WO2019162965A1 - Moissonneuse de microalgues régulée - Google Patents

Moissonneuse de microalgues régulée Download PDF

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Publication number
WO2019162965A1
WO2019162965A1 PCT/IN2019/050139 IN2019050139W WO2019162965A1 WO 2019162965 A1 WO2019162965 A1 WO 2019162965A1 IN 2019050139 W IN2019050139 W IN 2019050139W WO 2019162965 A1 WO2019162965 A1 WO 2019162965A1
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Prior art keywords
microalgae
harvester
detection
aqueous solution
detection module
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PCT/IN2019/050139
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English (en)
Inventor
Achintya RANJAN
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Ranjan Achintya
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Publication of WO2019162965A1 publication Critical patent/WO2019162965A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M21/00—Bioreactors or fermenters specially adapted for specific uses
    • C12M21/02—Photobioreactors
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M33/00—Means for introduction, transport, positioning, extraction, harvesting, peeling or sampling of biological material in or from the apparatus
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
    • C12M41/48—Automatic or computerized control

Definitions

  • the invention generally relates to the field of microalgae harvesting and more particularly to a method and a system for harvesting of microalgae.
  • Microalgae are considered as one of the potential and valuable source of nutritional supplement, favorable for human consumption.
  • One such microalgae that is more popularly cultivated is the Spirulina.
  • One of the predominant source for harvesting microalgae are the naturally existing ecosystems conducive for growth of the microalgae.
  • commercial scale harvesting of the microalgae can lead to alteration of the balance of the ecosystem.
  • Cultivation and harvesting of microalgae is a tedious manual procedure. Further, harvesting of the microalgae involves a critical step of determining the time of harvest and requires monitoring by a skilled person.
  • An apparatus for culturing microalgae is disclosed in W020091341 14, assigned to HO, Tet and Shin.
  • the apparatus consists of pH sensors for determining acidity of the culture broth and light sensors for determining light intensity in the culture tank.
  • the light sensors are specifically used to determine the time to initiate the removal of the cultivated microalgae.
  • a significant disadvantage of the apparatus is the variations in the value read by multiple sensors are dependent on the light conditions outside the tank.
  • FIG. 1 shows a schematic representation of the regulated microalgae harvester according to an embodiment of the invention.
  • FIG. 2 shows a schematic representation of an automated microalgae detection system, according to an embodiment of the invention.
  • FIG. 3 shows a plot of light intensity of the aqueous solution containing microalgae, according to an embodiment of the invention.
  • FIG. 4 shows a schematic representation of automatic plateau detection module, according to an embodiment of the invention.
  • FIG. 5 shows a schematic representation of automatic clumping detection module, according to an embodiment of the invention.
  • FIG. 6 shows a schematic representation of automatic grade classification module, according to an embodiment of the invention.
  • FIG. 7 shows a schematic representation of harvestability index computation module, according to an embodiment of the invention.
  • FIG. 8 shows a schematic representation of mixing determination module, according to an embodiment of the invention.
  • the harvester includes a reservoir containing aqueous solution of microalgae, an agitator coupled to the reservoir and a plurality of automated microalgae detection systems.
  • the automated microalgae detection systems are provided within the harvester for determining an optimal time duration for harvest of the said microalgae thereby regulating the growth of microalgae within the harvester.
  • the automated microalgae detection system includes a first detection module and a second detection module.
  • the first detection module is configured for measuring at least one fluid parameter in respect of a volume of the aqueous solution containing microalgae flowing within the first detection module.
  • the second detection module is configured for detection of at least one growth related parameter of the microalgae contained within the said aqueous solution.
  • Various embodiments of the invention provide a method and a system for regulated harvesting of microalgae.
  • the method includes the step of selecting an aqueous solution containing microalgae.
  • the aqueous solution containing the microalgae is then subjected to a growth cycle. Subsequent to the initiation of the growth cycle, a plurality of growth parameters are measured. The measured parameters are then analysed to determine the optimal time for harvesting the microalgae.
  • the method described hereinabove briefly shall be explained in detail.
  • the method includes selecting aqueous solution containing microalgae.
  • microalgae in the aqueous solution include but are not limited to spirulina, chlorella, blue-green algae, red algae, dulse and sea weeds.
  • the aqueous solution containing the microalgae is then subjected to a growth cycle. Subsequent to the initiation of the growth cycle, a plurality of growth parameters is measured. Examples of growth parameters include but are not limited to microalgae clumping, microalgae grading, microalgae mixing and microalgae harvestability.
  • the microalgae clumping is determined by measuring the intensity mean, intensity variance, and intensity distribution of the aqueous solution of the microalgae. The extent of microalgae clumping is then used to determine the grade of the microalgae. The grade of the microlagae is related to the clumping, as well as to the intensity values. The grade of the microalgae determined is subsequently used to determine the optimal time for harvesting the microalgae. Further, the harvestability of the microalgae is dependent on the mixing of the microalgae. The mixing of the microalgae is determined as an inverse function of the variance of the harvestability distribution function.
