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Experimental Investigation of Natural Plant Extracts as A Green Corrosion Inhibitor in Steel Muhamad Akrom
Journal of Renewable Energy and Mechanics Vol. 5 No. 01 (2022): REM VOL 5 NO 01 2022
Publisher : UIR PRESS

Natural plant extracts show excellent performance in inhibiting steel corrosion. Steel has a relatively weak resistance to corrosion when in a corrosive environment. The use of natural plant extracts as green corrosion inhibitors is able to provide good performance with high inhibition efficiency. Experimental methods such as gravimetric analysis, potentiodynamic polarization, and electrochemical impedance spectroscopy are commonly used to investigate corrosion inhibition based on corrosion activity. A comparative literature review of this work is important for the development and utilization of green inhibitors. Experimental studies still do not clearly reveal the mechanism of adsorption and inhibition of molecular inhibitors at the atomic level. For this reason, future developments are very important to study theoretically at the atomic level of the main components of natural plant extracts as corrosion inhibitors, so that it is hoped that there will be a clear and systematic understanding of the mechanism of corrosion inhibition in steel.

Investigasi DFT pada Ekstrak Tanaman Cengkeh dan Tembakau sebagai Inhibitor Korosi Hijau Muhamad Akrom
Science Tech: Jurnal Ilmu Pengetahuan dan Teknologi Vol 8 No 1 (2022): Februari
Publisher : Universitas Sarjanawiyata Tamansiswa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30738/st.vol8.no1.a11775

In the present work, corrosion inhibition of Syzygium aromaticum and Nicotiana tabacum extract was studied by DFT method. Frontier molecular orbitals (FMO) plots, quantum chemical descriptors such as highest occupied molecular orbital energy (EHOMO), lowest unoccupied molecular orbital energy (ELUMO), energy gap (ΔE), dipole moment (l) and partial charge analysis were calculated. The results obtained show that the eugenol as extract of Syzygium aromaticum and nicotine as extract of Nicotiana tabacum acts as an effective green corrosion inhibitor. In general, nicotine has a better potential as an green corrosion inhibitor than eugenol based on chemical reactivity. However, further studies are needed to gain insight into the mechanism of corrosion inhibition through the study of the adsorption of eugenol and nicotine molecules on metal surfaces.

Investigation of Natural Product Extracts as Green Corrosion Inhibitors on Steels and Alloys: Experimental and Theoretical Approach Muhamad Akrom; Wahyu Aji Eko Prabowo
Prosiding ESEC Vol. 3 No. 1 (2022): Seminar Nasional (ESEC) 2022
Publisher : Prosiding ESEC

Show Abstract | Download Original | Original Source | Check in Google Scholar

Natural plant extracts show excellent performance as green corrosion inhibitors on steel with high inhibition efficiency. Experimental and theoretical methods are able to synergize in investigating corrosion inhibition performance. This report is a comparative literature for the development of natural-based inhibitors. The development of future investigations is expected to show the interaction of plant extract molecules with metal surfaces at the atomic level, so that a systematic and detailed understanding of the mechanism of corrosion inhibition in metal is obtained.

Investigasi Model Machine Learning Berbasis QSPR pada Inhibitor Korosi Pirimidin Muhamad Akrom; Totok Sutojo
Eksergi Vol 20, No 2 (2023)
Publisher : Prodi Teknik Kimia, Fakultas Teknologi Industri, UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/e.v20i2.9864

Since corrosion causes considerable losses in many fields, including the economy, environment, society, industry, security, and safety, it is a major concern for the industrial and academic sectors. Damage control of materials based on organic compounds is currently a field of great interest. Because it is non-toxic, affordable, and effective in a variety of corrosive situations, pyrimidine has potential as a corrosion inhibitor. It takes a lot of time and resources to carry out experimental investigations in the exploration of potential corrosion inhibitor candidates. In this study, we evaluate the gradient boosting regressor (GBR), support vector regression (SVR), and k-nearest neighbor (KNN) algorithms as predictive models for corrosion inhibition efficiency using a machine learning (ML) approach based on the quantitative structure-property relationship model (QSPR). Based on the metric coefficient of determination (R2) and root mean square error (RMSE), we found that the GBR model had the best predictive performance compared to the SVR and KNN models as well as models from the literature for pyrimidine compound datasets. Overall, our study offers a new perspective on the ability of ML models to predict corrosion inhibition of iron surfaces

