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刘 浪,方治余,王双明,等. 煤矿充填固碳理论基础与技术构想[J]. 煤炭科学技术,2024,52(2):292−308

. DOI: 10.12438/cst.2023-1485
引用本文:

刘 浪,方治余,王双明,等. 煤矿充填固碳理论基础与技术构想[J]. 煤炭科学技术,2024,52(2):292−308

. DOI: 10.12438/cst.2023-1485

LIU Lang,FANG Zhiyu,WANG Shuangming,et al. Theoretical basis and technical conception of backfill carbon fixation in coal mine[J]. Coal Science and Technology,2024,52(2):292−308

. DOI: 10.12438/cst.2023-1485
Citation:

LIU Lang,FANG Zhiyu,WANG Shuangming,et al. Theoretical basis and technical conception of backfill carbon fixation in coal mine[J]. Coal Science and Technology,2024,52(2):292−308

. DOI: 10.12438/cst.2023-1485

煤矿充填固碳理论基础与技术构想

基金项目: 

国家自然科学基金资助项目(52074212, 51874229, 51674188)

详细信息
    作者简介:

    刘浪: (1985—),男,陕西靖边人,教授,博士生导师。Tel:029-85583143,E-mail:liulang@xust.edu.cn

    通讯作者:

    王双明: (1955—),男,陕西岐山人,中国工程院院士。Tel:029-85587131,E-mail:sxmtwsm@163.com

  • 中图分类号: TD801

Theoretical basis and technical conception of backfill carbon fixation in coal mine

Funds: 

National Natural Science Foundation of China(52074212, 51874229, 51674188)

  • 摘要:

    在国家“双碳”目标背景下,如何减少煤炭行业的碳排放、实现碳封存已成为亟待解决的难题。煤炭行业作为高碳化石能源生产者和主体碳排放源提供者,在生产和消费过程中引发的大宗固废堆存、大型采空区形成和大量CO2排放是制约煤炭可持续开发利用与绿色健康发展的瓶颈所在。为协同解决二氧化碳封存与矿山固废消纳问题,将大宗固废处置、固废高值化利用、CO2封存、采空区利用有机结合,提出了二氧化碳充填的理念,从碳汇能力评估角度界定了二氧化碳充填的3种类型。具体开展工作包括:① 分析了CO2充填料浆输运过程和矿化反应过程涉及到的基础理论,给出了各个过程的数学方程以及碳封存量计算公式,指出了温度、湿度等因素对矿化反应机理、碳封存量和充填体强度的影响规律。② 总结了现阶段CO2矿化的工艺方法、主要碱性工业固废的CO2封存能力和CO2矿化强化措施。在此基础上提出了基于直接湿法矿化和间接矿化的2种CO2充填材料制备工艺,满足矿井充填的流动性、凝固特性和强度要求。③ 针对CO2充填过程中的CO2物理封存问题,提出了窄条带式胶结充填和综采架后胶结充填2种技术路径,前者通过在弱充填条带中构筑多贯通孔隙的充填体CO2物理封存,后者借助充填支架和链式自行充填挡板在长壁工作面采空区中间断构筑充填带,控制顶板垮落,形成CO2物理化学封存空间。④ 为了评估CO2充填的碳平衡效果,依据全生命周期法界定了CO2充填中碳足迹及碳消纳的计算边界。然后,梳理了CO2充填过程中的碳足迹及碳消纳,分别考虑了CO2的来源、用量、损耗、转化等因素。给出了包括原料运输、充填料浆制备、井下注入与充填等过程中的碳足迹及碳消纳计算方法。研究成果有望降低CO2封存的能耗及成本,对煤炭绿色开采及其可持续开发利用具有深远的意义。

    Abstract:

    Under the national “dual carbon” goal, how to reduce the carbon emissions of the coal industry and achieve carbon storage has become an urgent problem to be solved. The coal industry is the producer of high carbon fossil energy and the main carbon emission source provider. In the process of production and consumption, the accumulation of bulk solid waste, the formation of large goaf and a large amount of CO2 emissions are the bottlenecks that restrict the sustainable development and utilization and the green and healthy development of the coal industry. In order to solve the problem of carbon dioxide storage and mine waste consumption, the bulk solid waste disposal, high-value solid waste utilization, CO2 storage and goaf utilization were organically combined, the concept of carbon dioxide backfill was put forward, and three types of dioxide backfill were defined from the perspective of carbon sink capacity assessment. ① The basic theories involved in the transportation process of CO2 filling slurry and mineralization reaction process are analyzed. The mathematical equations for each process and calculation formulas for carbon sequestration amount are provided. The influence of factors such as temperature and humidity on the mineralization reaction mechanism, carbon sequestration amount, and strength of the backfill body are pointed out. ② The carbonation technological approaches, CO2 sequestration capacity of major alkaline industrial solid wastes and enhancing measures of CO2 mineralization are summarized. On this premise, two types of CO2 backfill materials preparation technique based on direct carbonationand or indirect carbonationand are presented, which can fulfill the criterion of mine backfill in fluidity, solidification characteristics and strength. ③ To solve the problem of decomposed CO2 escaping during CO2 backfill, two technical paths of strip roadway paste backfilling and intermittent backfilling behind packed hydraulic support have been proposed. The former sequesters decomposed CO2 by constructing backfilling bodies with multiple through holes in the weak backfilling strip, while the latter uses packed hydraulic support and chain self-filling baffling to construct backfilling strips in longwall goaf to control roof caving and form a CO2 physicochemical storage space. ④ In order to evaluate the carbon balance effect of CO2 backfill, the calculation boundary of carbon footprint and carbon sequestration in CO2 backfill was defined according to the life cycle method, including the stages of raw material mining, transportation, processing, injection, solidification, etc. Then, the carbon footprint and carbon sequestration in the CO2 backfill process were sorted out, and factors such as the source, dosage, loss, and conversion of CO2 were considered. Next, the calculation methods of carbon footprint and carbon sequestration in the processes of raw material transportation, filling slurry preparation, underground injection and filling were given. The research results are expected to reduce the energy consumption and cost of CO2 storage, and have far-reaching significance for green coal mining and sustainable development and utilization.

  • 煤炭在我国一次能源结构中的占比仍超过50%,是保障我国能源安全的压舱石[1],而随着开采深度的增加,煤层赋存地质环境愈加恶劣,加之复杂的开采扰动影响,导致底板奥灰水突涌风险加剧,严重威胁着深部煤层安全高效回采[2]。据不完全统计,华北地区约20%的煤炭资源遭受底板岩溶水的影响[3],存在带压开采问题,以邯郸和邢台地区的煤矿尤甚,一旦管理不当常常诱发工作面突水灾害。为此,国内外学者开展诸多研究[4-6],并取得了可喜成果。其中突水系数理论得到了广泛认可和应用,底板有效隔水层厚度是突水系数理论的关键参数之一,合理确定底板破坏深度对煤层底板突水风险评价[7-9]具有重要意义。

    煤层底板破坏深度传统计算方法有:现场实测法[10],公式法[11-12],数值模拟法[13-14]以及室内模型试验[15]等。近年来,机器学习被广泛引入到煤层底板破坏深度预测研究中,于小鸽等[16]提出的BP神经网络预测模型;施青龙等[17]构建了PCA-GWO-BP (Principal Component Analysis - Grey Wolf Optimization - Back Propagation Neural Network)神经网络预测模型;邵良杉等[18]构建了基于PSO-ELM-Boosting (Particle Swarm Optimization- Extreme Learning Machine- Boosting)的底板破坏深度预测模型;WANG等[19]基于模糊神经网络建立了底板破坏深度预测模型。相比传统方法,智能预测模型考虑了更多的影响因素,对原始数据进行了更深层次的挖掘;预测精度和效率都有很大提高。但上述成果中没有考虑底板奥灰水的影响。同时煤层底板破坏相关实测数据的获取还存在着成本高、难度大以及随机性强等问题。少量的样本难以覆盖整个有效空间,存在信息不完整和不充足等缺陷。小样本问题严重影响煤层底板破坏深度预测模型的精度。通过撷取小样本数据间隙中存在的潜在信息,产生适当数量虚拟样本,即虚拟样本生成技术 (Virtual Sample Generation, VSG),实现小样本数据增强,进而能够提高训练样本对总体数据特征的表征能力和模型的学习与泛化能力。

