Fadhilah Abdul Razak

Personal Information

Biography

A researcher specialized in thermal systems optimization, energy efficiency, and the integration of Artificial Intelligence (AI) and Digital Twin technologies for sustainable engineering solutions. With a strong foundation in both experimental and simulation-based design, current work focuses on the development of smart, adaptive systems for energy and environmental applications.

Proficient in a wide range of optimization methodologies, including Response Surface Methodology (RSM), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and hybrid AI techniques such as ANN-GA and PSO-ANN, applied to improve the performance of thermal systems. Research also includes Artificial Neural Networks (ANN), Support Vector Machines (SVM), deep learning, and reinforcement learning for predictive modeling, classification, and adaptive system control.

Currently engaged in cutting-edge research on the implementation of Digital Twin technology for renewable energy systems and waste management optimization. This work involves the creation of real-time, data-driven virtual models that mirror physical systems to enable predictive maintenance, efficiency enhancement, and decision-making support. Applications include:

1. Digital twins of solar thermal and photovoltaic systems for energy forecasting and fault detection

2. Waste-to-energy process optimization through integrated sensor data and AI models

3. Lifecycle management of energy assets using real-time monitoring and virtual prototyping

Dedicated to advancing smart, sustainable technologies that align with global environmental goals, with active involvement in multi-institutional collaborations, industry partnerships, and grant-funded research. Committed to translating research outcomes into high-impact publications and scalable solutions for green energy, circular economy, and intelligent infrastructure.