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Kinetic modeling and thermogravimetric investigation of Phoenix dactylifera and Phyllanthus emblica non-edible oil seeds: artificial neural network (ANN) prediction modeling

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Abstract

In the present study, Phoenix dactylifera (PD) or Indian Amla and Phyllanthus emblica (PE) or Dry dates were thermally studied to determine their potential as pyrolysis feedstocks using theoretical kinetic modeling, then the artificial neural network (ANN) was used on TGA data to predict the best fitting. TG experiments were conducted at three different heating rates of 10, 20, and 30 °C/min for both the seeds. Kinetic modeling of the PD and PE seeds was also performed using FWO, KAS, FRM, KN model-free isoconversional methods and C-R model fitting method. Further, the thermodynamic parameters were also determined for the aforementioned heating rates. The average values of activation energy were found to be in incremental pattern of KN < FRM < C-R < KAS < FWO methods and KN < KAS < FWO < C-R < FRM methods, respectively for PD and PE seeds. The values of ∆G, ∆S, and ∆H were observed in the similar range as other oilseeds utilized in pyrolysis process. The TGA data were accurately simulated by ANN modeling at three different heating rates as the value of R2 were found in the range of 0.99–1 for both the seeds. The kinetic modeling, thermodynamic parameters, and ANN modeling also proved that the PD and PE waste biomass seeds could definitely be used to obtain liquid fuels or other valuable chemicals besides reducing the environmental degradation and value-addition to waste seeds towards a circular economy.

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Data availability

The authors declare that all data supporting the findings of this study are available within the article and its supplementary data file.

Abbreviations

PD:

Phoenix dactylifera

PE:

Phyllan thus emblica

TGA:

Thermogravimetric analyzer

DTG:

Differential thermogravimetric

FRM:

Friedman

KAS:

Kissinger–Akahira–Sunose

FWO:

Flynn–Wall–Ozawa

KN:

Kissinger

C-R:

Coats-Redfern

ANN:

Artificial neural network

Wt :

Total weight loss

A:

Pre-exponential factor

α:

Degree of conversion

Ea :

Activation energy

β:

Heating rate

-dw/dtmax :

Maximum degradation rate

ΔG:

Gibbs free energy change

ΔH:

Enthalpy change

ΔS:

Entropy change

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Authors and Affiliations

  1. Department of Energy Engineering, Central University of Jharkhand, Ranchi, India

    Indra Mohan & Sachin Kumar

  2. Department of Chemical Engineering, Indian Institute of Technology, Delhi, India

    Abhisek Sahoo

  3. School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea

    Sandip Mandal

  4. Department of Energy Engineering and Centre of Excellence - Green and Efficient Energy Technology (CoE-GEET), Central University of Jharkhand, Ranchi, India

    Sachin Kumar

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  1. Indra Mohan
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  2. Abhisek Sahoo
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  3. Sandip Mandal
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  4. Sachin Kumar
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Contributions

Indra Mohan: conceptualization, formal analysis, investigation, methodology, software, visualization, data curation, writing – original draft. Abhisek Sahoo: data curation, supervision, writing – review & editing. Sandip Mandal: data curation, supervision, writing – review & editing. Sachin Kumar: project administration, supervision, validation, resources, writing – review & editing.

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Correspondence to Sachin Kumar.

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Highlights

• An innovative approach to effectively utilize the waste biomass for the domestic carbon resource in a biorefinery concept.

• Pyrolysis kinetics was studied for two novel waste seeds such as Phoenix dactylifera and Phyllanthus emblica seed.

• Thermodynamics parameters were also estimated for pyrolysis of waste biomass.

• First study concerning the prediction modeling using Artificial Neural Network (ANN) pyrolysis of waste Phoenix dactylifera and Phyllanthus emblica seeds.

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Mohan, I., Sahoo, A., Mandal, S. et al. Kinetic modeling and thermogravimetric investigation of Phoenix dactylifera and Phyllanthus emblica non-edible oil seeds: artificial neural network (ANN) prediction modeling. Biomass Conv. Bioref. (2023). https://doi.org/10.1007/s13399-023-04094-z

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