Culturing algae with a computational algal growth forecasting model

Abstract

A computer-implemented method for culturing algae in a raceway pond is provided, the method comprising receiving input data indicative of one or more properties of the raceway pond, predicting a state of the raceway pond using a model, based on the input data, and automatically adjusting one or more operating conditions of the raceway pond, based on the predicted state, by outputting a control signal to at least one apparatus, wherein the control signal is calculated to increase or maintain the growth rate of the algae. Also provided is a computer-implemented method, comprising receiving first data indicative of one or more properties of a raceway pond, predicting a state of the raceway pond using a model, based on the received first data, and iteratively receiving further data indicative of one or more properties of the raceway pond from a raceway pond sensing / monitoring apparatus, updating the predicted state of the raceway pond using the model, based on the received further data, and providing an output to a display based on the updated predicted state. Corresponding devices, computer-readable media and systems are also disclosed.

Description

[0001] CULTURING ALGAE WITH A COMPUTATIONAL ALGAL GROWTH FORECASTING MODEL

[0002] All documents cited herein are incorporated by reference in their entirety.

[0003] TECHNICAL FIELD

[0004] The present invention relates to methods for culturing algae in land-based mariculture with a computational algal growth forecasting model. Specifically, the present invention relates to methods for culturing algae in at least one raceway pond in which a computer-implemented process models the pond and provides an appropriate output, such as outputting a control signal to an apparatus.

[0005] BACKGROUND

[0006] The current Intergovernmental Panel on Climate Change (IPCC) Synthesis Reports (AR6, UN) have demonstrated that it is difficult to attain the 1.5°C or the 2.0°C Paris Agreement targets through mitigation measures, or measures that reduce carbon emissions, alone. Demand for energy is increasing as developing and developed nations alike are increasing consumption of primary energy due to population increases and a continuous global increase in living standards. While the installation and utilisation of renewable energies is rapidly increasing, it is not at a rate that is sufficient to provide for both the increased demand for, and the replacement of, thermal energy production.

[0007] There is a need for anthropogenic CO2 removal technologies to remove CO2 from the atmosphere, and durably store it. Such technologies are needed to compensate for residual emissions to reach net zero greenhouse gas emissions.

[0008] Previously, a marine phytoplankton cultivation method was designed to work in nutrient rich upwelling seawater locations with very rapidly growing ‘bloom’ forming organisms. However, there remains a pressing need to develop more cost-efficient methods for sequestering atmospheric CO2.

[0009] SUMMARY OF THE INVENTION

[0010] The inventors have devised a method for carbon sequestration that permits culturing local, wildtype marine microalgae that have natural nutrient accumulation capabilities under both nutrient saturating and oligotrophic conditions. These organisms are highly efficient in terms of nutrient demand to cell growth rate, with some isolates even able to fix many of their own nutrients, instead of consuming them from surface seawater. The inventors’ method advantageously increases the mass culture of marine organisms for the purpose of low-cost carbon sequestration, without the negative externalities of land, energy, or freshwater use, at even lower cost than previous applications of algal raceway ponds. The cultivation method works principally with fast-growing ‘r-strategist’ microalgae that grow exponentially in relatively nutrient-rich ‘upwelled’ seawater. Both approaches typically utilise very large algal raceway ponds, with a defined depth and a white geomembrane liner. See, for example, GB2464763B (“Method of Carbon Sequestration”).

[0011] The inventors’ method is an ‘additive’ technology that dramatically increases productivity of algae in large desert areas using abundantly available and unutilised natural inputs. “Additive” here means that this form of algal cultivation enables effective and efficient algal growth at large scale, where it would not have otherwise grown, without displacing pre-existing primary productivity (or algae growth). Such methods rely on using a computational model to predict a state of the raceway pond, e.g., a future state of the raceway pond, based on received input data indicative of one or more raceway pond properties. Based on this forecasting, it is possible to automatically adjust operating conditions, operating parameters and / or operating decisions in a proactive way, rather than a reactive way. In other words, in some embodiments, a predicted future state of the raceway pond can be determined using the model, and consequently a control signal can be output to adjust one or more operating conditions in anticipation of the future state, without having to wait for this future state to arrive. The algal growth rate can thus be increased (e.g., maximised) or maintained to provide optimal carbon capture.

