scholarly journals Bio-Mitigation of Carbon Dioxide Using Desmodesmus sp. in the Custom-Designed Pilot-Scale Loop Photobioreactor

2021 ◽  
Vol 13 (17) ◽  
pp. 9882
Author(s):  
Abhishek Anand ◽  
Kaustubh Tripathi ◽  
Amit Kumar ◽  
Suresh Gupta ◽  
Smita Raghuvanshi ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Global Warming ◽  
Lipid Content ◽  
Algal Biomass ◽  
Scale Up ◽  
Biomass Productivity ◽  
Co2 Concentration ◽  
Management Systems ◽  
Fixation Rate ◽  
Chl A

Today’s society is faced with many upfront challenges such as the energy crisis, water pollution, air pollution, and global warming. The greenhouse gases (GHGs) responsible for global warming include carbon dioxide (CO2), methane (CH4), nitrous oxide (NOx), water vapor (H2O), and fluorinated gases. A fraction of the increased emissions of CO2 in the atmosphere is due to agricultural and municipal solid waste (MSW) management systems. There is a need for a sustainable solution which can degrade the pollutants and provide a technology-based solution. Hence, the present work deals with the custom design of a loop photobioreactor with 34 L of total volume used to handle different inlet CO2 concentrations (0.03%, 5%, and 10% (v/v)). The obtained values of biomass productivity and CO2 fixation rate include 0.185 ± 0.004 g L−1 d−1 and 0.333 ± 0.004 g L−1 d−1, respectively, at 10% (v/v) CO2 concentration and 0.084 ± 0.003 g L−1 d−1 and 0.155 ± 0.003 g L−1 d−1, respectively, at 5% (v/v) CO2 concentration. The biochemical compositions, such as carbohydrate, proteins, and lipid content, were estimated in the algal biomass produced from CO2 mitigation studies. The maximum carbohydrate, proteins, and lipid content were obtained as 20.7 ± 2.4%, 32.2 ± 2.5%, and 42 ± 1.0%, respectively, at 10% (v/v) CO2 concentration. Chlorophyll (Chl) a and b were determined in algal biomass as an algal physiological response. The results obtained in the present study are compared with the previous studies reported in the literature, which indicated the feasibility of the scale-up of the process for the source reduction of CO2 generated from waste management systems without significant change in productivity. The present work emphasizes the cross-disciplinary approach for the development of bio-mitigation of CO2 in the loop photobioreactor.

Influence of Carbon Dioxide Concentration on Microalgal Growth in a Bubble Column Photobioreactor

2012 ◽  
Vol 599 ◽  
pp. 137-140 ◽  
Author(s):  
Shu Wen Li ◽  
Sheng Jun Luo ◽  
Rong Bo Guo
Keyword(s):  
Carbon Dioxide ◽  
Global Warming ◽  
Chlorella Vulgaris ◽  
Co2 Sequestration ◽  
High Efficiency ◽  
Bubble Column ◽  
Carbon Dioxide Concentration ◽  
Co2 Concentration ◽  
Dry Biomass ◽  
Bubble Column Photobioreactor

The CO2 sequestration by microalgae is thought to be one of the most sustainable strategies to relieve global warming. To produce 1 ton of microalgal dry biomass, 2 ton of CO2 is required. However, insufficient supply of CO2 will limit microalgal growth, and excessive CO2 both means wasting and inhibits microalgal growth. In the present study, the dissolved CO2 concentration in culture limiting and inhibiting microalgal growth (Chlorella vulgaris) in a bubble column photobioreactor was studied. The experimental results showed that the dissolved CO2 concentration ranging from 107μmol/L to 1500 μmol/L could meet microalgal growth’s need, which provides the guidance for microalgal CO2 biofixation with high efficiency.

