Modeling the light distribution in airlift photobioreactors under simultaneous external and internal illumination using the two-flux model

Lighting the way to sustainable development: Physiological response and light control strategy in microalgae-based wastewater treatment under illumination

The Sustainable Development Goals link pollutant control with carbon dioxide reduction. Toward the goal of pollutant and carbon reduction, microalgae-based wastewater treatment (MBWT), which can simultaneously remove pollutants and convert carbon dioxide into biomass with value-added metabolites, has attracted considerable attention. The photosynthetic organism microalgae and the photobioreactor are the functional body and the operational carrier of the MBWT system, respectively; thus, light conditions profoundly influence its performance. Therefore, this review takes the general rules of how light influences the performance of MBWT systems as a starting point to elaborate the light-influenced mechanisms in microalgae and the light control strategies for photobioreactors from the inside out. Wavelength, light intensity and photoperiod solely or interactively affect biomass accumulation, pollutant removal, and value-added metabolite production in MBWT. Physiological processes, including photosynthesis, photooxidative damage, light-regulated gene expression, and nutrient uptake, essentially explain the performance influence of MBWT and are instructive for specific microalgal strain improvement strategies. In addition, light causes unique reactions in MBWT systems as it interacts with components such as photooxidative damage enhancers present in types of wastewater. In order to provide guidance for photobioreactor design and light control in a large-scale MBWT system, wavelength transformation, light transmission, light source distribution, and light-dark cycle should be considered in addition to adjusting the light source characteristics. Finally, based on current research vacancies and challenges, future research orientation should focus on the improvement of microalgae and photobioreactor, as well as the integration of both.

Modeling and Simulation of Photobioreactors with Computational Fluid Dynamics—A Comprehensive Review

Computational Fluid Dynamics (CFD) have been frequently applied to model the growth conditions in photobioreactors, which are affected in a complex way by multiple, interacting physical processes. We review common photobioreactor types and discuss the processes occurring therein as well as how these processes have been considered in previous CFD models. The analysis reveals that CFD models of photobioreactors do often not consider state-of-the-art modeling approaches. As a comprehensive photobioreactor model consists of several sub-models, we review the most relevant models for the simulation of fluid flows, light propagation, heat and mass transfer and growth kinetics as well as state-of-the-art models for turbulence and interphase forces, revealing their strength and deficiencies. In addition, we review the population balance equation, breakage and coalescence models and discretization methods since the predicted bubble size distribution critically depends on them. This comprehensive overview of the available models provides a unique toolbox for generating CFD models of photobioreactors. Directions future research should take are also discussed, mainly consisting of an extensive experimental validation of the single models for specific photobioreactor geometries, as well as more complete and sophisticated integrated models by virtue of the constant increase of the computational capacity.

Analysis on the Influence of China’s Energy Consumption on Economic Growth

Many studies have shown that energy consumption has a great influence on economic growth. This paper divides China’s energy into coal, oil, natural gas and clean energy (hydroenergy, nuclear energy, wind energy and solar energy), and then studies the influences of China’s coal, oil, natural gas and clean energy on economic growth quantitatively using econometric models. This paper uses three methods. The first method is correlative degree analysis. The paper calculates the correlative degrees between four energy consumption and economic growth (GDP), and then compares the influences of four different kinds of energy consumption on economic growth in terms of the correlative degree. The second method is multiplier analysis. The paper uses the lagged variable regression model to calculate four energy consumption’s current multipliers, dynamic multipliers and long-term multipliers for economic growth, and then compares the influences of four kinds of energy consumption on economic growth in terms of marginal effect. The third method is contribution rate analysis. The paper calculates the rates of contribution of four kinds of energy consumption to economic growth and then compares the influences of four energy consumption on economic growth in terms of input and output. The paper makes an empirical analysis on influences of China’s energy consumption on economic growth. Analysis results show that in terms of correlative degree, natural gas has the greatest influence on GDP, followed by clean energy, oil and coal; in terms of the multiplier effect, natural gas has the biggest current multiplier and long-term multiplier, followed by clean energy, oil and coal; in terms of contribution rate, clean energy has the biggest contribution rate, followed by natural gas, oil and coal. Overall, China’s natural gas consumption and clean energy consumption have more influence on economic growth than coal consumption and oil consumption, and show a rising trend.