Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal IJIMCE : International Journal of Innovation in Mechanical Construction and Energy
Hyginus Unegbu
Department of Mechanical Engineering, Faculty of Engineering, Ahmdu Bello University Nigeria
Automotive Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Conversion of Waste Tyres into High-Energy Fuel Products Using Pyrolysis Technology Hyginus Unegbu; Danjuma YAWAS
IJIMCE : International Journal of Innovation in Mechanical Construction and Energy Vol. 2 No. 3 (2025): IJIMCE : International Journal of Innovation in Mechanical Construction and Ene
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ijimce.v2i3.71

Abstract

The accumulation of waste tyres poses a critical environmental and public health concern due to their non-biodegradability, complex composition, and fire hazard potential. This study explores the thermochemical valorization of end-of-life tyres into high-energy fuel products through pyrolysis, emphasizing process optimization, product quality assessment, and environmental performance. A fixed-bed pyrolysis system was operated across a temperature range of 350°C to 550°C to evaluate its influence on product distribution and yield characteristics. The maximum oil yield of 52.3% was obtained at 500°C, representing the optimal balance between primary depolymerization and suppression of secondary cracking reactions. The pyrolysis oil exhibited a high heating value (43.6 MJ/kg), suitable for energy applications, but exceeded international standards for sulfur content and viscosity, necessitating upgrading prior to use in transportation fuels. The gaseous fraction, enriched with hydrogen and light hydrocarbons, showed strong potential for process self-sufficiency and co-generation. Solid char demonstrated favorable physicochemical properties for application as a fuel or adsorbent material. Statistical modeling using linear regression accurately predicted oil yield trends, and Aspen Plus® simulations closely aligned with experimental results, validating the process model for scale-up. A comparative life cycle assessment revealed that pyrolysis significantly outperforms incineration in terms of greenhouse gas reduction, energy recovery, and particulate matter emissions. These findings confirm that tyre pyrolysis is a technically feasible and environmentally advantageous approach for sustainable waste management and fuel generation. Further integration with refining technologies and renewable energy inputs is recommended to enhance commercial viability.
Physics-Informed Neural Networks for Predictive Maintenance in Nigerian Hydropower Infrastructure Hyginus Unegbu; Danjuma YAWAS
IJIMCE : International Journal of Innovation in Mechanical Construction and Energy Vol. 2 No. 3 (2025): IJIMCE : International Journal of Innovation in Mechanical Construction and Ene
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ijimce.v2i3.72

Abstract

Ensuring the operational integrity of hydropower infrastructure is critical for maintaining energy security and grid stability in Nigeria. However, conventional predictive maintenance frameworks are hindered by inconsistent data availability, poor sensor coverage, and a lack of physical interpretability. This study presents a robust Physics-Informed Neural Network (PINN) architecture tailored for predictive maintenance in Nigerian hydropower systems. By embedding domain-specific physical laws—namely Bernoulli’s principle, the turbine power equation, and Fourier’s law of heat conduction—directly into the model’s loss function, the proposed PINN integrates physical reasoning with deep learning to produce accurate and explainable degradation forecasts. Simulated operational data reflective of real-world hydropower conditions were used to train and evaluate the model. Comparative analysis against Long Short-Term Memory (LSTM) networks and Random Forest (RF) regressors demonstrated the superior performance of the PINN, which achieved an RMSE of 4.75 days and an R² value of 0.88. Furthermore, physics residuals across all governing constraints were consistently below 0.04, indicating strong physical consistency. The model accurately predicted failure in three fault scenarios—runner blade erosion, stator insulation decay, and penstock pressure surges—with lead times ranging from 7.5 to 11 days, thereby enabling actionable intervention before catastrophic breakdown. A real-time monitoring interface was developed to visualize model outputs, risk thresholds, and residual dynamics, facilitating operator trust and integration into existing maintenance workflows. This research establishes the PINN as a scalable and domain-aware solution, well-suited for advancing predictive maintenance capabilities in Nigeria’s evolving hydropower infrastructure.
Co-Authors Danjuma YAWAS