2006年发生了趋势突变,1982年后人类活动强度增大导致上游年径流量减小,2006年后黑河流域综合治理效果显现,人类活动对年径流量的负面效应减弱;1982—2020年期间的气候变化影响中,蒸散发与降水对径流的贡献分别占46.57%与53.43%。
Abstract:ObjectiveThe primary objective of this study is to conduct an in-depth investigation into the impact of human activities and climate change on the annual runoff in the upper reaches of the Heihe River of northwestern China, with the aim of providing scientific support for regional water resource conservation and planning.
MethodThis study employed a comprehensive approach involving the Mann-Kendall non-parametric statistical test, Pettitt test, and sliding t-test methods to assess the trends in the annual runoff series in the upper reaches of the Heihe River from 1954 to 2020. The objective was to identify abrupt change points in the annual runoff series and delineate the reference period and analysis period. Building upon this foundation, we employed the BP neural network model, the grey time series model, and the multivariate linear regression model to simulate the annual runoff variations during the reference period. We then selected the model with the best simulation performance. Subsequently, utilizing the selected model and runoff attribution methods, we quantitatively analyzed the driving mechanisms of human activities and climate change factors on the annual runoff variations.
ResultTrend analysis revealed that the annual runoff series experienced abrupt changes around 1982 and 2006. Consequently, the annual runoff series in the upper reaches of the Heihe River can be divided into three phases: 1954–1982 (reference period), 1982–2006 (analysis period 1), and 2006–2020 (analysis period 2). In the simulation of the annual runoff series during the reference period, the BP neural network model exhibited a clear advantage over the other two models in three parameters during the validation period: relative error (0.79%), Nash-Sutcliffe efficiency coefficient (0.84), and goodness of fit (0.84). Utilizing the neural network model for annual runoff attribution analysis, it was determined that human activities led to an average decrease of 7.56 × 108 m3 in annual runoff between 1982 and 2020. However, during the period of 2006 to 2020, the adverse contribution of human activities in the upper reaches of the Heihe River to runoff decreased by approximately 18.00% compared with the period from 1982 to 2006. A detailed analysis of the impact of climate change on annual runoff revealed that between 2006 and 2020, precipitation and evapotranspiration contributed approximately 11.00% and 8.00% more, respectively, to annual runoff compared with the period from 1954 to 1982.
ConclusionThe BP neural network model demonstrates a strong performance in simulating the annual runoff series of the upper reaches of the Heihe River, achieving a simulation accuracy of 94.23% with a maximum error of only 1.36%. The annual runoff series in the upper Heihe River Basin exhibited trend transitions in 1982 and 2006. Increased human activities after 1982 lead to a reduction in annual runoff, while the comprehensive river basin management measures implemented after 2006 result in a mitigation of the negative impacts of human activities on annual runoff. Regarding the influence of climate change during the period from 1982 to 2020, evapotranspiration and precipitation contribute 46.57% and 53.43%, respectively to runoff.
