Work

nRTD: Determining complex residence time distributions from experimental data using convolutional neural networks

Year: 2025

Type: article

Abstract: Obtaining quantitative information about residence time behavior (i.e., the residence time distribution function) in realistic experimental systems is oftentimes experimentally challenging and numerically complex. The conventional way is to conduct very simple pulse or step tracer experiments or construct elaborate compartment models. In this work a workflow is proposed and demonstrated to obtain accurate residence time distributions without the need for trivial step experiments or complex compartment models. The workflow consists of a systematic experimental program and a novel machine learning algorithm to fit residence time distributions via convolutional neural networks which the authors call nRTD – neural residence time distribution. nRTD is demonstrated for literature models and a dynamically operated test rig used for kinetic experiments.

Source: Chemical Engineering Science

DOI: 10.1016/j.ces.2025.122454

Authors: Hannes Stagge, Max Gäßler, Tuana Oyuncu, Robert Güttel

Institution: Universität Ulm

Language: English

Topic: Fluid Dynamics and Mixing

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,050

Funder: Deutsche Bundesstiftung Umwelt