A genetic algorithm-based optimal selection and blending ratio of plastic waste for maximizing economic potential

Abstract

Pyrolysis of plastic waste presents an innovative solution to convert plastics into valuable fuels like oil and gas, thereby contributing to circular economy and sustainability. The efficiency of this process highly depends on the types and compositions of plastics utilized, as each type reacts uniquely during pyrolysis, affecting fuel yield and quality. However, previous studies have not thoroughly examined the impact of plastic selection and blending ratios on the pyrolysis outcome, as this requires extensive experimentation due to the numerous possible combinations. Optimizing these factors, along with operational conditions, is essential for enhancing the economic viability of plastic recycling via pyrolysis. This study introduces an optimal selection and blending ratio for plastic waste in pyrolysis fuel production to enhance economic viability using the surrogate model-based genetic algorithm (GA). The optimization process involves (1) generating data via a process model of the plastic waste pyrolysis reactor, (2) preprocessing data, (3) developing a surrogate model based on deep neural networks (DNNs), and (4) deriving the optimal solution using GA. The results reveal an optimal blending ratio: 4 wt% LDPE, 30 wt% HDPE, 43 wt% PP, and 24 wt% PS. Additionally, the optimal pyrolysis temperature is 607 ℃. By adopting this optimal solution, the net profit could increase by 20% compared to the conventional scenario, resulting in an additional annual net profit of US$ 407,811, thus highlighting the potential this approach offers to attain maximum economic feasibility in industrial applications.