TY  - INPR
KW  - Science & Technology
KW  -  Life Sciences & Biomedicine
KW  -  Environmental Sciences
KW  -  Environmental Sciences & Ecology
KW  -  E -waste recycling
KW  -  Resource recovery
KW  -  Mathematical optimization
KW  -  Circular economy
KW  -  Machine learning
KW  -  LITHIUM-ION BATTERIES
KW  -  VALUABLE METALS
KW  -  ELECTRONIC-WASTE
KW  -  COMPLEX-MIXTURES
KW  -  MODEL-REDUCTION
KW  -  RECOVERY
KW  -  SIMULATION
KW  -  PYROLYSIS
KW  -  ENERGY
KW  -  COPPER
TI  - Towards circular economy of wasted printed circuit boards of mobile phones fuelled by machine learning and robust mathematical optimization framework
UR  - http://dx.doi.org/10.1016/j.rcradv.2024.200226
Y1  - 2024/11//
PB  - ELSEVIER
VL  - 23
A1  - Ashraf, Waqar Muhammad
A1  - Jadhao, Prashant Ram
A1  - Panda, Ramdayal
A1  - Pant, Kamal Kishore
A1  - Dua, Vivek
N2  - Estimating the operating conditions using conventional process analysis techniques for the maximum metal extraction from the wasted printed circuit boards (WPCB) can provide sub-optimal solutions leading to the low yield of the process. In this paper, we present a closed-loop methodological framework built on machine learning and robust mathematical optimization technique, that offers the mathematical rigour, to determine the optimum operating conditions for the maximum Cu and Ni recovery from the WPCB. Alkali leaching based novel metals recovery process from the WPCB is designed, and the experiments are conducted to collect the data on the percentage recovery of Cu and Ni against the operating levels of the process input variables (ammonia concentration (NH3 conc. (g/L)), ammonium sulfate concentration ((NH4)2SO4 conc. (g/L)), H2O2 concentration (H2O2 conc. (M)), time (h), liquid to solid ratio (L/S ratio, (mL/g)), temperature (Temp. (°C)), and stirring speed (rpm)). The experimental data is deployed to construct the functional mapping between the nonlinear output variables of metals recovery process with the hyperdimensional input space through artificial neural network (ANN) based modelling algorithm ? a powerful universal function approximator. Well-predictive ANN models for Cu and Ni recovery are developed having co-efficient of determination (R2) value more than 0.90. Partial derivative-based sensitivity analysis is then carried out to establish the order of the significance of the input variables that is backed by the domain knowledge, thus promotes the interpretability of the trained ANN models. The hybridization of ANN with NLP (nonlinear programming) framework is implemented for the determination of optimized operating conditions to extract maximum Cu and Ni under separate and combined model of metal extraction. The robustness of the determined solutions is verified, the determined optimized solutions for the metal recovery are validated in the lab, and the maximum metal recovery, i.e., 100 % Cu and 90 % Ni is extracted from the WPCB. This research demonstrates the effective utilization of ANN model-based robust optimization approach for the metal recovery from the WPCB that supports the circular economy for the metal extraction industry.
JF  - Resources, Conservation & Recycling Advances
EP  - 13
AV  - public
ID  - discovery10196894
SN  - 2667-3789
N1  - © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
ER  -