An intelligent decision model for optimizing industrial power consumption using q-fraction fuzzy information.

The minimization of power consumption poses a very important challenge to industries which aim at reducing power consumption and achieving their ecological sustainability objectives. The study proposes a superior decision-making model of the q-fractional fuzzy logic with reference to energy consumption in the industrial setting. The proposed model is the combination of multiple criteria including the power requirement of a machine, operating efficiency, temporal limitations and energy prices. The framework is concerned with uncertainties and imprecision in energy consumption data using the concepts of the q-fractional fuzzy aggregation operators, e.g., Weighted Average (WA), Weighted Geometric (WG), Ordered Weighted Average (OWA), Ordered Weighted Geometric (OWG), Hybrid Average (HA) and Hybrid Geometric (HG). The model will seek to attain high level of quality and cost efficient strategies in production due to energy saving. The result of its performance is confirmed by its case studies and simulations and has a huge decrease in energy consumption as compared to the traditional methods of optimization. These findings indicate how this other q-fractional fuzzy set can be a feasible and intelligent technology to manage energy during the industrial processes towards ecological sustainability, and it must be based on minimal human interjections as much as feasible. The empirical findings show that the advised q-fraction fuzzy verdict paradigm significantly reduces industrial power usage and outperforms the competitive CoCoSo mode in terms of reliability and effectiveness.