  • microalgae selected is spirulina.
  • the regulated harvesting of spirulina shall be explained below in detail.
  • FIG. 1 shows a schematic representation of the regulated microalgae harvester, according to an embodiment of the invention.
  • the regulated microalgae harvester includes a large reservoir 101 to contain the aqueous solution containing microalgae 103, an agitator 105 and a plurality of automated microalgae detection systems 107.
  • the plurality of automated microalgae detection systems 107 are provided within the harvester for determining an optimal time duration for harvest of the said microalgae thereby regulating the growth of microalgae within the harvester.
  • the reservoir is selected from a list including but not limited to a glass reservoir, a translucent fiberglass tank, a plastic tank, a tubular structure and a bio- reactor.
  • the reservoir stores aqueous solution containing microalgae.
  • the microalgae is selected from the list including but is not limited to spirulina, chlorella, blue-green algae, red algae, dulse and sea weeds.
  • An agitator is attached to the reservoir to help mixing of the aqueous solution contained in the reservoir.
  • FIG. 2 shows a schematic representation of an automated microalgae detection system, according to an embodiment of the invention.
  • Each of the automated microalgae detection systems includes a first detection module and a second detection module.
  • the first detection module is configured for measuring at least one fluid parameter in respect of a volume of the aqueous solution containing microalgae flowing within the first detection module.
  • the fluid parameter described herein includes but is not limited to velocity, turbulence, viscosity and density of the aqueous solution containing microalgae.
  • the second detection module is configured for detection of at least one growth related parameter of the said microalgae contained within the said aqueous solution.
  • the growth related parameter described herein includes but are not limited to plateau detection, clumping detection, grade classification, harvestability index, and mixing determination.
  • the automated microalgae detection system shall be explained in detail.
  • the automated microalgae detection system is a Light Source-Detector Assembly, hereinafter referred to as LSDA.
  • Each of the LSDA is placed manually or automatically at the appropriate angle by determining the direction of flow at that location.
  • Each LSDA consists of a first set of hollow cylinder 201 perpendicular to a second set of two hollow cylinders 203.
  • the first set of hollow cylinder 201 consists of an inlet 205 and an outlet 207.
  • the aqueous solution containing spirulina flows across the first set of hollow cylinders.
  • the second set of hollow cylinder 203 is placed perpendicular to the first set of hollow cylinders.
  • the first set of hollow cylinder 201 consists of flow sensors 209 to measure the velocity and turbulence of the flowing aqueous solution containing microalgae.
  • the second set of hollow cylinder consists of an assembly of an array of light-source 21 1 and light detectors 213.
  • the LSDA 107 further consists of fins 215.
  • the purpose of the fins is to ensure that the fluid flow into the LSDA is laminar and in a direction perpendicular to the line joining the light source and detectors.
  • the fins 215 are rotated by means of an electromagnetic motor (not shown).
  • the speed of the electromagnetic motor is adjusted by the speed controller.
  • the speed controller takes as input the measurements recorded by the flow sensors.
  • Each LSDA 107 is further encoded with an identifier and fitted with a provision for wireless data transfer to an external controller (not shown).
  • the data transferred includes the ID of the LSDA, flow sensor data, fin speed data and the array of intensities as detected by the light-detector array, and the time-stamp of the transmission.
  • the plurality of LSDA’s is synchronized with a single external clock.
  • the plurality of LSDA can also be synchronized to transmit data at specific input pulses driven from an external controller.
  • the LSDA 107 is used to determine the grade of the microalgae in the reservoir.
  • FIG. 3 shows a plot of sample intensity distribution of the aqueous solution containing spirulina from a single LSDA 107, according to an embodiment of the invention.
  • An intensity reference scale is calibrated for the specific LSDA 107. The intensity level when there is no spirulina in the water is at the highest level and calibrated as“no spirulina”. As the quantity of spirulina is increased, the intensity of light comes down and this scale is calibrated for various growth stages of spirulina, with the lowest intensity corresponding to dead spirulina. From each LSDA 107, the intensity distribution obtained is then taken as input to a automatic plateau detection module.
  • FIG. 4 shows an automatic plateau detection module, according to an embodiment of the invention.
  • the automatic plateau detection module 403 is attached either to the LSDA 107. Alternatively, the automatic plateau detection module 403 can be connected to an independent external unit which operates on the data transmitted from the LSDA 107.
  • the inputs to the automatic plateau detection module 403 are the intensity mean, intensity variance, and area obtained from an intensity distribution module 401.
  • the automatic plateau detection module 403 works on a threshold method, fuzzy method, probabilistic segmentation method and/or combinations thereof.
  • an image segmentation method is used for plateau detection. A plurality of plateaus are obtained as an output. Each output plateau is characterized by width, and the distribution of intensity values within the plateau. Each of the output plateau is then compared with a threshold.
  • the threshold is pre-computed, based on a standardization procedure.
  • the threshold can be determined through an adaptive / feedback learning algorithm.