[0012] The inventors have provided guidance on methods for culturing algae in United Kingdom Patent Application Nos. 2207837.2, 2303156.0, 2212805.2, 2308480.9, 2212809.4 and 2308479.1 , as well as International Patent Application Nos. PCT / GB2023 / 051392, PCT / GB2023 / 052278 and PCT / GB2023 / 052279. The inventors have also provided guidance on culturing algae with remote optical monitoring in United Kingdom Patent Application No. 2303165.1 and International Patent Application No. PCT / GB2024 / 050542. Each of these patent applications is incorporated herein by reference.

[0013] In a first aspect of the invention, there is provided a method for culturing algae in at least one raceway pond, wherein the method comprises receiving input data indicative of one or more properties of the raceway pond; predicting a state of the raceway pond using a model, based on the input data; and automatically adjusting one or more operating conditions of the raceway pond, based on the predicted state, by outputting a control signal to at least one apparatus; wherein the control signal is calculated to increase or maintain the growth rate of the algae.

[0014] In some embodiments, automatically adjusting the one or more operating conditions comprises one or more of controlling the speed of a motor coupled to a paddlewheel, controlling a sluice gate at an inlet pipe of the raceway pond, controlling a sluice gate at an outlet pipe of the raceway pond, controlling a gate at an exit of a nutrient storage container, controlling a gate at an exit of an acid storage container, controlling the start time of a dilution operation, controlling the rate of a dilution operation, controlling the output of a heating or cooling element, controlling the time of a dilution operation to indirectly regulate the daily mean pond temperature, controlling means for adding hydroxide to the raceway pond, or actuating an air ventilation system.

[0015] In some embodiments, automatically adjusting the one or more operating conditions may comprise controlling a sluice gate at an inlet pipe of the raceway pond, said inlet pipe being configured to provide water to the raceway pond. The water may be seawater. In some embodiments, automatically adjusting the one or more operating conditions may comprise controlling a sluice gate at an outlet pipe of the raceway pond, said outlet pipe leading into another raceway pond or into one or more harvesting screens.

[0016] In some embodiments, the algae are bloom-forming microalgae or diatoms that have the ability to grow exponentially or with a cell division rate that exceeds one division per day. In some embodiments, the algae are diatoms, diazotrophic phytoplankton and / or diatom-diazotroph assemblages (DDAs). In such embodiments, the diatoms may be Rhopalodiaceae sp. Alternatively, in such embodiments, the bloom-forming microalgae and diazotrophic phytoplankton may be cyanobacteria or Trichodesmium sp. Alternatively, in such embodiments, the bloom-forming microalgae, bloom-forming diatoms, diazotrophic phytoplankton and / or DDAs may comprise: (i) one or more of the following diazotrophs: Richelia sp., Calothrix sp., Crocosphaera sp. and Candidatus Atelocynaobacterium Thalassa, and / or (ii) one or more of the following diatoms: Hemiaulus sp. and Climacodium sp. (iii) one or more of the following: Skeletonema spp., Chaetoceros spp., Thalassiosira spp., Coscinodiscus spp., Navicula spp., Synedra sp. and Nitzschia spp., or (iv) Dunaliella spp. Alternatively, in such embodiments, DDAs may comprise Hemiaulus sp. {e.g., an assemblage of Richelia sp. and Hemiaulas sp.).

[0017] In some embodiments, the raceway pond comprises one or more structural features, the one or more structural features comprising a paddlewheel, a side wall, a base, a divider, a flow diverter, an inlet pipe, and a drainpipe. In such embodiments, the raceway pond may comprise a white base and white side wall. In some embodiments, the at least one raceway pond comprises a series of connected raceway ponds, arranged in stages. In such embodiments, the series of connected raceway ponds may comprise a first stage comprising one or more covered raceway ponds and a second stage comprising one or more stages of open raceway ponds.

[0018] In some embodiments, the one or more operating conditions may comprise water temperature, ambient air temperature, ambient relative humidity, dissolved inorganic carbon speciation, dilution rate, dilution volume, nutrient supplementation amount / rate, paddlewheel speed, inoculum density, standing stock / cell density / biomass density of a specific organism, irradiation rate, solar exposure, acid supplementation amount / rate, frequency / time for moving algae out of the pond and / or performing maintenance, alkalinity, or residence time in the raceway pond after dilution. In such embodiments, the nutrient of the nutrient supplement rate / amount may be one or more of: nitrogen, phosphorous, iron or silicon / silicate. The acid may be a mineral acid. The alkalinity may be a total alkalinity of the raceway pond.

