Abstract

Powders acquire a high electrostatic charge during transport and processing. Consequently, in the aftermath of dust explosions, electrostatic discharge is often suspected to be the ignition source. However, definite proof is usually lacking since the rise of electrostatic charge cannot be seen or smelled, and the explosion destroys valuable evidence. Moreover, conventional methods to measure the bulk charge of powder flows, such as the Faraday pail, provide only the aggregate charge for the entire particle ensemble. Our simulations show that, depending on the flow conditions, contacts between particles lead to bipolar charging. Bipolar charged powder remains overall neutral; thus, a Faraday pail detects no danger, even though individual particles are highly charged. To address this gap, we have developed a machine learning-enhanced measurement technology to resolve the powder charge spatially. The first measurements have revealed a critical discovery: a localized charge peak near the inner wall of the conveying duct that is 85 times higher than the average charge that would be measured by the Faraday pail. This finding underscores the possibility of extremely high local charges that can serve as ignition sources, even though they remain undetected by conventional measurement systems. Our new technology offers a solution by spatially resolving the charge distribution within powder flows, unmasking hidden ignition sources, and preventing catastrophic incidents in the industry.

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