scholarly journals Soft-computing modeling and multiresponse optimization of microalgal biomass and lipid productivity

Author(s):  
S. M. Zakir Hossain ◽  
Nahid Sultana ◽  
Shaikh Razzak ◽  
Mohammad Hossain
2022 ◽  
Vol 45 ◽  
pp. 102490
Author(s):  
S. M. Zakir Hossain ◽  
Nahid Sultana ◽  
Majeed S. Jassim ◽  
Gulnur Coskuner ◽  
Lujain M. Hazin ◽  
...  

2020 ◽  
Vol 12 (21) ◽  
pp. 9083
Author(s):  
Zahra Shokravi ◽  
Hoofar Shokravi ◽  
Ong Hwai Chyuan ◽  
Woei Jye Lau ◽  
Seyed Saeid Rahimian Koloor ◽  
...  

Microalgae have received widespread interest owing to their potential in biofuel production. However, economical microalgal biomass production is conditioned by enhancing the lipid accumulation without decreasing growth rate or by increasing both simultaneously. While extensive investigation has been performed on promoting the economic feasibility of microalgal-based biofuel production that aims to increase the productivity of microalgae species, only a handful of them deal with increasing lipid productivity (based on lipid contents and growth rate) in the feedstock production process. The purpose of this review is to provide an overview of the recent advances and novel approaches in promoting lipid productivity (depends on biomass and lipid contents) in feedstock production from strain selection to after-harvesting stages. The current study comprises two parts. In the first part, bilateral improving biomass/lipid production will be investigated in upstream measures, including strain selection, genetic engineering, and cultivation stages. In the second part, the enhancement of lipid productivity will be discussed in the downstream measure included in the harvesting and after-harvesting stages. An integrated approach involving the strategies for increasing lipid productivity in up- and down-stream measures can be a breakthrough approach that would promote the commercialization of market-driven microalgae-derived biofuel production.


2015 ◽  
Author(s):  
Balamati Choudhury ◽  
Rakesh Mohan Jha
Keyword(s):  

Author(s):  
Shafagat Mahmudova

The study machine learning for software based on Soft Computing technology. It analyzes Soft Computing components. Their use in software, their advantages and challenges are studied. Machine learning and its features are highlighted. The functions and features of neural networks are clarified, and recommendations were given.


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