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Appraising different models for predicting biomethane potential: the case of avocado oil processing by-products
Biomethane potentials (BMPs) for avocado oil processing by-products were determined using six different theoretical BMP prediction models and results were compared with empirical values found in literature. The by-products were classified as kernels, skins, decanter pomace and decanter wastewater pr...
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Published in: | Journal of material cycles and waste management 2021, Vol.23 (1), p.409-415 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Biomethane potentials (BMPs) for avocado oil processing by-products were determined using six different theoretical BMP prediction models and results were compared with empirical values found in literature. The by-products were classified as kernels, skins, decanter pomace and decanter wastewater prior to physicochemical characterisation and BMP calculation of individual by-products. The estimated BMP values for the different by-products ranged between 152 and 889 mLCH
4
/gVS using the different prediction models across substrates. These values compare favourably with biomethane potentials for popular biogas plant substrates such as cow manure and food waste-based floatable oil whose biomethane potentials are 150 and 847 mLCH
4
/gVS, respectively. Results from the nutritional based and statistically derived canonical mixtures theoretical BMP prediction models for kernels (289 mLCH
4
/gVS) closely matched empirical values (284 mLCH
4
/gVS) from literature. We conclude that anaerobic digestion for biogas production can be preliminarily considered as a viable waste-to-energy technology option for managing avocado oil processing by-products. The statistically derived (nutritional based) theoretical BMP prediction models offer the best approach for evaluating these substrates’ candidature for biomethane production. |
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ISSN: | 1438-4957 1611-8227 |
DOI: | 10.1007/s10163-020-01116-0 |