基于煤中活性和惰性组分非岩相高效分离的炼焦配煤新方法及其机理研究

Coking coal blending method is one of the core and key issues to reduce the coke cost, improve the coke quality and optimize resource allocation of scarce coal of coking industry in China. Conventional blending methods using volatile (Vdaf), caking index (G) and maximum thickness of the glial layer (Y) as the indicators is mainly dominated by the experiences. When the coal types change or coal properties fluctuate, the method will seriously affect the coke quality. Relatively advanced coal maceral blending is also geographical limitations because there is not a wide range and reliable way to evaluate accurately the content and quality of the active ingredient with the binding property in coal. Based on the method of the separation all-component groups in coal developed by the applicant, the project will reasonably and efficiently separate the high sticky component, light sticky component and no-sticky component with different sticky activities from coal, and establish the corresponding indicators of evaluating accurately the degree, inert capacity and content of active component from a new angle of non-lithofacies. It aims to research and establish of the coke quality prediction model, propose the method of coking coal blending, simultaneously reveal deeply the coking mechanism based on the method to make the coking coal blending method acquire new breakthrough both in the technical and theoretical levels through the coking coal blending experiments.

炼焦配煤方法是我国焦化行业降低焦炭成本、提高焦炭质量、优化稀缺煤资源配置所需要解决的核心和关键问题之一。以挥发分Vdaf 、黏结指数G和最大胶质层厚度Y为指标的常规配煤方法，多由经验主导，当煤种变化或煤质出现波动时，便严重影响焦炭质量；相对先进的煤岩配煤，也因未找到一种广泛且可靠的办法来准确评价煤中有黏结性的活性组分的含量和质量，而存在着地域局限性。本项目将在申请者发明的煤全组分族分离方法基础上，从非岩相的新角度，合理、高效地分离出煤中具有不同黏结活性的强黏结组分、弱黏结组分和不黏结组分，并建立起能准确评价这些组分的活性程度、惰性能力及其含量的相应指标；通过配煤炼焦实验，研究并建立焦炭质量预测模型，提出炼焦配煤新方法；同时深度揭示基于该方法基础上的煤黏结成焦机理，使炼焦配煤方法在技术和理论层面均获得新的突破。

炼焦配煤方法是我国焦化行业降低焦炭成本、提高焦炭质量、优化稀缺煤资源配置所需要解决的核心和关键问题之一。以挥发分Vdaf 、黏结指数G和最大胶质层厚度Y为指标的常规配煤方法，多由经验主导，当煤种变化或煤质出现波动时，便严重影响焦炭质量；相对先进的煤岩配煤，也因未找到一种广泛且可靠的办法来准确评价煤中有黏结性的活性组分的含量和质量，而存在着地域局限性。本项目在发明的煤全组分族分离方法基础上，从非岩相的新角度，合理、高效地分离出了煤中具有不同黏结活性的强黏结组分、弱黏结组分和不黏结组分，建立了能较准确评价这些组分的活性程度、惰性能力及其含量的相应指标；通过配煤炼焦实验，研究并建立了焦炭质量预测模型，提出了炼焦配煤新方法；同时揭示了基于该方法基础上的煤黏结成焦机理。结果表明：萃取反萃取法是合适的非岩相的炼焦煤活性/惰性组分分离方法，由其获得的疏中质组和密中质组是非岩相的活性组分，而重质组是非岩相的惰性组分；其中疏中质组是提供煤热解膨胀性的来源物质，密中质组是提供煤热解流动性的来源物质，且疏中质组几乎不提供液相，而密中质组几乎不膨胀；膨胀质、流动质和惰性质三者间的适度配合是煤黏结成焦的关键所在；在炼焦煤范围内，脂肪链越短或支链化程度越高，越有利于形成煤的黏结性；含-OH（或-NH）的氢键缔合结构则可以与脂肪链协同作用于煤黏结性；煤黏结性形成的机理即在于这两部分的官能团结构特点，使其在热解过程中生成大量液相物质，从而非常有利于胶质体的流动、黏连和固化为半焦，并最终获得优越的黏结性；所设计的疏密比LDR和活惰比AIR指标可较好地反映煤的黏结能力；通过BP神经网络所构建的基于非岩相活性/惰性组分的焦炭质量预测模型及其炼焦配煤新方法，具有较高的预测精度，对指导生产实践有良好借鉴意义。