    基于整体趋势扩散的虚拟样本生成技术 (Mega-Trend-Diffusion, MTD)[20-21]是一种常用的VSG技术,其属于随机样本生成技术的一种改进形式,可实现对真实数据信息间隔填补,主要通过隶属函数来估计样本变量的真实数据空间,即通过隶属函数计算其相应的虚拟样本信息的左边界LB和右边界RB,从而在该范围内生成虚拟样本信息。但是该方法较少考虑真实样本与虚拟样本之间变量的相似特征,从而导致预测模型的过度拟合现象。此外,实际中先验知识的获取存在一定的困难性,实测样本中的变量之间往往并非完全独立,导致虚拟样本的生成难以满足变量间的独立性假设。且由于虚拟样本的引入存在误差的累积效应,虚拟样本的生成数量也是影响模型预测精度的因素之一。综上,少量实测样本、变量独立性以及虚拟样本生成数量均是影响煤层底板破坏深度预测模型精度的关键因素,如何消除原始样本数据特征量之间的关联性、生成与实测样本信息分布类似的虚拟样本以及确定合理的虚拟样本数量等问题值得进一步深入研究。

    鉴于此,基于文献[22]调研获得带压开采煤层底板破坏深度实测数据和影响因子集,共计50组数据;借助PCA算法优化实测数据样本空间,消除原始数据样本中各变量间的相关性;分析PCA (Principal Component Analysis)优化后的数据样本中主成分分布特征,通过引入基于MTD类分布生成技术,生成类似分布的虚拟样本,并通过K-S (Kolmogorov-Smirnov)检验验证虚拟样本与实测样本分布的相似性,进而实现对实测数据小样本的扩充;采用SaDE-ELM混合优化算法,生成虚拟样本输出,构建影响煤层底板破坏深度的混合数据样本;基于SaDE-ELM、GA-PSO-BP、BP 3种算法,构建底板破坏深度预测模型,对比分析数据增强前后的模型预测精度。相关研究成果对煤层底板突水风险评价和预测预警能力的提升具有一定的支撑作用。

    采用已搜集到的50组煤层底板破坏深度相关数据,见文献[22]。实测数据集中包含底板破坏深度影响因子和评价指标集2部分。其中影响因子集包含工程地质因素 (导水构造发育程度f1、陷落柱发育程度f2、断层发育程度f3和断层落差H1)、水文地质因素 (底板含水层水压力P、初始底板隔水层厚度h、隔水层砂性百分比p1、隔水层泥性百分比p2、隔水层灰性百分比p3、断裂导水特性k1、构造充水性k2和最大涌水量Q)和工作面开采条件 (开采深度H、煤层倾角α、煤层厚度M、工作面长度L1、走向长度L2、工作面采高m、月推进步距L3) 等19种影响因子。值得说明的是,此处的初始底板隔水层厚度是指地质勘探的结果,不包含开采扰动的影响。

    对实测数据集进行皮尔逊相关性检验,如图1所示。由图分析可知标有星号(*)的某些变量间的确存在相关性,如底板含水层水压力P和初始底板隔水层厚度h,工作面采高m和开采深度H、最大涌水量Q等。考虑到MTD类分布生成技术的独立性假设要求,采用PCA算法对50组原始数据样本进行主成分分析,降低样本数据之间的关联性。借助SPSS软件对影响因子集进行PCA降维优化处理,确定协方差矩阵R的特征值及对应的特征向量,结果见表1表2。根据主成分累计贡献率选取前9个主成分作为输入变量,并根据式(1)计算各主成分。

    图  1  影响因子相关性热图
    Figure  1.  Influence factor correlation heat map
    表  1  总方差解释
    Table  1.  Total variance explanation
    主成分 特征值 方差百分比/% 累积/%
    5.541 29.163 29.163
    2.464 12.971 42.134
    2.121 11.163 53.297
    1.534 8.072 61.370
    1.322 6.957 68.327
    1.215 6.396 74.723
    0.933 4.912 79.635
    0.823 4.331 83.966
    0.743 3.910 87.876
    0.588 3.097 90.973
    ⅩⅠ 0.453 2.382 93.355
    ⅩⅡ 0.310 1.633 94.989
    ⅩⅢ 0.286 1.508 96.497
    ⅩⅣ 0.267 1.404 97.901
    ⅩⅤ 0.176 0.925 98.827
    ⅩⅥ 0.122 0.641 99.468
    ⅩⅦ 0.079 0.415 99.882
    ⅩⅧ 0.022 0.118 100.000
    ⅩⅨ 0.000 0.000 100.000
    下载: 导出CSV 
    | 显示表格
    表  2  成分矩阵
    Table  2.  Component matrix
    主成分
    f1 0.377 0.526 −0.410 0.078 0.086 0.324 0.131 −0.246 0.072
    f2 −0.039 0.159 −0.346 −0.009 0.350 0.613 −0.325 0.429 0.123
    f3 0.035 0.521 0.098 0.386 0.106 0.366 0.052 −0.405 −0.354
    H1 0.620 0.202 −0.237 0.097 −0.408 0.043 −0.109 0.076 −0.347
    P 0.695 0.070 0.300 0.220 −0.348 0.091 −0.271 0.193 −0.061
    h 0.661 −0.042 0.187 0.221 −0.387 0.125 −0.342 −0.014 0.194
    p1 0.453 −0.162 −0.768 0.204 0.159 −0.265 −0.071 −0.008 0.052
    p2 −0.515 0.167 0.323 −0.147 −0.575 0.327 0.308 0.068 0.133
    p3 −0.036 0.034 0.751 −0.124 0.482 −0.011 −0.279 −0.073 −0.245
    k1 0.280 0.755 −0.161 −0.148 −0.003 −0.325 −0.147 0.064 0.030
    k2 0.219 0.741 0.150 0.148 0.046 −0.301 0.052 −0.145 0.314
    Q 0.662 −0.087 0.151 0.182 0.318 0.233 0.387 0.213 −0.021
    H 0.789 0.098 0.408 −0.027 0.140 −0.224 −0.005 0.125 0.011
    α −0.116 0.151 0.283 0.745 0.125 −0.091 0.260 0.276 0.185
    M 0.782 −0.464 −0.056 0.157 −0.064 −0.030 0.125 −0.098 −0.143
    L1 0.629 −0.064 0.245 −0.312 0.092 0.194 −0.163 −0.234 0.346
    L2 0.717 0.017 −0.031 −0.452 0.062 0.193 0.266 −0.085 0.120
    m 0.765 −0.515 0.038 0.109 0.026 0.002 0.107 −0.138 0.068
    L3 0.559 0.380 0.003 −0.473 −0.021 −0.118 0.248 0.330 −0.247
    下载: 导出CSV 
    | 显示表格
    $$ \boldsymbol{A}_{\rm{m k}}=\left[{a}_{{ij}}^{\prime}\right]_{{m} \times {n}} \frac{\boldsymbol{W}_{\rm{n k}}}{\sqrt{{\boldsymbol{\lambda}}_{{j}}}} $$ (1)

