Bio-based bulk chemicals such as carboxylic acids continue to struggle to compete with their fossil counterparts on an economic basis. One possibility to improve the economic feasibility is the use of crude substrates in biorefineries. However, impurities in these substrates pose challenges in fermentation and purification, requiring interdisciplinary research. This work demonstrates a holistic approach to biorefinery process development, using itaconic acid production on thick juice based on sugar beets with Ustilago sp. as an example. A conceptual process design with data from artificially prepared solutions and literature data from fermentation on glucose guides the simultaneous development of the upstream and downstream processes up to a 100 L scale. Techno-economic analysis reveals substrate consumption as the main constituent of production costs and therefore, the product yield is the driver of process economics. Aligning pH-adjusting agents in the fermentation and the downstream process is a central lever for product recovery. Experiments show that fermentation can be transferred from glucose to thick juice by changing the feeding profile. In downstream processing, an additional decolorization step is necessary to remove impurities accompanying the crude substrate. Moreover, we observe an increased use of pH-adjusting agents compared to process simulations.
Reliable prediction of flooding conditions
is needed for sizing
and operation of sieve plate extraction columns. Due to the complex
interplay of chemical properties, the extraction column geometry and
material and the pulsation intensity, the development of physical
models and semiempirical correlations for a broad validity range is
complicated. Available models and correlations may fail in predicting
the flooding curve accurately. To overcome this problem, a data-driven
model has been developed, which is capable of predicting flooding
with a higher accuracy than conventional correlations from the literature.
The optimized black-box approach, a purely data-driven approach, is
a Gaussian process with a root mean squared error of 1.65 × 10–3 m/s and a coefficient of determination of 0.942.
The combination of the data-driven model with additional physical
models improves the accuracy not significantly. This gray-box approach
results in a Gaussian process with a root mean squared error of 1.61
× 10–3 m/s and a coefficient of determination
of 0.944. The data-driven model is able to calculate correct flooding
curves for different representative chemical systems and extraction
column geometries.
Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.
Axial backmixing lowers the efficiency of packed countercurrent high-pressure extraction columns. To quantify backmixing, a method of measuring the residence time distribution and calculating the axial dispersion coefficient in high-pressure extraction columns is introduced. Using a design of experiments, the effect of supercritical and liquid mass flow rates as well as the pressure at a constant temperature on the mean residence time and the axial dispersion coefficient are evaluated for the system water/supercritical CO 2. The experimental data is correlated to the Reynolds and Schmidt number.
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