RESUMEN
The stability of emulsions is a critical concern across multiple industries, including food products, agricultural formulations, petroleum, and pharmaceuticals. Achieving prolonged emulsion stability is challenging and depends on various factors, with particular emphasis on droplet size, shape, and spatial distribution. Addressing this issue necessitates an effective investigation of these parameters and finding solutions to enhance emulsion stability. Image analysis offers a powerful tool for researchers to explore these characteristics and advance our understanding of emulsion instability in different industries. In this review, we highlight the potential of state-of-the-art deep learning-based approaches in computer vision and image analysis to extract relevant features from emulsion micrographs. A comprehensive summary of classic and cutting-edge techniques employed for characterizing spherical objects, including droplets and bubbles observed in micrographs of industrial emulsions, has been provided. This review reveals significant deficiencies in the existing literature regarding the investigation of highly concentrated emulsions. Despite the practical importance of these systems, limited research has been conducted to understand their unique characteristics and stability challenges. It has also been identified that there is a scarcity of publications in multimodal analysis and a lack of a complete automated in-line emulsion characterization system. This review critically evaluates the existing challenges and presents prospective directions for future advancements in the field, aiming to address the current gaps and contribute to the scientific progression in this area.
Asunto(s)
Inteligencia Artificial , Emulsiones , Estudios Prospectivos , Composición de Medicamentos/métodosRESUMEN
Liquid-liquid emulsions are used in a variety of industry sectors, including personal care, home care, food, and nutrition. The development of compact and modular systems and devices for creating emulsions with desired droplet size distribution (DSD) is becoming increasingly important. In this work, we have shown use of vortex-based cavitation devices for producing emulsions at nominal flow rate of 1 LPM and 20 LPM. We present new experimental results providing quantitative information on influence of multiple passes through the vortex based hydrodynamic cavitation (HC) device, type of oil and device scale on the breakage process and resulting DSDs. Multiple pass experiments were performed for generating oil-in-water emulsions containing 5 and 15% of oil. Rapeseed oil (RO) and tetrachloroethylene (TCE) were used as oil phases with densities of 915 and 1620 kg/m3, respectively. The effect of pressure drop across the HC device in the range of 50-250 kPa on DSD was examined. The HC device was shown to exhibit significant higher efficiency compared to alternative emulsion making devices (i.e., homogenizers, venturi, and orifice-based HC devices), and the Sauter mean drop size was found to reduce from 66 µm to less than 2 µm after about 50 passes in all the device scales. The DSD of the RO-water system showed a bimodal nature, whereas monomodal DSD was found for TCE-water system. Preliminary simulations using the computational fluid dynamics-population balance model (CFD-PBM) models developed in the previous work indicated the inadequacy of developed models to capture the influence of cavitation on DSDs. By carrying out Hinze scale analysis of bimodal DSD, we for the first time showed the existence of two different mechanisms (one based on conventional turbulent shear and the other based on collapsing cavities) of droplet breakage in HC devices. The order of magnitude of turbulence energy dissipation rates generated due to collapsing cavity estimated using Hinze scale analysis showed good agreement with the values reported from cavity dynamics models. The presented experimental results and analysis will be useful for researchers and engineers interested in developing computational models and compact devices for producing emulsions of the desired DSD.