C balance, carbon dioxide emissions and global warming potentials in LCA-modelling of waste management systems

2009 ◽  
Vol 27 (8) ◽  
pp. 707-715 ◽  
Author(s):  
Thomas H. Christensen ◽  
Emmanuel Gentil ◽  
Alessio Boldrin ◽  
Anna W. Larsen ◽  
Bo P. Weidema ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Global Warming ◽  
Waste Management ◽  
Carbon Dioxide Emissions ◽  
Management Systems ◽  
C Balance ◽  
Global Warming Potentials

scholarly journals Modeling of Carbon Dioxide Fixation Rate by Micro Algae Using Hybrid Artificial Intelligence and Fuzzy Logic Methods and Optimization by Genetic Algorithm

2021 ◽  
Author(s):  
Omkar Singh Kushwaha ◽  
Haripriyan Uthayakumar ◽  
Karthigaiselvan Kumaresan
Keyword(s):  
Carbon Dioxide ◽  
Genetic Algorithm ◽  
Fuzzy Inference ◽  
Scale Up ◽  
Hybrid Approach ◽  
Coefficient Of Determination ◽  
Anfis Model ◽  
Fixation Rate ◽  
Inference System ◽  
Prediction Capability

Abstract In this study we are reporting a prediction model for the estimation of carbon dioxide (CO2) fixation based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) hybrid approach. The experimental parameters such as temperature and pH conditions of the micro-algae-based carbon dioxide uptake process were taken as the input variables and the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and formation of the model structure were performed by genetic algorithm (GA) algorithm in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as RMSE, R2 and AARD. According to the analysis, GA-ANFIS model depicted a superior prediction capability over ANFIS optimized model. The Root Mean Square Error (RMSE), coefficient of determination (R2) and AARD for GA-ANFIS were determined as 0.000431, 0.97865 and 0.044354 in the training phase and 0.00056, 0.98457 and 0.032156 in the testing phase, respectively for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having very high potential to accurately calculate CO2 fixation rate and the exploration of the industrial scale-up process for commercial activities.

Starch and triacylglycerol accumulation in the cells of the stain Chlorella sp. IPPAS C-1210

2021 ◽  
pp. 1-7
Author(s):  
Lidia A. Bobrovnikova ◽  
Maria S. Pakholkova ◽  
Roman A. Sidorov ◽  
Maria A. Sinetova
Keyword(s):  
Carbon Dioxide ◽  
Lipid Content ◽  
Food Industry ◽  
Nitrogen Starvation ◽  
Total Lipid Content ◽  
Biomass Productivity ◽  
Chlorella Sp ◽  
Phosphorus Source ◽  
Triacylglycerol Accumulation ◽  
In Cells

Strain Сhlorella sp. IPPAS C-1210 is an effective lipid and triacilglycerols (TAG) producer. The strain could be used eventually in such industries as bioenergetics, food industry and agriculture. The objective of this work was investigation of conditions in which the strain Сhlorella sp. IPPAS C-1210 accumulates the most starch and TAG in cells with a view to optimise its growth and productivity. The following cultivation parameters were investigated in order to figure out their influence on accumulation of starch and TAG: nitrogen- and phosphorous-starvation and cultivation on media with different nitrogen (nitrate, urea) and carbon (carbon dioxide, bicarbonate) sources. Pigments, starch, protein and lipid content in cells were measured. The exclusion of nitrogen or phosphorus source from medium decreased the biomass productivity significantly, caused chlorosis and reduction of protein content. Total lipid content increased slightly after phosphorous starvation and stayed almost constant under nitrogen starvation, however a greater TAG increase was observed during nitrogen starvation. Both nitrogen and phosphorous starvations caused the increase of the amount of reserve carbohydrates: during phosphorous starvation increase was insignificant, whereas the latter almost doubled the amount of reserve carbohydrates. The highest biomass and lipid productivity was observed in cells grown in bicarbonate supplement medium and the highest starch productivity was observed in cells grown in standard BBM-3N medium.

scholarly journals Utilisation of CO2 from Sodium Bicarbonate to Produce Chlorella Vulgaris Biomass in Tubular Photobioreactors for Biofuel Purposes

2021 ◽  
Vol 13 (16) ◽  
pp. 9118
Author(s):  
Patryk Ratomski ◽  
Małgorzata Hawrot-Paw ◽  
Adam Koniuszy
Keyword(s):  
Sodium Bicarbonate ◽  
Carbon Content ◽  
Optical Density ◽  
Chlorella Vulgaris ◽  
Lipid Content ◽  
Biomass Productivity ◽  
Culture Conditions ◽  
Fixation Rate ◽  
Spectrophotometric Methods ◽  
Wide Range

