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Scientific Journal of Engineering Research
Published by PT. Teknologi Futuristik Indonesia
ISSN : -     EISSN : 31091725     DOI : https://doi.org/10.64539/sjer
Core Subject : Engineering,
Engineering
The Scientific Journal of Engineering Research (SJER) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The journal is committed to publishing high-quality articles in all fundamental and interdisciplinary areas of engineering, with a particular emphasis on advancements in Information Technology. It encourages submissions that explore emerging fields such as Machine Learning, Internet of Things (IoT), Deep Learning, Artificial Intelligence (AI), Blockchain, and Big Data, which are at the forefront of innovation and engineering transformation. SJER welcomes original research articles, review papers, and studies involving simulation and practical applications that contribute to advancements in engineering. It encourages research that integrates these technologies across various engineering disciplines. The scope of the journal includes, but is not limited to: Mechanical Engineering Electrical Engineering Electronic Engineering Civil Engineering Architectural Engineering Chemical Engineering Mechatronics and Robotics Computer Engineering Industrial Engineering Environmental Engineering Materials Engineering Energy Engineering All fields related to engineering By fostering innovation and bridging knowledge gaps, SJER aims to contribute to the development of sustainable and intelligent engineering systems for the modern era.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 43 Documents
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Isolation Forest–Based Intrusion Detection for Cyber-Physical Systems Oise, Godfrey Perfectson; Konyeha, Susan; Uloko, Felix Oshiorenoya; Pius, Kevin Chinedu; Eferoba–Idio, Enovwo; Edobor, Michael Uyiosa; Mintah, Evans; Ukpebor, Osahon; Sokoya, Oludare; Jessa, Tejiri
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.434

Abstract

Cyber-physical engineering systems (CPES) form the backbone of critical infrastructures such as power generation, industrial automation, and water treatment facilities. Because cyber intrusions in these environments can directly disrupt physical processes, reliable intrusion detection mechanisms are essential for maintaining operational safety and system resilience. However, many existing intrusion detection approaches rely on supervised learning techniques that require large volumes of labeled attack data, which are rarely available in real industrial environments. In addition, advanced detection methods often introduce significant computational overhead, limiting their practicality for deployment in resource-constrained cyber-physical systems. To address these challenges, this study proposes a one-class anomaly detection framework based on the Isolation Forest algorithm for monitoring cyber-physical engineering systems. The proposed approach learns the statistical distribution of normal operational behavior using multivariate sensor, actuator, and control signals, and identifies deviations from this learned pattern as potential cyber intrusions. The framework is evaluated using the Hardware-in-the-Loop–based Augmented Industrial Control System (HAI) Security Dataset, which provides realistic industrial process measurements under both normal and attack scenarios. Experimental results show that the model achieves overall accuracy (0.89) and strong performance in identifying normal operational states (F1-score = 0.94). However, attack detection shows moderate recall (0.48) but low precision (0.04) due to class imbalance and overlapping anomaly score distributions. These findings indicate that Isolation Forest serves as a computationally efficient baseline anomaly detection mechanism for real-time CPS monitoring, while highlighting the need for hybrid and temporally aware detection strategies to improve attack discrimination in industrial cyber-physical environments.
Artificial Intelligence–Driven Simulation Models for Industrial Accident Prevention in Chemical Process Engineering Acuña Acuña, Edwin Gerardo
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.405

Abstract

Industrial accidents in chemical process engineering continue to pose a significant issue despite the widespread use of Industry 4.0 technology and data-driven monitoring systems. Traditional safety frameworks often depend on either purely empirical machine learning models or deterministic first-principles simulations, creating a methodological split that constrains prediction reliability in uncommon, high-impact situations. This work bridges the structural gap by incorporating physics-informed artificial intelligence into a digital twin architecture for the avoidance of industrial accidents. A methodological framework driven by simulation was established, integrating first-principles process modeling, synthetic data generation with controlled fault injection, supervised and unsupervised learning, and reinforcement learning for safety-constrained optimization. Physics-based limitations were included into the learning aim to maintain thermodynamic and transport consistency. The model's performance was assessed using safety-oriented criteria, such as detection delay, false negative rate, resilience to sensor noise, and stability amid parametric uncertainty. Results demonstrate that physics-informed models significantly reduce detection latency and false negatives in accident precursor regimes compared to purely data-driven baselines. The integration of constraint-aware learning improves extrapolation stability under class imbalance conditions typical of industrial safety datasets. Furthermore, explainable AI mechanisms enhance interpretability and regulatory transparency. These findings indicate that AI-enhanced simulation models reconfigure accident prevention strategies by shifting from reactive threshold systems to proactive, mechanism-consistent risk anticipation frameworks applicable to safety-critical chemical processes.
Probabilistic Finite Element Analysis of Temperature-Dependent Corrosion in Oil and Gas Pipelines Nrior, Million Matthew; Nitonye, Samson; Adumene, Sidum; Orji, Charles Ugochukwu
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.427

Abstract

Marine pipeline systems are continually exposed to operating conditions that accelerate internal corrosion, posing risks to flow assurance and structural integrity. This study applies finite element modelling to evaluate the influence of operating temperature on corrosion progression and pipeline performance. The study addressed gaps in temperature-based corrosion propagation in a pipeline using ANSYS Design Modeler, meshing, and exporting for flow-corrosion modelling in ANSYS Fluent. A one-way coupling was established between Fluent and ANSYS Mechanical to assess the mechanical response under operating conditions. The base case at 62 °C showed a corrosion rate of 6.0 mm/year. To investigate the role of temperature, simulations were conducted at 30 °C, 50 °C, 62 °C, and 70 °C, representing the typical temperature range of Niger Delta fluid systems.  Results indicate that lower temperatures significantly increase corrosion rates, leading to pronounced wall thinning and elevated stress concentrations. Conversely, higher temperatures reduce corrosion intensity by promoting the formation of protective corrosion films. However, localized stress elevations at higher temperatures were also observed, which may be attributed to combined thermal expansion effects and residual corrosion-induced weakening. This demonstrates a non-linear interaction between temperature, corrosion progression, and stress response. The study recommends maintaining sufficiently high fluid temperatures to mitigate corrosion. Further studies are needed to define the temperature range where corrosion behaves linearly, to support optimal design and operation while preventing conditions that could impair system performance and flow assurance. The result provides technical insight for the development of an integrity management strategy for optimum pipeline safety.

Page 5 of 5 | Total Record : 43

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