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活性碳纤维作为一种新型吸附材料,以比表面积高、吸附容量大、吸附脱附速率快、耐热耐酸碱等优点,被广泛应用于环境净化、催化剂载体、储能材料等领域[1-2]。活性碳纤维的孔隙结构是影响各项性能的关键因素。在活化过程中,碳基体与活化剂之间的反应导致大量孔隙的生成,而不仅仅是纤维表面的烧失,这表明活化反应具有选择性。作为活化前驱体,炭化过程的产物碳纤维由乱层石墨多晶结构组成[3],其微观晶体结构对活化孔结构形成具有重要的影响。在活化过程中,活化剂优先侵蚀碳纤维的无定形区、晶胞缺陷处、晶界处和初始孔隙处,而后进入有序化较高的微晶区域。上述位置的碳基体以不同的速率与活化剂反应,从而形成孔洞,随着活化继续进行,孔结构进一步变深、扩宽[4-5]。
近年,为缓解石化资源危机,利用林业生物质资源制备活性碳纤维受到了广泛关注。其中,基于木材液化物制备的活性碳纤维具有丰富的孔隙结构[6-7],且在污染物净化[8-10]、抗菌性能[11-14]、电化学特性[15-17]等方面表现出优良的性能。目前,针对木材液化物纤维在炭化、活化过程中微晶结构的变化已进行了一系列研究。马晓军等[18]研究表明:800 ~ 1 000 ℃炭化温度下,木材液化物原丝形成大量多苯稠环结构,碳网重组并进行有序化生长,石墨化程度显著提高。赵广杰[19]指出:木材苯酚液化物原丝分子网状交联结构在300 ~ 600 ℃炭化过程中被破坏并发生重排,进而形成初步的碳网结构;700 ℃以上,碳网继续生长,聚合度逐渐提高。Liu等[20]指出:较高的活化温度或较长的活化时间会导致木材液化物活性碳纤维乱层石墨结构的破坏,使其结晶化程度降低。Li等[21]研究了CO2活化过程中木材液化物活性碳纤维的微晶结构随着活化温度升高的变化规律,由微晶尺寸数据推断600 ℃之前微晶结构正在经历芳环结构向多层石墨堆叠结构的转变,而600 ℃以后,石墨网有序化程度逐步提高,乱层石墨晶体结构逐渐稳定,晶胞尺寸逐渐增大。Liu等[22]比较了利用木材液化物原丝和碳纤维分别制备的活性碳纤维微晶结构的区别,得出:前者具有更大的晶体尺寸和更致密的乱层石墨结构,而后者微孔数量较多,微孔孔径较大。Ma等[23]通过添加木炭制备中孔木材液化物活性碳纤维,微晶结构研究表明:木炭的添加打断了活性碳纤维的碳平面,限制了乱层石墨片层的生长和排列,进而影响了乱层石墨片层的发育和有序堆积,导致了中孔结构的增加。Liu等[24]研究指出:木材液化物活性碳纤维中孔结构源于纤维缺陷的扩大,中孔结构的形成加剧了乱层石墨微晶结构的瓦解。以上研究充分证实了不同炭化与活化过程中纤维微晶结构变化与孔结构形成的相关性,然而尚未详细阐明微晶结构演变对孔结构形成的作用及影响规律。
为进一步揭示木材液化物活性碳纤维孔结构形成机制,本研究通过控制炭化温度获得具有不同微晶结构的杉木液化物碳纤维,并以水蒸气作为活化剂在800 ℃下进行活化,采用元素分析仪、X射线衍射仪和氮气吸附仪分别考察了随着活化时间延长,杉木液化物活性碳纤维元素组成、微晶结构和孔结构的变化,探讨了微晶结构演变和孔结构形成两者之间的作用机制及影响规律。
1. 材料与方法
1.1 材 料
将杉木(Cunninghamia lanceolata)木粉粉碎至20 ~ 80目,并在(105 ± 5) ℃下干燥24 h。苯酚(分析纯)为北京笃信精细制剂厂生产。磷酸(分析纯),质量分数37%,北京化工厂生产。六次甲基四胺(分析纯)为西陇化工股份有限公司生产。甲醛(分析纯),质量分数37%,广东光华科技股份有限公司生产。盐酸(分析纯),质量分数37%,北京化工厂生产。
1.2 研究方法
1.2.1 杉木苯酚液化物的制备
将20 g杉木木粉与苯酚按质量比1∶6混合加入三口烧瓶中,并加入苯酚质量8%的磷酸作为催化剂进行杉木木粉液化工艺。开启冷凝器,在1 053 r/min的搅拌速率下,液化混合物以5 ℃/min的升温速率在油浴中加热至160 ℃并保温2.5 h。而后撤去油浴,待三口烧瓶冷却至室温,撤去冷凝器,将液化产物通过50 mL砂芯漏斗(直径为8 cm,G3型,孔径为15 ~ 40 μm),抽真空过滤制得杉木苯酚液化物。
1.2.2 纺丝与固化
