Neural network based prediction of the efficacy of ball milling to separate cable waste materials

Abstract

Material recycling technologies are essential for achieving a circular economy while reducing greenhouse gas emissions. However, most of them remain in laboratory development. Machine learning (ML) can promote industrial application while maximising yield and environmental performance. Herein, an asynchronous-parallel recurrent neural network was developed to predict the dynamic behaviour when separating copper and poly(vinyl chloride) components from the cable waste. The model was trained with six datasets (treatment conditions) at 3600 epochs. High accuracy was confirmed based on a mean-square error of 0.0015–0.0145 between the prediction and experimental results. The quantitative relationship between the input features and the separation yield was identified using sensitivity analysis. The charged weight of cables and impact energy were determined as the critical factors affecting the separation efficiency. The ML framework can be widely applied to recycling technologies to reveal the process mechanism and establish a quantitative relationship between process variables and treatment outputs.

Quality Control of Cement Clinker through Operating Condition Classification and Free Calcium Oxide Content Prediction

“…Artificial intelligence (AI) technologies and machine learning (ML) algorithms have been increasingly utilized to solve hard problems that inherit nondeterminism and randomness in engineering practice of a variety of fields including complex system control [19,20], object detection [21], communication networks [22], oil pipeline monitoring [23] and leak detection [24], industrial control system protection [25], waste material recycling [26], etc. In recent years, more and more AI/ML practitioners collaborate with experts in different industrial domains to apply data-driven methods to help solve domain-specific problems and achieve the "Smart Manufacturing" and "Industry 4.0" goals of integrating computing machine intelligence into their respective manufacture production processes [1][2][3][4][5][6][7][10][11][12][13][14][15].…”

Ultrafast and highly selective gold recovery with high capture capacity from electronic waste by upconversion of a silsesquioxane-based hybrid luminescent aerogel

“…WEEE has reportedly become one of the world's fastest-growing wastes, with 75 million tons expected to be generated by 2030. 8 Currently, the pathways for recovering gold from WEEE include primarily chemical precipitation, 9 electrochemical reduction, 10 solvent extraction, 11 chemical/physical adsorption, 12 and so on. Among them, adsorption is a more environmentally friendly and energy-efficient treatment.…”