Search Results (4)

Disassembly is a crucial step in the remanufacturing of end-of-life (EoL) electronic products. Disassembly depth refers to the disassembly stop point determined by the disassembly sequence. For the disassembly depth optimization of EoL electronic products, a feasibility model with a fast convergence and low mean squared error (MSE) is needed to improve optimization accuracy. However, the use of a backpropagation neural network (BPNN) model or mathematical model often results in a slow convergence and high MSE due to the randomness of the initial weights and biases. In this study, an improved method for the disassembly depth optimization of smartphones based on a Particle Swarm Optimization-BPNN (PSO-BPNN) predictive model is proposed. Compared with the traditional BPNN optimization method, the proposed method in this study is that the BPNN predictive model is optimized by using PSO, which shows a superior predictive performance and reduces the MSE. The case of ‘Huawei P7’ is used to verify the feasibility of the method. The results show that the method maintains disassembly profit while reducing the disassembly time and carbon emissions by 17.1% and 7.8%, respectively. Compared with the BPNN model, the PSO-BPNN model converges 18.6%, 32.8%, and 16.6% faster in predicting the disassembly time, profit, and carbon emissions, respectively, with MSE reductions of 92.95%, 96.51%, and 92.74%, respectively. Full article

Research on smartphone lifespan is of high interest nowadays due to the growing number of smartphone users and the environmental impact associated with device turnover. Although the concept of smartphone lifespan varies in the literature, research defines the in-use lifespan as the period during which the user finds the device useful, i.e., until it becomes obsolete. Smartphone obsolescence is primarily influenced by both technological and psychological factors, making them the key determinants of the device’s lifespan. For this reason, it is essential to dedicate more research efforts to understanding smartphone lifespan and to developing clear guidelines and policies that can shift users’ perceptions, thereby improving lifespan estimation and encouraging prolonged use. This review synthesizes EU regulations and scientific literature, gathering comprehensive knowledge on key segments and events that affect the in-use lifespan of smartphones. Based on the data, several statistics were generated to provide a better understanding of the term “lifespan,” its multiple estimations, obsolescence issues, and factors affecting the estimated length of the in-use lifespan. Additionally, the research material was used to design a smartphone lifecycle model within the business process management software ARIS and to identify the End of Life (EoL) phase accordingly. Full article

Small waste from electrical and electronic equipment (WEEE) such as waste mobile phones are rich in plastic components. Recycling mobile phones is particularly challenging, since the main interest for recyclers is printed circuit boards, rich in valuable metals, while the plastic components are usually destined for thermal recovery. This study is dedicated to the assessment of the recyclability potential of the plastic fractions of end-of-life (EoL) mobile phones according to the European Union’s (EU) Restriction of Hazardous Substances (RoHS) and Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) directives. A total of 275 plastic items (inventoried as casings, frames, and screens) were dismantled from 100 EoL mobile phones and analyzed to identify the type and abundance of polymers via Fourier-transform infrared spectroscopy (FTIR) and the presence of hazardous elements such as Br, Cl, Pb, and Cd via X-ray fluorescence (XRF). Polycarbonate (PC) (57% of samples) and polymethyl methacrylate (PMMA) (27% of the items) were identified as the most common prevalent polymers. In total, 67% of the items contained Cl (0.84–40,700 mg/kg), and 26% contained Br (0.08–2020 mg/kg). Hg was detected only in one item (17 mg/kg). Cr was found in 17% of the items, with concentrations between 0.37 mg/kg and 915 mg/kg, while Pb was found in 15% of the items in low concentrations (1–90 mg/kg). In conclusion, while hazardous elements are present in the plastic fractions of EoL mobile phones (with higher values in smartphones), their concentrations were below the regulatory limits, suggesting compliance with recycling regulations in the EU. Full article

The disassembly of end-of-life (EoL) products is of high concern in sustainability research. It is important to obtain reasonable disassembly depth during the disassembly process. However, the overall safety of the disassembly process is not considered during the disassembly depth optimization process, which leads to an inability to accurately obtain a reasonable disassembly depth. Considering this, a multi-objective disassembly depth optimization method for EoL smartphones considering the overall safety of the disassembly process is proposed to accurately determine a reasonable disassembly depth in this study. The feasible disassembly depth for EoL smartphones is first determined. The reasonable disassembly process for EoL smartphones is then established. A multi-objective function for disassembly depth optimization for EoL smartphones is established based on the disassembly profit per unit time, the disassembly energy consumption per unit time and the overall safety rate of the disassembly process. In order to increase solution accuracy and avoid local optimization, an improved teaching–learning-based optimization algorithm (ITLBO) is proposed. The overall safety of the disassembly process, disassembly time, disassembly energy consumption and disassembly profit are used as the criteria for the fuzzy analytic hierarchy process (AHP) to evaluate the disassembly depth solution. A case of the ‘Xiaomi 4’ smartphone is used to verify the applicability of the proposed method. The results show that the searchability of the non-inferior solution and the optimal solution of the proposed method are improved. The convergence speeds of the ITLBO algorithm are 50.00%, 33.33% and 30.43% higher than those of the TLBO algorithm, and the optimal solution values of the ITLBO algorithm are 3.91%, 5.10% and 3.45% higher than those of the TLBO algorithm in three experiments of single objective optimization. Full article