Faina, Andres

Garcia, Rodrigo Moreno

Støy, Kasper

English

Liquid-liquid extraction is a well-known and widely used process in chemistry and laboratory settings.The manual version of this process relies on visual inspection of the mixture, looking for interface formation and stabilization, and for draining the liquids separately. Different automatic devices have been developed for the purpose of separating immiscible liquids, however these solutions are often designed with a specific chemical process in mind. An automatically adjustable LLE device, that can detect and adapt to different substances can shorten adjustment time and enable automatic experimentation in laboratory settings. These kinds of devices are a key part of AI orchestrated, automatic experimentation and screening systems.To enable adaptation in a robust way, an interface detection setup that is able to detect a range of interfaces is presented in this work. The use of a light sensor, that encompasses an array of photo detectors for different light wavelengths, and an arrangement that takes advantage of the optical properties of liquids is investigated for detecting different interfaces with varying optical features. Light sensors have the advantage of not coming in contact with the chemicals involved, as they can be located outside glass vessels. An array of simple sensors reduces the complexity of the signals to be analyzed while at the same time providing enough relevant information to feed more elaborated detection techniques, i.e., machine learning approaches.A dataset is created from sensor measures, characterized, and related to different interface features. Data is automatically generated using a normal glass separatory funnel, connected to a peristaltic pump for controlling the level of liquid inside, but the setup can be adapted for different kinds of vessels and existing automation tools. An analysis of light refraction and reflection inside the funnel is performed. The light source is located on the opposite side to the light sensor and the whole setup is shielded from external light. The dataset obtained will allow the creation of machine learning models (e.g. neural networks) to improve the detection capabilities of the sensor and generalize it to not seen before interfaces.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 95718

The IT University of Copenhagen's Repository

Photoelectric sensor setup and characterization for general interface detection in laboratory scale liquid-liquid extraction processesAcknowlegedmentsThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957189  Rodrigo Moreno, Andres Faina, Kasper StoyIT University of Copenhagen  Introduction Sensor SetupReferencesA photoelectric sensor array (Sparkfun Triad Spectroscopy) is used to detect the interface. A normal separatory funnel is illuminated by a diffused light source (Diffused 450-700nm LEDs) from the opposite side. The optical properties of the liquid and of the interface determine the light that is cast in the sensor array.     CharacterizationDifferent mixtures of oil and water, simulating organic and watery phases, are used to test the sensor capabilities, the main limitation of the sensor arrangement comes when the two liquids are very similar optically and with almost no emulsion in between (Upper left). In this case the sensor must rely on the interface reflection. Emulsive interfaces, which are difficult to spot with cameras [1], cast a shadow in the sensor, making them easier to spot. Measurements are recorded as the separated liquids are drained with a dosing pump at a rate of approximately 1ml per step.     DatasetConclusionThe measures portrayed, including substance composition data, can be found in the following repository.   A photoelectric sensor setup for detecting liquid-liquid interfaces in liquid-liquid extraction processes, capable of detecting different interfaces and aimed to be used in battery chemical synthesis, is described.Liquids with similar optical features limit the sensor performance.Emulsive interfaces, which are difficult to detect with cameras, cast a shadow in the sensor, making them easier to spot.The sensor setup can be adapted to different machines and tools.   Liquid-liquid extraction involves adding a solvent in a mixture to extract a substance of interest, and is widely used in different processes, including battery chemical synthesis.The solvent and the rest of the liquid are immiscible, thus they separate after some time.Automatic extraction devices aim to detect the liquid-liquid interface between the remaining liquids, but are often tailored to specific industrial processes, which are either of a too big scale or a too small scale.A sensor for detecting different interfaces with varying optical features at laboratory scale, attached to a separatory funnel, is investigated.  No EmulsionLiquid-liquid Interface EmulsionLiquid-liquid InterfaceLight SourceMineral oilWaterVegetable OilYellow ink Water Vegetable OilWaterMineral oilWaterBlack oil paintSugar solution1. Eppel, S., & Kachman, T. (2014). Computer vision-based recognition of liquid surfaces and phase boundaries in transparent vessels, with emphasis on chemistry applications. 1–36. http://arxiv.org/abs/1404.7174     Light Sensor ArrayReflectionEmulsionGraphs in process of publication

Photoelectric sensor setup and characterization for general interface detection in laboratory scale liquid-liquid extraction processes

https://pure.itu.dk/portal/files/86275868/Poster.pdf

Photoelectric sensor setup and characterization for general interface detection in laboratory scale liquid-liquid extraction processes

Abstract

Similar works

Full text

Available Versions

The IT University of Copenhagen's Repository