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Wastewater Biosolid Composting Optimization Based on UV-VNIR Spectroscopy Monitoring
Conventional wastewater treatment generates large amounts of organic matter–rich sludge that requires adequate treatment to avoid public health and environmental problems. The mixture of wastewater sludge and some bulking agents produces a biosolid to be composted at adequate composting facilities....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5134578/ https://www.ncbi.nlm.nih.gov/pubmed/27854280 http://dx.doi.org/10.3390/s16111919 |
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author | Temporal-Lara, Beatriz Melendez-Pastor, Ignacio Gómez, Ignacio Navarro-Pedreño, Jose |
author_facet | Temporal-Lara, Beatriz Melendez-Pastor, Ignacio Gómez, Ignacio Navarro-Pedreño, Jose |
author_sort | Temporal-Lara, Beatriz |
collection | PubMed |
description | Conventional wastewater treatment generates large amounts of organic matter–rich sludge that requires adequate treatment to avoid public health and environmental problems. The mixture of wastewater sludge and some bulking agents produces a biosolid to be composted at adequate composting facilities. The composting process is chemically and microbiologically complex and requires an adequate aeration of the biosolid (e.g., with a turner machine) for proper maturation of the compost. Adequate (near) real-time monitoring of the compost maturity process is highly difficult and the operation of composting facilities is not as automatized as other industrial processes. Spectroscopic analysis of compost samples has been successfully employed for compost maturity assessment but the preparation of the solid compost samples is difficult and time-consuming. This manuscript presents a methodology based on a combination of a less time-consuming compost sample preparation and ultraviolet, visible and short-wave near-infrared spectroscopy. Spectroscopic measurements were performed with liquid compost extract instead of solid compost samples. Partial least square (PLS) models were developed to quantify chemical fractions commonly employed for compost maturity assessment. Effective regression models were obtained for total organic matter (residual predictive deviation—RPD = 2.68), humification ratio (RPD = 2.23), total exchangeable carbon (RPD = 2.07) and total organic carbon (RPD = 1.66) with a modular and cost-effective visible and near infrared (VNIR) spectroradiometer. This combination of a less time-consuming compost sample preparation with a versatile sensor system provides an easy-to-implement, efficient and cost-effective protocol for compost maturity assessment and near-real-time monitoring. |
format | Online Article Text |
id | pubmed-5134578 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-51345782017-01-03 Wastewater Biosolid Composting Optimization Based on UV-VNIR Spectroscopy Monitoring Temporal-Lara, Beatriz Melendez-Pastor, Ignacio Gómez, Ignacio Navarro-Pedreño, Jose Sensors (Basel) Article Conventional wastewater treatment generates large amounts of organic matter–rich sludge that requires adequate treatment to avoid public health and environmental problems. The mixture of wastewater sludge and some bulking agents produces a biosolid to be composted at adequate composting facilities. The composting process is chemically and microbiologically complex and requires an adequate aeration of the biosolid (e.g., with a turner machine) for proper maturation of the compost. Adequate (near) real-time monitoring of the compost maturity process is highly difficult and the operation of composting facilities is not as automatized as other industrial processes. Spectroscopic analysis of compost samples has been successfully employed for compost maturity assessment but the preparation of the solid compost samples is difficult and time-consuming. This manuscript presents a methodology based on a combination of a less time-consuming compost sample preparation and ultraviolet, visible and short-wave near-infrared spectroscopy. Spectroscopic measurements were performed with liquid compost extract instead of solid compost samples. Partial least square (PLS) models were developed to quantify chemical fractions commonly employed for compost maturity assessment. Effective regression models were obtained for total organic matter (residual predictive deviation—RPD = 2.68), humification ratio (RPD = 2.23), total exchangeable carbon (RPD = 2.07) and total organic carbon (RPD = 1.66) with a modular and cost-effective visible and near infrared (VNIR) spectroradiometer. This combination of a less time-consuming compost sample preparation with a versatile sensor system provides an easy-to-implement, efficient and cost-effective protocol for compost maturity assessment and near-real-time monitoring. MDPI 2016-11-15 /pmc/articles/PMC5134578/ /pubmed/27854280 http://dx.doi.org/10.3390/s16111919 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Temporal-Lara, Beatriz Melendez-Pastor, Ignacio Gómez, Ignacio Navarro-Pedreño, Jose Wastewater Biosolid Composting Optimization Based on UV-VNIR Spectroscopy Monitoring |
title | Wastewater Biosolid Composting Optimization Based on UV-VNIR Spectroscopy Monitoring |
title_full | Wastewater Biosolid Composting Optimization Based on UV-VNIR Spectroscopy Monitoring |
title_fullStr | Wastewater Biosolid Composting Optimization Based on UV-VNIR Spectroscopy Monitoring |
title_full_unstemmed | Wastewater Biosolid Composting Optimization Based on UV-VNIR Spectroscopy Monitoring |
title_short | Wastewater Biosolid Composting Optimization Based on UV-VNIR Spectroscopy Monitoring |
title_sort | wastewater biosolid composting optimization based on uv-vnir spectroscopy monitoring |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5134578/ https://www.ncbi.nlm.nih.gov/pubmed/27854280 http://dx.doi.org/10.3390/s16111919 |
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