Impeller Fermenter CFD Optimization for Oxygen Transfer and Power Use
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Solution Overview
Problem
Existing methods for optimizing large-scale fermenters for amino acid fermentation are limited by physical constraints and high energy consumption, requiring costly experiments to find optimal operating conditions for oxygen transfer and energy efficiency.
Innovation Solution
A CFD-based optimization method and system that simulates fermenter flow phenomena to predict oxygen transfer coefficients and power consumption, adjusting impeller heights and other variables to optimize oxygen supply efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the diameter or rotational speed of the impeller is increased to ensure rapid oxygen transfer, then oxygen transfer efficiency is improved, but energy consumption increases significantly
Solution Approach 1:
The patent applies parameter changes by systematically varying impeller diameter, rotational speed, and aeration rate to identify optimal operating conditions. CFD simulations calculate oxygen transfer coefficients and power consumption for different parameter combinations, enabling selection of conditions that balance oxygen transfer efficiency with energy consumption without requiring physical experiments on large-scale fermenters.
2Measurement precision
If physical experiments are conducted on large-scale fermenters to analyze oxygen transfer and energy consumption, then accurate data is obtained, but production must be halted and significant costs are incurred
Solution Approach 1:
The patent uses CFD simulations as virtual copies of large-scale fermenters to conduct experiments and collect data without physical manipulation of actual equipment. The simulations replicate flow phenomena, oxygen transfer, and energy consumption characteristics, providing accurate data while avoiding production interruption and high experimental costs associated with physical testing.
Solution Approach 2:
The patent replaces physical mechanical experiments with computational fluid dynamics simulations. Instead of conducting hands-on experiments that require stopping production and modifying large-scale fermenters, the invention uses numerical models to predict oxygen transfer coefficients and power consumption under various operating conditions, eliminating the need for physical intervention.
3Productivity
If multiple experiments are conducted to analyze various operating conditions, then comprehensive optimization data is obtained, but time and resource requirements increase
Solution Approach 1:
The patent performs preliminary CFD simulations to predict optimal operating conditions before conducting any physical experiments. By pre-calculating oxygen transfer coefficients and power consumption for various impeller configurations and operating parameters, the invention identifies promising conditions that can be directly applied, minimizing the need for iterative physical experimentation and reducing overall optimization time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves productivity and reduces power consumption by calculating optimal impeller configurations, enabling efficient oxygen transfer and fermentation time reduction.
Implementation Method 1
calculating a primary value, which is at least one of oxygen diffusion coefficient, liquid density, liquid viscosity, gas hold-up, bubble diameter, energy dissipation rate, and torque
Implementation Method 2
calculating a primary value, which is at least one of oxygen diffusion coefficient, liquid density, liquid viscosity, gas hold-up, bubble diameter, energy dissipation rate, and torque
Data Source
AI summary
A CFD-based optimization method for a fermenter for amino acid fermentation according to an embodiment of the present invention includes: a first step of setting an objective variable, a constraint variable, and a design variable with respect to an optimization objective of at least one impeller fermenter; a second step of generating a three-dimensional shape for a flow region of the impeller fermenter for each condition of the constraint variable and the design variable according to the objective variable; a third step of calculating a primary value, which is at least one of oxygen diffusion coefficient, liquid density, liquid viscosity, gas hold-up, bubble diameter, energy dissipation rate, and torque, by computational fluid dynamics analysis for the flow region based on information about the three-dimensional shape; a fourth step of calculating secondary values, which are an oxygen transfer coefficient and a power consumption ratio (power ratio), using the primary value; and a fifth step of repeating the second, third, and fourth steps while varying the design variable of the first step in various ways, and finding an optimal point based on a comparison value obtained by comparing the secondary values before and after the variation.