    式中:${a}_{{ij}}^{\prime} $为标准化后的数据,i=1,2,…,mj=1,2,…,nm为样本个数;n为样本特征数;Amk为样本的主成分;Wnk为特征向量根据特征值大小按列降序排列,选取前k列组成的矩阵;λj 为特征向量,j=1,2,…,n

    SPXY (Sample set partitioning based on joint x-y distance) 算法 [23]是广泛应用的数据集划分方法,是基于KS (Kennard-Stone) 算法提出的一种改进方法,该方法能够同时计算不同样本的x向量方向和y向量方向的欧氏距离,并通过正则化将xy方向的距离结合,更加全面的评估和划分数据集,能够有效地减小过拟合问题,提高机器学习模型的泛化能力。笔者采用该方法进行样本的训练集和测试集划分。

    1)基于MTD类分布生成技术的基本原理

    MTD的基本原理[20-21]图2所示,所生成的虚拟样本的左边界LB和右边界RB按式(2)和式(3)计算。

    图  2  MTD原理
    Figure  2.  MTD schematic diagram
    $$ \mathrm{LB}=\left\{ \begin{array}{cc} {\mathrm{C L}}-{Skew}_{\mathrm{L}} \times \sqrt{-2 \times \dfrac{\hat{S}_x^2}{N_{\mathrm{L}}} \times \ln \left(10^{-20}\right)} & {\mathrm{L B}} \leqslant \min \\ \mathrm{min} & \mathrm{LB}>\min \end{array}\right. $$ (2)
    $$ \mathrm{RB}=\left\{ \begin{array}{cc} {\mathrm{C L}}+{Skew}_{\mathrm{R}} \times \sqrt{-2 \times \dfrac{\hat{S}_x^2}{N_{\mathrm{R}}} \times \ln \left(10^{-20}\right)} & {\mathrm{R B}} \geqslant \text {min} \\ \min & \mathrm{RB}< \mathrm{min} \end{array}\right. $$ (3)

    其中:

    $$ {\mathrm{CL}}=(\max-\min)/2 $$
    $$ {Skew}_{{\mathrm{L}}}=\frac{{N}_{{\mathrm{L}}}}{{N}_{{\mathrm{L}}}+{N}_{{\mathrm{R}}}} $$
    $$ {Skew}_{{\mathrm{R}}}=\frac{{N}_{{\mathrm{R}}}}{{N}_{{\mathrm{L}}}+{N}_{{\mathrm{R}}}} $$
    $$ {\hat{S}}_{x}^{2}=\frac{\displaystyle\sum _{i=1}^{n}{\left({x}_{i}-\bar{x}\right)}^{2}}{n-1} $$

    式中:min 为样本特征最小边界;max为样本特征最大边界;LB 为虚拟样本左边界;CL 为样本数据中心点;RB 为虚拟样本右边界;NLNR分别为中心点数据CL左边和右边的样本数量;${\hat{S}}_{x}^{2} $为样本方差;xi 为样本,i=1,2,…,n;$\bar x $为样本均值;n 为样本数量。

    一般情况下,虚拟样本的生成方法采用插值法,根据插值方法的不同可分为三角分布插值法,均匀分布插值法、正态分布插值法以及混合插值方法。采用上述方法生成的虚拟样本中的变量分布类型为既定类型,没有考虑到真实样本数据的潜在分布规律等情况的影响,虚拟样本与真实样本的分布不同,训练出来的模型可能会过度拟合虚拟样本的特征,无法准确预测真实样本。因此基于MTD提出类分布虚拟样本生成技术,其核心是通过分析实测样本数据潜在分布类型,进而生成与实测样本相似分布的虚拟样本集,可按照式(4)计算生成。

    $$ X_{V S G}={\mathrm{L B}}+g({\mathrm{R B}}-{\mathrm{L B}})+\Delta x$$ (4)

    式中:g为与实测样本分布相似的随机数,g = (0,1);$\Delta x $为修正量,由于真实样本存在边界分布不平衡的问题,可能出现少量极端样本,这会导致MTD生成的拓展域上的虚拟样本不能很好的反应实测样本的分布,生成的虚拟样本分布情况与实测样本存在一定偏移,故对虚拟样本增加一个修正因子$\Delta x $。

    2)实测样本训练集的潜在分布特征

    实测样本训练集中主成分Ⅰ~Ⅸ的频率直方图和累计分布曲线,如图3所示。由图中分析可知,除主成分Ⅰ出现明显两端分布大、中间分布少的特点外,其他主成分分布两头低、中间高,与正态分布相似。故主成分Ⅰ采用双正态分布组合的形式进行模拟,其他主成分采用正态分布进行模拟。

    图  3  主成分的频率直方图和累计分布曲线
    Figure  3.  Frequency histogram and cumulative distribution curve of principal components

    3)虚拟样本合理性K-S检验

    为进一步验证生成的虚拟样本与实测样本分布的相似性,采用K-S检验方法[24]对虚拟样本与实测样本训练集进行分布检验,该方法适用于样本量小的非参数检验。

    设原假设:两个样本来自同一连续分布;备择假设:两个样本来自不同的连续分布。虚拟样本生成数量分别为50、200和500,限于图幅要求,文中仅给出虚拟样本数量为200时,虚拟样本与实测样本中主成分的累积分布曲线对比结果,如图4所示。由图中分析可知,九个主成分的分布显著性水平均在0.8以上,故接受虚拟样本与实测样本同分布假设。

    图  4  虚拟样本数为200时实测样本和虚拟样本中的主成分累积分布曲线对比分析
    Figure  4.  Comparative analysis of principal component cumulative distribution curves in measured samples and virtual samples with a virtual sample size of 200

    3)虚拟样本输出生成

    通过SPXY算法划分生成的训练集为 (xy),采用MTD类分布技术获得得到的虚拟样本为 (Txy(T(x)))。为了给虚拟样本输入生成更合理的虚拟样本输出,首先采用SaDE-ELM算法对训练集 (xy) 进行回归训练,通过调整模型超参数,使预测均方根误差RMSE降低到0.1以下,利用训练好的模型为虚拟样本生成输出值。

    将虚拟样本集与训练集组成的的混合训练集,分别采用SaDE-ELM、BP、GA-PSO-BP 3种智能算法构建数据增强前后的带压开采煤层底板破坏深度预测模型,并对模型预测精度进行对比分析,模型实现的具体流程如图5所示。

    图  5  模型实现流程
    Figure  5.  Model implementation process

    为验证引入虚拟样本对模型的优化效果,采用绝对误差Ea、相对误差δ、均方根误差RMSE三个指标评价模型预测精度,评价指标的计算方法见式(5)—式(7)。

    $$ {{\mathrm{E}}}_{{\mathrm{a}}}=\left|{\widehat{b}}_{{\mathrm{i}}}-{b}_{{\mathrm{i}}}\right| $$ (5)
    $$\text{ δ} =\frac{\left|{\widehat{b}}_{{\mathrm{i}}}-{b}_{{\mathrm{i}}}\right|}{{b}_{{\mathrm{i}}}}\times 100\% $$ (6)
    $$ {\mathrm{RMSE}}=\sqrt{\frac{\displaystyle\sum _{i}^{n}{\left({\widehat{b}}_{{\mathrm{i}}}-{b}_{{\mathrm{i}}}\right)}^{2}}{n-1}} $$ (7)

    式中:n为测试集样本个数;Ea 为绝对误差;δ 为相对误差;RMSE 为均方根误差;bi 为真实输出;$ {\widehat{b}}_{{\mathrm{i}}} $ 为预测输出。

    虚拟样本的数量对模型计算效率和预测精度具有一定程度的影响,不同虚拟样本数量下模型的误差分布变化规律,如图6所示。由图中分析可知,虚拟样本数量超过80后,模型预测误差变化基本稳定,而未考虑虚拟样本增强的模型预测误差始终波动变化,综合考虑计算效率及模型预测误差的影响,本次分析中虚拟样本数量均取100。

