气候变化领域集成服务门户(CCPortal): Multi-attribute decision-making approach for Aegle marmelos pyrolysis process using TOPSIS and Grey Relational Analysis: Assessment of engine emissions through novel Infrared thermography

CCPortal

DOI	10.1016/j.jclepro.2019.06.188
	Multi-attribute decision-making approach for Aegle marmelos pyrolysis process using TOPSIS and Grey Relational Analysis: Assessment of engine emissions through novel Infrared thermography
	Baranitharan P.; Ramesh K.; Sakthivel R.
发表日期	2019
ISSN	9596526
起始页码	315
结束页码	328
卷号	234
英文摘要	This research focuses on the selection of the optimum process parameters for Aegel marmelos (AM) pyrolysis experiment based on multi-objective decision-making techniques. This investigation presents the optimization report for obtaining maximum pyrolysis oil from AM de-oiled seed cake through thermochemical conversion (pyrolysis) process. The pyrolysis process has been conducted according to L27 orthogonal array with chosen input control factors such as pyrolysis temperature (°C), heating rate (°C/min) and biomass particle size (mm). The output response parameters measured are the bio-oil yield, bio-char yield and biogas yield. The multi-objective decision-making approach namely Technique for order preference by similarity to ideal solution (TOPSIS) and Grey relational analysis (GRA) techniques are employed to determine the optimum pyrolysis process parameters to maximize the yield of AM bio-oil. The optimized values of pyrolysis temperature (PT), heating rate (HR) and feedstock particle size (PS) are 600 °C, 10 °C/min and 0.6 mm. At peak engine loading condition, 20% AM bio-oil + 80% diesel fuel blend (AM20) emit lower carbon dioxide (CO2 = 8.68%) and oxides of nitrogen (NOx = 1401 ppm) emissions as compared with diesel (D) CO2 (10.33%) and NOx (1511 ppm) emissions. The association between exhaust gas temperature and NOx emission was inferred using a novel approach of thermal imager by sensing the infrared rays from the hot surface of the exhaust port. Infrared thermal images are captured during the engine operations fuelled with bio-oil at the optimum pyrolysis conditions concluded by TOPSIS and GRA results (PT = 600 °C, HR = 10 °C/min and PS = 0.6 mm). According to the thermal imaging result, AM20 blend produces the lower amount of NOx emissions compared with neat diesel and it is suggested that AM bio-oil can be used as engine fuel instead in order to preserve the eco-system stability and biodiversity. Elsevier Ltd
英文关键词	Aegle marmelos; GRA; NOx emission; Pyrolysis; Thermal imager; TOPSIS
scopus关键词	Biodiversity; Carbon dioxide; Decision making; Diesel engines; Heating rate; Infrared radiation; Nitrogen oxides; Particle size; Particle size analysis; System stability; Thermography (imaging); Aegle marmelos; Grey relational analyses (GRA); Multi attribute decision making; Multi objective decision making; NOx emissions; Technique for order preference by similarity to ideal solutions; Thermal imagers; TOPSIS; Pyrolysis
来源期刊	Journal of Cleaner Production
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/176322
作者单位	Department of Mechanical Engineering, Government College of Technology, Coimbatore, 641013, India; Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India
推荐引用方式 GB/T 7714	Baranitharan P.,Ramesh K.,Sakthivel R.. Multi-attribute decision-making approach for Aegle marmelos pyrolysis process using TOPSIS and Grey Relational Analysis: Assessment of engine emissions through novel Infrared thermography[J],2019,234.
APA	Baranitharan P.,Ramesh K.,&Sakthivel R..(2019).Multi-attribute decision-making approach for Aegle marmelos pyrolysis process using TOPSIS and Grey Relational Analysis: Assessment of engine emissions through novel Infrared thermography.Journal of Cleaner Production,234.
MLA	Baranitharan P.,et al."Multi-attribute decision-making approach for Aegle marmelos pyrolysis process using TOPSIS and Grey Relational Analysis: Assessment of engine emissions through novel Infrared thermography".Journal of Cleaner Production 234(2019).