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Robust multi-objective optimization of gasifier and solid oxide fuel cell plant for electricity production using wood

机译:气化炉和固体氧化物燃料电池装置的稳健多目标优化,以利用木材发电

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摘要

Biomass is an attractive renewable and stored energy that can be converted to transportation fuels, chemicals and electricity using bio-chemical and thermo-chemical conversion routes. Notably, biofuels have relatively lower greenhouse gas emissions compared to the fossil fuels. A biomass gasifier can convert lignocellulosic biomass such as wood into syngas, which can be used in Solid Oxide Fuel Cell (SOFC) to produce heat and electricity. SOFC has very good thermodynamic conversion efficiency for converting methane or hydrogen into electricity, and integration of SOFC with gasifier gives heat integration opportunities that allow one to design systems with electricity production efficiencies as high as 70%. Generally, process design and operational optimization problems have conflicting performance objectives, and Multi-Objective Optimization (MOO) methods are applied to quantify the trade-offs among the objectives and to obtain the optimal values of design and operating parameters. This study optimizes biomass gasifier and SOFC plant for annual profit and annualized capital cost, simultaneously. A Pareto front has been obtained by solving Moo problem, and then net flow method is used to identify some optimal solutions from the Pareto front for the implementation into next phase. The constructed composite curves, which notify maximum amount of possible heat recovery, and first law efficiency also indicate better performance of the integrated plant. Uncertainty of market and operating parameters has been added to the optimization problem, and robust MOO of the integrated plant has been performed, which retains less sensitive Pareto solutions during the optimization. Finally, Pareto solutions obtained via normal and robust MOO approaches are considered for uncertainty analysis, and Pareto solutions obtained via robust MOO found to be less sensitive. (C) 2017 Elsevier Ltd. All rights reserved.
机译:生物质是一种有吸引力的可再生和存储的能源,可以通过生物化学和热化学转化途径转化为运输燃料,化学物质和电力。值得注意的是,与化石燃料相比,生物燃料的温室气体排放量相对较低。生物质气化炉可将木质纤维素生物质(例如木材)转化为合成气,可用于固体氧化物燃料电池(SOFC)中以产生热量和电能。 SOFC具有很好的将甲烷或氢转化为电能的热力学转化效率,并且SOFC与气化炉的集成提供了热集成机会,使人们可以设计发电效率高达70%的系统。通常,过程设计和操作优化问题具有相互矛盾的性能目标,因此采用多目标优化(MOO)方法来量化目标之间的折衷,并获得设计和操作参数的最佳值。该研究同时优化了生物质气化炉和SOFC装置的年利润和年化资本成本。通过求解Moo问题获得了Pareto前沿,然后使用净流方法从Pareto前沿识别出一些最佳解决方案,以实施到下一阶段。所构造的复合曲线可通知最大可能的热量回收,并且第一定律效率也表明集成工厂的性能更好。市场和操作参数的不确定性已添加到优化问题中,并且已执行了集成工厂的强大MOO,这在优化过程中保留了敏感性较低的Pareto解决方案。最后,将通过常规和鲁棒的MOO方法获得的Pareto解考虑用于不确定性分析,而通过鲁棒的MOO获得的Pareto解则较不敏感。 (C)2017 Elsevier Ltd.保留所有权利。

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