将10 g杉木苯酚液化物和0.5 g六次甲基四胺混合加入纺丝管,开启搅拌器,以5 ℃/min的升温速率在空气中加热至(175 ± 2) ℃,并保温20 min合成纺丝液,而后在120 ~ 130 ℃进行纺丝制备初始纤维。将得到的初始纤维浸入含有7.4 mL甲醛、6 mL盐酸和1.4 mL蒸馏水的酸溶液中,以0.25 ℃/min的升温速率加热至90 ℃,保温2 h进行固化工艺。待固化结束后取出纤维,经蒸馏水洗涤后放入(85 ± 2) ℃烘箱干燥2 h,得到原丝。
1.2.3 炭化过程
将约5 g的原丝以2 ℃/min的升温速率在N2保护下分别加热至设定的炭化温度(500、700、900 ℃)并保温1 h。之后以4 ℃/min的速率将温度调整至800 ℃,结束炭化过程。待炭化产物冷却至室温,取出炭化产物得到杉木苯酚液化物碳纤维(liquefied wood carbon fibers,LWCFs)。LWCFs-500、LWCFs-700和LWCFs-900分别代表炭化温度为500、700、900 ℃的LWCFs。
1.2.4 活化过程
将约5 g的前驱体纤维以2 ℃/min的升温速率在N2保护下分别加热至设定的炭化温度(500、700、900 ℃)并保温1 h。之后以4 ℃/min的速率将温度调整至800 ℃,此时开启水蒸气(流量为6.69 g/min),活化20、40 min制备活性碳纤维。待活化产物冷却至室温,取出活化产物得到杉木苯酚液化物活性碳纤维(activated liquefied wood carbon fibers,ALWCFs)。ALWCFs-500-20代表炭化温度为500 ℃,活化时间为20 min的ALWCFs,其他样品名称含义与之相同。
1.2.5 元素分析检测
采用美国公司(Thermo)生产的A FLASH EA1112型元素分析仪对所有样品的碳、氢、氮元素质量分数进行测试。测试条件为:以He为载气,碳、氢、氮元素分解温度为950 ℃。氧元素质量分数计算公式如下:
WO=(1−WC−WH−WN)×100% 式中:WC、WH、WN分别表示碳、氢、氮元素的质量分数。
1.2.6 X射线衍射仪检测
采用日本公司(SHIMAZU)生产的XRD-6000型X射线衍射仪采集样品的X射线衍射图谱。具体操作如下:将约0.5 g的干燥样品在研钵中研磨15 min,放入样品台中压实后开始检测。铜靶辐射(辐射管额定电压为40 kV,额定电流为30 mA,波长为0.154 nm),扫描频率为2 (°)/min,2θ角扫描区间值为15° ~ 60°,测量步长为0.2°。
样品的石墨层间距(d002)、石墨层堆叠厚度(Lc002)由(002)衍射面求得,石墨网横向尺寸(La110)由(110)衍射面求得,依据Scherrer公式[25]计算。
d002=λ/(2sinθ002) Lc002=0.94λ/(βcosθ002) La110=1.84λ/(βcosθ110) 式中:λ表示入射X射线的波长,取0.154 nm;β表示该晶面衍射峰的半峰宽;θ为该衍射峰所对应的衍射角。
1.2.7 孔结构检测
采用美国公司(Quantachrome)生产的Autosorb iQ型氮气吸附分析仪进行测定。将0.0500 g的ALWCFs待测样品装入干燥的测试管,经300 ℃脱气3 h,在−196 ℃下测定不同相对压强下的N2吸附/脱附等温线。采用Brunauer-Emmett-Teller法[26]计算BET比表面积SBET;由相对压力约为0.99时的液氮吸附量换算成液氮体积,得到总孔容Vt;采用t-plot法[27]计算微孔比表面积Smi和微孔孔容Vmi;采用Barrett, Joyner & Halenda(BJH)法[28]计算中孔孔容VBJH;采用Horvath-Kawazoe(HK)法[29]计算峰值孔径DHK;由BET法计算平均孔径Da;由Density Functional Theory(DFT)法[30]计算孔径大小与分布。
2. 结果与分析
2.1 元素组成变化
