Skip to main content

Advertisement

SpringerLink
Log in

Slow pyrolysis of pistachio-waste pellets: combined phenomenological modeling with environmental, exergetic, and energetic analysis (3-E)

  • Original Article
  • Published:
Biomass Conversion and Biorefinery Aims and scope Submit manuscript

Abstract

Slow pyrolysis of a pellet of pistachio waste was studied using a macro-thermogravimetric analysis. Experiments were conducted at different heating rates (5, 10, and 15 K/min), measuring the evolution of mass weight loss and CO release. Based on a dimensionless number analysis, a numerical model was formulated, comprising heat and mass balances. A kinetic expression for the release of CO was proposed. Additionally, a 3-E (environmental, exergetic, and energetic) analysis for the processing of 20 kg/h of bio-waste (case study) was applied. Experimental results showed that biochar and gas yields decreased with an increase in the heating rate (43 to 36% and 28 to 24%, respectively), while the bio-oil yield increased (29 to 40%). The slow pyrolysis model presented a good agreement with experimental results of weight loss. Furthermore, a comparison with the contracting volume model showed that internal heat transport should control the global process. The proposed kinetic model for CO release showed a good fit to experimental data, where activation energy values were 29.88 (5 K/min), 17.44 (10 K/min), and 28.79 kJ/mol (15 K/min). Finally, from the 3-E analysis and the experimental results, it can be suggested that an increase in the heating rate resulted in a higher pyrolysis exergetic efficiency (70%). It is due to an increase in the bio-oil yield with high-energy content.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The datasheets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Characteristics of used materials are also available on request.

Abbreviations

A b :

Pre-exponential factor for bio-waste decomposition, s−1

A CO :

Pre-exponential factor for CO release, s−1

b :

Geometric factor, dimensionless

Cbiochar :

Carbon content of biochar, %

Cbio-oil :

Carbon content of bio-oil, %

Cbio-waste :

Carbon content of bio-waste, %

Cpg :

Gas heat capacity, J/(kg K)

Cpi :

Gas heat capacity of i-th gaseous specie, J/(kmol K)

Cpj :

Gas heat capacity of j-th reagent, J/(kmol K)

Cpk :

Gas heat capacity of k-th inert species, J/(kmol K)

Cpm :

Gas heat capacity of m-th product, J/(kmol K)

CpP :

Pellet heat capacity, J/(kg K)

F CO :

Volumetric flow of CO release, m3/s

h conv :

Convective heat transfer coefficient, J/(kg m.2 s K)

H biochar :

Hydrogen content of biochar, %

H bio-oil :

Hydrogen content of bio-oil, %

H bio-waste :

Hydrogen content of bio-waste, %

HHV :

Higher heating value, MJ/Kg

HHV bio-waste :

Higher heating value of bio-waste, MJ/kg

HHV m :

Higher heating value of m-th product of pyrolysis, MJ/kg

I RE :

Energy index, dimensionless

K :

\(=\frac{{\lambda }_{{P}}}{{\uprho }_{{P}}{{C}}_{{{p}}_{{p}}}}\); Thermal diffusivity, m2/s

L C :

Characteristic length, m

LHV m, prod :

Lower heating value of bio-waste, bio-oil and biochar, MJ/kg

l P :

Height, m

MW :

Molecular weight, kg/kmol

MW gas :

Average molecular weight of gas mixture, kg/kmol

Nu :

\(=\frac{{h}_{{conv}}{L}_{{C}}}{{\lambda }_{{g}}}\); Nusselt number, dimensionless

O biochar :

Oxygen content of biochar, %

O bio-oil :

Oxygen content of bio-oil, %

O bio-waste :

Oxygen content of bio-waste, %

Pr :

\(=\frac{{{C}}_{{{p}}_{{g}}}{\mu }_{{g}}}{{\uplambda }_{{g}}}\); Prandtl number, dimensionless

Q bio-waste :

Energy content of bio-waste, MJ/h

Q h :

Energy of heat supplied to the reactor, MJ/h

Q m :

