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)