  • a definite plateau is selected for characterization.
  • the plateau generated can be used to measure different parameters including but not limited to spirulina clumping, spirulina grading, spirulina aqueous solution mixing, spirulina harvestability, spirulina growth rate and health.
  • the output 407 is used to determine the extent of spirulina clumping.
  • FIG. 5 shows automatic clumping detection module, according to an embodiment of the invention.
  • the clumping detection module 501 is a computational module.
  • the clumping detection module 501 is attached either to the LSDA or at an independent external unit which operates on the data transmitted from the LSDA 107.
  • the plateau width, along with the intensity distribution parameters such as mean and variance from output 407 is sent to the automatic clumping detection module 501 to determine the extent of clumping.
  • the clumping is determined as a fuzzy membership or probability function.
  • the fuzzy membership or probability function values are determined through a prior process of learning and sample clustering.
  • the clumping extent can also be determined through an unsupervised classification process.
  • the output 503 is then analyzed for determining the extent of clumping. A plateau of large size and uniform intensity indicates clumping.
  • the output 503 of the clumping detection module 501 is further used to determine the grade of the spirulina.
  • FIG. 6 shows automatic grade classification module, according to an embodiment of the invention.
  • the automatic grade classification module 601 is a computational module.
  • the grade classification module 601 is attached either to the LSDA or at an independent external unit which operates on the data transmitted from the LSDA 107.
  • the inputs to the grade classification module 601 are the plateaus detected in the plateau detection module 403 as well as the clumping detection module 501 values.
  • the grade is determined by a combination of clumping probability and area of the plateau.
  • the grade is determined by a weighted average of the two values, followed by a threshold, wherein the threshold is decided prior during the process of standardization.
  • the output 603 of the grade classification module 601 is then used to determine the harvestability of the spirulina.
  • FIG.7 shows harvestability index computation module, according to an embodiment of the invention.
  • the harvestability of the spirulina is related to the grade and the intensity values.
  • the harvestability is determined through a harvestability index computation module 701 .
  • the harvestability index module 701 is a computational module.
  • the harvestability index module 701 is attached either to the LSDA 107 or at an independent external unit which operates on the data transmitted from the LSDA 107.
  • the input to the harvestability index computation module 701 is the plateaus detected in the plateau detection module 403, output 603 from the grade classification module 601 and the grade function values per plateau and the number of such plateaus per unit time. The number of such plateaus per unit time can be inferred from the time stamp of the data from each LSDA 107.
  • the harvestability is a probability function or a fuzzy membership function which determines the harvestability per grade as a function of the quantity of each grade per unit time.
  • the output 703 of the harvestability harvestability index computation module 701 is then used to determine the mixing of the aqueous solution of spirulina.
  • FIG. 8 shows mixing determination module, according to an embodiment of the invention.
  • the mixing determination module 801 is a computational module.
  • the mixing determination module 801 is an independent external unit which operates on the data transmitted from one or more LSDAs 107.
  • the input to the mixing determination module is the output 703 of the harvestability harvestability index computation module 701 and/or the output 603 as determined by the grade classification module 601 . From the distribution of the quantity per grade, the output 803 is then analyzed for determining the overall for mixing rate.
  • the mixing is determined as an inverse function of the variance of the harvestability distribution function. If the probability of all grades is roughly uniform, then the mixing is very high. If there is sufficient variance between the probability values as determined in the harvestability detection module, then the mixing is low.
  • the mixing may also be determined as a difference between the expected distribution as a function of the initial concentration, light intensity distribution over time, tank dimensions and the actual distribution as obtained by the grade and / or harvestability index distribution.
  • the overall mixing value can also be used to adjust the agitation rate of the tank.
  • the agitation is a standard feature of most tanks, and the mixing value determined can provide a quantitative way to determine the optimal agitation of the tank.
  • the overall rate of growth of spirulina is also measured as the number of microalgae plateaus measured as a function of time, and the rate of agitation of the medium.
  • the method and system for regulated harvesting of microalgae advantageously provides a user to selectively harvest the spirulina, for consumption of the same, as a nutritional supplement.
  • An additional advantage of the harvester is the automation of the system that enables an user with no skill in the art to consume the right quantity of spirulina without any additional need for determining the quality of the spirulina harvested.
  • the harvester is easy to install and maintain. Further, the harvester is portable and does not require elaborate set up.
  • the system can be used to determine the purity of the harvest by measuring various parameters such as extent of clumping of microalgae and grades of the microalgae. The locations where each grade is pre-dominant can also be determined. The extent to which grades are mixed can also be determined. Different grades/purity of microalgae can be simultaneously harvested from different locations.