Energy content of biochar, bio-oil or gas, MJ/h

Q recovery :

Recovered energy, MJ/h

r :

Cylindrical coordinate (radius direction), m

rc :

Radius at front of reaction, m

R :

Universal gas constant, 8.3144 × 10.−3 kJ/(mol K)

Re :

\(=\frac{{\uprho }_{{g}}{\nu }_{{g}}{L}_{C}}{{\mu }_{{g}}}\); Reynolds number, dimensionless

T :

Temperature, K

t :

Time, s

T 0 :

Initial temperature, K

T c :

Center temperature, K

T fs :

Final surface temperature, K

T g :

Gas temperature, K

T M :

Middle temperature, K

T P :

Pellet local temperature, K

T s :

Surface temperature, K

V P :

Volatile mass, kg

v g :

Lineal velocity of nitrogen, m/s

W i :

Mass of i-th component, kg

W gas :

Mass flowrate of gas, kg/h

W m,prod :

Mass flowrate of bio-waste, bio-oil or biochar, kg/h

W P :

Mass pellet at the time t, kg

W 0 :

Initial mass, kg

W P∞ :

Mass at the time t → ∞, kg

X m :

Mass yield of biochar, bio-oil or gas. dimensionless

x i :

Number of moles of i-th component, kmol

x j :

Number of moles of j-th reagent, kmol

x k :

Number of moles of k-th inert species, kmol

x m :

Number of moles of m-th product, kmol

\({\widetilde{\upvarepsilon }}_{{{ch}}_{i}}\) :

Molar chemical exergy of i-th component, MJ/kmol

\({\widetilde{\upvarepsilon }}_{{chgas}}\) :

Chemical exergy of gas, MJ/kmol

ɛ:

System exergy, MJ/h

ɛch :

Chemical exergy of system, MJ

ɛch m,prod :

Chemical exergy of biochar, bio-oil or gas, MJ/h

ɛchi :

Chemical exergy of i-th component, MJ/h

ɛin :

Input exergy, MJ/h

ɛph :

Pysical exergy of system, MJ

ɛphgas :

Physical exergy of gas, MJ/h

ɛphi :

Physical exergy of i-th component, MJ/h

ɛprod :

Products exergy, MJ/h

ΔH f ,i :

Molar formation enthalpy change of i-th gaseous component, kJ/kgmol

ΔH f ,j :

Molar formation enthalpy change of j-th reagent, kJ/kgmol

ΔH f ,k :

Molar formation enthalpy change of k-th inert species, kJ/kgmol

ΔH f ,m :

Molar formation enthalpy change of m-th product, kJ/kgmol

ΔH pyr :

Pyrolysis enthalpy, J/kg

λg :

Gas conductivity of nitrogen, W/(m K)

λP :

Thermal conductivity of pellet, W/(m K)

μg :

Kinematic viscosity of nitrogen, kg/(m s)

π:

Pi number = 3.14159

ρ0 :

Initial density of bio-waste, kg/m.3

ρg :

Gas density, kg/m.3

ρP :

Instantaneous density of bio-waste pellet, kg/m.3

ρP0 :

Density of bio-waste pellet at time t = 0, kg/m.3

ρP ∞ :

Density of bio-waste pellet at time t → ∞, kg/m.3

σ:

Stefan-Boltzmann constant, W/(m2 K.4)

τ:

Correlation factor, dimensionless

φ:

Porosity, dimensionless

Ψ:

Exergy efficiency, dimensionless

ωb :

Emissivity, dimensionless

0:

Initial

\(\infty\) :

At time \(t\to \infty\)

b:

Bio-waste

c:

Center

C:

Characteristic

ch:

Chemical

CO:

Carbon monoxide

f:

Formation

g:

Gas

i:

Gaseous products of pyrolysis

j:

Reagent

k:

Inert species

m:

Products of pyrolysis, biochar or bio-oil

m,i:

Product (biochar, bio-oil or gas)

P:

Pellet

Phi:

Physical

prod:

Product

pyr:

