eprintid: 10196894 rev_number: 9 eprint_status: archive userid: 699 dir: disk0/10/19/68/94 datestamp: 2024-09-13 08:52:26 lastmod: 2024-09-13 08:52:26 status_changed: 2024-09-13 08:52:26 type: article metadata_visibility: show sword_depositor: 699 creators_name: Ashraf, Waqar Muhammad creators_name: Jadhao, Prashant Ram creators_name: Panda, Ramdayal creators_name: Pant, Kamal Kishore creators_name: Dua, Vivek title: Towards circular economy of wasted printed circuit boards of mobile phones fuelled by machine learning and robust mathematical optimization framework ispublished: inpress divisions: UCL divisions: B04 divisions: F43 keywords: Science & Technology, Life Sciences & Biomedicine, Environmental Sciences, Environmental Sciences & Ecology, E -waste recycling, Resource recovery, Mathematical optimization, Circular economy, Machine learning, LITHIUM-ION BATTERIES, VALUABLE METALS, ELECTRONIC-WASTE, COMPLEX-MIXTURES, MODEL-REDUCTION, RECOVERY, SIMULATION, PYROLYSIS, ENERGY, COPPER note: © 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/). abstract: 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. date: 2024-11 date_type: published publisher: ELSEVIER official_url: http://dx.doi.org/10.1016/j.rcradv.2024.200226 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2308601 doi: 10.1016/j.rcradv.2024.200226 lyricists_name: Ashraf, Waqar lyricists_name: Dua, Vivek lyricists_id: WMAAS21 lyricists_id: VDUAX49 actors_name: Ashraf, Waqar actors_id: WMAAS21 actors_role: owner funding_acknowledgements: MFIRP179 [UCL-IIT Delhi Strategic Partner Fund] full_text_status: public publication: Resources, Conservation & Recycling Advances volume: 23 article_number: 200226 pages: 13 issn: 2667-3789 citation: Ashraf, Waqar Muhammad; Jadhao, Prashant Ram; Panda, Ramdayal; Pant, Kamal Kishore; Dua, Vivek; (2024) Towards circular economy of wasted printed circuit boards of mobile phones fuelled by machine learning and robust mathematical optimization framework. Resources, Conservation & Recycling Advances , 23 , Article 200226. 10.1016/j.rcradv.2024.200226 <https://doi.org/10.1016/j.rcradv.2024.200226>. (In press). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10196894/2/Ashraf_1-s2.0-S2667378924000257-main.pdf