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All Journal Rekayasa Mesin International Journal of Mechanical Engineering Technologies and Applications (MECHTA)
Anggara, Teuku
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Automotive Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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PENJADWALAN PERAWATAN DENGAN METODE CAMPBELL DUDEL SMITH (CDS) UNTUK MENINGKATKAN PRODUKSI MESIN RECYCLE WASTE TEMBAKAU Anggara, Teuku; Pratikto, Pratikto; Sonief, Achmad As?ad
Rekayasa Mesin Vol 11, No 1 (2020)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jrm.2020.011.01.12

Abstract

The Campbell Dudek Smith (CDS) method is commonly used by large companies to help them make Flowshop schedules. The purpose of this study is to design a more effective maintenance scheduling sequence in order to increase the amount of production with the most efficient use of time without having to stop production and be able to calculate the productivity of the machine it self. Partial Productivity (PP) analysis is used to determine the level of machine productivity by proving Partial Productivity (PP) after is better than Partial Productivity (PP) before. This research was conducted at PT. X, one of the leading national companies in producing cigarette products such as SKT cigarettes, SKM and SPM. As a result, this study has performed calculations using the Campbell Dudek Smith (CDS) algorithm and calculates the productivity of each iteration using Partial Productivity (PP). The recommended improvement of the engine maintenance scheduling sequence is by applying the scheduling sequence to the 5th iteration, J2-J4-J3-J1-J5.
THE PREDICTION OF HYDROGEN EVOLUTION REACTION FROM DYNAMIC MAGNETIC FIELD ASSISTED WATER ELECTROLYSIS ARTIFICIAL NEURAL NETWORK Nugroho, Willy Satrio; Purnami, Purnami; Schulze, Ajani Aiman; Anggara, Teuku; Schulze, Klauss
International Journal of Mechanical Engineering Technologies and Applications Vol. 6 No. 1 (2025)
Publisher : Mechanical Engineering Department, Engineering Faculty, Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/MECHTA.2025.006.01.1

Abstract

This study explores the prediction of Hydrogen Evolution Reaction (HER) performance in Dynamic Magnetic Field (DMF) assisted water electrolysis using Artificial Neural Networks (ANN). The integration of ANN models with experimental data from DMF-assisted electrolysis provides valuable insights into the complex interplay between magnetic fields and electrochemical processes. The results show significant enhancements in HER rates compared to conventional electrolysis, with static magnetic fields also contributing to performance improvements. The ANN models developed exhibit high accuracy in predicting HER performance under varying DMF rotational speeds, as evidenced by low Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and high R-squared values, demonstrating their strong predictive power and reliability. However, caution is advised regarding overfitting, and future research should focus on incorporating techniques like regularization and cross-validation to enhance model generalization. This study lays the foundation for further optimization of efficient hydrogen production technologies in the context of sustainable energy solutions.
THE PREDICTION OF HYDROGEN EVOLUTION REACTION FROM DYNAMIC MAGNETIC FIELD ASSISTED WATER ELECTROLYSIS ARTIFICIAL NEURAL NETWORK Nugroho, Willy Satrio; Purnami, Purnami; Schulze, Ajani Aiman; Anggara, Teuku; Schulze, Klauss
International Journal of Mechanical Engineering Technologies and Applications Vol. 6 No. 1 (2025)
Publisher : Mechanical Engineering Department, Engineering Faculty, Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/MECHTA.2025.006.01.1

Abstract

This study explores the prediction of Hydrogen Evolution Reaction (HER) performance in Dynamic Magnetic Field (DMF) assisted water electrolysis using Artificial Neural Networks (ANN). The integration of ANN models with experimental data from DMF-assisted electrolysis provides valuable insights into the complex interplay between magnetic fields and electrochemical processes. The results show significant enhancements in HER rates compared to conventional electrolysis, with static magnetic fields also contributing to performance improvements. The ANN models developed exhibit high accuracy in predicting HER performance under varying DMF rotational speeds, as evidenced by low Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and high R-squared values, demonstrating their strong predictive power and reliability. However, caution is advised regarding overfitting, and future research should focus on incorporating techniques like regularization and cross-validation to enhance model generalization. This study lays the foundation for further optimization of efficient hydrogen production technologies in the context of sustainable energy solutions.
Co-Authors Achmad Noerkhaerin Putra Pratikto Pratikto Purnami Purnami Schulze, Ajani Aiman Schulze, Klauss Sonief, Achmad As?ad Sonief, Achmad As’ad Willy Satrio Nugroho