Pyrolysis

RE:

Recovery energy

s:

Surface

b:

Geometric factor

I:

Inert component

n:

Reaction order

p:

Number of product species

rea:

Reagents

References

  1. Chen Y (2012) Biomass to fuels: thermo-chemical or bio-chemical conversion? Ferment Technol 01:7972. https://doi.org/10.4172/2167-7972.1000e104

    Article  Google Scholar 

  2. Zalazar-García D, Feresin GE, Rodriguez RA (2022) Optimal operation variables of phenolic compounds extractions from pistachio industry waste (Pistacia vera var. Kerman) using the response surface method. Biomass Conv Bioref 12:3761–3770. https://doi.org/10.1007/s13399-020-00862-3

    Article  Google Scholar 

  3. Zalazar-García D, Torres E, Rodriguez-Ortiz L, Deng Y, Soria J, Bucalá V, Rodriguez R, Mazza G (2020) Cleaner and sustainable processes for extracting phenolic compounds from bio-waste. J Environ Manage 273:111154. https://doi.org/10.1016/j.jenvman.2020.111154

    Article  Google Scholar 

  4. Vyas D, Sayyad F, Khardiwar M, Kumar S (2015) Physicochemical properties of briquettes from different feed stock. Curr World Environ 10:263–269. https://doi.org/10.12944/cwe.10.1.32

  5. Theerarattananoon K, Xu F, Wilson J, Staggenborg S, McKinney L, Vadlani P, Pei ZJ, Wang D (2012) Impact of pelleting and acid pretreatment on biomass structure and thermal properties of wheat straw, corn stover, big bluestem, and sorghum stalk. Trans ASABE 55:1845–1858

    Article  Google Scholar 

  6. Wang X, Zhai M, Guo H, Panahi A, Dong P, Levendis YA (2021) High-temperature pyrolysis of biomass pellets: the effect of ash melting on the structure of the char residue. Fuel 285:119084. https://doi.org/10.1016/j.fuel.2020.119084

    Article  Google Scholar 

  7. Van de Velden M, Baeyens J, Brems A, Janssens B, Dewil R (2010) Fundamentals, kinetics and endothermicity of the biomass pyrolysis reaction. Renew Energy 35:232–242. https://doi.org/10.1016/J.RENENE.2009.04.019

    Article  Google Scholar 

  8. Soria J, Li R, Flamant G, Mazza GD (2019) Influence of pellet size on product yields and syngas composition during solar-driven high temperature fast pyrolysis of biomass. J Anal Appl Pyrol 140:299–311. https://doi.org/10.1016/j.jaap.2019.04.007

    Article  Google Scholar 

  9. Bucalá V, Saito H, Howard JB, Peters WA (1996) Products compositions and release rates from intense thermal treatment of soil. Ind Eng Chem Res 35:2725–2734. https://doi.org/10.1021/ie9505726

    Article  Google Scholar 

  10. Rodriguez-Ortiz L, Torres E, Zalazar D, Zhang H, Rodriguez R, Mazza G (2020) Influence of pyrolysis temperature and bio-waste composition on biochar characteristics. Renew Energy 155:837–847. https://doi.org/10.1016/j.renene.2020.03.181

    Article  Google Scholar 

  11. Torres E, Rodriguez-Ortiz L, Zalazar-García D, Echegaray M, Rodriguez R, Zhang H, Mazza G (2020) 4-E (Environmental, Economic, Energetic and Exergetic) analysis of 1 slow pyrolysis of lignocellulosic waste. Renew Energy 161:296–307. https://doi.org/10.1016/j.renene.2020.07.147

    Article  Google Scholar 

  12. Daouk E, Van De Steene L, Paviet F, Salvador S (2014) Oxidative pyrolysis of a large wood particle: effects of oxygen concentration and of particle size. Chem Eng Trans 37:73–78. https://doi.org/10.3303/CET1437013

