2018
DOI: 10.1016/j.fuel.2017.10.082
|View full text |Cite
|
Sign up to set email alerts
|

Optimal use of condensed parameters of ultimate analysis to predict the calorific value of biomass

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

2
25
0
1

Year Published

2018
2018
2024
2024

Publication Types

Select...
6
2

Relationship

0
8

Authors

Journals

citations
Cited by 102 publications
(28 citation statements)
references
References 27 publications
2
25
0
1
Order By: Relevance
“…These models give relationship between the biomass HHV and the proximate analysis results (contents of moisture, volatile matter, fixed carbon, ash, and sulfur) or ultimate analysis (contents of C, H, O, N, S elements ) or results of both analysis respectively [11]. Ultimate analysis based models for HHV predicting were applied among others by Setyawati et al [12] for tropical peat and by Ozyuguran et al [9] for fruit juice pulps and fruit, vegetables and nut residues, stalk, stems etc. As far as proximate analysis based correlations are concerned Cordero et al [13] used it for HHV prediction of lignocellulosic and carbonaceous materials, whereas Özyuğuran and Yaman [3] applied it for different biomass sub-classes.…”
Section: *Corresponding Author: Krzysztof_gornicki@sggwplmentioning
confidence: 99%
See 1 more Smart Citation
“…These models give relationship between the biomass HHV and the proximate analysis results (contents of moisture, volatile matter, fixed carbon, ash, and sulfur) or ultimate analysis (contents of C, H, O, N, S elements ) or results of both analysis respectively [11]. Ultimate analysis based models for HHV predicting were applied among others by Setyawati et al [12] for tropical peat and by Ozyuguran et al [9] for fruit juice pulps and fruit, vegetables and nut residues, stalk, stems etc. As far as proximate analysis based correlations are concerned Cordero et al [13] used it for HHV prediction of lignocellulosic and carbonaceous materials, whereas Özyuğuran and Yaman [3] applied it for different biomass sub-classes.…”
Section: *Corresponding Author: Krzysztof_gornicki@sggwplmentioning
confidence: 99%
“…The design and operation of biomass combustion systems depend on such biomass characteristics as chemical composition, moisture content, ash amounts, and heating value [7,8]. Heating values of biomass, especially its higher heating value (HHV), belongs to the most important parameters that describes the fuel quality [9]. The HHV of a fuel evaluates the amount of heat released during combustion with the original and generated water that is in a condensed state.…”
Section: Introductionmentioning
confidence: 99%
“…The HHV of fuels can be determined experimentally by burning a specimen in a adiabatic oxygen bomb calorimeter under controlled conditions. The calorimeter measures the changes between reactants and products enthalpy [2,4]. The higher heating value can be also approximately predicted using empirical models.…”
Section: Introductionmentioning
confidence: 99%
“…These are however models considering impact of several elements on HHV, e.g. : carbon, hydrogen, and oxygen [10,13], carbon, hydrogen, oxygen, and nitrogen [4,14] or carbon, hydrogen, oxygen, nitrogen, chloride, and phosphor [15].…”
Section: Introductionmentioning
confidence: 99%
“…Where, is the number of heat energy (kJ/hr), is the amount of fuel used during the process (kg/hr), LHV is thelower heating value of fuel (kJ/kg), is the combustion efficiency (0.90 ÷ 0.97).The combustion efficiency can be demonstrated by the performance furnace are stated at PHU (Percent Heat utilized) or SC (specifik consumption) whose value can be determined by testing Water Boiling Test (WBT) This test includes the ripening amount of water (usually expressed one liter of water) pan on the heat and boiling condition, with initial water temperature measurement, temperature rise during the test a final temperature of water (temperature simering, ± 90 °C), the number of initial water, amount residual water (1 kg water produces, 0.1 kg of water vapor) and fuel consumption following Eq (4), [34,35]:…”
Section: Introductionmentioning
confidence: 99%