PREDICTING HIGHER HEATING VALUE OF AGRO-INDUSTRIAL WASTE: CLASSIFICATION AND MODELING BASED ON PROXIMATE AND ULTIMATE ANALYSIS

Oliveira, Augusto C. L. de; Renato, Natalia dos S.; Ribeiro Júnior, Rogerio S.; Ildefonso, Luna L. H.

doi:10.1590/1809-4430-Eng.Agric.v43nepe20220139/2023

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Scientific Paper, Special Issue: Energy in Agriculture • Eng. agríc. 43 (spe) • July 2023 • https://doi.org/10.1590/1809-4430-Eng.Agric.v43nepe20220139/2023 copy

PREDICTING HIGHER HEATING VALUE OF AGRO-INDUSTRIAL WASTE: CLASSIFICATION AND MODELING BASED ON PROXIMATE AND ULTIMATE ANALYSIS

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Bioenergy production relies on resources such as agricultural waste, which has prompted extensive research on biofuels. The heating value is a crucial parameter for evaluating energy sources. This study aims to analyze and propose formulations for estimating the higher heating value (HHV) based on proximate and ultimate chemical analysis of biomass. A database consisting of 142 samples was created, and 14 formulas available in the literature were initially tested. The datasets for each composition type were classified using the k-means algorithm, and the new sample spaces were validated. For proximate analysis data, specific multiple linear regression models were developed for two classes, one with an average SE of 1.05 MJ kg ^-1 , and the other with an average SE of 1.27 MJ kg ^-1 . For samples with ultimate analysis, a general model was formulated with an average SE of 1.11 MJ kg ^-1 . Sample classification for proximate analysis did not significantly affect the fit of models. Considering that proximate analysis is less expensive than ultimate analysis, the proposed method shows promise in optimizing and reducing costs for determining the HHV of biomass for energy production. of 0.697 and of 0.678 and of 0.701 and

biomass energy; higher heating value; proximate analysis; ultimate analysis

Reference	Equation of HHV	Nº
R1	$19, 914 - 0, 2324 A s h$	(1)
R1	$- 3, 0368 + 0, 2218 V M + 0, 2601 F C$	(2)
R2	$0, 3536 F C + 0, 1559 V M - 0, 0078 A s h$	(3)
R1	$0, 3259 C + 3, 4597$	(4)
R1	$- 1, 3675 + 0, 3137 C + 0, 7009 H + 0, 03180$	(5)
R3	$0, 00355 C^{2} - 0, 232 C - 2, 23 H + 0, 0512 C \times H + 0, 131 N + 20, 6$	(6)
R4	$0, 3491 C + 1, 1783 H + 0, 1005 S + 0, 1034 O + 0, 0151 N + 0, 0211 A s h$	(7)
R5	$0, 3543 F C + 0, 1708 V M$	(8)
R5	$35, 43 - 0, 1835 V M - 0, 3543 A s h$	(9)
R6	$- 10, 8141 + 0, 3133 (V M + F C)$	(10)
R7	$- 0, 763 + 0, 301 C + 0, 525 H + 0, 0640$	(11)
R8	$0, 4373 C - 1, 6701$	(12)
R9	$0, 1905 V M + 0, 2521 F C$	(13)
R9	$0, 2949 C + 0, 825 H$	(14)

Equation	R²		SE (MJ kg^-1)
(1)	0.319	0.314	1.853
(2)	0.117	0.102	2.757
(3)	0.120	0.097	3.262
(8)	0.121	0.106	3.319
(9)	0.134	0.119	3.263
(10)	0.099	0.083	3.455
(13)	0.127	0.111	2.641

Equation	R²		SE (MJ kg^-1)
(4)	0.428	0.421	1.621
(5)	0.675	0.664	1.208
(6)	0.201	0.155	2.758
(7)	0.643	0.623	1.689
(11)	0.610	0.597	1.406
(12)	0.428	0.421	1.907
(14)	0.698	0.695	1.142

Equation	R²		SE (MJ kg^-1)
(1)	0.636	0.630	1.201
(2)	0.538	0.522	1.328
(3)	0.565	0.541	1.423
(8)	0.563	0.547	1.747
(9)	0.691	0.680	1.759
(10)	0.507	0.489	1.502
(13)	0.554	0.538	1.410

Equation	R²		SE (MJ kg^-1)
(1)	0.396	0.384	1.632
(2)	0.321	0.293	1.687
(3)	0.541	0.513	1.721
(8)	0.533	0.514	1.399
(9)	0.594	0.578	1.282
(10)	0.186	0.154	1.968
(13)	0.404	0.380	1.688

Equation	R²		SE (MJ kg^-1)
(4)	0.505	0.493	0.970
(5)	0.565	0.530	1.016
(6)	0.536	0.471	0.987
(7)	0.576	0.517	1.088
(11)	0.544	0.508	1.278
(12)	0.505	0.493	0.991
(14)	0.571	0.560	0.927

Equation	R²		SE (MJ kg^-1)
(4)	0.699	0.692	1.343
(5)	0.716	0.695	1.317
(6)	0.724	0.688	1.356
(7)	0.711	0.673	1.690
(11)	0.704	0.683	1.409
(12)	0.699	0.692	1.325
(14)	0.720	0.713	1.299

Parameters	Estimate	t -test statistic	p -value
β₀	30.951	5.0866	$4.7137 \times 10^{- 6}$
β₁	-0.13594	-2.1342	0.037386
β₂	-0.3504	-5.1358	$3.9554 \times 10^{- 6}$
β₃	-0.018214	-0.30759	0.75958

Number of observations: 58		Number of degrees of freedom: 54

SE: 1.06 MJ kg^-1		: 0.692

Parameters	Estimate	t -test statistic	p -value
β₀	30.278	2.4943	0.016042
β₁	-0.13202	-1.1055	0.27434
β₂	-0.31373	-2.4982	0.015885
β₃	0.042992	0.31341	0.7553

Number of observations: 53		Number of degrees of freedom: 49

SE: 1.28 MJ kg^-1		: 0.571

Parameters	Estimate	t -test statistic	p -value
β₀	0.12524	0.077918	0.93807
β₁	0.26983	11.53	$2.3634 \times 10^{- 19}$
β₂	0.9326	9.1872	$1.5388 \times 10^{- 19}$
β₃	0.0037024	0.24838	0.80441

Number of observations: 93		Number of degrees of freedom: 89

SE: 1.11 MJ kg^-1		: 0.698

Associação Brasileira de Engenharia Agrícola SBEA - Associação Brasileira de Engenharia Agrícola, Departamento de Engenharia e Ciências Exatas FCAV/UNESP, Prof. Paulo Donato Castellane, km 5, 14884.900 | Jaboticabal - SP, Tel./Fax: +55 16 3209 7619 - Jaboticabal - SP - Brazil
E-mail: revistasbea@sbea.org.br

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[1] * Corresponding author. Department of Agricultural Engineering - Federal University of Viçosa/Viçosa - MG, Brazil. E-mail: natalia.renato@ufv.br | ORCID ID: https://orcid.org/0000-0001-6168-8257