不同炭化–活化过程中LWCFs与ALWCFs的元素质量分数及变化趋势分别见表1和图1。在炭化样品中,随着炭化温度升高,碳元素质量分数逐渐增加,氢、氧元素质量分数减少。其中,炭化温度由700 ℃升高至900 ℃过程中,氢、氧元素质量分数的减少十分明显,这表明脂肪族官能团和含氧官能团的热解在高于700 ℃以后变得剧烈。在同一活化时间下,随着炭化温度升高,碳元素质量分数逐渐增加,氢、氧元素质量分数逐渐减少。综上可知,炭化温度的提高促进了非碳元素的挥发,且有利于活化过程中碳元素的富集。
表 1 不同炭化–活化过程中LWCFs和ALWCFs的元素质量分数变化Table 1. Changes of element percentage compositions for LWCFs and ALWCFs by different carbonization-activation processes样品名称 Sample name 质量分数 Mass fraction/% C H N O LWCFs-500 88.76 1.15 0.61 9.49 LWCFs-700 89.21 1.17 0.64 9.00 LWCFs-900 91.71 0.84 0.64 6.82 ALWCFs-500-20 91.25 1.04 0.58 7.14 ALWCFs-700-20 93.23 1.03 0.62 5.12 ALWCFs-900-20 94.31 0.79 0.6 4.30 ALWCFs-500-40 93.45 1.14 0.57 4.86 ALWCFs-700-40 94.25 0.99 0.64 4.13 ALWCFs-900-40 95.10 0.69 0.57 3.64 图 1 不同炭化–活化过程中LWCFs和ALWCFs的元素质量分数变化图LWCFs对应的活化时间为0 min,ALWCFs对应的是活化时间为20 ~ 40 min。LWCFs correspond to the activation time of 0 min, and ALWCFs correspond to the activation time of 20–40 min.Figure 1. Changes of element percentage compositions for LWCFs and ALWCFs by different carbonization-activation processes2.2 微晶结构变化
图2为不同炭化–活化过程中LWCFs与ALWCFs的XRD衍射图谱。从图中可以看出:LWCFs与ALWCFs在2θ为19° ~ 21°和44°附近均出现衍射峰,分别对应于石墨碳层的(002)衍射面和(110)衍射面[31-32]。这两处衍射峰和形态特点说明LWCFs与ALWCFs的微晶结构均由乱层石墨微晶堆叠而成,属于多晶乱层石墨结构。
不同炭化–活化过程中LWCFs与ALWCFs的微晶结构参数见表2。相关微晶结构参数在不同炭化–活化过程中的变化趋势分别见图3和图4。LWCFs的d002值均大于石墨微晶的层间距(0.335 4 nm),表明石墨化程度较低[33-34]。
表 2 不同炭化–活化过程LWCFs和ALWCFs的微晶结构参数Table 2. Microcrystalline structure parameters for LWCFs and ALWCFs prepared by different carbonization-activation processes样品名称
Sample name2θ/(°) d002/nm Lc002/nm Lc002/d002 La110/nm La110/Lc002 LWCFs-500 20.3 0.437 0.505 1.16 1.683 3.33 LWCFs-700 20.8 0.427 0.509 1.19 1.606 3.16 LWCFs-900 20.8 0.427 0.515 1.21 1.651 3.21 ALWCFs-500-20 20.6 0.430 0.512 1.19 2.365 4.62 ALWCFs-700-20 20.6 0.430 0.512 1.19 2.274 4.44 ALWCFs-900-20 19.9 0.446 0.501 1.12 2.870 5.72 ALWCFs-500-40 19.4 0.457 0.538 1.18 2.245 4.17 ALWCFs-700-40 20.2 0.439 0.464 1.06 2.217 4.78 ALWCFs-900-40 21.4 0.415 0.447 1.08 2.779 6.23 注:θ为衍射峰所对应的衍射角,d002为墨层间距,Lc002为石墨层堆叠厚度,La110为石墨网横向尺寸。