The biochemical methane potential (BMP) of primary and biological sludge varies in a wide range, mostly depending on location, sewer characteristics, wastewater treatment plant design and operating conditions. BMP tests are useful to verify the performance of a full scale digester, but they are not yet a common procedure in the operation of most Italian facilities because of cost and test duration. Changes in the composition of sewage sludge can lead to a high variation of biogas production. Aimed at developing BMP predictive models based on low cost and fast analyses, this study investigated the chemical composition of 20 sludge samples by means of principal component and multiple linear regression analyses. Three preliminary predictive models were developed based on soluble organic nitrogen, volatile solids, carbohydrates, proteins, lipids and an operational parameter, the sludge retention time: the explained variance and the standard errors of prediction of BMP are in the range 77-81% and 21-34 NmLCH4·gVS-1, respectively. Models were evaluated on five additional samples: errors ranged 2-15% for four samples and about 54% for one sample, collected from a peculiar facility. Further data and variables describing the operation mode of the waterline would certainly improve the reliability and robustness of the models.
Development of statistical predictive models for estimating the methane yield of Italian municipal sludges from chemical composition: a preliminary study
Catenacci, A;Azzellino, A;Malpei, F
2019-01-01
Abstract
The biochemical methane potential (BMP) of primary and biological sludge varies in a wide range, mostly depending on location, sewer characteristics, wastewater treatment plant design and operating conditions. BMP tests are useful to verify the performance of a full scale digester, but they are not yet a common procedure in the operation of most Italian facilities because of cost and test duration. Changes in the composition of sewage sludge can lead to a high variation of biogas production. Aimed at developing BMP predictive models based on low cost and fast analyses, this study investigated the chemical composition of 20 sludge samples by means of principal component and multiple linear regression analyses. Three preliminary predictive models were developed based on soluble organic nitrogen, volatile solids, carbohydrates, proteins, lipids and an operational parameter, the sludge retention time: the explained variance and the standard errors of prediction of BMP are in the range 77-81% and 21-34 NmLCH4·gVS-1, respectively. Models were evaluated on five additional samples: errors ranged 2-15% for four samples and about 54% for one sample, collected from a peculiar facility. Further data and variables describing the operation mode of the waterline would certainly improve the reliability and robustness of the models.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
