(194c) Operating Condition Optimization of a Full-Scale Stirred Tank for Biofuel Production

Global warming and greenhouse gas emissions as well as the exhaustion of easily accessible fossil fuel resources are calling for effective carbon dioxide (CO2) mitigation technologies and clean and renewable energy sources. One promising technology is the conversion of CO2 by microalgae into biofuels. This process has several advantages but still suffers from economic feasibility.

In this study we are looking into this process by using Computational Fluid Dynamics (CFD) to understand and optimize the operating conditions in an aerated full-scale mixing vessel. The operating conditions are the key factors for a more efficient and economic process. The goal of the optimization is to reduce the power consumption for aeration and the impellers as well as to supply the best environment for the microalgae. Their growth and activity is affected by several parameters like the availability of dissolved CO2, sunlight (energy supply), further components, pH-value as well as shear forces, which can lead to significant cell damage.

Numerical pre-studies [1] have shown, that especially in full-scale the description of the interfacial area between the gas bubbles and the liquid phase is important for momentum, energy and mass transfer (CO2) and therefor for the amount of dissolved CO2. And this can typically only be achieved with a bubble size distribution including coalescence and breakup phenomena, which is considered in this study.

For the CFD simulation STAR-CCM+ by Siemens PL is used in combination with the design space exploration tool HEEDS to find the best configuration which matches the requirements. The requirements here are to minimize the power input but still to supply sufficient CO2 and avoid excessive shear acting on the microalgae. Based on the achieved results the Pareto-front is discussed and the effects on the efficiency of the process is shown. Furthermore the transferability to other reactor concepts is discussed.

[1] Aglave, R., & Eppinger, T. (2016, December). Influence of Bubble Size Distribution in Gas-Liquid Flows. In High Performance Computing Workshops. IEEE.