Estimating best nanomaterial for energy harvesting through reinforcement learning DQN coupled with fuzzy PROMETHEE under road-based conditions

Energy harvesters based on nanomaterials are getting more and more popular, but on their way to commercial availability, some crucial issues still need to be solved. The objective of the study is to select an appropriate nanomaterial. Using features of the Reinforcement Deep Q-Network (DQN) in conjunction with Fuzzy PROMETHEE, the proposed model, we present in this work a hybrid fuzzy approach to selecting appropriate materials for a vehicle-environmental-hazardous substance (EHS) combination that operates in roadways and under traffic conditions. The DQN is able to accumulate useful experience of operating in a dynamic traffic environment, accordingly selecting materials that deliver the highest energy output but at the same time bring consideration to factors such as durability, cost, and environmental impact. Fuzzy PROMETHEE allows the participation of human experts during the decision-making process, going beyond the quantitative data typically learned by DQN through the inclusion of qualitative preferences. Instead, this hybrid method unites the strength of individual approaches, as a result providing highly resistant and adjustable material selection to real EHS. The result of the study pointed out materials that can give high energy efficiency with reference to years of service, price, and environmental effects. The proposed model provides 95% accuracy with a computational efficiency of 300 s, and the application of hypothesis and practical testing on the chosen materials showed the high efficiency of the selected materials to harvest energy under fluctuating traffic conditions and proved the concept of a hybrid approach in True Vehicle Environmental High-risk Substance scenarios.