    Article  Google Scholar 

  13. Pattanotai T, Watanabe H, Okazaki K (2015) Effects of particle aspect ratio on pyrolysis and gasification of anisotropic wood cylinder. Fuel 150:162–168. https://doi.org/10.1016/j.fuel.2015.02.017

    Article  Google Scholar 

  14. Okekunle P, Olasunkanmi A (2016) Numerical investigation of the effects of some selected thermo-physical properties on products evolution and yields during biomass pyrolysis. Biofuels 9:1–14. https://doi.org/10.1080/17597269.2016.1271633

    Article  Google Scholar 

  15. Sadhukhan AK, Gupta P, Saha RK (2008) Modelling and experimental studies on pyrolysis of biomass particles. J Anal Appl Pyrol 81:183–192. https://doi.org/10.1016/j.jaap.2007.11.007

    Article  Google Scholar 

  16. Li J, Paul MC, Younger PL, Watson I, Hossain M, Welch S (2015) Characterization of biomass combustion at high temperatures based on an upgraded single particle model. Appl Energy 156:749–755. https://doi.org/10.1016/j.apenergy.2015.04.027

    Article  Google Scholar 

  17. Mian I, Li X, Jian Y, Dacres OD, Zhong M, Liu J, Ma F, Rahman N (2019) Kinetic study of biomass pellet pyrolysis by using distributed activation energy model and Coats Redfern methods and their comparison. Biores Technol 294:122099. https://doi.org/10.1016/j.biortech.2019.122099

    Article  Google Scholar 

  18. Ramesh N, Murugavelh S (2020) A cleaner process for conversion of invasive weed (Prosopis juliflora) into energy-dense fuel: kinetics, energy, and exergy analysis of pyrolysis process. Biomass Convers Biorefin. https://doi.org/10.1007/s13399-020-00747-5

    Article  Google Scholar 

  19. Zisopoulos FK, Rossier-Miranda FJ, van der Goot AJ, Boom RM (2017) The use of exergetic indicators in the food industry – a review. Crit Rev Food Sci Nutr 57:197–211. https://doi.org/10.1080/10408398.2014.975335

    Article  Google Scholar 

  20. Bi H, Wang C, Lin Q, Jiang X, Jiang C, Bao L (2021) Pyrolysis characteristics, artificial neural network modeling and environmental impact of coal gangue and biomass by TG-FTIR. Sci Total Environ 751:142293. https://doi.org/10.1016/j.scitotenv.2020.142293

    Article  Google Scholar 

  21. Barry D, Barbiero C, Briens C, Berruti F (2019) Pyrolysis as an economical and ecological treatment option for municipal sewage sludge. Biomass Bioenerg 122:472–480. https://doi.org/10.1016/j.biombioe.2019.01.041

    Article  Google Scholar 

  22. Cheng F, Luo H, Colosi LM (2020) Slow pyrolysis as a platform for negative emissions technology: an integration of machine learning models, life cycle assessment, and economic analysis. Energy Convers Manage 223:113258. https://doi.org/10.1016/j.enconman.2020.113258

    Article  Google Scholar 

  23. Mazza G, Falcoz Q, Gauthier D, Flamant G (2009) A particulate model of solid waste incineration in a fluidized bed combining combustion and heavy metal vaporization. Combust Flame 156:2084–2092. https://doi.org/10.1016/j.combustflame.2009.04.003

    Article  Google Scholar 

  24. Groleau G, Tanguay-Rioux F, Spreutels L, Héroux M, Legros R (2019) Development of a MSW gasification model for flexible integration into a MFA-LCA framework. Detritus 7:44–54. https://doi.org/10.31025/2611-4135/2019.13850

  25. Wang X, Demirel Y (2018) Feasibility of power and methanol production by an entrained-flow coal gasification system. Energy Fuels 32:7595–7610. https://doi.org/10.1021/acs.energyfuels.7b03958

    Article  Google Scholar 

  26. ASTM (1996) Standard test method for moisture in the analysis sample of coal and coke. ASTM D3173–87. https://www.astm.org/d3173-87r96.html

