Effects of temperature on the astaxanthin productivity and light harvesting characteristics of the green alga Haematococcus pluvialis

Giannelli, Luca; Yamada, Hiroyuki; Katsuda, Tomohisa; Yamaji, Hideki

doi:10.1016/j.jbiosc.2014.09.002

Cited by 88 publications

(32 citation statements)

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“…'red stage') the stress environmental conditions of increased light intensity and temperature are required. At 27°C H. pluvialis has demonstrated the highest astaxanthin production, while the saturation intensity at these temperature levels corresponds to 500 μmol m −2 s −1 [27,34].…”

Section: Parameters Affecting Microalgae Growthmentioning

confidence: 97%

“…'green stage') of H. pluvialis, a temperature of 20°C and a saturation intensity of 250 μmol m −2 s −1 are proposed [34]. Saturation intensity refers to the light intensity value, above which (slightly greater) irreversible damage of the parts in algae cells that are responsible for photosynthesis occurs, leading to a reduction of the biomass growth rate [83].…”

Section: Parameters Affecting Microalgae Growthmentioning

confidence: 99%

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Commercial astaxanthin production derived by green alga Haematococcus pluvialis : A microalgae process model and a techno-economic assessment all through production line

2016

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a b s t r a c tThe freshwater green microalgal strain Haematococcus pluvialis is the richest source for the production of astaxanthin. Astaxanthin is member of the xanthophyll family of carotenoids and constitutes the highest value product derived by microalgae. So far, algal astaxanthin amounts to b1% of the global market, since the synthetic alternative involves lower production costs. In this study, the technical and economic performance throughout large scale astaxanthin production, for two European cities (Livadeia, Greece and Amsterdam, the Netherlands), is investigated. The techno-economic assessment was facilitated by creating a theoretical process model, which simulated all phases of the production process. A hybrid system for photoautotrophic cultivation comprised by a photobioreactor (PBR) fence and a raceway pond complex was assumed for the 'green' and the 'red stage' respectively. The area covered by each cultivation system was assumed as 1 ha. The technical part included the massenergy flows associated with the production process. The most important mass inflow refers to freshwater. More specifically, 63,526 m 3 /year and 23,793 m 3 /year are needed for the production of 426 kg/year and 143 kg/year astaxanthin in Livadeia and Amsterdam respectively. Regarding total energy needs, they were calculated at 751.2 MWh/year and 396.5 MWh/year for the Greek and the Dutch city respectively. With respect to the economic performance, a Profit and Loss (P&L) analysis was conducted applying three scenarios (worst-, base-and best case). Determining CAPEX and annual OPEX, the return of investment (ROI) for different market prices of astaxanthin was calculated. It was found that only in Livadeia high economic viability can be achieved for all market prices. The costs per kilogram of natural astaxanthin for Livadeia and Amsterdam were calculated at €1536/ kg ASTAX and €6403/kg ASTAX respectively (best case scenario), rendering natural astaxanthin unable to compete with the synthetic alternative (€880/kg ASTAX ) yet, at least for feeding purposes.

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Section: Parameters Affecting Microalgae Growthmentioning

confidence: 97%

Section: Parameters Affecting Microalgae Growthmentioning

confidence: 99%

Commercial astaxanthin production derived by green alga Haematococcus pluvialis : A microalgae process model and a techno-economic assessment all through production line

2016

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“…For a number of decades, microalgae have attracted significant interest as feedstocks for the production of renewable bioenergy and sustainable high‐value bioproducts . One of their main advantages is their ability to directly utilize solar energy to convert atmospheric CO 2 into biorenewable products, ranging from biofuels (e.g., biodiesel, biohydrogen) to valuable food additives and pharmaceutical ingredients (e.g., astaxanthin, lutein) . Moreover, recent development of genetically engineered algal strains, which are able to excrete bioproducts directly into the medium, show great potential to further reduce the downstream biorenewables' separation cost …”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

