Review of Dielectric Properties Optimization of RTV Silicone Rubber Using Coal Fly Ash Filler with the Backpropagation Neural Network-Genetic Algorithm Method
摘要
Indonesia’s high-voltage transmission network, essential for reliable power delivery in tropical environments, faces challenges from humidity, pollution, and UV radiation. Room Temperature Vulcanized (RTV) silicone rubber, valued for its hydrophobicity and dielectric strength, is a cornerstone for modern insulators. This systematic literature review synthesizes research from multiple databases to evaluate coal fly ash–a sustainable byproduct of Indonesia’s coal-fired power plants–as a filler to enhance RTV silicone rubber’s dielectric properties, specifically breakdown voltage and dielectric permittivity. The review underscores fly ash’s silica content for improving charge distribution. The novelty lies in proposing a Backpropagation Neural Network-Genetic Algorithm (BPNN-GA) framework to optimize fly ash formulations, a pioneering approach absent in prior RTV-fly ash research, which leverages GA’s evolutionary optimization from polymer composite studies to address non-linear filler-matrix interactions and promote sustainable insulator design. By integrating BPNN’s predictive modeling with GA, this method reduces experimental costs. Findings show fly ash significantly enhances dielectric performance. Gaps in long-term performance, hybrid filler systems, and standardized testing protocols are identified, with recommendations for cyclic testing and field validations. This work advances eco-friendly, high-performance insulators, aligning with Indonesia’s circular economy and energy security goals.