  27. ASTM (2021) Standard Practice for Proximate Analysis of Coal and Coke. ASTM-D3172–13. https://www.astm.org/d3172-13r21e01.html

  28. Sheng C, Azevedo JLT (2005) Estimating the higher heating value of biomass fuels from basic analysis data. Biomass Bioenerg 28:499–507. https://doi.org/10.1016/j.biombioe.2004.11.008

    Article  Google Scholar 

  29. Fernandez A, Saffe A, Pereyra R, Mazza G, Rodriguez R (2016) Kinetic study of regional agro-industrial wastes pyrolysis using non-isothermal TGA analysis. Appl Therm Eng 106:1157–1164. https://doi.org/10.1016/j.applthermaleng.2016.06.084

    Article  Google Scholar 

  30. Bird RB, Warren ES, Lightfoot EN (2002) Transport phenomena, 2nd edn. John Wiley & Sons Inc., New York

  31. Soria J, Zeng K, Asensio D, Gauthier D, Flamant G, Mazza (2017) Comprehensive CFD modelling of solar fast pyrolysis of beech wood pellets. Fuel Process Technol 158:226–237.https://doi.org/10.1016/J.FUPROC.2017.01.006

  32. Whitaker S (1972) Forced convection heat transfer correlations for flow in pipes, past flat plates, single. AIChE J 18:361–371

    Article  Google Scholar 

  33. Rodriguez RA, Udaquiola SM (2012) Combustion of dry sewage sludge particle in a fluidized bed reactor. Lat Am Appl Res 42:359–364

    Google Scholar 

  34. Li X, Yin C, Knudsen Kær S, Condra T (2020) A detailed pyrolysis model for a thermally large biomass particle. Fuel 278:118397. https://doi.org/10.1016/j.fuel.2020.118397

    Article  Google Scholar 

  35. Biswas AK, Umeki K (2015) Simplification of devolatilization models for thermally-thick particles: differences between wood logs and pellets. Chem Eng J 274:181–191. https://doi.org/10.1016/j.cej.2015.03.131

    Article  Google Scholar 

  36. Pan Y, Kong S-C (2017) Predicting effects of operating conditions on biomass fast pyrolysis using particle-level simulation. Energy Fuels 31:635–646. https://doi.org/10.1021/acs.energyfuels.6b02445

    Article  Google Scholar 

  37. Anca-Couce A (2016) Reaction mechanisms and multi-scale modelling of lignocellulosic biomass pyrolysis. Prog Energy Combust Sci 53:41–79. https://doi.org/10.1016/j.pecs.2015.10.002

    Article  Google Scholar 

  38. Pyle DL, Zaror CA (1984) Heat transfer and kinetics in the low temperature pyrolysis of solids. Chem Eng Sci 39:147–158. https://doi.org/10.1016/0009-2509(84)80140-2

    Article  Google Scholar 

  39. Manovic V, Komatina M, Oka S (2008) Modeling the temperature in coal char particle during fluidized bed combustion. Fuel 87:905–914. https://doi.org/10.1016/j.fuel.2007.05.020

    Article  Google Scholar 

  40. Zaror CA (1983) Studies of the pyrolysis of wood at low temperatures. Dissertation, University of London

  41. Sundaram E, Natarajan E (2009) Pyrolysis of coconut shell: an experimental investigation. J Eng Res 6:33–39. https://doi.org/10.24200/tjer.vol6iss2pp33-39

  42. Carslaw HS, Jaeger JC (1959) Heat conduction in solids, 2nd edn. Clarendon Press, Oxford

    Google Scholar 

  43. Gröber H, Sigmund E (1961) Fundamentals of heat transfer, 3rd edn. McGraw-Hill series in mechanical engineering, New York

    Google Scholar 

  44. Çengel YA, Ghajar AJ (2014) Heat and mass transfer: fundamentals and applications, 5th Edition. McGraw-Hill Professional, New York

  45. Fernandez A, Soria J, Rodriguez R, Baeyens J, Mazza G (2019) Macro-TGA steam-assisted gasification of lignocellulosic wastes. J Environ Manage 233:626–635. https://doi.org/10.1016/j.jenvman.2018.12.087

