Process simulation based life cycle assessment for bioethanol production from cassava, cane molasses, and rice straw
Process simulation based life cycle assessment for bioethanol production from cassava, cane molasses, and rice straw
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基于过程模拟的木薯、甘蔗糖蜜和稻草生物乙醇生产生命周期评价
DOI：
10.1016/j.jclepro.2018.04.152
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发表时间：
2018-07
影响因子：
11.1
通讯作者：
M. Rathnayake;Thanapat Chaireongsirikul;A. Svang-ariyaskul;L. Lawtrakul;P. Toochinda
中科院分区：
文献类型：
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作者：
M. Rathnayake;Thanapat Chaireongsirikul;A. Svang-ariyaskul;L. Lawtrakul;P. Toochinda
本研究将制程模拟技术整合至生命周期评估中，以减少制程资料收集中的参数偏差，并在不同制程间进行公平的比较。以木薯、甘蔗糖蜜和稻草为原料生产生物乙醇的过程为例，说明了过程模拟的实用性。将模拟结果与实际生物乙醇装置数据进行比较，验证了该技术的可靠性。该研究是一项全面的LCA比较，基于模拟的无偏过程数据，以及其他设置中的最小变化，即，工艺燃料类型，废物回收方案等的结果表明，清洁的工艺设计，使用模拟与可再生工艺能源和可持续废物回收，提高能源效率，可再生性，和环境友好的三个方面的生物乙醇原料。木薯生物乙醇的净能量比（1.34），可再生性（5.16），并减少温室气体排放量（410千克CO2 eq/1000 L）的最佳值。无偏LCA结果的净能量分析，环境影响评价，包括排放/热点识别和敏感性分析进行了全面的讨论。在这项研究中的模拟技术也适用于未来的新的/修改后的工艺LCA。
The process simulation technique is integrated into life cycle assessment (LCA) in this study to reduce biased parameters in process data collection and perform a fair comparison among different processes. Bioethanol production processes from cassava, cane molasses, and rice straw are studied as an example, to show the usability of process simulation. The simulation results are compared with actual bioethanol plant data to validate the reliability of the technique. The study is a comprehensive LCA comparison, based on unbiased process data from simulations, and minimum variations in other settings, i.e., process fuel type, waste recovery options, etc. The results show that cleaner process designs, using simulations with renewable process energy and sustainable waste recovery, improve the energy efficiency, renewability, and environmentally-benign aspects of the three bioethanol feedstocks. Bioethanol from cassava shows the best values of net energy ratio (1.34), renewability (5.16), and reduced greenhouse gas emissions (410 kg CO2eq/1000 L). The unbiased LCA results from net energy analysis, and environmental impact assessment, including emissions/hotspot identification and sensitivity analysis, are comprehensively discussed. The simulation technique in this study is also adaptable for future LCA of new/modified processes.