Biofuels
The constant growth of the world population has caused the increase on demand for energy. Industry and transportation sectors are the most end-use energy consumption in the world which require a large amount of liquid fuels. In this way, the combustion of fossil fuels has led to the emission and accumulation of greenhouse gases (GHG) causing negative effects to environment, such as global warming. Therefore, to mitigate the GHG emissions and eventual depletion of petroleum, new technologies and infrastructure have been developed to produce sustainable, efficient, economically viable and renewable sources of liquid fuels (H. Chen et al., 2017).On this basis, the use of renewable energy sources has presented a growth of 2.8% annually since 2008 and it is expected to increase according to the objectives of The Paris Agreement in 2016 (Paris Agreement, 2016). However, the global energy and economics are still based in fossil fuel utilization. Petro-oil represents the highest consumption of energy available for final use in industry transport and home (Total Final Consumption, TFC), compared with other energy sources, followed by electricity and natural gas as Figure 1 exhibits.
Figure 1. World TFC by fuel (♦) Oil, (⁎) Electricity, (▲) Natural Gas, (●) Biofuels and Waste, (■) Coal, (▼) Other, from 2000 to 2016. Adapted from (IEA, 2019b)
Within the alternative energy sources, biomass transformation into energy is an important option to consider. Biomass from crops and agroindustry residues can be converted into biofuels which are a suitable and ecological friendly option since they are inexpensive, clean, sustainable and they could improve the rural economy (Sunde, Brekke, & Solberg, 2011). Also, the emitted CO2 can be reintegrated into the carbon cycle by photosynthesis and it can be mixed with fossil fuels as additives which would reduce SOx and NOx emissions. According to the International Energy Agency (IEA), it is expected that biofuels may provide the 27% of the world transportation fuel by 2050, which means that nearly 3 billion tons of biomass will be needed annually (IEA, 2011; IEA, 2019). Even so, the production of biofuels requires to triple -from 130 to 400 billion liters per year; Figure 2- its growth to achieve the 2030 under the Suitable Development Scenario (SDS) (IEA, 2019a). It is important to point out that biofuels may not replace the total transportation fossil fuel demand (Furimsky, 2013). Nonetheless, the potential of these fuels resides in that they can be blended with typical petroleum-based fuels, in such way that they can be distributed and used in the current infrastructure and combustion systems
Figure 2. Global historical (2010-2016), forecast (2017-2023) and SDS targets (2025-2030) biofuels production (IEA, 2019a)
In this sense, four generations of biofuels have been developed based on the raw material and method of production to overcome the agricultural issues and to supply fuel depending in the resources of each ecosystem (Correa, Beyer, Possingham, Thomas-Hall, & Schenk, 2017). Figure 3 summarizes the biofuel generations and main products.
Figure 3. Biofuels and generation classification and main products
First generation biofuels (1G) are produced from sugar and vegetable oils to produce bioethanol and biodiesel. In contrast, second generation biofuels (2G) are generated form the organic waste from agroindustry into fuel. Third and fourth biofuel generations (3G and 4G respectively) produce biomass (sugars, waste and triglycerides) from micro and macro algae, which is transformed into fuel by similar processes used in 1G and 2G. The 4G biofuels have been developed by improvement of the photosynthetic microorganisms to produce sustainable and renewable fuels by biological engineering. Table 1 displays the pros and cons of each biofuel generation.
Table 1. Comparison between biofuel generations (Adapted from (Abdullah et al., 2019))
| Topic | Biofuels |
| 1G | 2G | 3G | 4G |
| Competition with food | Yes | No | No | No |
| Land footprint | Large area | Large area | Small area | Small area |
| Conversion to biofuels | Easy | Difficult | Easy | Easy |
| Environment impact | Use of fertilizers | Deforestation | Marine eutrophication | Release GM organisms |
| Commercialization | Produced | Produced | Non produced | Non produced |
| Financial input | Low | Low | Large initial and cultivation costs | Large initial and cultivation costs |
Biorefineries appear as conceptual models for biofuel production which integrate different upgrading conversion units of biomass. It is necessary to adapt them into biomass feedstocks according to the local needs to improve the scientific and technological advancements to build an economically and sustainable transformation systems (S. K. Maity, 2015). Hence, new challenges for optimization in the production capacity of the biorefinery and technical advances are ahead. Several authors have agreed that integration of biorefineries with fossil fuel industries and renewable energy sources can be made to simplify the scalation and reduce investment costs (Palmeros Parada, Osseweijer, & Posada Duque, 2017; Yang & Yu, 2013). These advanced plants are still in demonstration or pilot-plant stage an requires experiments to settle down the optimal conditions and operation variables. Thus, the essential optimization experiments and predicting models are needed to accelerate the understanding of the multifaceted biofuels production systems
The use of Design of Experiments (DoE) in the biofuel industry has been applied to obtain critical information from the system in order to improve the production including the catalytic synthesis, the profits and capability, the performance and the manufacturing costs and reduce design and development time (Cox & Reid, 2000). The attention on application of DoE in biofuels development is directed to the screening and optimization of combining factors such as the harvesting biomass and optimal operational conditions. The present chapter is focused on the review, comparation and discussion of the application of different DoE methodologies in the biofuel production optimization to analyze and compile information of the factors and optimal results published by different authors.