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Resources 2018, 7(3), 39; https://doi.org/10.3390/resources7030039

Regression Model to Predict the Higher Heating Value of Poultry Waste from Proximate Analysis

1
Industrial and Systems Engineering Department, Morgan State University, 1700 East Cold Spring Lane, Baltimore, MD 21251, USA
2
Center for Advanced Energy Systems and Environmental Technologies, School of Engineering, Morgan State University, 5200 Perring Parkway, Baltimore, MD 21239, USA
3
Department of Chemistry, Towson University, 8000 York Road, Towson, MD 21252, USA
*
Author to whom correspondence should be addressed.
Received: 18 May 2018 / Revised: 12 June 2018 / Accepted: 23 June 2018 / Published: 26 June 2018
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Abstract

Improper land application of excess poultry waste (PW) causes environmental issues and other problems. Meanwhile there is an increasing trend of using PW as an alternative energy resource. The Higher Heating Value (HHV) is critical for designing and analyzing the PW conversion process. Several proximate-based mathematical models have been proposed to estimate the HHV of biomass, coal, and other solid fuels. Nevertheless, only a small number of studies have focused on a subclass of fuels, especially for PW. The aim of this study is to develop proximate-based regression models for an HHV prediction of PW. Sample data of PW were collected from open literature to develop regression models. The resulting models were then validated by additional PW samples and other published models. Results indicate that the most accurate model contains linear (all proximate components), polynomial terms (quadratic and cubic of volatile matter), and interaction effect (fixed carbon and ash). Moreover, results show that best-fit regression model has a higher R2 (91.62%) and lower estimation errors than the existing proximate-based models. Therefore, this new regression model can be an excellent tool for predicting the HHV of PW and does not require any expensive equipment that measures HHV or elemental compositions. View Full-Text
Keywords: poultry waste; energy resources; higher heating value; proximate analysis; regression model; estimation errors poultry waste; energy resources; higher heating value; proximate analysis; regression model; estimation errors
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Qian, X.; Lee, S.; Soto, A.-M.; Chen, G. Regression Model to Predict the Higher Heating Value of Poultry Waste from Proximate Analysis. Resources 2018, 7, 39.

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