Exploring sustainable fuel production through thermal behavior analysis using tga and artificial neural network in the co-pyrolysis of polystyrene and coconut sawmill residue

Joselito A. Olalo

Author(s)

Joselito A. Olalo Department of Mechanical Engineering, College of Engineering, Camarines Norte State College,,Philippines

Abstract

This study employs Thermogravimetric Analysis (TGA) to explore co-pyrolysis potential using polystyrene (PS) and coconut sawmill residue (CSR) for liquid fuel production. Two distinct degradation stages are observed in CSR-PS blends, mirroring pure CSR samples: the initial phase (200-400°C) decomposes biomass components, while the second stage (400-550°C) targets the synthetic polymer PS within CSR-PS blends. Analyzing thermal degradation parameters reveals insights. 100% PS exhibits the highest weight loss and activation energy, highlighting PS's formidable decomposition. Conversely, 100% CSR shows the lowest weight loss and activation energy due to its organic composition. Artificial Neural Network (ANN) modeling indicates varying correlation accuracies for different blend compositions. Surprisingly, 100% PS exhibits lower correlation accuracy in predicting weight loss compared to the 80% PS blend, which achieves a perfect correlation. Conversely, 100% CSR, with simpler decomposition, has the lowest correlation accuracy. These findings illuminate the complex thermal behavior of CSR-PS blends, emphasizing the distinct degradation characteristics of PS and CSR. Implications extend to material applications and disposal strategies, emphasizing tailored approaches based on blend compositions and thermal profiles. This research advances co-pyrolysis as a sustainable avenue for liquid fuel production, providing insights for future research and practical applications.

Publication Details

Page(s) 2540-2548

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 18, Issue: 23, Year: 2023

Request the full text from PASTIC

Keywords

TGA, polystyrene, ANN, coconut sawmill residue, co-pyrolysis

References

Chen W.,Biswas P.,Kwon E.,Park Y.,Rajendran S.,Gnanasekaran L.,Chang J. .2023 .Optimization of the process parameters of catalytic plastic pyrolysis for oil production using design of experiment approaches: A review. Chemical Engineering Journal, 471 : 144695.

Dahal R.,Uusi-Kyyny P.,Pokki J.,Alopaeus V. .2023 .Conceptual design of a distillation process for the separation of styrene monomer from polystyrene pyrolysis oil: experiment and simulation. Chemical Engineering Research and Design, 195 : 65-75.

Escalante J.,Chen W.,Tabatabaei M.,Hoang A.,Kwon E.,Lin K.,Saravanakumar A. .2022 .Pyrolysis of lignocellulosic, algal, plastic, and other biomass wastes for biofuel production and circular bioeconomy: A review of thermo gravimetric analysis (TGA) approach. , 169 : 112914.

Zhou Fan Z.,X. Fan Z.,Wan Z.,S. Z.,Deng Z. F.,Cheng S.,X. S. .2023 .Co-pyrolysis technology for enhancing the functionality of sewage sludge biochar and immobilizing heavy metals. Chemosphere, 317 : 137929.

Flynn J. and Wall L. .1966 .A quick, direct method for the determination of Activation energy from' thermogravimetric data. Polymer Letter, 4 : 323-328.

Giezen A.,Wiegmans B. .2020 .Alternative logistics chains to accommodate plastic waste recycling: An economic evaluation. Transportation Research Interdisciplinary Perspectives. 5, : 100115.

Im J.,Lee S.,Jo I.,Kang J. W.,Kim K. .2022 .Structural characteristics and thermal properties of regenerated cellulose, hemicellulose and lignin after being dissolved in ionic liquids. Journal of Industrial and Engineering Chemistry, 107 : 365-375.

Inayat A.,Fasolini A.,Basile F.,Fridrichova D.,Lestinsky P. D. .2022 .Chemical recycling of waste polystyrene by thermo-catalytic pyrolysis: A description for different feedstocks, catalysts and operation modes. Polymer Degradation and Stability, 201 : 109981.

Jerzak W.,Kuznia M. .2020 .Examination of inorganic gaseous species and condensed phases during coconut husk combustion based on thermodynamic equilibrium predictions. Renewable Energy, 167 : 497-507.

Mankeed P.,Onsree T.,Naqvi S. R.,Shimpalee S.,Tippayawong N. .2022 .Kinetic and thermodynamic analyses for pyrolysis of hemp hurds using discrete distributed activation energy model. Case Studies in Thermal Engineering, 31 : 101870.

Ogunbo J. N.,Alagbe O. A.,Oladapo M. I.,Shin C. .2020 .N-hidden layer artificial neural network architecture computer code: geophysical application example. Heliyon, 6 : .

Prasertpong P.,Onsree T.,Huenkaeo N.,Tippayawong N.,Lauterbach J. .2023 .Exposing and understanding synergistic effects in co-pyrolysis of biomass and plastic waste via machine learning. Bioresource Technology, 369 : 128419.

Vo T. A.,Ly H. V.,Hwang I.,Hwang H. T.,Kim J,Kim S. .2023 .Enhancement of biofuel quality via conventional and catalytic co-pyrolysis of bamboo with polystyrene in a bubbling fluidized bed reactor: Coupled experiments and artificial neural network modeling. Fuel, 346 : 128403.

Zhang Y.,Liang Y.,Li S.,Yuan Y.,Zhang D.,Wu Y.,Xie H.,Brindhadevi K.,Pugazhendhi A.,Xia C. .2023 .A review of biomass pyrolysis gas: Forming mechanisms, influencing parameters, and product application upgrades. Fuel, 347 : 128461.