کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5479439 | 1522087 | 2017 | 27 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Chemometric analysis and NIR spectroscopy to evaluate odorous impact during the composting of different raw materials
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کلمات کلیدی
VOCsOdor emissionSNVdetrendOFMSWMSCDRIDETOERWWTPPLSNIRPCA - PCAStandard normal variate - استاندارد عادیDynamic olfactometry - الفات سنجی پویاNear infrared reflectance - انعکاس مادون قرمز نزدیکPrincipal components analysis - تجزیه و تحلیل اجزای اصلیPrincipal component analysis - تحلیل مولفههای اصلی یا PCAVolatile organic compounds - ترکیبات آلی فرارmultiplicative scatter correction - تصحیح پراکندگی multiplicativeWastewater treatment plant - تصفیه خانه فاضلاب volatile solids - جامدات فرارPartial least squares - حداقل مربعات جزئی Multivariate regression - رگرسیون چند متغیرهNIR spectroscopy - طیف سنجی NIRSewage sludge - لجنprincipal component - مولفه های اصلیorganic fraction of municipal solid waste - کسر آلی از ضایعات جامد شهری
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
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چکیده انگلیسی
This study evaluated odor generated during the first stage of the composting process in a dynamic respirometer using different raw materials such as the organic fraction of municipal solid waste (OFMSW), a mixture of this organic fraction with orange peel waste (OFMSW-OPW), sewage sludge with bulking agent (SL) and a mixture of strawberry extrudate, fish waste, sewage sludge and bulking agent (SFWSL). The combination of near infrared reflectance (NIR) spectroscopy and chemometric analysis is proposed to correlate the chemical composition and the operational variables of each raw material to odor generated during the composting process. The operational variables temperature, dynamic respirometric index (DRI), airflow (Q), odor concentration (OC) and odor emission rate (OER) were monitored. Adequate linear correlations were obtained between temperature and DRI for each compostable substrate within a confidence interval of 10% and 30%. Operational variables were statistically analyzed by principal component analysis with 87% of total variance explained and from which the substrates were clearly grouped. Near infrared reflectance spectroscopy provided the chemical composition of each raw material, and was found to be an advantageous technique to predict the relationship between odor emissions and absorption bands. Odor emissions were also predicted from the operational variables by multivariate regression, with temperature and DRI being the most influential variables.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Cleaner Production - Volume 167, 20 November 2017, Pages 154-162
Journal: Journal of Cleaner Production - Volume 167, 20 November 2017, Pages 154-162
نویسندگان
M. Toledo, M.C. Gutiérrez, J.A. Siles, J. GarcÃa-Olmo, M.A. MartÃn,