    图  6  模型预测误差随虚拟样本数量的变化规律
    Figure  6.  Variation of model prediction error with number of virtual samples

    根据SPXY算法样本集划分结果,对黑山矿3号(真实破坏深度:9.34 m)、夏庄矿3号(7.83 m)、夏庄矿5号(12.58 m)、夏庄矿7号(9.34 m)、夏庄矿8号(13.66 m)、夏庄矿10号(13.66 m)、夏庄矿11号(13.66 m)、夏庄矿12号(11.50 m)、夏庄矿13号(6.10 m)、双山大井4号(8.80 m)10个样本进行预测。为避免模型的随机性,进行5次试验,结果取平均。采用虚拟样本增强前后,各模型的误差对比分析结果如图7图9表3所示。由图表分析可知,数据增强前,SaDE-ELM模型预测的底板破坏深度分别为9.33577.868112.59409.254913.625513.584613.679311.55096.12088.8046 m,GA-PSO-BP模型预测的底板破坏深度分别为9.55887.779012.71429.262413.526213.640013.490111.36075.98808.7881 m,BP模型预测的底板破坏深度分别为9.46676.512210.80618.358212.721911.721911.63539.21936.14968.0830 m;数据增强后,SaDE-ELM模型预测的底板破坏深度分别为9.34357.840112.56489.310613.648613.613513.705911.52286.11148.8015 m,GA-PSO-BP模型预测的底板破坏深度分别为9.25297.733912.64559.274513.713413.585513.556711.49196.30558.7799 m,BP模型预测的底板破坏深度分别为10.11147.954212.88559.684612.361813.713213.656911.49986.75258.8053 m。相较于虚拟样本增强前,增强后SaDE-ELM模型、GA-PSO-BP模型、BP模型的平均绝对误差分别降低约42.95%、27.09%、70.7%,平均相对误差分别降低约47.08%、16.3%、65.9%,均方根误差分别降低约51.27%、27.77%、36.46%,可见采用虚拟样本增强后的预测模型精度显著提升,且SaDE-ELM模型预测精度最优。

    图  7  模型预测结果
    Figure  7.  Model prediction results
    图  8  模型预测绝对误差
    Figure  8.  Absolute error of model prediction
    图  9  模型预测相对误差
    Figure  9.  Relative error of model prediction
    表  3  模型预测误差
    Table  3.  Model predictive error
    预测性能指标 数据样本 SaDE-ELM GA-PSO-BP BP
    误差 误差降低/% 误差 误差降低/% 误差 误差降低/%
    平均绝对误差/m 实测训练集 0.034678 42.95328 0.106853 27.09214 1.214402 70.69971
    增强训练集 0.019783 0.077904 0.355823
    平均相对误差/% 实测训练集 0.333537 47.08235 1.044385 16.30162 10.74378 65.89809
    增强训练集 0.1765 0.874134 3.663832
    均方根误差 实测训练集 0.057211 51.27344 0.2437 27.7698 1.539483 36.45541
    增强训练集 0.027877 0.176025 0.978258
    下载: 导出CSV 
    | 显示表格

    以邯郸云驾岭煤矿九号煤层为研究对象。根据云驾岭矿9煤层工程地质调查结果,目前正在生产的工作面为19105工作面。相邻的19103工作面已被完全开采。19105工作面存在多处局部断层,最小落差为1 m。煤底板以砂岩为主,抗拉强度5 MPa,单轴抗压强度33.26 MPa。煤层下方存在奥陶系灰岩含水层,工作面距该含水层顶界面31.41 m。含水层底部界面水压为1.49 MPa。工作面采动期间,平均涌水量约为5 m3/h,最大涌水量约为10 m3/h。根据MENG等[25]p1p2p3的值由式(8)计算得出。根据地质勘探结果和MENG[25]的方法计算,可以确定19105工作面影响因子值,见表4

    表  4  19105工作面影响因子值
    Table  4.  Impact factor values of 19105 working face
    影响因子 影响因子 影响因子
    f1 0.5 P /MPa 1.49 H /m 346
    f2 0 hl /m 34.41 α 13
    f3 1 p1 1 M /m 2.88
    H1 /m 1 p2 0 L1 /m 69
    Q /(m3·h−1) 10 p3 0 L2 /m 199
    k1 0.5 m1 /m 2.88
    k2 0.5 L3 /m 30
    下载: 导出CSV 
    | 显示表格
    $$ {p}_{{\mathrm{i}}}\text=\frac{{h}_{\text{i}}}{{h}_{1}}\text{}\text{×100\%} $$ (8)

    式中:pi 为岩层厚度占不透水层总厚度的比例;hi 为层状岩层厚度。

    采用规范经验方法(CWIC)[25]、基于滑移场理论的解析解(ASSF)[26]和本方法对19105工作面底板突水风险进行预测和评估,如图10所示。可以看出,对于云驾岭煤矿19105工作面实际工况,本方法预测结果为13.4794 m,CWIC预测结果为8.09 m,ASSF预测结果为12.85 m。所提方法的预测结果略大于CWIC和ASSF,主要是综合考虑了地质和建设因素的影响。该方法的评价结果偏向危险性,有利于工程安全。

    图  10  19105工作面破坏深度预测结果对比
    Figure  10.  Comparison of failure depth prediction results of 19105 working face

    1)针对底板破坏深度实测样本量少、预测模型精度低和泛化能力弱等问题,通过采用MTD类分布虚拟样本生成技术,增强煤层底板破坏深度实测样本,通过K-S检验方法检验了虚拟样本数据集的合理性。

    2)采用SaDE-ELM、GA-PSO-BP、BP 3种算法,构建了虚拟样本增强前后煤层底板破坏深度预测模型。

    3)采用虚拟样本增强后的煤层底板破坏深度预测模型精度显著提高,模型预测误差可降低42.95%~51.27%,其中基于MTD类分布小样本增强的PCA-SaDE-ELM预测模型的预测效果最优。

    4)相较于其他方法,所提方法预测获得的云驾岭19105工作面底板破坏深度相对较大,有利于工作面安全生产管理。

  • 图  1   煤矿二氧化碳充填示意

    Figure  1.   CO2 backfill diagram of coal mine

    图  2   充填体内物质分布及矿化反应示意

    Figure  2.   Schematic of material distribution and mineralization reaction in the backfill

    图  3   钢渣溶解、反应及强度形成示意[29]

    Figure  3.   Schematic of steel slag dissolution, reaction, and strength formation[29]

    图  4   直接湿法矿化法制备CO2充填材料工艺示意

    Figure  4.   Process diagram of CO2 backfill material prepared by direct aqueous carbonation method

    图  5   间接矿化法制备CO2充填材料工艺示意

    Figure  5.   Process diagram of CO2 backfill material prepared by indirect carbonation method

    图  6   CO2充填技术

    Figure  6.   CO2 backfill technology

    图  7   CO2条带式巷道胶结充填

    Figure  7.   CO2 strip roadway cemented backfilling

    图  8   综采架后间断充填方法

    Figure  8.   Intermittent backfilling behind packed hydraulic support

    图  9   链式自行充填挡板的概念简图

    Figure  9.   Conceptual diagram of chain self-walking backfilling baffle

    图  10   CO2充填全生命周期的碳足迹及碳消纳

    Figure  10.   Carbon footprint and carbon absorption throughout the life cycle of CO2 backfill

    图  11   CO2充填全生命周期计算边界

    Figure  11.   LCA calculation boundary of CO2 backfill

    表  1   充填体内主要发生的水化/矿化反应[10,19-22]

