Continuous track paths reveal additive evidence integration in multistep decision making

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Tool Design for Electronic Product Dismantling

AbstractIn industrialized countries, waste electrical and electronic equipment is mostly processed in shredder-based processes, which are characterized by a low recovery of precious metals, rare earth elements and flame retardant plastics. To increase the recycling efficiency for these materials, a dismantling tool has been developed. The development process of the dismantling tool was guided by in-depth analysis of the required disassembly time for LCD TVs and laptops. The results of practical experiments demonstrate that the use of the dismantling tool enable to reduce the dismantling time for plastic housing components and PWBs with respectively 36% and 45% for LCD TVs

Streamlining life cycle assessments: an emerging need for simplification

At an ever increasing rate innovative chemistry and technology platforms are reshaping manufacturing environments to become factories of the future by being more productive, lean and flexible. The use of Life Cycle Assessment (LCA) in early process development phases has been challenged many a time to assess whether or not this willingness to strive for innovation is an environmentally sustainable one, often due to a lack of process data. This study takes the streamlining of LCA one step further and proposes an optimal complexity of modelled product systems in terms of their optimal set of predictor variables. Out of more than 2,800 Basic Operations (BOs) in pharmaceutical synthesis steps, candidate predictor variables were identified to forecast the environmental burden (in this case natural resource consumption) of a production step per unit of output. By means of backwards stepwise linear regression modelling, combinations of candidate predictors were tested and evaluated based on their predictive power (R²) and the model uncertainty. It was proven that at least the amount of organic solvents used, the molar efficiency and the time duration of the synthesis step should be included in the model (R² = 0.87) as being the most significant predictor variables. Including additional predictors however imposes no guarantee to contribute to the predictive power and eventually weakens the model interpretation and its simplicity. The results of the study were evaluated in the light of the product-specific versus product group approach debate. Should LCAs be generalized to such an extent that an extensively diversified product group is to be represented with an averaged burden, while fairly simplified and streamlined methods can represent product-specific impact assessments with a reasonable need for data, time and knowledge? The trade-off between simplicity and accuracy will be dealt with quantitatively in the oral presentation. Ideally, an organization should be able to derive its environmental impacts from readily available Enterprise Resource Planning (ERP) data, linking supply chains back to the cradle of resource extraction, excluding the need for an approximation with product group averages. While this study has taken a step in that direction, more research should be conducted especially on how efforts towards sustainable development should be addressed with care to valorise them efficiently in the supply chain and its sectors, beyond any company borders

Simultaneous mass estimation and class classification of scrap metals using deep learning

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksWhile deep learning has helped improve the performance of classification, object detection, and segmentation in recycling, its potential for mass prediction has not yet been explored. Therefore, this study proposes a system for mass prediction with and without feature extraction and selection, including principal component analysis (PCA). These feature extraction methods are evaluated on a combined Cast (C), Wrought (W) and Stainless Steel (SS) image dataset using state-of-the-art machine learning and deep learning algorithms for mass prediction. After that, the best mass prediction framework is combined with a DenseNet classifier, resulting in multiple outputs that perform both object classification and object mass prediction. The proposed architecture consists of a DenseNet neural network for classification and a backpropagation neural network (BPNN) for mass prediction, which uses up to 24 features extracted from depth images. The proposed method obtained 0.82 R2, 0.2 RMSE, and 0.28 MAE for the regression for mass prediction with a classification performance of 95% for the C&W test dataset using the DenseNet+BPNN+PCA model. The DenseNet+BPNN+None model without the selected feature (None) used for the CW&SS test data had a lower performance for both classification of 80% and the regression (0.71 R2, 0.31 RMSE, and 0.32 MAE). The presented method has the potential to improve the monitoring of the mass composition of waste streams and to optimize robotic and pneumatic sorting systems by providing a better understanding of the physical properties of the objects being sorted.Peer ReviewedPostprint (author's final draft