Investigasi Efisiensi Penghambatan Korosi Senyawa Quinoxaline Berbasis Machine Learning

Vicenzo Frendyatha Adiprasetya, Muhamad Akrom, Gustina Alfa Trisnapradika

Abstract


Korosi memberikan kekhawatiran serius bagi sektor industri dan akademik karena mempunyai dampak negatif yang signifikan terhadap sejumlah bidang, termasuk perekonomian, lingkungan, masyarakat, industri, keamanan, dan keselamatan. Saat ini, banyak peminat topik pengendalian kerusakan bahan berbasis molekul organik. Quinoxaline mempunyai potensi sebagai inhibitor korosi karena tidak beracun, mudah diproduksi, dan efektif dalam berbagai kondisi korosif. Mengeksplorasi kemungkinan kandidat penghambat korosi melalui penelitian eksperimental adalah proses yang memakan waktu dan sumber daya yang intensif. Dengan menggunakan pendekatan machine learning (ML) berdasarkan model quantitative structure-property relationship (QSPR), kami mengevaluasi beragam algoritma linier dan non-linier sebagai model prediktif nilai corrosion inhibition efficiency (CIE) dalam penelitian ini. Kami menemukan bahwa, untuk kumpulan data senyawa quinoxaline, model non-linier Gradient Boosting Regressor (GBR) mengungguli keseluruhan model linier dan non-linier, serta hasil dari literatur dalam hal kinerja prediksi berdasarkan metrik root mean squared error (RMSE), mean squared error (MSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE) dan coefficient of determination (R2). Secara keseluruhan, penelitian kami memberikan sudut pandang baru tentang kapasitas model ML untuk memperkirakan kemampuan penghambatan korosi pada permukaan besi oleh senyawa organik quinoxaline.

Keywords


Korosi, inhibitor, machine learning, quinoxaline.

Full Text:

PDF

References


Akrom, M. (2022). Investigation Of Natural Extracts As Green Corrosion Inhibitors In Steel Using Density Functional Theory. In Jurnal Teori dan Aplikasi Fisika (Vol. 10, Issue 01).

Akrom, M., Rustad, S., & Kresno Dipojono, H. (2023). Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors. Results in Chemistry, 101126. https://doi.org/10.1016/J.RECHEM.2023.101126

Akrom, M., Rustad, S., Saputro, A. G., & Dipojono, H. K. (2023). Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors. Computational and Theoretical Chemistry, 1229, 114307. https://doi.org/10.1016/J.COMPTC.2023.114307

Akrom, M., Rustad, S., Saputro, A. G., Ramelan, A., Fathurrahman, F., & Dipojono, H. K. (2023a). A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds. Materials Today Communications, 35. https://doi.org/10.1016/j.mtcomm.2023.106402

Akrom, M., Rustad, S., Saputro, A. G., Ramelan, A., Fathurrahman, F., & Dipojono, H. K. (2023b). A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds. Materials Today Communications, 35, 106402. https://doi.org/10.1016/J.MTCOMM.2023.106402

Akrom, M., Sudibyo, U., Kurniawan, A. W., Setiyanto, N. A., Herowati, W., Pertiwi, A., Safitri, A. N., Hidayat, N., Al Azies, H., & Nuswantoro, U. D. (n.d.). Artificial Intelligence Berbasis QSPR Dalam Kajian Inhibitor Korosi. 07(01), 15–20. https://doi.org/10.

Akrom, M., & Sutojo, T. (2023). Investigasi Model Machine Learning Berbasis QSPR pada Inhibitor Korosi Pirimidin Investigation of QSPR-Based Machine Learning Models in Pyrimidine Corrosion Inhibitors. Eksergi, 20(1).

