Characterizing a Novel Food Waste Recovery Process Using an Electrostatic Separator

Abstract

Organic food waste compounds in landfills can heavily pollute the soil, aquifer, and air, mainly due to anaerobic transformation. This paper presents the outcomes of research on the waste separation process and recovery of food waste (FW) using an electrostatic separator. The separation process sorts the charged bodies from the uncharged under an intensive electric field. We studied influences of various parameters such as applied voltage, rotation speed, and electrode angle on the separation process. Results revealed that separation efficiency relies on a uniform electric field and separator configuration. To our best knowledge, this is the first characterization report for food waste recovery using an electrostatic separator.

Driving Management of Novel Foods: A Network Analysis Approach

“…Otherwise, those who used fractional counting (Lai et al, 2016), in accordance with its functionality (Van Eck and Waltman, 2018), mostly encountered data skimming needs. Sometimes, it is because they use only one keyword (Martinho, 2021) for the collection of the articles, or, in other cases, their search output resulted in a much larger number of papers (Dardonville et al, 2021) than those in this paper.…”

An Artificial Neural Network Approach to Predicting Electrostatic Separation Performance for Food Waste Recovery

“…The separation process relies on a number of forces, e.g., centrifugal force due to the rotation of the roller, lifting force due to the attraction by the electrostatic plate, and gravity force and pinning force due to the ionized effect on the insulative particles [16]. When the roller delivers the particles through the zone, the dissipative FW lose their charge rapidly, keeping them from being pinned for a longer time than the insulative non-FW.…”

“…In this study, an ANN model was constructed to predict the recovered food waste (W f ) and middling products (W m ). The Taguchi OA design considers three independent input factors: the voltage level of the DC power supply (kV), the rotation speed of the roller (rpm), and the corona electrode angle (degree) as determined by [16]. The recovered FW and middling mass were selected as the network responses.…”