Microalgae are one of the most promising sources of renewable substrates used for energy purposes. Biomass and components accumulated in their cells can be used to produce a wide range of biofuels, but the profitability of their production is still not at a sufficient level. Significant costs are generated, i.a., during the cultivation of microalgae, and are connected with providing suitable culture conditions. This study aims to evaluate the possibility of using sodium bicarbonate as an inexpensive alternative CO2 source in the culture of Chlorella vulgaris, promoting not only the increase of microalgae biomass production but also lipid accumulation. The study was carried out at technical scale using 100 L photobioreactors. Gravimetric and spectrophotometric methods were used to evaluate biomass growth. Lipid content was determined using a mixture of chloroform and methanol according to the Blight and Dyer method, while the carbon content and CO2 fixation rate were measured according to the Walkley and Black method. In batch culture, even a small addition of bicarbonate resulted in a significant (p ≤ 0.05) increase in the amount of biomass, productivity and optical density compared to non-bicarbonate cultures. At 2.0 g∙L–1, biomass content was 572 ± 4 mg·L−1, the maximum productivity was 7.0 ± 1.0 mg·L–1·d–1, and the optical density was 0.181 ± 0.00. There was also an increase in the lipid content (26 ± 4%) and the carbon content in the biomass (1322 ± 0.062 g∙dw–1), as well as a higher rate of carbon dioxide fixation (0.925 ± 0.073 g·L–1·d–1). The cultivation of microalgae in enlarged scale photobioreactors provides a significant technological challenge. The obtained results can be useful to evaluate the efficiency of biomass and valuable cellular components production in closed systems realized at industrial scale.

scholarly journals Growth of Haematococcus pluvialis on a Small-Scale Angled Porous Substrate Photobioreactor for Green Stage Biomass

2021 ◽  
Vol 11 (4) ◽  
pp. 1788
Author(s):  
Thanh-Tri Do ◽  
Binh-Nguyen Ong ◽  
Tuan-Loc Le ◽  
Thanh-Cong Nguyen ◽  
Bich-Huy Tran-Thi ◽  
...  
Keyword(s):  
Light Intensity ◽  
Algal Biomass ◽  
Haematococcus Pluvialis ◽  
Biomass Productivity ◽  
Pilot Scale ◽  
Small Scale ◽  
Porous Substrate ◽  
Low Light ◽  
Green Algal ◽  
Green Stage

In the production of astaxanthin from Haematococcus pluvialis, the process of growing algal biomass in the vegetative green stage is an indispensable step in both suspended and immobilized cultivations. The green algal biomass is usually cultured in a suspension under a low light intensity. However, for astaxanthin accumulation, the microalgae need to be centrifuged and transferred to a new medium or culture system, a significant difficulty when upscaling astaxanthin production. In this research, a small-scale angled twin-layer porous substrate photobioreactor (TL-PSBR) was used to cultivate green stage biomass of H. pluvialis. Under low light intensities of 20–80 µmol photons m−2·s−1, algae in the biofilm consisted exclusively of non-motile vegetative cells (green palmella cells) after ten days of culturing. The optimal initial biomass density was 6.5 g·m−2, and the dry biomass productivity at a light intensity of 80 µmol photons m−2·s−1 was 6.5 g·m−2·d−1. The green stage biomass of H. pluvialis created in this small-scale angled TL-PSBR can be easily harvested and directly used as the source of material for the inoculation of a pilot-scale TL-PSBR for the production of astaxanthin.

scholarly journals Developing Eco-Friendly and Cost-Effective Porous Adsorbent for Carbon Dioxide Capture

Molecules ◽  
2021 ◽  
Vol 26 (7) ◽  
pp. 1962
Author(s):  
Mahboubeh Nabavinia ◽  
Baishali Kanjilal ◽  
Noahiro Fujinuma ◽  
Amos Mugweru ◽  
Iman Noshadi
Keyword(s):  
Carbon Dioxide ◽  
Surface Area ◽  
Co2 Capture ◽  
High Surface ◽  
Scale Up ◽  
Polymer Networks ◽  
High Surface Area ◽  
Excellent Thermal Stability ◽  
Doped Polymers ◽  
Metal Doped

