PLASTIC WASTE CONVERSION TO LIQUID FUELS OVER MODIFIED-RESIDUAL CATALYTIC CRACKING CATALYSTS: MODELING AND OPTIMIZATION USING HYBRID ARTIFICIAL NEURAL NETWORK – GENETIC ALGORITHM

Istadi Istadi; Luqman Buchori; Suherman Suherman

doi:10.14710/reaktor.13.3.131-139

DOI: https://doi.org/10.14710/reaktor.13.3.131-139

PLASTIC WASTE CONVERSION TO LIQUID FUELS OVER MODIFIED-RESIDUAL CATALYTIC CRACKING CATALYSTS: MODELING AND OPTIMIZATION USING HYBRID ARTIFICIAL NEURAL NETWORK – GENETIC ALGORITHM

*Istadi Istadi - Laboratory of Energy and Chemical Process Engineering, , Indonesia

Luqman Buchori - Laboratory of Energy and Chemical Process Engineering, , Indonesia

Suherman Suherman - Laboratory of Energy and Chemical Process Engineering, , Indonesia

Published: 6 May 2011.

BibTex Citation Data :

@article{Reaktor3178,
    author = {Istadi Istadi and Luqman Buchori and Suherman Suherman},
    title = {PLASTIC WASTE CONVERSION TO LIQUID FUELS OVER MODIFIED-RESIDUAL CATALYTIC CRACKING CATALYSTS: MODELING AND OPTIMIZATION USING HYBRID ARTIFICIAL NEURAL NETWORK – GENETIC ALGORITHM},
    journal = {Reaktor},
  volume = {13},
    number = {3},
    year = {2011},
    keywords = {artificial neural network; central composite design; genetic algorithm; optimization; plastic waste; Residual Catalytic Cracking},
    abstract = {  The plastic waste utilization can be addressed toward different valuable products. A promising technology for the utilization is by converting it to fuels. Simultaneous modeling and optimization representing effect of reactor temperature, catalyst calcinations temperature, and plastic/catalyst weight ratio toward performance of liquid fuel production was studied over modified catalyst waste. The optimization was performed to find optimal operating conditions (reactor temperature, catalyst calcination temperature, and plastic/catalyst weight ratio) that maximize the liquid fuel product. A Hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) method was used for the modeling and optimization, respectively. The variable interaction between the reactor temperature, catalyst calcination temperature, as well as plastic/catalyst ratio is presented in surface plots. From the GC-MS characterization, the liquid fuels product was mainly composed of C 4  to C 13  hydrocarbons.      KONVERSI LIMBAH PLASTIK MENJADI BAHAN BAKAR CAIR DENGAN METODE PERENGKAHAN KATALITIK MENGGUNAKAN KATALIS BEKAS YANG TERMODIFIKASI: PEMODELAN DAN OPTIMASI MENGGUNAKAN GABUNGAN METODE  ARTIFICIAL NEURAL NETWORK  DAN  GENETIC ALGOR      ITHM .   Pemanfaatan limbah plastik dapat dilakukan untuk menghasilkan produk yang lebih bernilai tinggi. Salah satu teknologi yang menjanjikan adalah dengan mengkonversikannya menjadi bahan bakar. Permodelan, simulasi dan optimisasi simultan yang menggambarkan efek dari suhu reaktor, suhu kalsinasi katalis, dan rasio berat plastik/katalis terhadap kinerja produksi bahan bakar cair telah dipelajari menggunakan katalis bekas termodifikasi Optimisasi ini ditujukan untuk mencari kondisi operasi optimum (suhu reaktor, suhu kalsinasi katalis, dan rasio berat plastik/katalis) yang memaksimalkan produk bahan bakar cair. Metode Hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) telah digunakan untuk permodelan dan optimisasi simultan tersebut. Inetraksi antar variabel suhu reaktor, suhu kalsinasi katalis, dan rasio berat plastik/katalis digambarkan dalam bentuk plot surface. Berdasarkan karakterisasi GC-MS, produk bahan bakar yang diperoleh terdiri dari komponen-komponen hidrokarbon C 4 -C 13 .  },
   issn = {2407-5973},   pages = {131--139}  doi = {10.14710/reaktor.13.3.131-139},
    url = {https://ejournal.undip.ac.id/index.php/reaktor/article/view/3178}
}

Citation Format:

Abstract

The plastic waste utilization can be addressed toward different valuable products. A promising technology for the utilization is by converting it to fuels. Simultaneous modeling and optimization representing effect of reactor temperature, catalyst calcinations temperature, and plastic/catalyst weight ratio toward performance of liquid fuel production was studied over modified catalyst waste. The optimization was performed to find optimal operating conditions (reactor temperature, catalyst calcination temperature, and plastic/catalyst weight ratio) that maximize the liquid fuel product. A Hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) method was used for the modeling and optimization, respectively. The variable interaction between the reactor temperature, catalyst calcination temperature, as well as plastic/catalyst ratio is presented in surface plots. From the GC-MS characterization, the liquid fuels product was mainly composed of C₄ to C₁₃ hydrocarbons.

