A novel Euler-Lagrangian (EL) computational fluid dynamics (CFD) finite volume-based model to simulate the gas mixing of sludge for anaerobic digestion is developed and described. Fluid motion is driven by momentum transfer from bubbles to liquid. Model validation is undertaken by assessing the flow field in a labscale model with particle image velocimetry (PIV). Conclusions are drawn about the upscaling and applicability of the model to full-scale problems, and recommendations are given for optimum application.

The performance of KM static mixers has been assessed for the blending of Newtonian and time‐independent non‐Newtonian fluids using planar laser induced fluorescence (PLIF). A stream of dye is injected at the mixer inlet and the distribution of dye at the mixer outlet is analyzed from images obtained across the pipe cross section. The effect of number of mixing elements, fluid rheology, and apparent viscosity ratio for two‐fluid blending have been investigated at constant mixture superficial velocity of 0.3 m s⁻¹. Aqueous solutions of glycerol and Carbopol 940 are used as the working fluids, the latter possessing Herschel–Bulkley rheology. The PLIF images have been analyzed to determine log variance and maximum striation thickness to represent the intensity and scale of segregation, respectively. Conflicting trends are revealed in the experiments, leading to the development of an areal‐based distribution of mixing intensity. For two‐fluid blending, the addition of a high viscosity stream into the lower viscosity main flow causes very poor mixing performance, with unmixed spots of this component observable in the PLIF image. © 2013 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 60: 332–342, 2014

The performance of Kenics KM static mixers has been determined for the blending of two shear-thinning fluid streams with identical or different rheology. Planar Laser Induced Fluorescence (PLIF) has been used to obtain the concentration distribution at the mixer outlet by doping one fluid stream with fluorescent dye upstream of the mixer inlet. The effect of scale of the static mixer, total flow rate, flow ratio between the fluid streams and inlet configuration have been investigated. The applicability of different methods to characterise mixing performance is examined by comparing conventional mixing measures such as coefficient of variation and maximum striation area against recent alternative methods presented in the literature, such as the areal distribution method developed by Alberini et al. (2014). A method of characterising individual striations by determining their distribution as a function of size and concentration is also presented. These findings illustrate the complexity of information-rich PLIF images, and highlight how different methods of analysis may be appropriate given the dependencies of the downstream process

Operations involving gas–liquid agitated vessels are common in the biochemical and chemical industry; ensuring good contact between the two phases is essential to process performance. In this work, a methodology to compute acoustic emission data, recorded using a piezoelectric sensor, to evaluate the gas–liquid mixing regime within gas–liquid and gas–solid–liquid mixtures was developed. The system was a 3L stirred tank equipped with a Rushton Turbine and a ring sparger. Whilst moving up through the vessel, gas bubbles collapse, break or coalesce generating sound waves transmitted through the wall to the acoustic transmitter. The system was operated in different flow regimes (non-gassed condition, loaded and complete dispersion) achieved by varying impeller speed and gas flow rate, with the objective to feed machine learning algorithms with the acoustic spectrum to univocally identify the different conditions. The developed method allowed to successfully recognise the operating regime with an accuracy higher than 90% both in absence and presence of suspended particles.