The dynamics of heterotrophic algal cultures

Siegler, Hector De la Hoz; Ben-Zvi, Amos; Burrell, Robert E.; McCaffrey, William C.

doi:10.1016/j.biortech.2011.01.081

Cited by 40 publications

(15 citation statements)

References 33 publications

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“…Most of the previously published cultivation models were developed based on the Monod (Monod, ) kinetics and its extensions, for example, the Droop (Droop, ) kinetic model, which have the advantage of being simple and concise. Since the original models of the Monod and the Droop focus only on the relationship between external nutrient and cell growth, several modifications have been proposed to predict lipid production from microalgae (De la Hoz Siegler, Ben‐Zvi, Burrell, & McCaffrey, ; Packer et al, ). Though these models with proper adjustment of parameters can predict basic growth behavior of microalgae fairly well, they are highly empirical in nature and provide limited insights on the fundamental behavior of microalgae culture.…”

Section: Introductionmentioning

confidence: 99%

A mathematical model of intracellular behavior of microalgae for predicting growth and intracellular components syntheses under nutrient‐replete and ‐deplete conditions

Ryu

Sung

Kim

et al. 2018

Biotech & Bioengineering

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Microalgae is a promising biomass source for renewable fuels and chemicals production. To describe microalgal behavior and improve their cultivation, various kinetic models have been proposed. However, previous works have focused on biomass formation and lipids production only, even though carbohydrates and proteins are also important products, not only for understanding the metabolic behavior of microalgae but also for enhancing the economic viability through value-added side products. In this study, a new mathematical model is proposed to explain core biological mechanisms of growth and macromolecules syntheses based on the central metabolism of carbon and nitrogen. In the model, microalgal growth is separated as hyperplasia and hypertrophy, to describe the cell growth more precisely under nutrient-replete and -deplete conditions. Sensitivity analysis performed using the model indicates that cell state (e.g., cell death rate) has a strong effect on the lipid production explaining the difficulty of reproducing a microalgae culture experiment.

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Section: Introductionmentioning

confidence: 99%

A mathematical model of intracellular behavior of microalgae for predicting growth and intracellular components syntheses under nutrient‐replete and ‐deplete conditions

Ryu

Sung

Kim

et al. 2018

Biotech & Bioengineering

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“…Lipid productivity was claimed to increase by about 2 times. However, comparison was made solely against photoautotrophic culture, by which the typical biomass concentration is in the range of 0.1 to 1.0 g/L, owing to the effect of mutual shading [7]. Alternatively, researchers are looking into the potential of mixotrophic (photoheterotrophic) mode upon realization of enhanced growth and unrepressed light-dependent bioproducts formation from the combined effects of photosynthesis and the cells' own ability to ingest either prey or dissolved organic materials [8].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analyses of Response Surface Methodology and Artificial Neural Network on Medium Optimization for Tetraselmis sp. FTC209 Grown under Mixotrophic Condition

Mohamed

Tan

Mohamad

et al. 2013

The Scientific World Journal

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Mixotrophic metabolism was evaluated as an option to augment the growth and lipid production of marine microalga Tetraselmis sp. FTC 209. In this study, a five-level three-factor central composite design (CCD) was implemented in order to enrich the W-30 algal growth medium. Response surface methodology (RSM) was employed to model the effect of three medium variables, that is, glucose (organic C source), NaNO3 (primary N source), and yeast extract (supplementary N, amino acids, and vitamins) on biomass concentration, X max, and lipid yield, P max/X max. RSM capability was also weighed against an artificial neural network (ANN) approach for predicting a composition that would result in maximum lipid productivity, Prlipid. A quadratic regression from RSM and a Levenberg-Marquardt trained ANN network composed of 10 hidden neurons eventually produced comparable results, albeit ANN formulation was observed to yield higher values of response outputs. Finalized glucose (24.05 g/L), NaNO3 (4.70 g/L), and yeast extract (0.93 g/L) concentration, affected an increase of X max to 12.38 g/L and lipid a accumulation of 195.77 mg/g dcw. This contributed to a lipid productivity of 173.11 mg/L per day in the course of two-week cultivation.

