Towards circular economy of wasted printed circuit boards of mobile phones fuelled by machine learning and robust mathematical optimization framework

IF 5.4 Q1 ENVIRONMENTAL SCIENCES
Waqar Muhammad Ashraf , Prashant Ram Jadhao , Ramdayal Panda , Kamal Kishore Pant , Vivek Dua
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引用次数: 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.

Abstract Image

在机器学习和稳健数学优化框架的推动下,实现废旧手机印刷电路板的循环经济
使用传统工艺分析技术估算从废印刷电路板(WPCB)中最大限度提取金属的操作条件,可能会提供次优解决方案,导致工艺产量低。在本文中,我们提出了一种建立在机器学习和稳健数学优化技术基础上的闭环方法框架,该框架具有数学严谨性,可确定最佳操作条件,以最大限度地从废印刷电路板中回收铜和镍。设计了基于碱浸出的从 WPCB 中回收金属的新工艺,并进行了实验,以收集与工艺输入变量(氨浓度(NH3 conc.(g/L)、硫酸铵浓度((NH4)2SO4 conc. (g/L))、H2O2 浓度(H2O2 conc. (M))、时间(h)、液固比(L/S 比,(mL/g))、温度(Temp.通过基于人工神经网络(ANN)的建模算法(一种强大的通用函数近似器),利用实验数据构建金属回收过程的非线性输出变量与超维输入空间之间的函数映射。针对铜和镍的回收开发出了具有良好预测性的 ANN 模型,其决定系数 (R2) 值大于 0.90。然后进行了基于偏导数的敏感性分析,以确定输入变量的重要性顺序,该顺序由领域知识支持,从而提高了训练有素的 ANN 模型的可解释性。将 ANN 与 NLP(非线性程序设计)框架进行混合,以确定在单独和组合金属提取模型下提取最大铜和镍的优化操作条件。确定的解决方案的稳健性得到了验证,确定的金属回收优化方案在实验室中得到了验证,并从 WPCB 中提取了最大的金属回收率,即 100% 的铜和 90% 的镍。这项研究表明,基于 ANN 模型的稳健优化方法可有效地从 WPCB 中回收金属,从而支持金属提取行业的循环经济。
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来源期刊
Resources, conservation & recycling advances
Resources, conservation & recycling advances Environmental Science (General)
CiteScore
11.70
自引率
0.00%
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0
审稿时长
76 days
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