Liquid holdup and dispersion are reported for a column of 2 mm internal diameter, filled with 0.1 mm spherical particles, for multiphase flows with hydrocarbon liquid flow rates of 10-100 lL/min and nitrogen gas flow rates of 50-1000 lL/min using different tracers with varying diffusion coefficients and vapor pressures. It was found that the liquid holdup (liquid volume/external void volume) was between 0.65 and 0.85, with variations between different experiments and limited impact of flow rate on the holdup. The dispersion characteristics were very similar to single-phase dispersion. The particle Peclet number for dispersion was close to 0.2. This value was of the same order of magnitude -just a factor of two to three lower -as the value that was obtained without gas flow. Tracer volatility did cause the tracer to elude earlier, but did not cause significant additional dispersion. The results suggest that the fluid mechanical interaction between the gas and the liquid was very limited.

The step response, including various startup procedures, in a three-phase microreactor of 2 mm internal diameter packed with nonporous particles of 100 μm is reported. We demonstrate that the bed behaves reproducibly through many cycles of operating conditions. Interestingly, we find that the different startup procedures have little effect on the steady state that is achieved. In other words, minimal hysteresis was observed, in sharp contrast to larger-scale reactors with larger particles where prewetting has a remarkable impact on the hydrodynamic behavior. The powder-packed beds have very high liquid saturation values, and prewetting is not needed. At least four liquid-residence times were needed to achieve stable pressure drop and dispersion values over the bed. This indicates that the hydrodynamic response into a stable operation may well be the limiting factor that determines the rate at which kinetic experiments can be performed in high-throughput equipment.

In this paper, the technical and economic advantages of combining conversion technologies into a multi-dimensional plant primarily using regional biomass residues are investigated. The main objective is to show how locally available biomass can be used more effi ciently as a source for renewable energy and bio-based products. Therefore, not only is the theoretical perspective considered, but also a reality check for the local situation is taken into account. Although industrial attitude toward biorefi neries is positive, the effi cient production of a portfolio of bio-based products has not yet been implemented. A biorefi nery concept for Moerdijk (the Netherlands) was developed, focusing on grass refi ning, production of pyrolysis oil, biodiesel production, and bio-LNG production. Grass refi ning is the most experimental technique of all proposed conversion techniques. In terms of development, pyrolysis oil and bio-LNG production are in the demonstration phase. Anaerobic digestion and biodiesel production are proven techniques. It is shown that this concept allows for synergies with regard to the utilization of residue fl ows from internal processes. Furthermore, it is demonstrated that by integrating different conversion technologies, an economically feasible concept can be developed in which technologies, currently residing in a demonstration phase, can also be brought to the market.

We describe the co-current flow pattern of gas and liquid through micro-fabricated beds of solid and pillars under variable (i) capillary number, (ii) contact angle or wettability and (iii) pillar arrangement, i.e. modifying the distance between pillars or their size and comparing regular with more chaotic systems.

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Infinite dilution diffusion coefficients of benzothiophene, dibenzothiophene, acridine, naphthalene, and anthracene in tetradecane have been measured in a temperature range from (313.2 to 473.2) K using the Taylor dispersion technique. Predicted values based on the correlations of Wilke-Chang and Tyn-Calus deviate up to 35 %. An Arrhenius type of equation was used to describe the temperature dependence of the diffusion coefficients with an accuracy on average of less than 5 %.

Relevance: Glaucoma is a group of diseases characterized by progressive, bilateral yet asymmetric optic neuropathy, which results in permanent vision loss when is not treated promptly; It is asymptomatic in the early stages; thus, unfortunately, the diagnosis is discovered when the compromise is already severe, and the condition is advanced. Because of this, it is crucial to conduct early screening using technologies that are accessible to the population. Artificial intelligence (AI), particularly deep learning (DL), plays an essential role in this issue. DL may be an efficient approach for glaucoma screenings with the proper training. Objective: Describe the development of AI and DL over time and their use and significance in glaucoma screening. Methods: A literature search was conducted in PUBMED/MEDLINE, EMBASE, and manuscript references in English and Spanish between January 2014 to July 2022 on the role and evolution of AI and DL over the years and the usefulness of deep learning for glaucoma diagnosis. Of the 1914 abstracts reviewed, 105 articles were selected that contained information on the history of AI in medicine and the applicability of this tool for the early diagnosis of glaucoma. Findings and conclusions: We can demonstrate that deep learning can outperform glaucoma specialists in diagnosing the condition through fundus imaging data; DL is an exciting tool in the screening and early diagnosis of glaucoma.

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