Notes:θ is the diffraction angle corresponding to the diffraction peak, d002 is the spacing between graphite layers, Lc002 is the stacking thickness of graphite layers, and La110 is the transverse size of graphite network. 随着炭化温度升高,LWCFs(002)衍射峰的2θ角从20.3°偏移至20.8°,d002值减少0.01 nm,Lc002值增加0.01 nm,轴向石墨层数Lc002/d002增加0.05。可见,炭化温度的升高使LWCFs的乱层石墨微晶结构更加致密,微晶间排列更趋于规整,且轴向尺寸逐渐增大。LWCFs的La110值与La110/Lc002随着炭化温度由500 ℃升至700 ℃逐渐减小,在此过程中LWCFs的化学结构因热解产生破坏并逐步向多层堆叠的乱层石墨结构转变。当炭化温度升高至900 ℃,LWCFs的La110值与La110/Lc002增大,此时乱层石墨微晶结构更趋有序、规整,横向微晶发生了生长。
活化过程中,当炭化温度为700和900 ℃时,Lc002值随着活化时间延长呈减小趋势,在活化时间高于20 min时,Lc002值的减少尤为明显,其中,ALWCFs-700-40的Lc002值较LWCFs-700减小了8.8%,ALWCFs-900-40的Lc002值较LWCFs-900减小了13.2%,这是由活化过程中水蒸气对乱层石墨轴向微晶的侵蚀所致,且随着活化时间的延长,侵蚀程度明显加重。与上述情况相反,当炭化温度为500 ℃时,Lc002随着活化时间的延长呈增加趋势,ALWCFs-500-40的Lc002值较LWCFs-500增加了6.5%。这是由于LWCFs-500纤维结构中有较多的晶体缺陷和较大的初始孔隙,活化反应优先在这些缺陷和孔隙处发生,以至于水蒸气没有充分进入微晶内部[4]。
随着活化时间延长至20 min,La110值增加显著,其中,900 ℃炭化时的增加率最高,ALWCFs-900-20较LWCFs-900增加了73.8%。La110值的升高表明乱层石墨网络横向尺寸的增大,这是源于微晶间碰撞所产生的横向重排,在900 ℃下最为剧烈,这与上文中LWCFs横向微晶结构的变化趋势一致,进一步证实900 ℃下乱层石墨碳网结构的充分生长[35]。随着活化时间延长至40 min,3种炭化温度下的La110值仅略微降低,表明该阶段微晶的横向结构基本稳定,这可能是由于此时乱层石墨结构的生长已趋于饱和,主要以碳原子的活化热解为主。
对于500和700 ℃炭化样品,d002值随活化时间的延长呈增大趋势,其中,ALWCFs-500-40的d002值较LWCFs-500增加了4.6%,ALWCFs-700-40的d002值较LWCFs-700增加了2.8%,可见活化作用没有使乱层石墨结构变得更为致密,这是由于活化过程中挥发物的蒸发以及孔结构的形成使微晶结构变得松散。而ALWCFs-900-40的d002值大幅减少,较LWCFs-900减少了2.8%,表明高温活化下,轴向微晶结构已经高度分散,并产生了塌陷或紧缩。轴向石墨层数Lc002/d002的减小与Lc002值的变化趋势相符,这进一步验证了活化过程是对轴向乱层石墨片层结构的侵蚀,且在900 ℃活化过程中侵蚀最为剧烈。
2.3 孔结构变化