et al. 2018

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Identifying optimal photobioreactor configurations and process operating conditions is critical to industrialize microalgae‐derived biorenewables. Traditionally, this was addressed by testing numerous design scenarios from integrated physical models coupling computational fluid dynamics and kinetic modeling. However, this approach presents computational intractability and numerical instabilities when simulating large‐scale systems, causing time‐intensive computing efforts and infeasibility in mathematical optimization. Therefore, we propose an innovative data‐driven surrogate modeling framework, which considerably reduces computing time from months to days by exploiting state‐of‐the‐art deep learning technology. The framework built upon a few simulated results from the physical model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then adopts a hybrid stochastic optimization algorithm to explore untested processes and find optimal solutions. Through verification, this framework was demonstrated to have comparable accuracy to the physical model. Moreover, multi‐objective optimization was incorporated to generate a Pareto‐frontier for decision‐making, advancing its applications in complex biosystems modeling and optimization. © 2018 American Institute of Chemical Engineers AIChE J, 65: 915–923, 2019

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“…Microalgae, configured as photobioreactors (PBRs), has attracted considerable attention as the basis of combined biological method for removal of nutrients (nitrogen N and phosphorus P) from wastewaters and CO 2 fixation from flue gases . The microalgae Chlorella vulgaris has been extensively studied for CO 2 mitigation under a range of operating conditions, including gas CO 2 concentration, light intensity and temperature . Nutrient removal using microalgae has been studied since the mid‐1970s .…”

Section: Introductionmentioning

confidence: 99%

Optimization of cultivation conditions for combined nutrient removal and CO₂fixation in a batch photobioreactor

ketife

Judd

Znad

2016

J. Chem. Technol. Biotechnol.

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BACKGROUND: The application of Chlorella vulgaris for simultaneous CO 2 biofixation and nutrient removal has been optimised using response surface methodology (RSM) based on Box Behnken design (BBD). Experimental conditions employed comprised CO 2 concentrations (C c,g ) of 0.03-22% CO 2 , irradiation intensities (I) of 100-400 E, temperatures of 20-30 ∘ C and nutrient concentrations of 0-56 and 0-19 mg L −1 nitrogen and phosphorus, respectively, the response parameters being specific growth rate , CO 2 uptake rate R c and %nutrient removal. RESULTS: Over 10 days the biomass concentration reached almost 3 g L −1 for C c,g of 5% CO 2 , with corresponding values of 0.74 g L −1 day −1 and 1.17 day −1 for R c and , respectively, and 100% nutrient (N and P) removals. At 22% CO 2 the R c and decreased by around an order of magnitude, and nutrient removal also decreased to 79% and 50% for N and P, respectively. CONCLUSION: Optimum values 5% CO 2 , 100 E and 22 ∘ C were identified for C c,g , I and T, respectively, with and R c reaching 1.53 day −1 and 1 g L −1 day −1 , respectively, along with associated nutrient removal of 100%. Regression analysis indicated a good fit between experimental and model data.

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Effects of temperature on the astaxanthin productivity and light harvesting characteristics of the green alga Haematococcus pluvialis

Cited by 88 publications

References 35 publications

Commercial astaxanthin production derived by green alga Haematococcus pluvialis : A microalgae process model and a techno-economic assessment all through production line

Commercial astaxanthin production derived by green alga Haematococcus pluvialis : A microalgae process model and a techno-economic assessment all through production line

Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

Optimization of cultivation conditions for combined nutrient removal and CO₂fixation in a batch photobioreactor

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Effects of temperature on the astaxanthin productivity and light harvesting characteristics of the green alga Haematococcus pluvialis

Cited by 88 publications

References 35 publications

Commercial astaxanthin production derived by green alga Haematococcus pluvialis : A microalgae process model and a techno-economic assessment all through production line

Commercial astaxanthin production derived by green alga Haematococcus pluvialis : A microalgae process model and a techno-economic assessment all through production line

Deep learning‐based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design

Optimization of cultivation conditions for combined nutrient removal and CO2fixation in a batch photobioreactor

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Product

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Optimization of cultivation conditions for combined nutrient removal and CO₂fixation in a batch photobioreactor