Detection of Cu particles volume fraction in vibrating Al-Cu mixtures by multiple-amplitude modulation implemented in impedance measurement system (&lt;i&gt;m&lt;/i&gt;AM-IMS)

Sejati, Prima Asmara; Baidillah, Marlin Ramadhan; Prayitno, Yosephus Ardean Kurnianto; Ibrahim, Kiagus Aufa; Kawashima, Daisuke; Takei, Masahiro

doi:10.1299/mej.21-00379

Cited by 3 publications

(1 citation statement)

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“…An effective recycling system is required to meet the high global demand for recycled aluminum (Al), copper (Cu), and plastic particles from electronic waste while considering economic and environmental factors [1]. In order to achieve an effective recycling system, a wet-type gravity vibration separator (WGS) is commonly provided to the recycling industry [2]. The WGS, as shown in Figure 1, has the ability to produce recycled particles with a high-purity grade from recycled materials through a separation process of an Al-Cu-plastic particle mixture [3].…”

Section: Introductionmentioning

confidence: 99%

Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration Separator

Ibrahim,

Sejati,

Darma

et al. 2023

Sensors

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The minor copper (Cu) particles among major aluminum (Al) particles have been detected by means of an integration of a generative adversarial network and electrical impedance tomography (GAN-EIT) for a wet-type gravity vibration separator (WGS). This study solves the problem of blurred EIT reconstructed images by proposing a GAN-EIT integration system for Cu detection in WGS. GAN-EIT produces two types of images of various Cu positions among major Al particles, which are (1) the photo-based GAN-EIT images, where blurred EIT reconstructed images are enhanced by GAN based on a full set of photo images, and (2) the simulation-based GAN-EIT images. The proposed metal particle detection by GAN-EIT is applied in experiments under static conditions to investigate the performance of the metal detection method under single-layer conditions with the variation of the position of Cu particles. As a quantitative result, the images of detected Cu by GAN-EIT ψ̿GAN in different positions have higher accuracy as compared to σ*EIT. In the region of interest (ROI) covered by the developed linear sensor, GAN-EIT successfully reduces the Cu detection error of conventional EIT by 40% while maintaining a minimum signal-to-noise ratio (SNR) of 60 [dB]. In conclusion, GAN-EIT is capable of improving the detailed features of the reconstructed images to visualize the detected Cu effectively.

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Section: Introductionmentioning

confidence: 99%