    Table  1   The main hydration/mineralization reactions occurring in the backfill[10,19-22]

    项目 化学式
    水化反应 $ {\text{CaO}} + {{\text{H}}_2}{\text{O}}\xrightarrow{{{r_{{\text{H,CaO}}}}}}{\text{Ca}}{\left( {{\text{OH}}} \right)_2} $
    $ {\text{2}}{{\text{C}}_{\text{3}}}{\text{S}} + {\text{6}}{{\text{H}}_{\text{2}}}{\text{O}}\xrightarrow{{{r_{{\text{H,}}{{\text{C}}_{\text{3}}}{\text{S}}}}}}{{{\rm{C-S-H}} + 3{\rm{Ca}}}}{\left( {{\text{OH}}} \right)_{\text{2}}} $
    $ {\text{2}}{{\text{C}}_{\text{2}}}{\text{S}} + {\text{4}}{{\text{H}}_{\text{2}}}{\text{O}}\xrightarrow{{{r_{{\text{H,}}{{\text{C}}_{\text{2}}}{\text{S}}}}}}{{{\rm{C-S-H}} + {\mathrm{Ca}}}}{\left( {{\text{OH}}} \right)_{\text{2}}} $
    $ \begin{gathered} {{\text{C}}_{\text{3}}}{\text{A}} + {\text{3}}\left( {{\text{CaS}}{{\text{O}}_{\text{4}}} \cdot {\text{2}}{{\text{H}}_{\text{2}}}{\text{O}}} \right) + {\text{26}}{{\text{H}}_{\text{2}}}{\text{O}}\xrightarrow{{{r_{{\text{H,}}{{\text{C}}_{\text{3}}}{\text{A}}}}}} \\ {\text{3CaO}} \cdot {\text{A}}{{\text{l}}_{\text{2}}}{{\text{O}}_{\text{3}}} \cdot {\text{CaS}}{{\text{O}}_{\text{4}}} \cdot {\text{32}}{{\text{H}}_{\text{2}}}{\text{O}} \\ \end{gathered} $
    $ \begin{array}{l}{\text{C}}_{\text{4}}\text{AF}+\text{2Ca}{\left(\text{OH}\right)}_{\text{2}}+\text{2}\left({\text{CaSO}}_{\text{4}}\cdot {\text{2H}}_{\text{2}}\text{O}\right)+{\text{18H}}_{\text{2}}\text{O} \xrightarrow{{r}_{\text{H},{\text{C}}_{\text{4}}\text{AF}}}\\ \text{6CaO}\cdot {\text{Al}}_{\text{2}}{\text{O}}_{\text{3}}\cdot {\text{Fe}}_{\text{2}}{\text{O}}_{\text{3}}\cdot {\text{2CaSO}}_{\text{4}}\cdot {\text{24H}}_{\text{2}}\text{O}\end{array} $
    $ {\text{MgO}} + {{\text{H}}_2}{\text{O}}\xrightarrow{{{r_{{\text{H,MgO}}}}}}{\text{Mg}}{\left( {{\text{OH}}} \right)_2} $
    矿化反应 $ {{\text{C}}_{\text{3}}}{\text{S}} + {\text{3C}}{{\text{O}}_{\text{2}}} + n{{\text{H}}_{\text{2}}}{\text{O}}\xrightarrow{{{r_{{\text{C,}}{{\text{C}}_{\text{3}}}{\text{S}}}}}}{\text{Si}}{{\text{O}}_{\text{2}}} \cdot n{{\text{H}}_{\text{2}}}{\text{O}} + {\text{3CaC}}{{\text{O}}_{\text{3}}} $
    $ {{\text{C}}_{\text{2}}}{\text{S + 2C}}{{\text{O}}_{\text{2}}} + n{{\text{H}}_{\text{2}}}{\text{O}}\xrightarrow{{{r_{{\text{C,}}{{\text{C}}_{\text{2}}}{\text{S}}}}}}{\text{Si}}{{\text{O}}_{\text{2}}} \cdot n{{\text{H}}_{\text{2}}}{\text{O + 2CaC}}{{\text{O}}_{\text{3}}} $
    $ {\rm{C-S-H}} + 3{{\text{CO}}_{\text{2}}}\xrightarrow{{{r_{{\text{C,CSH1}}}}}}3{\text{CaC}}{{\text{O}}_{\text{3}}} \cdot {\text{2Si}}{{\text{O}}_{\text{2}}} \cdot {\text{3}}{{\text{H}}_{\text{2}}}{\text{O}} $
    $ {{{\rm{C-S-H}}}}+{{\rm{CO}}_{\text{2}}}\xrightarrow{{{r_{{\text{C,CSH2}}}}}}{\text{CaC}}{{\text{O}}_{\text{3}}} + {\text{Si}}{{\text{O}}_{\text{2}}} \cdot {{\text{H}}_{\text{2}}}{\text{O}} $
    $ {\text{Ca}}{\left( {{\text{OH}}} \right)_{\text{2}}}{\text{ + C}}{{\text{O}}_{\text{2}}}\xrightarrow{{{r_{{\text{C,CH}}}}}}{\text{CaC}}{{\text{O}}_{\text{3}}}{\text{ + }}{{\text{H}}_{\text{2}}}{\text{O}} $
    $ {\text{Mg}}{\left( {{\text{OH}}} \right)_{\text{2}}}{\text{ + 2C}}{{\text{O}}_{\text{2}}}\xrightarrow{{{r_{{\text{C,MH1}}}}}}{\text{Mg}}{\left( {{\text{HC}}{{\text{O}}_{\text{3}}}} \right)_2} $
    $ {\text{Mg}}{\left( {{\text{HC}}{{\text{O}}_{\text{3}}}} \right)_2}{\text{ + 2}}{{\text{H}}_2}{\text{O}}\xrightarrow{{{r_{{\text{C,MH2}}}}}}{\text{MgC}}{{\text{O}}_3} \cdot {\text{3}}{{\text{H}}_{\text{2}}}{\text{O + C}}{{\text{O}}_{\text{2}}} \uparrow $
    $ {\text{5Mg}}{\left( {{\text{HC}}{{\text{O}}_{\text{3}}}} \right)_2}\xrightarrow{{{r_{{\text{C,MH3}}}}}} {\text{ 4MgC}}{{\text{O}}_3} \cdot {\text{Mg}}{\left( {{\text{OH}}} \right)_2} \cdot 4{{\text{H}}_{\text{2}}}{\text{O + 6C}}{{\text{O}}_{\text{2}}} \uparrow $
    下载: 导出CSV

    表  2   主要碱性工业固废的CO2封存能力

    Table  2   CO2 sequestration of major alkaline industrial solid wastes

    序号 碱性工业固废 原料质量分数/% CO2封存量/(kg·t−1) 文献
    CaO MgO
    1 粉煤灰 5.68~31.95 4.5~230 [4146]
    2 钢渣 34.29~64.73 5.75~6.33 26~361 [39, 42, 45, 4748]
    3 电石渣 82.1~90.9 382.21~613.4 [42, 45]
    4 镁渣 50.98 11.27 221.4 [49]
    5 石膏 30~32.49 224 [50]
    下载: 导出CSV

    表  3   二氧化碳充填技术碳足迹评估参数

    Table  3   Carbon footprint assessment parameters of CO2 backfill technology

    参数 参数说明 参数值 文献来源
    FE 电力碳排放因子 0.5703 tCO2/MWh [68]
    VLH1 柴油平均低位热值 4.265 2×107 MJ/t [69]
    CC1 柴油单位热值含碳量 2.02×10–2 tC/GJ [69]
    FO1 柴油氧化率 98% [69]
    Ect CO2管道运输能耗 1.3 kWh/(t·km) [70]
    CC1 CO2公路运输能耗(柴油) 0.184 8 kg/(t·km) [71]
    下载: 导出CSV

    表  4   不同植被与土壤碳密度[72]

    Table  4   Carbon density of different vegetation and soil[72]

    植被类型植被地上碳
    密度/(kg·m−2)
    植被地下碳
    密度/(kg·m−2)
    土壤有机碳
    密度/(kg·m−2)
    草地0.09300.78301.404 8
    湿地0.30802.484 21.626 1
    灌丛0.974 20.768 61.778 3
    农田1.434 9
    下载: 导出CSV
  • [1] 桑树勋,刘世奇,朱前林,等. CO2地质封存潜力与能源资源协同的技术基础研究进展[J]. 煤炭学报,2023,48(7):2700−2716.

