Gaussian smoothing strategy of thermogravimetric data of biomass materials in an air atmosphere

Liu, N; Chen, H; Shu, L; Zong, R; Yao, B; Statheropoulos, M

dc.contributor.author	Liu, N	en
dc.contributor.author	Chen, H	en
dc.contributor.author	Shu, L	en
dc.contributor.author	Zong, R	en
dc.contributor.author	Yao, B	en
dc.contributor.author	Statheropoulos, M	en
dc.date.accessioned	2014-03-01T01:20:33Z
dc.date.available	2014-03-01T01:20:33Z
dc.date.issued	2004	en
dc.identifier.issn	0888-5885	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15957
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-3242717666&partnerID=40&md5=b8593dc110e8f6cbca4b2cf59d38c646	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.other	Algorithms	en
dc.subject.other	Atmospheric composition	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Reaction kinetics	en
dc.subject.other	Thermogravimetric analysis	en
dc.subject.other	Gaussian smoothing	en
dc.subject.other	Kinetic parameters	en
dc.subject.other	Biomass	en
dc.subject.other	air	en
dc.subject.other	air	en
dc.subject.other	algorithm	en
dc.subject.other	article	en
dc.subject.other	atmosphere	en
dc.subject.other	biomass	en
dc.subject.other	decomposition	en
dc.subject.other	differential thermogravimetric curve	en
dc.subject.other	Gaussian smoothing	en
dc.subject.other	kinetics	en
dc.subject.other	mathematical optimum range	en
dc.subject.other	normal distribution	en
dc.subject.other	simulation	en
dc.subject.other	statistical analysis	en
dc.subject.other	statistical parameters	en
dc.subject.other	thermogravimetry	en
dc.title	Gaussian smoothing strategy of thermogravimetric data of biomass materials in an air atmosphere	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	In differential analysis, noises may induce high fluctuations in the differential thermogravimetric curve (DTG) curve and thereby induce considerable errors in the evaluation of kinetic parameters. This paper studies the strategy of using the Gaussian smoothing algorithm in the pretreatment of biomass decomposition data. Special attention is paid to the choice of the smoothing parameter a in both mathematical and kinetic senses. A mathematical optimum range (MOR), 3-5 less than or equal to a less than or equal to 10, is definitely suggested to achieve a high enough mathematical smoothing degree while keeping the data distortion at a low level. By simulation analysis, it is found that, in the range of MOR, lower values of a can achieve the best fit to the expected DTG peaks. The smoothing parameters within the central section of 3-10 are highly suggested to achieve the best kinetic parameter fitting. Higher values of a are required for the transition and end regions. A two-stage Gaussian smoothing strategy for biomass decomposition data is formally proposed. The effect of noises upon the differential methods is analyzed, and it is addressed that the choice of smoothing parameters should be considered in connection with the features of kinetic analysis methods.	en
heal.publisher	AMER CHEMICAL SOC	en
heal.journalName	Industrial and Engineering Chemistry Research	en
dc.identifier.isi	ISI:000222738000011	en
dc.identifier.volume	43	en
dc.identifier.issue	15	en
dc.identifier.spage	4087	en
dc.identifier.epage	4096	en