Chemical Engineering Science, Vol.185, 1-17, 2018
Numerical investigation of COD reduction in compact bioreactor with bubble plumes
Water purification using microbubbles has become an important topic, owing to its enhanced mass transfer effect. Therefore, a full 3D numerical model is developed for wastewater purification using microbubbles with bacterial biochemical reactions. Microbubbles are injected into the bioreactor, and the oxygen obtained from microbubble dissolution is used by the bacteria for substrate consumption. This consumption performance may depend on several factors, including physical parameters such as bubble size, column height, and injection type. Optimization of the bioreactor performance based on these parameters would lead to significantly faster purification. In this study, the dependences of these parameters are investigated numerically. The bioreactor model is provided by the mixed Eulerian-Lagrangian formulation for fluid flow and bubble motion tracking in the system. Mass transfer, gas dissolution, and mixing using the Sherwood number approach are employed in this model. Biochemical reactions based on various literatures, including activated sludge models, are used for the current simulation of wastewater purification. Simulations are carried out for an aerobic bacterial system with carbohydrates as the chemical oxygen demand source. The bioreactor height is varied from 1.1 to 4.1 to the base (similar to 0.1 m), the bubble size is varied from 200 mu m to 1 mm, and the central and uniform injection systems are compared. The analysis demonstrates that, for microbubbles with a uniform injection system, a drastic reduction in bioreactor height can be achieved without a performance reduction. An important conclusion is that, for a shorter bioreactor height with 200 mu m-microbubble injections, the uniform injection system offers significantly superior performance, while for a longer height with larger (500 mu m or 1 mm) bubbles, the central and uniform injections provide nearly the same performance. (C) 2018 Elsevier Ltd. All rights reserved.