    SANHG Shuxun,LIU Shiqi,ZHU Qianlin,et al. Research progress on technical basis of synergy between CO2 geological storage potential and energy resources[J]. Journal of China Coal Society,2023,48(7):2700−2716.

    [2] 谢和平,刘 涛,吴一凡,等. CO2的能源化利用技术进展与展望[J]. 工程科学与技术,2022,54(1):145−156.

    XIE Heping,LIU Tao,WU Yifan,et al. Progress and prospect of CO2 energy utilization technology[J]. Advanced Engineering Sciences,2022,54(1):145−156.

    [3] 王双明,刘浪,赵玉娇,等. “双碳”目标下赋煤区新能源开发:未来煤矿转型升级新路径[J]. 煤炭科学技术,2023,51(1):59−79.

    WANG Shuangming,LIU Lang,ZHAO Yujiao,et al. New energy exploitation in coal-endowed areas under the target of “double carbon”: a new path for transformation and upgrading of coal mines in the future[J]. Coal Science and Technology,2023,51(1):59−79.

    [4] 李 强,艾 锋,王 玺,等. 煤基固废协同矿山土壤生态修复的理论解析与实践探索:以陕西榆林市为例[J]. 西北地质,2023,56(3):70−77.

    LI Qiang,AI Feng,WANG Xi,et al. Theoretical analysis and practical exploration on ecological restoration of mines with multi-source solid wastes:example from Yulin City,Shaanxi Province[J]. Northwestern Geology,2023,56(3):70−77.

    [5] 张吉雄,张 强,周 楠,等. 煤基固废充填开采技术研究进展与展望[J]. 煤炭学报,2022,47(12):4167−4181.

    ZHANG Jixiong,ZHANG Qiang,ZHOU Nan,et al. Research progress and prospect of coal based solid waste backfilling mining technology[J]. Journal of China Coal Society,2022,47(12):4167−4181.

    [6] 杨 科,赵新元,何 祥,等. 多源煤基固废绿色充填基础理论与技术体系[J]. 煤炭学报,2022,47(12):4201−4216.

    YANG Ke,ZHAO Xinyuan,HE Xiang,et al. Basic theory and key technology of multi-source cola based solid waste for green backfilling[J]. Journal of China Coal Society,2022,47(12):4201−4216.

    [7] 柳晓娟,侯华丽,武 强,等. 绿色矿山经济效益核算理论与实证:以矿井充填开采技术为例[J]. 中国矿业,2022,31(9):61−67. doi: 10.12075/j.issn.1004-4051.2022.09.022

    LIU Xiaojuan,HOU Huali,WU Qiang,et al. Theory and empirical study on green mine economic benefit account taking the filling mining technology of coal mine as an example[J]. China Mining Magazine,2022,31(9):61−67. doi: 10.12075/j.issn.1004-4051.2022.09.022

    [8]

    YIN Shenghua,YAN Zepeng,CHEN Xun,et al. Active roof-contact:The future development of cemented paste backfill[J]. Construction and Building Materials,2023,370:130657.

    [9]

    FENG Yabin,QI Wenyue,ZHAO Qingxin,et al. Synthesis and characterization of cemented paste backfill:Reuse of multiple solid wastes[J]. Journal of Cleaner Production,2023,383:135376.

    [10] 刘 浪,王双明,朱梦博,等. 基于功能性充填的CO2储库构筑与封存方法探索[J]. 煤炭学报,2022,47(3):1072−1086.

    LIU Lang,WANG Shuangming,ZHU Mengbo,et al. CO2 storage-cavern construction and storage method based on functional backfill[J]. Journal of China Coal Society,2022,47(3):1072−1086.

    [11] 段圆圆. 煤基固废协同利用制备采空区充填膏体试验研究[D]. 包头:内蒙古科技大学,2021.

    DUAN Yuanyuan. Experimental study on preparation of goaf filling paste by synergistic utilization of coal-based solid waste[D]. Baotou:Inner Mongolia University of Science & Technology,2021.

    [12]

    LIU Shiqi,LIU Tong,ZHENG Sijian,et al. Evaluation of carbon dioxide geological sequestration potential in coal mining area[J]. International Journal of Greenhouse Gas Control,2023,122:103814.

    [13]

    CHEN Jiangzhi,MEI Shenghua. Gas-saturated carbon dioxide hydrates above sub-seabed carbon sequestration site and the formation of self-sealing cap[J]. Gas Science and Engineering,2023,111:204913.

    [14]

    CHEN Jing,XING Yi,WANG Yan,et al. Application of iron and steel slags in mitigating greenhouse gas emissions:A review[J]. Science of The Total Environment,2022,844:157041.

    [15] 方治余. 高温深井下含冰粒充填料浆流动沉降规律研究[D]. 西安:西安科技大学,2020.

    FANG Zhiyu. Investigation on the flow and settlement law of ice-containing cemented paste backfill slurry in high temperature and deep well[D]. Xi’an:Xian University of Science and Technology,2020.

    [16] 王 乐. 污水处理构筑物内多相流数值模拟及机理研究[D]. 成都:西南交通大学,2018.

    WANG Le. Numerical simulation and mechanism study of multiphase fluid dynamics in sewage treatment structures[D]. Chengdu:Southwest Jiaotong University,2018.

    [17] 刘志双. 充填料浆流变特性及其输送管道磨损研究[D]. 北京:中国矿业大学(北京),2018.

    LIU Zhishuang. Study on rheological properties of filling slurry and wear of conveying pipeline[D]. Beijing:China University of mining and technology-Beijing,2018.

    [18]

    Fluent 14.5. Theory Giuide [M]. Canonsburg,PA:Ansys Inc.,2012.

    [19] 王 鹏,CHEN Shen’en,陈占清,等. 二氧化碳在多孔水泥充填材料中的扩散与反应动力学响应[J]. 采矿与安全工程学报,2019,36(2):381−387.

    WANG Peng,CHEN Shen’en,CHEN Zhanqing. Dynamic response of carbon dioxide diffusion and reaction in porous cementitious back-filling material[J]. Journal of Mining & Safety Engineering,2019,36(2):381−387.

    [20]

    CHEN T,GAO X,QIN L. Mathematical modeling of accelerated carbonation curing of portland cement paste at early age[J]. Cement and Concrete Research,2019,120:187−197. doi: 10.1016/j.cemconres.2019.03.025

    [21]

    KASHEF–HAGHIGHI S,SHAO Y,GHOSHAL S. Mathematical modeling of CO2 uptake by concrete during accelerated carbonation curing[J]. Cement and Concrete Research,2015,67:1−10. doi: 10.1016/j.cemconres.2014.07.020

    [22] 陈闵敏,孙玉柱,宋兴福,等. 氢氧化镁碳化过程研究[J]. 华东理工大学学报(自然科学版),2022,48(5):600−608.

    CHEN Minmin,SUN Yuzhu,SONG Xingfu,et al. Carbonization process of magnesium hydroxide[J]. Journal of East China University of Science and Technology,2022,48(5):600−608.

