溶剂-溶质间协同竞争的物理化学耦合作用机制、调控及天然气脱硫溶剂分子组成智能化设计

Expediting the exploration of natural gas resource on a high safety, quality and efficiency level is a very important approach to building a Clean, Low-Carbon, Safe and Effective energy system. Traditionally, desulfurization solvents were developed by means of experimental screening as well as blending of components. However, such strategy cannot meet the requirement of natural gas exploration. There is an urgent need to explore an intelligent method for molecular composition design of solvent. This project is focused on mechanism of cooperative-competitive physical and chemical interplay between solvent and natural gas components by using simulation method coupled with multiple characterization techniques. The solvent-solute interplay is also expected to be regulated in present research proposal. By means of modeling calculation using COSMO-RS model coupled with experimental measurement, solubility parameters will be observed and a QSAR model can be established between solubility parameters and structural vectors of solvent molecules. In addition, applying Ab initio calculation assisted with experimental investigation, rate constants with respect to various reactions between solute and solvent molecules can be derived. Then a reaction network as well as its kinetics model can be obtained. Furthermore, the reaction network and the QSAR model will be combined to build an intelligent molecular composition design strategy for solvent development, which responds to the composition changes of the sulfides in raw materials. Such research not only provides a new and efficient approach to the molecular design and development of desulfurization solvent but also enriches the basic theory behind the complex interplay involved in solvent-solute systems.

加快天然气资源安全、优质、高效开发是构建“清洁低碳、安全高效”能源体系的重要途径。传统基于组分实验筛选及调配的脱硫溶剂研发已不能适应天然气开发需求，亟需发展智能化的溶剂组成设计方法。本课题拟通过计算模拟与多技术实验表征相结合研究并阐明溶剂与天然气组分间协同竞争的物理化学耦合作用机制，探索溶剂-溶质间交互作用的定向调控方法；采用COSMO-RS计算与实验相结合的方法获取溶解度参数，并建立溶解度参数与溶剂分子结构向量间QSAR模型；同时构建基于结构向量的溶剂溶质分子矩阵，利用Ab initio计算与实验相结合获取反应速率常数，并建立基于结构向量的溶剂-溶质分子间反应网络和动力学模型；进一步将反应网络与QSAR模型耦合，构建响应原料硫化物分布特征的智能化溶剂分子组成设计策略。为高效选择性脱硫溶剂设计开发发展新的研究思路和高效方法，同时拓展溶剂-溶质分子间复杂交互作用及硫化物选择性吸收的基础理论。

本项目针对传统脱硫溶剂开发效率无法适应行业发展要求、溶剂与有机硫等组分分子间相互作用的理论研究不足等问题，开展溶剂与天然气组分分子间复杂交互作用研究，以天然气中典型有机硫高效脱除的溶剂分子设计为实例，通过计算模拟与实验相结合的方法研究了溶剂与天然气中主要有机硫分子间相互作用；借助第一性原理分析，揭示了与硫醇和硫醚等有机硫溶解性能高度相关的溶剂分子描述符和关键分子信息，筛选有效分子描述符，构建了准确描述物理溶解度与溶剂分子结构参数的定量构效关系模型，揭示了溶剂分子结构参数对有机硫溶解性能的影响规律与调节机制；确定了COS与溶剂间的化学反应机理，构建了溶剂-溶质分子间反应网络，通过ab initio方法并结合实验获得反应动力学常数，自定义分子描述符构建可准确预测反应动力学常数的机器学习模型；阐明了复合溶剂与COS、硫醇等有机硫化物间的物理和化学耦合作用及其协同竞争溶解机制；在充分揭示有机硫溶解机理的基础上，开发了物理-化学耦合的有机硫溶解性能预测机器学习模型，构建了可响应原料有机硫分布变化的溶剂智能化设计策略；解决了脱硫溶剂组分有效设计及高效率开发中的关键科学问题和技术难点，同时丰富了溶剂-溶质组分间协同竞争的物理-化学耦合作用及选择性吸收基础理论；通过静态吸收平衡、吸收动力学及小试填料塔吸收性能评价等多尺度实验对模型的有机硫溶解性能预测效果进行检验；发展了脱硫溶剂智能化设计新方法，指导设计开发了系列新型高效脱硫溶剂，成功应用于油田伴生气、炼厂气等多套脱硫工业装置，解决了不同气源中有机硫高效吸收脱除技术难题，服务“川气东送”国家能源重点工程和新疆地区油气资源开发利用，对推动我国能源结构优化调整、实现“双碳”目标、助力经济和社会可持续发展、保护区域生态环境均具有重要意义。

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