图5为不同炭化–活化过程中ALWCFs的N2吸附/脱附等温曲线。从中可以看出:ALWCFs的等温线类型均属于Ⅰ型,表明微孔结构占据主导地位。表3列出了不同炭化–活化过程中ALWCFs的孔结构参数。从中可以看出:炭化温度的提高有利于ALWCFs孔结构的形成。较样品ALWCFs-500-20,ALWCFs-700-20和ALWCFs-900-20的SBET分别增加了12.5%和4.3%,Vt分别增加了15.9%和2.6%;较样品ALWCFs-500-40,ALWCFs-700-40和ALWCFs-900-40的SBET分别增加了7.9%和18.6%,ALWCFs-900-40 的Vt增加了12.5%。其中,炭化温度的提高对ALWCFs微孔结构形成有明显的促进作用,且活化时间越长,微孔结构增加越显著。较样品ALWCFs-500-20,ALWCFs-700-20和ALWCFs-900-20的Smi分别提高了8.1%和5.0%,Vmi分别提高了8.9%和4.6%;较样品ALWCFs-500-40,ALWCFs-700-40和ALWCFs-900-40的Smi分别提高了19.9%和25.1%,Vmi分别提高了17.3%和25.5%。结合微晶分析结果可以得出:水蒸气对乱层石墨轴向微晶内部的侵蚀是形成微孔结构主要途径。低炭化温度样品微晶结构有序化程度较低,水蒸气会优先侵蚀其微晶缺陷和初始孔隙处,这一定程度上减缓了水蒸气对轴向微晶内部的侵蚀。而高的炭化温度形成的微晶有序化程度较高,有助于加快水蒸气进入轴向微晶内部的速率。
表 3 不同活化时间制备的ALWCFs的孔结构参数Table 3. Pore structure parameters for ALWCFs prepared by different activation time样品名称
Sample nameSBET/
(m2·g−1)Vt/
(cm3·g−1)Smi/
(m2·g−1)Vmi/
(cm3·g−1)VBJH/
(cm3·g−1)DHK/nm Da/nm ALWCFs-500-20 861 0.464 650 0.259 0.230 0.443 2.15 ALWCFs-700-20 969 0.538 703 0.282 0.284 0.428 2.22 ALWCFs-900-20 898 0.476 683 0.271 0.226 0.448 2.12 ALWCFs-500-40 1 068 0.590 793 0.318 0.314 0.458 2.21 ALWCFs-700-40 1 152 0.562 951 0.373 0.241 0.463 1.95 ALWCFs-900-40 1 267 0.664 992 0.399 0.373 0.468 2.09 注:SBET为比表面积;Vt为总孔容;Smi为微孔比表面积;Vmi为微孔孔容;VBJH为中孔孔容;DHK为峰值孔径;Da为平均孔径。Notes:SBET is the specific surface area, Vt is the total pore volume, Smi is the micropore specific surface area, Vmi is the micropore volume, VBJH is the mesopore volume, DHK is the peak pore width, and Da is the average pore width. ALWCFs中孔结构随着炭化温度升高呈现不同的变化趋势。对于500和700 ℃炭化样品,活化初期的中孔结构主要来源于水蒸气对晶体缺陷或初始孔隙处的活化作用,活化20 min时,两者VBJH均高于ALWCFs-900-20。随着活化时间延长至40 min,ALWCFs-700-40的VBJH急剧下降,ALWCFs-900-40的VBJH明显提高,这是由于水蒸气对轴向微晶内部的侵蚀加重,前者的中孔结构受到破坏,而后者微孔结构进一步扩大。
ALWCF孔径大小和分布变化趋势进一步证实了以上结果。图6为不同活化时间ALWCF的DHK和Da的变化趋势图。3种炭化温度下,DHK均随着活化时间的延长而增大,且900 ℃炭化–活化样品的DHK最高,表明了900 ℃炭化样品在活化过程微孔结构扩大最显著。Da的变化表明,仅500 ℃炭化–活化样品的Da随活化时间的延长而增加,700 ℃炭化–活化样品的Da随活化时间的延长下降最为显著。图7为不同活化时间下的ALWCFs的DFT孔径大小分布图。从中可以观察到:当活化20 min时,ALWCFs中多数为孔径小于1 nm的微孔。这些微孔的孔径大小分布随着炭化温度的升高而逐渐扩宽。当活化40 min时,炭化温度的升高使孔径大小分布在0.6 ~ 1.5 nm的微孔范围内和2 ~ 2.5 nm的中孔范围内的逐渐扩大,样品ALWCFs-900-40的孔径大小分布扩大最为明显,这是由于随着活化时间的延长,轴向微晶的侵蚀加重促进了孔结构的形成与扩大,同时,碳基体中的孔隙通道变多变宽,水蒸气更容易到达活化位点,这进一步加剧了孔径的扩大。
