Knowledge Management System Of Institute of process engineering,CAS
生物甲烷技术通过厌氧发酵将低劣生物质转化为甲烷产品，兼具节能、减排、减污三重意义，是环境和可再生能源领域的研究热点。目前，生物甲烷技术的研究主要集中于单元技术的开发和优化，而同时考虑系统经济效益和环境影响的多目标优化还未见报道。基于上述背景，本论文建立了生物甲烷全系统的能耗、经济性、绿色度和产气率模型，并以生物甲烷系统的经济效益和环境影响为目标，进行了多目标优化。基于优化结果，研究了产气规模对系统能量-环境-经济指标的影响。本研究为生物甲烷技术的规模化应用提供了科学依据，具有重要的实用价值和理论意义。本论文主要研究成果如下：（1）设计了包含九种原料组成、三种供热技术和四种脱碳工艺的生物甲烷网络系统。基于文献和实验数据，建立了关键单元能耗、经济性、绿色度和产气率计算模型；基于流程模拟结果，建立了脱碳工艺关键参数的经验模型。为生物甲烷系统全流程模拟和优化提供了模型基础。（2）建立了以净现值（NPV）和绿色度（GD）为目标函数、以技术组合和工艺条件为决策变量的多目标优化模型。采用带精英策略的快速非支配排序遗传算法（NSGA-II）求解多目标优化问题，获得了Pareto解集。分析了最优技术组合、工艺条件以及系统的成本、能耗、绿色度分布。结果表明，优化后生物甲烷系统在经济效益和环境影响上均具有较高可行性；成本最高的设备为厌氧发酵罐；成本最高的单元为脱碳单元；能耗最高的单元依次为厌氧发酵单元、脱碳单元和沼液沼渣处理单元；厌氧发酵和沼液沼渣处理单元绿色度为正，其余单元绿色度为负。（3）基于优化结果，分析了两种发酵温度和七种发酵规模下系统的能量-环境-经济指标。结果表明，增加产气规模能提高系统能效和单位绿色度，降低生产成本，提高发酵温度，有助于提高能效和降低成本。高温发酵较中温发酵能效提高7.4%，成本降低3%，但绿色度降低10%。;Biomass waste via anaerobic digestion to biomethane can achieve energy saving, CO2 reduction and pollutant reduction simultaneously, hence has been accepted as a hot spot in the research fields of environment and renewable energy. At present, the research on biomethane technology mainly focus on the development and optimization of unit technology. However, multi-objective optimization for biomethane production system with respect to economic and environmental performance has not been reported. Thus, models of energy consumption, economic performance, green degree and biogas yield were established in this work for each processing section. Afterwards, the economic and environmental performance of biomethane production system were optimized and analyzed. Based on the optimization results, the influence of production scale on energy-environment-economy indicators was studied. The work provides scientific basis for the large application of biomethane technology and has important practical and theoretical significance. The main results of this thesis are as follows:(1) A biomethane network system including 9 kinds of feedstocks, 3 kinds of heat supply technologies and 4 kinds of decarbonization technologies was designed in this work. Mathematical models of energy consumption, economic benefit, green degree and biogas yield, were established for important processing sections based on literature and experiment data. Based on process simulation results, empirical models of key parameters has been established for decarbonization technologies. It provides model bases for simulation and optimization of biomethane production system.(2) We established a multi-objective optimization model for biomethane production system with net present value (NPV) and green degree (GD) as objective functions and technology combinations and process parameters as decision variables. Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to solve the model, from which Pareto solutions were obtained. Afterwards, we analyzed the optimal technology combinations as well as the distribution of investment cost, energy consumption and green degree. The results showed that the biomethane production system after optimization has relative high economic and environmental feasibility. The most expensive equipment is anaerobic digester; the most expensive section is anaerobic digestion; the most energy consuming section is anaerobic digestion, followed by decarbonization and waste management. Processing sections of anaerobic digestion and waste management has positive green degree while other sections presents negative green degree values.(3) We analyzed energy-environment-economy performance of biomethane production system under two digestion temperature and seven process scales. The results showed that the increase of biomethane production can improve the energy efficiency and specific green degree, and decrease the cost of methane production and digestion liquid processing. Energy efficiency increases with digestion temperature while production cost decreases. Compared with mesophilic digestion, the energy efficiency of thermophilic digestion increased by 7.4%, the methane production cost decreased 3%, the specific green degree decreased 10%.
|李维俊. 生物甲烷系统的全流程模拟与多目标优化[D]. 中国科学院研究生院,2018.|
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