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一种创新的太阳能-木质纤维素生物质集成多联产系统，结合生物炼制与固体氧化物电解池
An innovative optimal integrated solar-lignocellulosic biomass polygeneration system with biorefinery and solid oxide electrolyzer cell
语言:
中文摘要
摘要 多联产系统通过整合多种能源和工艺，在统一系统中生产多种产品，以满足居民和工业需求，相较于单一工艺系统具有更高的可持续性和韧性。优化此类系统可最大化能源资源的利用效率。本研究提出了一种综合性的多联产系统，能够生产八种产品，以满足电力、制冷、供热以及淡水、氢气、氧气、二氧化碳和乙醇等副产品的需求。该系统基于两种可再生能源——太阳能和生物质能，并结合传统的甲烷能源。通过对能量、㶲、㶲经济、㶲环境、能值经济和能值环境等多个维度进行综合评估，对该系统进行了全面评价。采用MATLAB进行数学建模，评估热力、经济和生态因素以实现优化。通过Thermoflex和Aspen Plus对来自玉米秸秆的乙醇与电力联产生物炼制过程进行模型验证。多目标优化采用机器学习与遗传算法相结合的方法，分两个阶段实施。第一阶段针对混合多效蒸馏吸附脱盐单元的三个目标进行优化，旨在提高淡水产量、性能比和降低能耗。结果表明，在最优工况下，淡水产率提高了8.2%，性能比提高了10.2%，单位能耗降低了9.2%。第二阶段对整个系统进行优化，目标是在最大化热力学性能的同时，最小化经济和环境指标。六目标优化结果显示，系统整体能量效率提高了2.54%，整体㶲效率提高了3.38%，喷射式制冷循环的性能系数改善了59.66%；同时，整体㶲成本降低了1.88%，整体环境影响减少了39.78%，系统的整体能值消耗降低了19.77%。因此，所采用的优化方法被证实是实现能源资源更高效利用的有效工具。
English Abstract
Abstract Polygeneration systems combine various energy sources and processes to produce multiple products for residential and industrial needs in a unified system, offering enhanced sustainability and resilience compared to single-process systems. Optimizing these systems maximizes energy resource utilization. This study introduces a comprehensive polygeneration system generating eight products to satisfy power, cooling, heating, and secondary product requirements like fresh water, hydrogen, oxygen, carbon dioxide, and ethanol. The system is based on two renewable energy sources, solar and biomass, as well as traditional methane energy. Through a comprehensive evaluation encompassing energy, exergy, exergo-economic, exergo-environmental, emergo-economic, and emergo-environmental considerations, the system is thoroughly evaluated. Mathematical modeling in MATLAB assesses thermal, economic, and ecological factors for optimization. Validation is performed using Thermoflex and Aspen Plus for an ethanol and power co-production biorefinery from corn stover. Multi-objective optimization employs a machine learning and genetic algorithm approach in two phases. The initial phase targets three objectives for the hybrid multi-effect distillation adsorption desalination unit to optimize fresh water production, performance ratio, and energy consumption. Our results demonstrate that in the optimal scenario, there is an 8.2% increase in fresh water production rate, a 10.2% increase in performance ratio, and a 9.2% reduction in specific energy consumption. In the second phase, the overall system is optimized with the objective of maximizing thermodynamic performance while minimizing economic and environmental indicators. The six-objective optimization results in a 2.54% increase in overall energy efficiency, a 3.38% increase in overall exergy efficiency, and a 59.66% improvement in the coefficient of performance of the ejector refrigeration cycle. Additionally, it leads to a 1.88% reduction in overall exergy costs, a 39.78% reduction in overall environmental impacts, and a 19.77% reduction in overall emergy for the system. Therefore, the selected optimization approach has been recognized as a valuable tool for achieving more efficient utilization of energy resources.
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SunView 深度解读
该多能互补系统整合太阳能与生物质能源,对阳光电源ST储能系统和SG光伏逆变器产品线具有重要参考价值。固体氧化物电解池制氢技术可与我司储能PCS协同,实现电-氢耦合优化。研究中的多目标优化方法(机器学习+遗传算法)可应用于iSolarCloud平台,提升能效2.54%、降低成本1.88%的成果验证了智能优化算法在多能源系统中的价值,为工商业光储氢一体化解决方案提供技术路径,支撑PowerTitan储能系统在综合能源场景的深度应用。