    [23]

    ASHRAF W. Carbonation of cement-based materials:challenges and opportunities[J]. Construction & Building Materials,2016,120:558−570.

    [24]

    ZHANG D,GHOULEH Z,SHAO Y. Review on carbonation curing of cement-based materials[J]. Journal of CO2 Utilization,2017,21:119−131. doi: 10.1016/j.jcou.2017.07.003

    [25]

    MO L,ZHANG F,DENG M,et al. Effectiveness of using CO2 pressure to enhance the carbonation of Porland cement-fly ash-MgO mortars[J]. Cement and Concrete Composites,2016,70:78−85. doi: 10.1016/j.cemconcomp.2016.03.013

    [26]

    SHI C,WU Y. Studies on some factors affecting CO2 curing of lightweight concrete products[J]. Resources Conservation & Recycling,2008,52(8/9):1087−1092.

    [27]

    EL–HASSAN H,SHAO Y,GHOULEH Z. Effect of initial curing on carbonation of lightweight concrete masonry units[J]. ACI Materials Journal,2013,110(4):441−450.

    [28]

    UNLUER C,AL–TABBAA A. Enhancing the carbonation of MgO cement porous blocks through improved curing conditions[J]. Cement and Concrete Research,2014,59:55−65. doi: 10.1016/j.cemconres.2014.02.005

    [29]

    NIELSEN P,BOONE MA,HORCKMANS L,et al. Accelerated carbonation of steel slag monoliths at low CO2 pressure–microstructure and strength development[J]. Journal of CO2 Utilization,2020,36:124−134. doi: 10.1016/j.jcou.2019.10.022

    [30]

    SEIFRITZ W. CO2 disposal by means of silicates[J]. Nature,1990,345:486

    [31]

    LACKNER KS,WENDT CH,BUTT DP,et al. Carbon dioxide disposal in carbonate minerals[J]. Energy,1995,20(11):1153−1170. doi: 10.1016/0360-5442(95)00071-N

    [32] 王宗华,张军营,徐 俊,等. CO2矿物碳酸化隔离的理论研究[J]. 工程热物理学报,2008,29(6):1063−1068.

    WANG Zonghua,ZHANG Junying,XU Jun,et al. A theoretical study on mineral carbonation for CO2 sequestration[J]. Journal of Engineering Thermophy-sics,2008,29(6):1063−1068.

    [33]

    RENFORTH P,Washbourne CL,TAYLDER J,et al. Silicate production and availability for mineral carbonation[J]. Environ Sci Technol,2011,45(6):2035−2041. doi: 10.1021/es103241w

    [34] 张兵兵,王慧敏,曾尚红,等. 二氧化碳矿物封存技术现状及展望[J]. 化工进展,2012,31(9):2075−2083.

    ZHANG bingbing,WANG Huimin,ZENG Shanghong,et al. Current status and outlook of carbon dioxide mineral carbonation technologies[J]. Chemical Industryand Engineering Progress,2012,31(9):2075−2083.

    [35]

    DAVAL D,SISSMANN O,MENGUY N,et al. Influence of amorphous silica layer formation on the dissolution rate of olivine at 90°C and elevated pCO2[J]. Chemical Geology,2011,284(1-2):193−209. doi: 10.1016/j.chemgeo.2011.02.021

    [36]

    HEMMATI A,SHAYEGAN J,BU J,et al. Process optimization for mineral carbonation in aqueous phase[J]. International Journal of Mineral Processing,2014,130:20−27. doi: 10.1016/j.minpro.2014.05.007

    [37]

    HEMMATI A,SHAYEGAN J,SHARRATT P,et al. Solid products characterization in a multi-step mineralization process[J]. Chemical Engineering Journal,2014,252:210−219. doi: 10.1016/j.cej.2014.04.112

    [38] 任京伟,王 涛,陈雨雷,等. CO2矿化研究现状及应用潜力[J]. 地球科学,2020,45(7):2413−2425.

    REN Jingwei,WANG Tao,CHEN Yulei,et al. Research status and application potential of CO2 mineralization[J]. Earth Science,2020,45(7):2413−2425.

    [39]

    RAHMANIHANZAKI M,HEMMATI A. A review of mineral carbonation by alkaline solidwaste[J]. International Journal of Greenhouse Gas Control,2022,121.

    [40] 冉武平,张永太,艾贤臣,等. 工业固体废弃物矿化封存CO2研究综述[J]. 科学技术与工程,2023,32(16):6718−6727.

    RAN Wuping,ZHANG Yongtai,AI Xianchen,et al. Review of CO2 sequestration research in industrial solid waste mineralization[J]. Science Technology and Engineering,2023,32(16):6718−6727.

    [41] 王秋华,吴嘉帅,张卫风. 碱性工业固废矿化封存二氧化碳研究进展[J]. 化工进展,2023,42(3):1572−1582.

    WANG Qiuhua,WU Jiashuai,ZHANG Weifeng. Research progress of alkaline industrial solid wastes mineralization for carbon dioxide sequestration[J]. Chemical Industry and Engineering Progress,2023,42(3):1572−1582.

    [42] 张亚朋,崔龙鹏,刘艳芳,等. 3 种典型工业固废的CO2 矿化封存性能[J]. 环境工程学报,2021,15(7):2344−2355.

    ZHANG Yapeng,CUI Longpeng,LIU Yanfang,et al. Comparison of three typical industrial solid wastes on the performance of CO2 mineralization and sequestration[J]. Chinese Journal of Environmental Engineering,2021,15(7):2344−2355.

    [43] 王晓龙,刘 蓉,纪 龙,等. 利用粉煤灰与可循环碳酸盐直接捕集固定电厂烟气中二氧化碳的液相矿化法[J]. 中国电机工程学报,2018,38(19):5787−5794.

    WANG Xiaolong,LIU Rong,JI Long,et al. A new direct aqueous mineralization process using fly ash and recyclable carbonate salts to capture and storage CO2 from flue-gas[J]. Proceedings of the CSEE,2018,38(19):5787−5794.

    [44] 蔡洁莹,李向东,李海红,等. 电厂粉煤灰固定二氧化碳实验研究[J]. 煤炭转化,2019,42(1):87−94.

    CAI Jieying,LI Xiangdong,LI Haihong et al. Experimental study on solidification of carbon dioxide by coal fly ash in power plant[J]. Coal Conversion,2019,42(1):87−94.

    [45] 武 鸽,刘艳芳,崔龙鹏,等. 典型工业固体废物碳酸化反应性能的比较[J]. 石油学报(石油加工),2020,36(1):169−178. doi: 10.3969/j.issn.1001-8719.2020.01.021

    WU Ge,LIU Yanfang,CUI Longpeng,et al. Comparison of the carbonation reaction properties of typical industrial solid wastes[J]. Acta Petrolei Sinica(Petroleum Processing Section),2020,36(1):169−178. doi: 10.3969/j.issn.1001-8719.2020.01.021

    [46]

    MAZZELLA A,ERRICO M,SPIGA D. CO2 uptake capacity of coal fly ash:Influence of pressure and temperature on direct gas-solid carbonation[J]. Journal of Environmental Chemical Engineering,2016,4(4):4120−4128. doi: 10.1016/j.jece.2016.09.020

    [47]

    YADAV S,MEHRA A. Experimental study of dissolution of minerals and CO2 sequestration in steel slag[J]. Waste Management,2017,64:348−357. doi: 10.1016/j.wasman.2017.03.032

    [48]

    CHENG C,HUANG W,XU H,et al. CO2 sequestration and CaCO3 recovery with steel slag by a novel two-step leaching and carbonation method[J]. Science of The Total Environment,2023,891.

    [49]

    YE J,LIU S,ZHAO Y,et al. Development of ultrafine mineral admixture from magnesium slag and sequestration of CO2[J]. Buildings,2023,13(1):204.