3. 结 论
本研究通过500 ~ 900 ℃炭化过程得到不同微晶结构的LWCFs,并将这些LWCFs经800 ℃水蒸气活化20 ~ 40 min,考察了不同炭化–活化过程中ALWCFs的元素组成、微晶结构和孔结构的变化,探讨了微晶结构对ALWCFs孔结构形成的作用机制及影响,揭示了微晶结构演变规律以及ALWCFs孔结构形成路径。得到如下结论:
(1)随着炭化温度的升高,LWCFs碳元素质量分数逐渐升高,氢、氧元素质量分数减少,高于700 ℃以后氢、氧元素质量分数减少显著;ALWCFs碳元素质量分数逐渐增加,氢、氧元素质量分数逐渐减少。以上表明,炭化温度的提高促进了非碳元素的挥发,且有利于活化过程中碳元素的富集。
(2)炭化温度的升高使LWCFs的乱层石墨微晶轴向尺寸逐渐增大,结构更加致密,900 ℃时横向微晶发生了生长。在活化过程中,高的炭化温度能够显著促进水蒸气对轴向微晶的侵蚀,且随着活化时间的延长,侵蚀程度加重。此外,活化初期微晶间进行碰撞产生横向重排,乱层石墨网络横向尺寸显著增大,炭化温度的升高利于活化过程中横向微晶的生长,而进一步延长活化时间横向微晶结构无显著变化。
(3)炭化温度的升高提高了ALWCFs的比表面积和总孔容,对微孔结构形成有明显的促进作用,且活化时间越长,微孔结构增加越显著。水蒸气对乱层石墨轴向微晶内部的侵蚀是形成微孔结构的主要途径。低的炭化温度(500和700 ℃)有利于ALWCFs在活化初期中孔结构的形成,主要来源于水蒸气对晶体缺陷或初始孔隙处的活化作用;随着活化时间的延长,轴向微晶的侵蚀加重,初期中孔发生了瓦解。高温(900 ℃)炭化样品在活化初期中孔结构较少,但随着活化时间的延长,微孔结构的逐步扩大导致了中孔结构明显增多。ALWCFs孔径大小和分布变化趋势进一步证实了上述结论。
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图 3 BP神经网络示意图
X1、X2、X3代表每个神经元的输入。z1、z2、z3代表隐含层中的蕴含条件。a1、a2、a3代表各对应输出层的输出结果。w11、w21、w31······则代表连接权值调节各个输入量的占重比,占重比指输入层与隐含层间进行训练调整的比值,下标11,21,31······分别对应第一个输入层与第一个隐含层、第二个输入层与第一个隐含层、第三个输入层与第一个隐含层,以此类推。X1, X2, and X3 represent the inputs of each neuron. z1, z2, and z3 represent the implication conditions in the hidden layer. a1, a2, a3 represent the output results of each corresponding output layer, while w11, w21, w31······ represent the weight ratio of the connection weight adjustment for each input quantity. The weight ratio refers to the ratio of training adjustment between the input layer and the hidden layer, with subscripts 11, 21, 31··· corresponding to the first input layer and the first hidden layer, the second input layer and the first hidden layer, the third input layer and the first hidden layer, and so on.