Peningkatan Keterampilan Penulisan Karya Ilmiah Bagi Guru Yayasan Hidayatullah Gunungpati Melalui Komunikasi Interaktif Gustina Alfa Trisnapradika; Achmad Wahid Kurniawan; Muhamad Akrom
Lok Seva: Journal of Contemporary Community Service Vol 1, No 1 (2022)
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/lok seva.v1i1.6352

Menulis karya ilmiah merupakan keterampilan yang sangat penting dalam pendidikan, sehingga hasil pemikiran dapat tersampaikan dalam bentuk informasi penting. Pentingnya kebutuhan karya ilmiah yang berkualitas, kreatif, dan inovatif kadang kala tidak sebanding dengan minat dan kompetensi seorang guru. Persepsi tentang rumitnya proses penulisan karya ilmiah menjadi masalah yang mendasar. Juga, beban mengajar serta beban administratif lainnya seringkali menyita waktu dan tenaga sehingga sulit mengembangkan kompetensi profesional seorang guru dalam bidang publikasi karya ilmiah. Tujuan kegiatan pengabdian masyarakat ini adalah untuk memberikan pelatihan guna meningkatkan keterampilan menulis karya ilmiah bagi para guru di Yayasan Hidayatullah Gunungpati. Pelatihan ini dilakukan agar dapat membantu menguraikan beberapa masalah mendasar diatas. Berdasarkan hasil pelatihan, dapat disimpulkan bahwa kegiatan pengabdian ini telah berhasil membangun motivasi para guru untuk menulis karya ilmiah. Hal ini dapat dinilai dari antusiasme peserta selama pelatihan dan diskusi interaktif. Selain itu, kegiatan ini telah merespon permasalahan para guru terhadap kesulitan dalam kaidah dasar penulisan ilmiah, sistematika penulisan karya ilmiah, melakukan penelitian tindakan kelas, mengidentifikasi jenis karya tulis ilmiah, dan tahapan publikasi. Peserta juga memulai untuk menulis karya imiah. Peserta berharap pelatihan dan pendampingan dapat berkelanjutan hingga berhasil memproduksi karya ilmiah yang diterbitkan

Artificial Intelligence Berbasis QSPR Dalam Kajian Inhibitor Korosi Muhamad Akrom; Usman Sudibyo; Achmad Wahid Kurniawan; Noor Ageng Setiyanto; Ayu Pertiwi; Aprilyani Nur Safitri; Novianto Hidayat; Harun Al Azies; Wise Herawati
JoMMiT Vol 7, No 1 (2023)
Publisher : Politeknik Negeri Media Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46961/jommit.v7i1.721

Baja termasuk material yang memiliki ketahanan rendah terhadap serangan korosi Ketika berada pada lingkungan korosif. Inhibitor organik mampu menghambat korosi dengan efisiensi inhibisi yang tinggi. Tinjauan komparatif penting bagi pengembangan metode evaluasi kinerja inhibitor disajikan dalam karya ini. Kami mereview perkembangan artificial intelligence berbasis mesin learning dengan model QSPR dalam kajian penghambatan korosi. Makalah ini menjelaskan bagaimana metode pembelajaran mesin berbasis data dapat menghasilkan model yang menghubungkan sifat-aktivitas molekuler dengan penghambatan korosi oleh inhibitor berbasis bahan alam (green inhibitor). Teknik ini dapat digunakan untuk memprediksi kinerja senyawa yang belum disintesis atau diuji. Keberhasilan model ini memberikan paradigma untuk penemuan senyawa baru yang cepat, penghambat korosi yang efektif untuk berbagai logam dan paduan.