To address the issue of global warming and climate change issues, recent research efforts have highlighted opportunities for capturing and electrochemically converting carbon dioxide (CO2). Despite metal doped polymers receiving widespread attention in this respect, the structures hitherto reported lack in ease of synthesis with scale up feasibility. In this study, a series of mesoporous metal-doped polymers (MRFs) with tunable metal functionality and hierarchical porosity were successfully synthesized using a one-step copolymerization of resorcinol and formaldehyde with Polyethyleneimine (PEI) under solvothermal conditions. The effect of PEI and metal doping concentrations were observed on physical properties and adsorption results. The results confirmed the role of PEI on the mesoporosity of the polymer networks and high surface area in addition to enhanced CO2 capture capacity. The resulting Cobalt doped material shows excellent thermal stability and promising CO2 capture performance, with equilibrium adsorption of 2.3 mmol CO2/g at 0 °C and 1 bar for at a surface area 675.62 m2/g. This mesoporous polymer, with its ease of synthesis is a promising candidate for promising for CO2 capture and possible subsequent electrochemical conversion.

Ammonium application mitigates the effects of elevated carbon dioxide on the carbon/nitrogen balance of Phoebe bournei seedlings

2021 ◽  
Author(s):  
Xiao Wang ◽  
Xiaoli Wei ◽  
Gaoyin Wu ◽  
Shengqun Chen
Keyword(s):  
Carbon Dioxide ◽  
Enzyme Activities ◽  
Atmospheric Carbon Dioxide ◽  
Global Climate ◽  
Co2 Concentration ◽  
Rubisco Activase ◽  
Plant Responses ◽  
N Metabolism ◽  
C And N ◽  
Phoebe Bournei

Abstract The study of plant responses to increases in atmospheric carbon dioxide (CO2) concentration is crucial to understand and to predict the effect of future global climate change on plant adaptation and evolution. Increasing amount of nitrogen (N) can promote the positive effect of CO2, while how N forms would modify the degree of CO2 effect is rarely studied. The aim of this study was to determine whether the amount and form of nitrogen (N) could mitigate the effects of elevated CO2 (eCO2) on enzyme activities related to carbon (C) and N metabolism, the C/N ratio, and growth of Phoebe bournei (Hemsl.) Y.C. Yang. One-year-old P. bournei seedlings were grown in an open-top air chamber under either an ambient CO2 (aCO2) (350 ± 70 μmol•mol−1) or an eCO2 (700 ± 10 μmol•mol−1) concentration and cultivated in soil treated with either moderate (0.8 g per seedling) or high applications (1.2 g per seedling) of nitrate or ammonium. In seedlings treated with a moderate level of nitrate, the activities of key enzymes involved in C and N metabolism (i.e., Rubisco, Rubisco activase and glutamine synthetase) were lower under eCO2 than under aCO2. By contrast, key enzyme activities (except GS) in seedlings treated with high nitrate or ammonium were not significantly different between aCO2 and eCO2 or higher under eCO2 than under aCO2. The C/N ratio of seedlings treated with moderate or high nitrate under eCO2was significantly changed compared with the seedlings grown under aCO2, whereas the C/N ratio of seedlings treated with ammonium was not significantly different between aCO2 and eCO2. Therefore, under eCO2, application of ammonium can be beneficial C and N metabolism and mitigate effects on the C/N ratio.

scholarly journals Estimating Causal Effects When the Treatment Affects All Subjects Simultaneously: An Application

2021 ◽  
Vol 5 (2) ◽  
pp. 22
Author(s):  
Chiara Binelli
Keyword(s):  
Machine Learning ◽  
Carbon Dioxide ◽  
Global Warming ◽  
Carbon Dioxide Emissions ◽  
Control Group ◽  
Sources Of Information ◽  
Average Global Temperature ◽  
Causal Impact ◽  
The Impact ◽  
Robust Evidence

Several important questions cannot be answered with the standard toolkit of causal inference since all subjects are treated for a given period and thus there is no control group. One example of this type of questions is the impact of carbon dioxide emissions on global warming. In this paper, we address this question using a machine learning method, which allows estimating causal impacts in settings when a randomized experiment is not feasible. We discuss the conditions under which this method can identify a causal impact, and we find that carbon dioxide emissions are responsible for an increase in average global temperature of about 0.3 degrees Celsius between 1961 and 2011. We offer two main contributions. First, we provide one additional application of Machine Learning to answer causal questions of policy relevance. Second, by applying a methodology that relies on few directly testable assumptions and is easy to replicate, we provide robust evidence of the man-made nature of global warming, which could reduce incentives to turn to biased sources of information that fuels climate change skepticism.

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