Figure 3. Schematic diagram of BP neural network
表 1 黑河上游年径流趋势划分与趋势方程
Table 1 Annual runoff trend division and trend equation in the upper reaches of the Heihe River
年份区间
Year range线性趋势方程
Linear trend equation1954—1982 y = −0.035 4x + 84.668 1982—2006 y = −0.039 9x + 96.283 2006—2020 y = 0.156 9x + 295.68 1954—2020 y = 0.083 6x − 149.29 注:x代表年份,y代表年径流量。Notes: x represents the year and y represents the annual runoff. 表 2 3种模型在径流模拟中的性能比较
Table 2 Performance comparison of three models in runoff simulation
不同时期 Different periods 灰色时间序列
Grey time series多元线性回归
Multiple linear regressionBP神经网络
BP neural networkRE/% NSE R2 RE/% NSE R2 RE/% NSE R2 率定期 Calibration period 7.93 0.46 0.66 8.27 0.59 0.71 3.81 0.62 0.89 验证期 Validation period 5.68 0.54 0.70 6.33 0.63 0.66 0.79 0.84 0.84 注:RE代表百分比偏差,又称相对误差;NSE是纳什效率系数;R2是拟合优度,反映模拟值与实测值的线性相关程度。Notes: RE represents percentage deviation, also known as relative error; NSE is the Nash efficiency coefficient; R2 is the goodness of fit, reflecting the degree of linear correlation between the simulated and the measured values. 表 3 1982—2006和2006—2020年气候变化与人类活动贡献率
Table 3 Contribution rates of climate change and human activities during 1982−2006 and 2006−2020
时期
PeriodQs Qi Qc ΔQc ΔQh ΔQ 气候变化贡献率
Contribution rate of climate change/%人类活动贡献率
Contribution rate of human activity/%1982—2006 16.90 16.71 15.09 1.81 −0.19 1.62 111.54 −11.54 2006—2020 20.24 20.08 15.09 5.15 −0.16 4.99 103.24 −3.24 注:ΔQ为分析期和基准期之间的年平均径流量变化,ΔQc和ΔQh分别代表这两个时期由于气候变化和人类活动引起的年平均径流量的变化;Qs表示分析期间的模拟径流;Qi和Qc分别代表分析期和基准期的观测径流。Notes: ΔQ represents the annual average runoff changes between the analysis period and the reference period, while ΔQc and ΔQh represent the changes in annual average runoff caused by climate change and human activities during these two periods, respectively. Qs represents the simulated runoff during the analysis period. Qi and Qc represent the observed runoff during the analysis period and the reference period, respectively. 表 4 1954—2020年黑河流域上游的降水、蒸散发和径流深度变化
Table 4 Precipitation, evapotranspiration, and runoff trends in the upper reaches of the Heihe River Basin during 1954−2020
不同时期
Different periodsP/mm ΔP/(mm·a−1)
ΔP/(mm·year−1)ET/mm ΔET/(mm·a−1)
ΔET/(mm·year−1)R/mm ΔR/(mm·a−1)
ΔR/(mm·year−1)1954—1982 103.34 0.55 1098.80 1.10 198.39 −18.30 1982—2006 115.32 −1.07 1093.09 1.96 219.70 33.01 2006—2020 130.71 0.45 1399.42 4.55 263.97 60.45 注:P代表年平均降水量,ΔP代表降水量的年际变化量,ET代表蒸散发,ΔET代表蒸散发年际变化量,R代表年平均径流深度,ΔR代表年际平均径流深度变化量。Notes: P represents annual average precipitation, ΔP represents interannual variation of precipitation, ET represents evapotranspiration, ΔET represents interannual variation of evapotranspiration, R represents annual runoff depth, and ΔR represents interannual average runoff depth variation. -
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