    Article  Google Scholar 

  46. Zeng J, Xiao R, Zhang H, Wang Y, Zeng D, Ma Z (2017) Chemical looping pyrolysis-gasification of biomass for high H2/CO syngas production. Fuel Process Technol 168:116–122. https://doi.org/10.1016/j.fuproc.2017.08.036

    Article  Google Scholar 

  47. Fernandez A, Rodriguez-Ortiz L, Asensio D, Rodriguez R, Mazza G (2020) Kinetic analysis and thermodynamics properties of air/steam gasification of agricultural waste. J Environ Chem Eng 8:103829. https://doi.org/10.1016/j.jece.2020.103829

    Article  Google Scholar 

  48. Ghodke P, Mandapati RN (2019) Investigation of particle level kinetic modeling for babul wood pyrolysis. Fuel 236:1008–1017. https://doi.org/10.1016/J.FUEL.2018.09.084

    Article  Google Scholar 

  49. Zalazar-Garcia D, Fernandez A, Rodriguez-Ortiz L, Rodriguez R, Mazza G (2022) Sustainable slow-pyrolysis simulation of 12 lignocellulosic bio-wastes: CO2 emission, energy, and water consumption. IOP Conf Ser: Earth Environ Sci 952 012008 https://doi.org/10.1088/1755-1315/952/1/012008

  50. Jankowiak L, Jonkman J, Rossier-Miranda FJ, van der Goot AJ, Boom RM (2014) Exergy driven process synthesis for isoflavone recovery from okara. Energy 74:471–483. https://doi.org/10.1016/j.energy.2014.07.013

    Article  Google Scholar 

  51. Adams P, Bridgwater T, Lea-Langton A, Ross A, Watson I (2018) Biomass Conversion Technologies. Report to NNFCC. In Greenhouse Gas Balances of Bioenergy Systems. Elsevier Inc

  52. Zhang Y, Ji G, Ma D, Chen C, Wang Y, Wang W, Li A (2020) Exergy and energy analysis of pyrolysis of plastic wastes in rotary kiln with heat carrier. Process Saf Environ Prot 142:203–211. https://doi.org/10.1016/j.psep.2020.06.021

    Article  Google Scholar 

  53. López A, de Marco I, Caballero BM, Laresgoiti MF, Adrados A (2011) Influence of time and temperature on pyrolysis of plastic wastes in a semi-batch reactor. Chem Eng J 173:62–71. https://doi.org/10.1016/j.cej.2011.07.037

    Article  Google Scholar 

  54. Demiral İ, Gülmezoglu N, Sensoz S (2009) Production of biofuel from soft shell of pistachio ( Pistacia vera L.). Chem Eng Commun 1–2:104–115. https://doi.org/10.1080/00986440802300984

    Article  Google Scholar 

  55. Chouchene A, Jeguirim M, Khiari B, Zagrouba F, Trouvé G (2010) Thermal degradation of olive solid waste: influence of particle size and oxygen concentration. Resour Conserv Recycl 54:271–277. https://doi.org/10.1016/j.resconrec.2009.04.010

    Article  Google Scholar 

  56. Louwes AC, Basile L, Yukananto R, Bhagwandas JC, Bramer EA, Brem G (2017) Torrefied biomass as feed for fast pyrolysis: an experimental study and chain analysis. Biomass Bioenerg 105:116–126. https://doi.org/10.1016/j.biombioe.2017.06.009

    Article  Google Scholar 

  57. Khuenkaeo N, Tippayawong N (2018) Bio-oil production from ablative pyrolysis of corncob pellets in a rotating blade reactor. IOP Conf Ser: Earth Environ Sci 159 012037. https://doi.org/10.1088/1755-1315/159/1/012037

  58. Xing X, Fan F, Jiang W (2018) Characteristics of biochar pellets from corn straw under different pyrolysis temperatures. Royal Soc Open Sci 5(8):172346. https://doi.org/10.1098/rsos.172346

