Recent Trends and Developments in Algal Biofuels and Biorefinery - Environmental Science and Engineering

This book presents technoeconomic challenges, recent trends, and developments toward sustainable algal biofuels and biorefinery. The exponential increase in population and thus the demand for energy, the ever-rising threats to climate change with conventional fossil fuels consumption, fluctuations in fossil fuels price and geopolitical instability have made each and every country of the world to think over its energy independence and security by increasing its own domestic energy production and reducing dependence on scarce fossil fuels. The global need to shift towards a sustainable source of energy has led to the discovery of third and fourth generation biofuels, in which microalgae and their genetically modified strains have been exploited. However, these simple photosynthetic organisms are difficult to select and cultivate for the production of biodiesel. Genetic engineering has opened provisions for the introduction of desired traits to such useful strains. There are also scopes for designing of novel photobioreactors (PBRs) having strain-specific physicochemical parameters, adequate CO₂ and nutrient supply, and so on. The role of microalgae in CO₂ sequestration from the environment is well known fact. Wastewater treatment plants can be used a raceway pond for the microalgal biomass production, and thus, obtained biomass can be used for biofuels and biochemical production.

Mathematical modelling can be used to understand the complex phenomena inside the photobioreactor, which can be a great help to overcome the limitations related to design and scale up of PBRs. Flow hydrodynamics in PBRs has significant effect on the microalgae growth. Computational fluid dynamics (CFD) can be used to study operating and geometry factors in PBRs that influence the flow dynamics, such as the inlet gas flow rate, mixing, mass transfer, reactor geometry. The use of artificial intelligence (AI) particularly artificial neural network model (ANN model), statistical and evolutionary learning-based techniques are some innovative approaches in manipulating and optimizing productivity and costs in algal biofuel production. This book is aimed to bring several aspects of algal biorefinery and microalgal biofuel production, challenges, and future perspective of microalgal biofuel production at a single place.