Budi, S., Akrom, M., Trisnapradika, G. A., Sutojo, T., & Prabowo, W. A. E. (2023). Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets. Scientific Journal of Informatics, 10(2), 151–158. https://doi.org/10.15294/sji.v10i2.43929

Chauhan, D. S., Singh, P., & Quraishi, M. A. (2020). Quinoxaline derivatives as efficient corrosion inhibitors: Current status, challenges and future perspectives. In Journal of Molecular Liquids (Vol. 320). Elsevier B.V. https://doi.org/10.1016/j.molliq.2020.114387

El Assiri, E. H., Driouch, M., Lazrak, J., Bensouda, Z., Elhaloui, A., Sfaira, M., Saffaj, T., & Taleb, M. (2020). Development and validation of QSPR models for corrosion inhibition of carbon steel by some pyridazine derivatives in acidic medium. Heliyon, 6(10). https://doi.org/10.1016/j.heliyon.2020.e05067

Haikal, F. M., Akrom, M., & Trisnapradika, G. A. (2023). Perbandingan Algoritma Multilinear Regression dan Decision Tree Regressor dalam Memprediksi Efisiensi Penghambatan Korosi Piridazin. Edumatic: Jurnal Pendidikan Informatika, 7(2), 307–315. https://doi.org/10.29408/edumatic.v7i2.22127

Sumarjono, C. A. P., Akrom, M., & Alfa Trisnapradika, G. (2023). Perbandingan Model Machine Learning Terbaik untuk Comparison of the Best Machine Learning Model to Predict Corrosion Inhibition Capability of Benzimidazole Compounds (Vol. 22, Issue 4).

Maidawati, M. (2023). Studi Eksperimen Dan Komputasi Senyawa Organik Sebagai Inhibitor Korosi : A Review. Jurnal Penelitian Dan Pengkajian Ilmiah Eksakta, 2(2), 148–153. https://doi.org/10.47233/jppie.v2i2.1030

Noor, M., Kasmui, , & Kusuma, S. (2016). ANALISIS HUBUNGAN KUANTITATIF STRUKTUR DAN AKTIVITAS ANTIMALARIA SENYAWA TURUNAN QUINOXALIN Info Artikel. Jurnal MIPA, 39(1), 51–56. http://journal.unnes.ac.id/nju/index.php/JM

Quadri, T. W., Olasunkanmi, L. O., Fayemi, O. E., Lgaz, H., Dagdag, O., Sherif, E. S. M., Alrashdi, A. A., Akpan, E. D., Lee, H. S., & Ebenso, E. E. (2022). Computational insights into quinoxaline-based corrosion inhibitors of steel in HCl: Quantum chemical analysis and QSPR-ANN studies. Arabian Journal of Chemistry, 15(7). https://doi.org/10.1016/j.arabjc.2022.103870

Sugeng, M. , M. I. F. , & P. U. J. (2022). ANALISIS PERBEDAAN LAJU KOROSI HASIL PENGUJIAN WEIGHT LOSS DAN POLARISASI PADA PIPA DENGAN PENGUJIAN KOROSI STANDAR ASTM G59 DAN ASTM G31. Tera Journal, 2(1), 48–56. https://jurnal.undira.ac.id/jurnaltera/article/view/14

Sutojo, T., Rustad, S., Akrom, M., Syukur, A., Shidik, G. F., & Dipojono, H. K. (2023). A machine learning approach for corrosion small datasets. Npj Materials Degradation, 7(1). https://doi.org/10.1038/s41529-023-00336-7

Verma, D. K. (2018). Density Functional Theory (DFT) as a Powerful Tool for Designing Corrosion Inhibitors in Aqueous Phase. In Advanced Engineering Testing. InTech. https://doi.org/10.5772/intechopen.78333




DOI: https://doi.org/10.31315/e.v21i2.10025

Refbacks

  • There are currently no refbacks.

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM


Eksergi p-ISSN  1410-394X, e-ISSN 2460-8203,  is published by "Prodi Teknik Kimia UPN Veteran Yogyakarta".

Contact  Jl. SWK 104 (Lingkar Utara) Condong catur Sleman Yogyakarta

 

 Creative Commons License

Eksergi by http://jurnal.upnyk.ac.id/index.php/eksergi/index/ is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

 

 

Lihat Statistik Jurnal Kami

slot gacor slot gacor hari ini slot gacor 2025 demo slot pg slot gacor slot gacor