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

Xuejun Qian, Seong Lee, Ana-maria Soto and Guangming Chen
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Xuejun Qian: Industrial and Systems Engineering Department, Morgan State University, 1700 East Cold Spring Lane, Baltimore, MD 21251, USA
Seong Lee: Industrial and Systems Engineering Department, Morgan State University, 1700 East Cold Spring Lane, Baltimore, MD 21251, USA
Ana-maria Soto: Department of Chemistry, Towson University, 8000 York Road, Towson, MD 21252, USA
Guangming Chen: Industrial and Systems Engineering Department, Morgan State University, 1700 East Cold Spring Lane, Baltimore, MD 21251, USA

Resources, 2018, vol. 7, issue 3, 1-14

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 R 2 (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.

Keywords: poultry waste; energy resources; higher heating value; proximate analysis; regression model; estimation errors (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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