Dynamic simulation of a 2D bubble column

Modeling and Simulation of Photobioreactors with Computational Fluid Dynamics—A Comprehensive Review

Computational Fluid Dynamics (CFD) have been frequently applied to model the growth conditions in photobioreactors, which are affected in a complex way by multiple, interacting physical processes. We review common photobioreactor types and discuss the processes occurring therein as well as how these processes have been considered in previous CFD models. The analysis reveals that CFD models of photobioreactors do often not consider state-of-the-art modeling approaches. As a comprehensive photobioreactor model consists of several sub-models, we review the most relevant models for the simulation of fluid flows, light propagation, heat and mass transfer and growth kinetics as well as state-of-the-art models for turbulence and interphase forces, revealing their strength and deficiencies. In addition, we review the population balance equation, breakage and coalescence models and discretization methods since the predicted bubble size distribution critically depends on them. This comprehensive overview of the available models provides a unique toolbox for generating CFD models of photobioreactors. Directions future research should take are also discussed, mainly consisting of an extensive experimental validation of the single models for specific photobioreactor geometries, as well as more complete and sophisticated integrated models by virtue of the constant increase of the computational capacity.

Changes in the Number of Membership Functions for Predicting the Gas Volume Fraction in Two-Phase Flow Using Grid Partition Clustering of the ANFIS Method

A 2D-bubble column reactor (BCR) including gas and liquid phases is simulated, and fluid characteristics such as gas-phase volume fraction and gas-phase turbulence are extracted from the CFD simulations. A type of heuristic algorithm called adaptive network-based fuzzy inference system (ANFIS) is applied here to simulate the gas-phase volume fraction in a physical system. Indeed, the x direction, the y direction, and gas-phase turbulence are considered as the ANFIS inputs. Changes in the number of inputs as well as membership functions are evaluated and studied to obtain a high level of ANFIS intelligence. By implementing the highest ANFIS intelligence, a surface is predicted, which suggests that the gas-phase volume fraction is based on x and y directions. It provides capability to achieve the amount of gas-phase volume fraction in different points of a 2D-BCR.

Prediction of multiphase flow pattern inside a 3D bubble column reactor using a combination of CFD and ANFIS

This work presents a combination of Computational Fluid Dynamics (CFD) and Adaptive Network-based Fuzzy Inference System (ANFIS) developed for flow characterization inside a cylindrical bubble column reactor.