    [50]

    DING W,CHEN Q,SUN H,et al. Modified mineral carbonation of phosphogypsum for CO2 sequestration[J]. Journal of CO2 Utilization,2019,34:507−515. doi: 10.1016/j.jcou.2019.08.002

    [51] 王中辉,苏 胜,尹子骏,等. CO2矿化及吸收–矿化一体化(IAM)方法研究进展[J]. 化工进展,2021,40(4):2318−2327.

    WANG Zhonghui,SU Sheng,YIN Zijun,et al. Research progress of CO2 mineralization and integrated absorption-mineralization(IAM) method[J]. Chemical Industry And Engineering Progress,2021,40(4):2318−2327.

    [52]

    TEIR S,REVITZER H,ELONEVA S,et al. Dissolution of natural serpentinite in mineral and organic acids[J]. International Journal of Mineral Processing,2007,83(1/2):36−46. doi: 10.1016/j.minpro.2007.04.001

    [53]

    ALEXANDER G,Mercedes Maroto-VALER M,Gafarova-AKSOY P. Evaluation of reaction variables in the dissolution of serpentine for mineral carbonation[J]. Fuel,2007,86(1/2):273−281. doi: 10.1016/j.fuel.2006.04.034

    [54]

    KAKIZAWA M,YAMASAKI A,YANAGISAWA Y. A new CO2 disposal process via artificial weathering of calcium silicate accelerated by acetic acid[J]. Energy,2001,26(4):341−354. doi: 10.1016/S0360-5442(01)00005-6

    [55]

    KUSAKA E,SUEHIRO R,IWAMIZU Y. Kinetics of calcium leaching from particulate steelmaking slag in acetic acid solution[J]. ISIJ International,2022,62(1):263−274. doi: 10.2355/isijinternational.ISIJINT-2021-121

    [56]

    MIAO E,DU Y,ZHENG X,et al. CO2 sequestration by direct mineral carbonation of municipal solid waste incinerator fly ash in ammonium salt solution:Performance evaluation and reaction kinetics[J]. Separation and Purification Technology,2023,309:123103. doi: 10.1016/j.seppur.2023.123103

    [57]

    O’Connor WK,Dahlin DC,Nilsen DN,et al. Carbon dioxide sequestration by direct mineral carbonation with carbonic acid[C]. Proceedings of the 25th International Technical Conference on Coal Utilization & FUEL Systems. Clearwater,Florida,UNITED States,2000.

    [58]

    PARK A HA,FAN L S. CO2 mineral sequestration:physically activated dissolution of serpentine and pH swing process[J]. Chemical Engineering Science,2004,59(22/23):5241−5247. doi: 10.1016/j.ces.2004.09.008

    [59]

    SOONG Y,Goodman AL,McCarthy-Jones JR,et al. Experimental and simulation studies on mineral trapping of CO2 with brine[J]. Energy Conversion and Management,2004,45(11/12):1845−1859. doi: 10.1016/j.enconman.2003.09.029

    [60]

    TEIR S,KUUSIK R,Fogelholm C–J,et al. Production of magnesium carbonates from serpentinite for long-term storage of CO2[J]. International Journal of Mineral Processing,2007,85(1/3):1−15. doi: 10.1016/j.minpro.2007.08.007

    [61]

    SOONG Y,Fauth DL,Howard BH,et al. CO2 sequestration with brine solution and fly ashes[J]. Energy Conversion and Management,2006,47(13/14):1676−1685. doi: 10.1016/j.enconman.2005.10.021

    [62] 纪 龙. 利用粉煤灰矿化封存二氧化碳的研究[D]. 北京:中国矿业大学(北京),2018.

    JI Long. Carbon dioxide sequestration by mineralisation of coal fly ash[D]. Beijing:China University of Mining & Technology−Beijing,2018.

    [63] 朱梦博,刘 浪,王双明,等. 短–长壁工作面充填无煤柱开采方法研究[J]. 采矿与安全工程学报,2022,39(6):1116−1124.

    ZHU Mengbo,LIU Lang,WANG Shuangming,et al. Short and long walls backfilling pillarless coal mining method[J]. Journal of Mining & Safety Engineering,2022,39(6):1116−1124.

    [64]

    ZHU Mengbo,XIE Geng,LIU Lang,et al. Strengthening mechanism of granulated blast-furnace slag on the uniaxial compressive strength of modified magnesium slag-based cemented backfilling material[J]. Process Safety and Environmental Protection,2023,174:722−733. doi: 10.1016/j.psep.2023.04.031

    [65]

    ZHANG Yongnian,PAN Jinghu,ZHANG Yongjiao,et al. Spatial-temporal characteristics and decoupling effects of China's carbon footprint based on multi-source data[J]. Journal of Geographical Sciences,2021,31(3):327−349. doi: 10.1007/s11442-021-1839-7

    [66] 董 雪,柯水发. 国内外碳足迹计算方法,评估标准及研究进展[C]// 绿色经济与林业发展论——第六届中国林业技术经济理论与实践论坛论文集,2012:1–9.

    DONG Xue,KE Shuifa. Methods,assessment standards and research progress of carbon footprint at China and abroad[C]// Green Economy And Forestry Development Forum – Proceedings of the Sixth China Forestry Technology and Economic Theory and Practice Forum,2012:1–9.

    [67] 杨博宇,白中科. 碳中和背景下煤矿区土地生态系统碳源/汇研究进展及其消纳对策[J]. 中国矿业,2021,30(5):1−9. doi: 10.12075/j.issn.1004-4051.2021.05.028

    YANG Boyu,BAI Zhongke. Research advances and emission reduction measures in carbon source and sink of land ecosystems in coal mining area under the carbon neutrality[J]. China Mining Magazine,2021,30(5):1−9. doi: 10.12075/j.issn.1004-4051.2021.05.028

    [68] 中华人民共和国生态环境部办公厅. 关于做好2023—2025年发电行业企业温室气体排放报告管理有关工作的通知[EB/OL]. [2023–02–07]. https://www.mee.gov.cn/xxgk2018/xxgk/xxgk06/202302/t20230207_1015569.html.
    [69] 国家统计局能源统计司. 中国能源统计年鉴2022[M]. 北京:中国统计出版社,2023.
    [70] 彭松水,陆诗建. CCS–EOR项目碳净消纳量方法学模型[J]. 油气田地面工程,2015,34(4):9−11.

    PENG Songshui,LU Shijian. Methodology model for carbon net consumption of CCS–EOR project[J]. Oil-Gas Field Surface Engineering,2015,34(4):9−11.

    [71] 顾清华,张 媛,卢才武,等. 低碳限制下综合成本最小的露天矿卡车运输优化研究[J]. 金属矿山,2019(8):157−161.

    GU Qinghua,ZHANG Yuan,LU Caiwu,et al. Truck transportation optimization research under the constraints of low carbon with the lowest comprehensive cost in open-pit mine[J]. Metal Mine,2019(8):157−161.

    [72] 徐 丽,何念鹏,于贵瑞,2010s中国陆地生态系统碳密度数据集[J]. 中国科学数据(中英文网络版),2019,4(1):90–96.

    XU Li,HE Nianpeng,YU Guirui. A dataset of carbon density in Chinese terrestrial ecosystems (2010s)[J]. Chinese scientific data,2019,4(1):90–96.

    [73]

    FANG Zhiyu,LIU Lang,ZHANG Xiaoyan,et al. Carbonation curing of modified magnesium-coal based solid waste backfill material for CO2 sequestration[J]. Process Safety and Environmental Protection,2023. https://doi.org/10.1016/j.psep.2023.10.049.

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  • 收稿日期:  2023-12-19
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