    Article  Google Scholar 

  59. Cetin E, Moghtaderi B, Gupta R, Wall TF (2004) Influence of pyrolysis conditions on the structure and gasification reactivity of biomass chars. Fuel 83:2139–2150. https://doi.org/10.1016/j.fuel.2004.05.008

    Article  Google Scholar 

  60. Guerrero M, Ruiz MP, Alzueta MU, Bilbao R, Millera A (2005) Pyrolysis of eucalyptus at different heating rates: studies of char characterization and oxidative reactivity. J Anal Appl Pyrol 74:307–314. https://doi.org/10.1016/j.jaap.2004.12.008

    Article  Google Scholar 

  61. Mermoud F, Golfier F, Salvador S, Van de Steene L, Dirion JL (2006) Experimental and numerical study of steam gasification of a single charcoal particle. Combust Flame 145:59–79. https://doi.org/10.1016/j.combustflame.2005.12.004

    Article  Google Scholar 

  62. Mazza G, Falcoz Q, Soria J, Gauthier D, Flamant G (2010) Nonisothermal particle modeling of municipal solid waste combustion with heavy metal vaporization. Combust Flame 157:2306–2317. https://doi.org/10.1016/j.combustflame.2010.07.005

    Article  Google Scholar 

  63. Baldwin RM, Magrini-Bair KA, Nimlos MR, Pepiot P, Donohoe BS, Hensley JE, Phillips SD (2012) Current research on thermochemical conversion of biomass at the National Renewable Energy Laboratory. Appl Catal B 115–116:320–329. https://doi.org/10.1016/j.apcatb.2011.10.033

    Article  Google Scholar 

  64. Mazza GD, Soria JM, Gauthier D, Reyes Urrutia A, Zambon M, Flamant G (2016) Environmental friendly fluidized bed combustion of solid fuels: a review about local scale modeling of char heterogeneous combustion. Waste Biomass Valori v. 7:237–266–2016 v.7 no.2. https://doi.org/10.1007/s12649-015-9461-5

  65. Bourke J, Manley-Harris M, Fushimi C, Dowaki K, Nunoura T, Antal MJ (2007) Do all carbonized charcoals have the same chemical structure? 2. A Model of the Chemical Structure of Carbonized Charcoal. Ind Eng Chem Res 46:5954–5967. https://doi.org/10.1021/ie070415u

    Article  Google Scholar 

  66. Keiluweit M, Nico PS, Johnson MG, Kleber M (2010) Dynamic molecular structure of plant biomass-derived black carbon (biochar). Environ Sci Technol 44:1247–1253. https://doi.org/10.1021/es9031419

    Article  Google Scholar 

  67. Toro-Trochez JL, Carrillo-Pedraza ES, Bustos-Martínez D, García-Mateos FJ, Ruiz-Rosas RR, Rodríguez-Mirasol J (2019) Bioresource technology reports thermogravimetric characterization and pyrolysis of soybean hulls. Bioresour Technol Rep 6:183–189. https://doi.org/10.1016/j.biteb.2019.02.009

    Article  Google Scholar 

  68. Saffe A, Fernandez A, Echegaray M, Mazza G, Rodriguez R (2019) Pyrolysis kinetics of regional agro-industrial wastes using isoconversional methods. Biofuels 10:245–257. https://doi.org/10.1080/17597269.2017.1316144

    Article  Google Scholar 

  69. Maroušek J, Strunecký O, Stehel V (2019) Biochar farming: defining economically perspective applications. Clean Technol Environ Policy 21:1389–1395. https://doi.org/10.1007/s10098-019-01728-7

    Article  Google Scholar 

  70. Yu H, Zou W, Chen J, Chen H, Yu Z, Huang J, Tang H, Wei X, Gao B (2019) Biochar amendment improves crop production in problem soils: a review. J Environ Manage 232:8–21. https://doi.org/10.1016/j.jenvman.2018.10.117