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“…De la Hoz Siegler et al consider that microalgae growth rate is a double sigmoidal function of the external glucose concentration and the internal nitrogen content,

\overset{q}{true}

μ = μ_{m} \frac{S_{2}}{K_{S_{2}} + S_{2} + S_{2}^{2} / K_{i 2}} \frac{\overset{true˜}{q}}{K_{\overset{true˜}{q}} + \overset{q}{true}} exp ((), - \frac{([], \overset{q}{true} ((), {normalt}_{0}) - \int_{0}^{t} \overset{q}{true} d normalt) / t}{K_{i 1}}) .

…”

Section: Microalgae Growth Modelsmentioning

confidence: 99%

“…They consider the following processes, as taken from Ref. , p. 5768: Cells are composed of three main compartments: metabolically active biomass ( x ), lipid body ( p ), and a nitrogen pool ( q ). Nitrogen source is taken up into the nitrogen pool and later converted, at a constant yield Y x / q , into active biomass. Carbon source is taken up by the cells and directly converted either into active biomass or oil, at constant yields Y x / s or Y p / s , respectively. Oil stored in the lipid bodies can be used to support growth at a constant yield Y x / p . …”

Section: Microalgae Growth Modelsmentioning

confidence: 99%

“…In this light, the mathematical model is

\frac{d}{d normalt} s_{1} = - italicρx + s_{1}^{i} \frac{f_{1}^{i}}{V} - s_{1} D

\frac{d}{d normalt} s_{2} = - \frac{1}{Y_{x / s}} italicμx + s_{2}^{i} \frac{f_{2}^{i}}{V} - k_{m} x - \frac{1}{Y_{p / s}} italicπx - s_{2} D

\frac{d}{d normalt} x = italicμx - italicxD

\frac{d}{d normalt} p = italicπx - \frac{1}{Y_{x / p}} italicμx - italicpD

\frac{d}{d normalt} q = italicρx - \frac{1}{Y_{x / q}} italicμx - italicqD

\frac{d}{d normalt} V = italicVD - f_{0},

where

D ≔ ((), f_{1}^{i} + f_{2}^{i}) / V

is the dilution rate, due to the influxes

f_{1}^{i}

and

f_{2}^{i}

; in turn ρ models nitrogen uptake, which is better described by a Michaelis–Menten reaction rate, Section 3.4.3. Furthermore, the oil production rate π , seems to be better described by a Michaelis–Menten reaction rate along with a decreasing function of the oil content:

…”

Section: Microalgae Growth Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Biogas purification via optimal microalgae growth: A literature review

Cea-Barcia

López‐Caamal

Zúñiga

et al. 2018

Biotechnology Progress

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In this review, we compare works from the current decade that address the CO2‐removal from biogas by means of microalgae. The microalgae culture process acts as a biochemical absorption process; it is potentially competitive with respect to classical and commercial absorption methods due to its additional benefits such as availing CO2 for the production of valuable microalgae biomass and being an environmentally friendly technique. Nevertheless, the low yield of biogas purification translates into the need to use optimal operation strategies that render the whole biogas production process economically feasible. A class of these strategies requires models capable of reproducing key traits of the dynamical behavior of microalgae growth. Thus, without overlooking the classical physico‐chemical methods for biogas purification, our literature review addresses: (i) biogas purification via microalgae and different microalgae growth conditions, (ii) approaches that maximize microalgae growth, in order to increase CO2‐consumption, and (iii) different models that describe the representative characteristics of microalgae growth. This investigation traces a pathway to future considerations on optimal biogas purification alternatives by microalgae culture processes. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1513–1532, 2018

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The dynamics of heterotrophic algal cultures

Cited by 40 publications

References 33 publications

A mathematical model of intracellular behavior of microalgae for predicting growth and intracellular components syntheses under nutrient‐replete and ‐deplete conditions

A mathematical model of intracellular behavior of microalgae for predicting growth and intracellular components syntheses under nutrient‐replete and ‐deplete conditions

Comparative Analyses of Response Surface Methodology and Artificial Neural Network on Medium Optimization for Tetraselmis sp. FTC209 Grown under Mixotrophic Condition

Biogas purification via optimal microalgae growth: A literature review

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