Data-driven surrogate modeling for global sensitivity analysis and the design optimization of medical waste shredding systems

Excessive medical waste is generated in various medical facilities, especially post-Covid. Recently, sterilization-based shredding systems are being widely used to treat medical waste; however, such systems are commonly over-designed, due to load variation among various treatment facilities. To overcome this challenge, a data-driven surrogate model framework is proposed to perform sensitivity analysis and design optimization based on different loading environments and capacity requirements. The stress estimation surrogate model was generated using the Latin hypercube sampling (LHS), which can represent the overall information of the design area with a limited sample. Furthermore, the data-driven model significantly reduced the computational time as increased numbers of samples were generated from the data-driven surrogate model, instead of finite element analysis (FEA). Two distinct design capacities of the shredding system were used for assessing the effectiveness of the present framework. The results demonstrated that surrogate model-based sensitivity analysis is an efficient approach to developing system designs based on various input and output conditions. The proposed approach mitigates the tremendous potential of the surrogate model, significantly reduces computational costs associated with sensitivity analysis, and yields promising accuracy for the optimization process. This method suggests a computationally efficient optimization method for different shredding capacities. Additionally, the proposed method does not remain at simply optimizing the existing system but provides optimization values for various capacity systems using the data of the existing design, therefore it can be applied to any mechanical system for designing an optimized and compact system.