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  • Chemical & Material Sciences (AREA)
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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Organic Chemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
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  • Wood Science & Technology (AREA)
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  • Sustainable Development (AREA)
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Abstract

L'invention concerne une moissonneuse de microalgues régulée. La moissonneuse comprend un réservoir contenant une solution aqueuse de microalgues, un agitateur couplé au réservoir et une pluralité de systèmes de détection de microalgues automatisés. Les systèmes de détection de microalgues automatisés sont disposés à l'intérieur de la moissonneuse pour déterminer une durée de temps optimale pour la récolte desdites microalgues, ce qui permet de réguler la croissance des microalgues à l'intérieur de la moissonneuse. Le système de détection de microalgues comprend un premier module de détection et un second module de détection. Le premier module de détection est configuré pour mesurer au moins un paramètre de fluide par rapport à un volume de la solution aqueuse contenant des microalgues s'écoulant à l'intérieur du premier module de détection. Le second module de détection est configuré pour détecter au moins un paramètre lié à la croissance des microalgues contenues dans ladite solution aqueuse.
PCT/IN2019/050139 2018-02-22 2019-02-22 Moissonneuse de microalgues régulée WO2019162965A1 (fr)

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IN201841006816 2018-02-22
IN201841006816 2018-02-22

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6156561A (en) * 1997-09-16 2000-12-05 Spirulina Biological Lab., Ltd. System and method for culturing algae
US20120162650A1 (en) * 2010-12-22 2012-06-28 Endress + Hauser Conducta Inc. Self-Aligning Light Source and Detector Assembly
WO2013100756A2 (fr) * 2011-12-29 2013-07-04 Tet Shin Ho Procédé et système de culture en masse de microalgues avec une meilleure efficacité photosynthétique

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6156561A (en) * 1997-09-16 2000-12-05 Spirulina Biological Lab., Ltd. System and method for culturing algae
US20120162650A1 (en) * 2010-12-22 2012-06-28 Endress + Hauser Conducta Inc. Self-Aligning Light Source and Detector Assembly
WO2013100756A2 (fr) * 2011-12-29 2013-07-04 Tet Shin Ho Procédé et système de culture en masse de microalgues avec une meilleure efficacité photosynthétique

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