Dynamic Simulation and Optimization for Arthrospira platensis Growth and C-Phycocyanin Production

This is the accepted manuscript. The final version is available at http://pubs.acs.org/doi/abs/10.1021/acs.iecr.5b03102.C-phycocyanin is a high-value bioproduct synthesized from cyanobacterium Arthrospira platensis. To facilitate its application, advanced dynamic models were built to simulate the complex effects of light intensity, light attenuation and nitrate concentration on cell growth and pigment production in the current research. By comparing these models against the experimental results, their accuracy was verified in both batch and fed-batch processes. Three key findings are presented in this work. First, a noticeable difference between the optimal light intensity for cell growth (282 μmol m-2 s-1) and phycocyanin synthesis (137 μmol m-2 s-1) is identified. Second, light attenuation is demonstrated to be the primary factor causing the decrease of intracellular phycocyanin content instead of nitrate concentration in the fed-batch process, while it has no significant effect on total phycocyanin production. Finally, although high nitrate concentration can enhance cell growth, it is demonstrated to suppress intracellular phycocyanin accumulation in a long-term operation.Author E. A. del Rio-Chanona is funded by CONACyT scholarship No. 522530 and the Secretariat of Public Education and the Mexican government. This work was also supported by the National High Technology Research and Development Program 863, China (No. 2014AA021701) and the National Marine Commonwealth Research Program, China (No. 201205020-2)

Dynamic modeling and optimization of cyanobacterial C-phycocyanin production process by artificial neural network

This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.algal.2015.11.004Artificial neural networks have been widely applied in bioprocess simulation and control due to their advantageous properties. However, their feasibility in long-term photo-fermentation process modelling and prediction as well as their efficiency on process optimisation have not been well studied so far. In the current study, an artificial neural network was constructed to simulate a 15-day fed-batch process for cyanobacterial C-phycocyanin production, which to the best of our knowledge has never been conducted. To guarantee the accuracy of artificial neural network, two strategies were implemented. The first strategy is to generate artificial data sets by adding random noise to the original data set, and the second is to choose the change of state variables as training data output. In addition, the first strategy showed the distinctive advantage of reducing the experimental effort in generating training data. By comparing with current experimental results, it is concluded that both strategies give the network great modelling and predictive power to estimate the entire fed-batch process performance, even when few original experimental data are supplied. Furthermore, by optimising the operating conditions of a 12-day fed-batch process, a significant increase of 85.6% on C-phycocyanin production was achieved compared to previous work, which suggests the high efficiency of artificial neural network on process optimisation.Author E. A. del Rio-Chanona is funded by CONACyT scholarship No. 522530 from the Secretariat of Public Education and the Mexican government. Author D. Zhang gratefully acknowledges the support from his family. This work was also supported by the National High Technology Research and Development Program 863, China (No. 2014AA021701) and the National Marine Commonwealth Research Program, China (No. 201205020-2)

Bioreactor for microalgal cultivation systems: strategy and development

Microalgae are important natural resources that can provide food, medicine, energy and various bioproducts for nutraceutical, cosmeceutical and aquaculture industries. Their production rates are superior compared to those of terrestrial crops. However, microalgae biomass production on a large scale is still a challenging problem in terms of economic and ecological viability. Microalgal cultivation system should be designed to maximize production with the least cost. Energy efficient approaches of using light, dynamic mixing to maximize use of carbon dioxide (CO2) and nutrients and selection of highly productive species are the main considerations in designing an efficient photobioreactor. In general, optimized culture conditions and biological responses are the two overarching attributes to be considered for photobioreactor design strategies. Thus, fundamental aspects of microalgae growth, such as availability of suitable light, CO2 and nutrients to each growing cell, suitable environmental parameters (including temperature and pH) and efficient removal of oxygen which otherwise would negatively impact the algal growth, should be integrated into the photobioreactor design and function. Innovations should be strategized to fully exploit the wastewaters, flue-gas, waves or solar energy to drive large outdoor microalgae cultivation systems. Cultured species should be carefully selected to match the most suitable growth parameters in different reactor systems. Factors that would decrease production such as photoinhibition, self-shading and phosphate flocculation should be nullified using appropriate technical approaches such as flashing light innovation, selective light spectrum, light-CO2 synergy and mixing dynamics. Use of predictive mathematical modelling and adoption of new technologies in novel photobioreactor design will not only increase the photosynthetic and growth rates but will also enhance the quality of microalgae composition. Optimizing the use of natural resources and industrial wastes that would otherwise harm the environment should be given emphasis in strategizing the photobioreactor mass production. To date, more research and innovation are needed since scalability and economics of microalgae cultivation using photobioreactors remain the challenges to be overcome for large-scale microalgae production

Connected learning: a refugee assessment

Connected learning offers the opportunity to expand access to higher education for refugees, benefiting both individuals and communities