    Article  Google Scholar 

  71. Puente Torres J, Crespo Sariol H, Yperman J, Brito Sauvanell Á, Carleer R, Navarro Campa J (2018) A novel X-ray radiography approach for the characterization of granular activated carbons used in the rum production. J Anal Sci Technol 9:1. https://doi.org/10.1186/s40543-017-0133-x

    Article  Google Scholar 

  72. Li L, Yao Z, You S, Wang CH, Chong C, Wang X (2019) Optimal design of negative emission hybrid renewable energy systems with biochar production. Appl Energy 243:233–249. https://doi.org/10.1016/j.apenergy.2019.03.183

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to thank the support of the following argentine institutions: the University of San Juan, Faculty of Engineering; the University of Comahue, Faculty of Engineering; National Scientific and Technical Research Council, CONICET and ANPCyT- FONCYT.

Daniela Zalazar-Garcia has a post-doctoral fellowship from CONICET, Argentina. Anabel Fernandez, Lucas Cavaliere, José Soria, Rosa Rodriguez, and Germán Mazza are Research Members of CONICET, Argentina.

Funding

The research leading to these results received funding from the University of San Juan, San Juan, Argentina, under Grant Agreement No. PDTS Res. 1054/18 and the University of Comahue, Neuquén, Argentina, under Grant Agreement No. PIN 2022–04/I260. This study was also funded by the National Scientific and Technical Research Council, CONICET (Grant Agreements: PUE PROBIEN CONICET 22920150100067, PIP 2021–2023-No. 11220200100950CO and SYNSOLGAS Project-CONICET-MINCyT-CNRS). This work was also supported by ANPCyT-FONCYT (grant numbers PICT 2017–2047 and PICT 2019–01810).

Author information

Authors and Affiliations

  1. Chemical Engineering Institute - Faculty of Engineering, Research Group associated with PROBIEN (CONICET-UNCo), National University of San Juan, 5400, San Juan, Argentina

    Daniela Zalazar-Garcia, Anabel Fernandez & Rosa Rodriguez

  2. Institute for Research and Development in Process Engineering, Biotechnology, and Alternative Energies, PROBIEN (CONICET-National University of Comahue), 8300, Neuquén, Argentina

    Lucas Cavaliere, José Soria & Germán Mazza

  3. KULeuven, Department of Chemical Engineering, Process and Environmental Technology Lab, 2860, Sint-Katelijne-Waver, Belgium

    Yimin Deng

Authors
  1. Daniela Zalazar-Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anabel Fernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lucas Cavaliere
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yimin Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. José Soria
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rosa Rodriguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Germán Mazza
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Daniela Zalazar-Garcia, Anabel Fernandez, José Soria, Yimin Deng, Rosa Rodriguez, Germán Mazza; methodology: Daniela Zalazar-Garcia, Anabel Fernandez, Yiming Deng, Rosa Rodriguez; formal analysis and investigation: Daniela Zalazar-Garcia, Anabel Fernandez, Lucas Cavaliere, José Soria, Germán Mazza; writing—original draft preparation: Daniela Zalazar-Garcia, Anabel Fernandez; software: Daniela Zalazar-Garcia, Lucas Cavaliere; writing—review and editing: Rosa Rodriguez, Germán Mazza; funding acquisition: Rosa Rodriguez, Germán Mazza; resources: Rosa Rodriguez, Germán Mazza; project administration: Rosa Rodriguez, Germán Mazza; supervision: Germán Mazza.

Corresponding author

Correspondence to Germán Mazza.

Ethics declarations

Ethical approval

Ethics approval was not required for the present investigation. It does not involve human participants and/or animals.

Conflict of interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 276 KB)

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zalazar-Garcia, D., Fernandez, A., Cavaliere, L. et al. Slow pyrolysis of pistachio-waste pellets: combined phenomenological modeling with environmental, exergetic, and energetic analysis (3-E). Biomass Conv. Bioref. 14, 9197–9215 (2024). https://doi.org/10.1007/s13399-022-03232-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13399-022-03232-3

Keywords

Search

Navigation