Optimization of production rate, productivity, and product concentration for a simulated moving bed process aimed atfucose separation using standing-wave-design and genetic algorithm

Effect of radial distribution of injected flow on simulated moving bed performance

In the modeling of a simulated moving bed, several assumptions are considered, the key assumption is there are no radial concentration gradients based on perfect mixing. However, it is difficult to achieve perfect mixing because the injected flowrate of the bed is periodically changed in the process. In this study, the performance of the simulated moving bed process was analyzed when the injected flow such as the feed or desorbent stream was unevenly distributed. To this end, the distribution function of the injected flow was calculated and applied to the model. Two types of distribution functions were obtained using the experimental results of a previous study, and the simulation results were compared with classical modeling assuming perfect mixing. In the base case simulation, the purity was similar in all cases, the productivity was higher more than 5% in the even distribution case compared to the most uneven distribution case. The effect of distribution was analyzed through sensitivity analysis by changing the overall flow rate, switching time, bed length, and flow rate of sections 2 and 3. As a result, regardless of the distribution applied, the trends of the performance parameters were the same. However, the more uneven the distribution, the greater the difference in productivity, recovery, and desorbent consumption compared to the even distribution case. It was confirmed that the design that distributes the injected flow more evenly has a better performance.

Production of Algal Biomass and High-Value Compounds Mediated by Interaction of Microalgal Oocystis sp. KNUA044 and Bacterium Sphingomonas KNU100

There is growing interest in the production of microalgae-based, high-value by-products as an emerging green biotechnology. However, a cultivation platform for Oocystis sp. has yet to be established. We therefore examined the effects of bacterial culture additions on the growth and production of valuable compounds of the microalgal strain Oocystis sp. KNUA044, isolated from a locally adapted region in Korea. The strain grew only in the presence of a clear supernatant of Sphingomonas sp. KNU100 culture solution and generated 28.57 mg/l/d of biomass productivity. Protein content (43.9 wt%) was approximately two-fold higher than carbohydrate content (29.4 wt%) and lipid content (13.9 wt%). Oocystis sp. KNUA044 produced the monosaccharide fucose (33 μg/mg and 0.94 mg/l/d), reported here for the first time. Fatty acid profiling showed high accumulation (over 60%) of polyunsaturated fatty acids (PUFAs) compared to saturated (29.4%) and monounsaturated fatty acids (9.9%) under the same culture conditions. Of these PUFAs, the algal strain produced the highest concentration of linolenic acid (C18:3 ω3; 40.2%) in the omega-3 family and generated eicosapentaenoic acid (C20:5 ω3; 6.0%), also known as EPA. Based on these results, we suggest that the application of Sphingomonas sp. KNU100 for strain-dependent cultivation of Oocystis sp. KNUA044 holds future promise as a bioprocess capable of increasing algal biomass and high-value bioactive by-products, including fucose and PUFAs such as linolenic acid and EPA.

Application of evolutionary algorithms to optimise one- and two-dimensional gradient chromatographic separations

We report on the performance of three classes of evolutionary algorithms (genetic algorithms (GA), evolution strategies (ES) and covariance matrix adaptation evolution strategy (CMA-ES)) as a means to enhance searches in the method development spaces of 1D- and 2D-chromatography. After optimisation of the design parameters of the different algorithms, they were benchmarked against the performance of a plain grid search. It was found that all three classes significantly outperform the plain grid search, especially in terms of the number of search runs needed to achieve a given separation quality. As soon as more than 100 search runs are needed, the ES algorithm clearly outperforms the GA and CMA-ES algorithms, with the latter performing very well for short searches (<50 search runs) but being susceptible to convergence to local optima for longer searches. It was also found that the performance of the ES and GA algorithms, as well as the grid search, follow a hyperbolic law in the large search run number limit, such that the convergence rate parameter of this hyperbolic function can be used to quantify the difference in required number of search runs for these algorithms. In agreement with one's physical expectations, it was also found that the general advantage of the GA and ES algorithms over the grid search, as well as their mutual performance differences, grow with increasing difficulty of the separation problem.