KONVERSI LIMBAH PLASTIK MENJADI BAHAN BAKAR CAIR DENGAN METODE PERENGKAHAN KATALITIK MENGGUNAKAN KATALIS BEKAS YANG TERMODIFIKASI: PEMODELAN DAN OPTIMASI MENGGUNAKAN GABUNGAN METODE ARTIFICIAL NEURAL NETWORK DAN GENETIC ALGOR

ITHM. Pemanfaatan limbah plastik dapat dilakukan untuk menghasilkan produk yang lebih bernilai tinggi. Salah satu teknologi yang menjanjikan adalah dengan mengkonversikannya menjadi bahan bakar. Permodelan, simulasi dan optimisasi simultan yang menggambarkan efek dari suhu reaktor, suhu kalsinasi katalis, dan rasio berat plastik/katalis terhadap kinerja produksi bahan bakar cair telah dipelajari menggunakan katalis bekas termodifikasi Optimisasi ini ditujukan untuk mencari kondisi operasi optimum (suhu reaktor, suhu kalsinasi katalis, dan rasio berat plastik/katalis) yang memaksimalkan produk bahan bakar cair. Metode Hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) telah digunakan untuk permodelan dan optimisasi simultan tersebut. Inetraksi antar variabel suhu reaktor, suhu kalsinasi katalis, dan rasio berat plastik/katalis digambarkan dalam bentuk plot surface. Berdasarkan karakterisasi GC-MS, produk bahan bakar yang diperoleh terdiri dari komponen-komponen hidrokarbon C₄-C₁₃.

Fulltext View|Download

Keywords: artificial neural network; central composite design; genetic algorithm; optimization; plastic waste; Residual Catalytic Cracking

Article Metrics:

Article Info

Section: Research Article

Language : EN

In Volume 13, Nomor 3, Juni 2011

Recent articles

KINETIKA MIKRO DEKOMPOSISI METANA MENJADI KARBON NANOTUBE PADA PERMUKAAN KATALIS Ni-Cu-Al AKTIVASI ZEOLIT ALAM SEBAGAI ADSORBEN PADA ALAT PENGERING BERSUHU RENDAH PENGARUH PROSES PENGERINGAN RIMPANG TEMULAWAK (Curcuma xanthorriza ROXB) TERHADAP KANDUNGAN DAN KOMPOSISI KURKUMINOID More recent articles

Most cited articles

SYNTHESIS AND CHARACTERIZATION OF POLYIMIDE-ZEOLITE MIXED MATRIX MEMBRANE KARAKTERISASI FILM KOMPOSIT ALGINAT DAN KITOSAN AKTIVASI ZEOLIT ALAM SEBAGAI ADSORBEN PADA ALAT PENGERING BERSUHU RENDAH Solid And Liquid Pineapple Waste Utilization For Lactic Acid Fermentation Synthesis of Matrix Si-K-HAs Gel from Geothermal Sludge and Peat More cited articles

Last update:

Application of computational approach in plastic pyrolysis kinetic modelling: a review
Sabino Armenise, Syieluing Wong, José M. Ramírez-Velásquez, Franck Launay, Daniel Wuebben, Bemgba B. Nyakuma, Joaquín Rams, Marta Muñoz. Reaction Kinetics, Mechanisms and Catalysis, 134 (2), 2021. doi: 10.1007/s11144-021-02093-7
Thermal and catalytic cracking of plastic waste: a review
Zahraa A. Hussein, Zaidoon M. Shakor, Mohammed Alzuhairi, Farooq Al-Sheikh. International Journal of Environmental Analytical Chemistry, 103 (17), 2023. doi: 10.1080/03067319.2021.1946527
Chemical characterization of waste tire pyrolysis products
Gediz UĞUZ, Abdülkadir AYANOĞLU. International Advanced Researches and Engineering Journal, 5 (2), 2021. doi: 10.35860/iarej.856112
Optimization of process parameters by response surface methodology (RSM) for catalytic pyrolysis of waste high-density polyethylene to liquid fuel
Sachin Kumar, R.K. Singh. Journal of Environmental Chemical Engineering, 2 (1), 2014. doi: 10.1016/j.jece.2013.12.001

Last update: 2025-02-01 12:02:42

No citation recorded.

In order for REAKTOR to publish and disseminate research articles, we need non-exclusive publishing rights (transferred from the author(s) to the publisher). This is determined by a publishing agreement between the Author(s) and REAKTOR. This agreement deals with transferring or licensing the publishing copyright to REAKTOR while Authors still retain significant rights to use and share their published articles. REAKTOR supports the need for authors to share, disseminate, and maximize the impact of their research and these rights in any databases.

As a journal author, you have the right to use your article for many purposes, including by your employing institute or company. These Author rights can be exercised without the need to obtain specific permission. Authors publishing in BCREC journals have wide rights to use their works for teaching and scholarly purposes without needing to seek permission, including, but not limited to:

use for classroom teaching by the Author or the Author's institution and presentation at a meeting or conference and distributing copies to attendees;
use for internal training by the author's company;
distribution to colleagues for their research use;
use in a subsequent compilation of the author's works;
inclusion in a thesis or dissertation;
reuse of portions or extracts from the article in other works (with full acknowledgment of the final article);
preparation of derivative works (other than commercial purposes) (with full acknowledgment of the final article);
voluntary posting on open websites operated by the author or the author’s institution for scholarly purposes,

(but it should follow the open access license of Creative Common CC-by-SA License).

Authors/Readers/Third Parties can copy and redistribute the material in any medium or format and remix, transform, and build upon the material for any purpose, even commercially. Still, they must give appropriate credit (the name of the creator and attribution parties (authors detail information), a copyright notice, an open access license notice, a disclaimer notice, and a link to the material), provide a link to the license, and indicate if changes were made (Publisher indicates the modification of the material (if any).

Authors/Readers/Third Parties can read, print and download, redistribute or republish the article (e.g., display in a repository), translate the article, download for text and data mining purposes, reuse portions or extracts from the article in other works, sell or re-use for commercial purposes, remix, transform, or build upon the material, they must distribute their contributions under the same license as the original Creative Commons Attribution-ShareAlike (CC BY-SA).