کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8869628 | 1622610 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Monitoring of the composting process of different agroindustrial waste: Influence of the operational variables on the odorous impact
ترجمه فارسی عنوان
نظارت بر فرآیند کمپوست زباله های مختلف زراعتی: تاثیر متغیرهای عملیاتی بر تاثیر بویایی
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کلمات کلیدی
OFMSWOdor emission rateTNSCICTKNSPSSCTCDRIOERNH4+ - NH4 +PCA - PCADynamic olfactometry - الفات سنجی پویاStatistical Package for the Social Sciences - بسته های آماری برای علوم اجتماعیPrincipal components analysis - تجزیه و تحلیل اجزای اصلیPrincipal component analysis - تحلیل مولفههای اصلی یا PCASour - ترشPhysico-chemical characterization - خصوصیات فیزیکی و شیمیاییMultivariate regression - رگرسیون چند متغیرهSewage sludge - لجنprincipal component - مولفه های اصلیorganic fraction of municipal solid waste - کسر آلی از ضایعات جامد شهری
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
مهندسی ژئوتکنیک و زمین شناسی مهندسی
چکیده انگلیسی
Composting is a conventional but economical and environmentally friendly way to transform organic waste into a valuable, organic soil amendment. However, the physico-chemical characterization required to monitor the process involves considerable investment in terms of cost and time. In this study, 52 samples of four compostable substrates were collected randomly during the composting process and analyzed physico-chemically. The physico-chemical characterization was evaluated and reduced by principal component analysis (PCA) (PC1â¯+â¯PC2: 70% variance). Moreover, a study of the relationship between odor and the raw material and odor and the operational variables was carried out at pilot scale using PCA and multivariate regression. The substrates were grouped by PCA (PC1â¯+â¯PC2: 87% variance). The odor emission rate (OER) and dynamic respirometric index (DRI) were found to be the most influential variables in the sample variance, being relevant to identify the different emission sources. Dynamic respirometry and multivariate regression could be suitable tools to predict these odor emissions for the majority of compostable substrates, identifying successfully the emission source.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Waste Management - Volume 76, June 2018, Pages 266-274
Journal: Waste Management - Volume 76, June 2018, Pages 266-274
نویسندگان
M. Toledo, J.A. Siles, M.C. Gutiérrez, M.A. MartÃn,