Predicting Methanol Space-Time Yield from CO? Hydrogenation Using Machine Learning: Statistical Evaluation of Penalized Regression Techniques Harun Al Azies; Muhamad Akrom; Setyo Budi; Gustina Alfa Trisnapradika; Aprilyani Nur Safitri
International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i2.1341

This study investigates the effectiveness of machine learning techniques, specifically penalized regression models Ridge Regression, Lasso Regression, and Elastic Net Regression in predicting methanol space-time yield (STY) from CO? hydrogenation data. Using a dataset derived from Cu-based catalyst research, the study implemented a comprehensive preprocessing approach, including data cleaning, imputation, outlier removal, and normalization. The models were rigorously evaluated through 10-fold cross-validation and tested on unseen data. Ridge Regression outperformed the other models, achieving the lowest Root Mean Squared Error (RMSE) of 0.7706, Mean Absolute Error (MAE) of 0.5627, and Mean Squared Error (MSE) of 0.5938. In comparison, Lasso and Elastic Net Regression models exhibited higher error metrics. Feature importance analysis revealed that Gas Hourly Space Velocity (GHSV) and Molar Masses of Support significantly influence catalytic activity. These findings suggest that Ridge Regression is a promising tool for accurately predicting methanol production, providing valuable insights for optimizing catalytic processes and advancing sustainable practices in chemical engineering.

Harnessing Quantum SVR on Quantum Turing Machine for Drug Compounds Corrosion Inhibitors Analysis Akbar Priyo Santosa; Muhammad Reesa; Lubna Mawaddah; Muhamad Akrom
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.601

Corrosion is an issue that has a significant impact on the oil and gas industry, resulting in significant losses. This is worth investigating because corrosion contributes to a large part of the total annual costs of oil and gas production companies worldwide, and can cause serious problems for the environment that will impact society. The use of inhibitors is one way to prevent corrosion that is quite effective. This study is an experimental study that aims to implement machine learning (ML) on the efficiency of corrosion inhibitors. In this study, the use of the Quantum Support Vector Regression (QSVR) algorithm in the ML approach is used considering the increasingly developing quantum computing technology with the aim of producing better evaluation matrix values than the classical ML algorithm. From the experiments carried out, it was found that the QSVR algorithm with a combination of (TrainableFidelityQuantumKernel, ZZFeatureMap/ PauliFeatureMap, and linear entanglement) obtained better Root Mean Square Error (RMSE) and model training time with a value of 6,19 and 92 compared to other models in this experiment which can be considered in predicting the efficiency of corrosion inhibitors. The success of the research model can provide a new insights of the ability of quantum computer algorithms to increase the evaluation value of the matrix and the ability of ML to predict the efficiency of corrosion inhibitors, especially on a large industrial scale.

Investigasi Efisiensi Penghambatan Korosi Senyawa Quinoxaline Berbasis Machine Learning Vicenzo Frendyatha Adiprasetya; Muhamad Akrom; Gustina Alfa Trisnapradika
Eksergi Vol 21 No 2 (2024)
Publisher : Prodi Teknik Kimia, Fakultas Teknik Industri, UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/e.v21i2.10025

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.

Harnessing Quantum SVR on Quantum Turing Machine for Drug Compounds Corrosion Inhibitors Analysis Akbar Priyo Santosa; Muhammad Reesa; Lubna Mawaddah; Muhamad Akrom
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.601

Corrosion is an issue that has a significant impact on the oil and gas industry, resulting in significant losses. This is worth investigating because corrosion contributes to a large part of the total annual costs of oil and gas production companies worldwide, and can cause serious problems for the environment that will impact society. The use of inhibitors is one way to prevent corrosion that is quite effective. This study is an experimental study that aims to implement machine learning (ML) on the efficiency of corrosion inhibitors. In this study, the use of the Quantum Support Vector Regression (QSVR) algorithm in the ML approach is used considering the increasingly developing quantum computing technology with the aim of producing better evaluation matrix values than the classical ML algorithm. From the experiments carried out, it was found that the QSVR algorithm with a combination of (TrainableFidelityQuantumKernel, ZZFeatureMap/ PauliFeatureMap, and linear entanglement) obtained better Root Mean Square Error (RMSE) and model training time with a value of 6,19 and 92 compared to other models in this experiment which can be considered in predicting the efficiency of corrosion inhibitors. The success of the research model can provide a new insights of the ability of quantum computer algorithms to increase the evaluation value of the matrix and the ability of ML to predict the efficiency of corrosion inhibitors, especially on a large industrial scale.

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