[0019] In some embodiments, outputting the control signal to the at least one apparatus may comprise outputting an electrical signal to an electrically actuated apparatus. In some embodiments, the apparatus may belong to an industrial control or SCADA system. In some embodiments, the predicted state may be a forecast of a future state of the raceway pond, and / or may comprise a predicted growth rate of the algae in the raceway pond.

[0020] Predicting the state of the raceway pond can be based on one or more physical conditions. In some embodiments, predicting the state of the raceway pond may be based on one or more of a temperature condition, a light condition, a rate of gas exchange across an air-water interface, a chemical condition of water in the raceway pond or in an inlet used for a dilation operation, or a biological condition of the algae. A temperature condition may comprise a temperature of water in the raceway pond, a temperature of water in an inlet used for a dilution operation, an ambient wind speed, an ambient wind direction, and / or an ambient air temperature. A light condition may comprise a solar irradiation rate, a total daily solar exposure, a level of photosynthetically active radiation, a spectral property, and / or a depth value; optionally the light condition may comprise a level of photosynthetically active radiation spectrally corrected to the pond, at a representative pond depth. A chemical condition of water in the raceway pond or in an inlet used for a dilation operation may comprise a nutrient density, a pond / inlet temperature, a level of salinity, a pH value, a carbon dioxide content, a total dissolved inorganic carbon content, a total alkalinity, and / or a bicarbonate content. In various embodiments, at least one temperature, light, chemical or biological condition and / or rate of gas exchange may be calculated from the input data using a sub-model of the model.

[0021] In some embodiments, the input data may comprise data supplied by a raceway pond sensing / monitoring apparatus. Optionally, the raceway pond sensing / monitoring apparatus may be disposed within, above or adjacent to the raceway pond. Optionally, the input data may comprise a water temperature, a paddlewheel speed, one or more nutrient densities, a pH level, a turbidity level, a cell density, a biomass density, an ambient air temperature, a local depth, an inlet water property, a sensed light level, and / or aerial imaging data.

[0022] In some embodiments, the input data may comprise remotely supplied data. Optionally, the input data may comprise wind speed and / or direction data, weather forecast data, oceanic forecast data, sunrise or sunset timing data, solar zenith angle data, cloud cover data, UV light exposure data, light attenuation data such as aerosols data or sea haze data, and / or sunlight intensity data.

[0023] In some embodiments, the input data may comprise data supplied by a user input. Optionally, the input data may comprise an identified type of the algae and / or pond geometry data such as width, depth, length, volume, geomembrane reflective index, geomembrane roughness index, paddlewheel speed or surface area data for the raceway pond.

[0024] In some embodiments, the method may further comprise receiving second input data indicative of one or more properties pertaining to the environment surrounding the raceway pond.

[0025] In some embodiments, a higher algal growth rate informs an increase in dilution rate, increase in dilution volume, and / or decrease in resident time in the raceway pond, and vice versa. In some embodiments, a higher algal growth rate informs an increase in the system excess volume. The system excess volume is a volume of water removed and / or harvested during a pond transfer process in order to accommodate the full dilution volume in the raceway pond without it overflowing.

[0026] In some embodiments, a higher algal growth rate in combination with a lower nutrient density informs an increase in nutrient supplementation rate, and vice versa.

[0027] In some embodiments, a higher algal growth rate in combination with a lower acidity (higher pH) informs an increase in acid supplementation amount / rate, and vice versa. The supplied acid may be a mineral acid.

[0028] In some embodiments, the method further comprises: determining productivity of the algae based on the algal net growth rate. In such embodiments, a lower productivity may inform an increase in inoculum density, an increase in dilution rate, an increase in nutrient supplementation, and / or an increase in residence time in the raceway pond, and vice versa. The determined productivity may be gross primary productivity. A lower productivity may inform an increase in paddlewheel speed, in order to enhance CO2 diffusion into the raceway pond. Additionally or alternatively, a lower productivity may inform an increase in a rate / volume of acid addition (e.g., mineral acid addition), in order to make more seawater CO2 available to thereby enhance algal growth. In a further aspect of the invention, there is provided a computer-implemented method comprising receiving first data indicative of one or more properties of a raceway pond; predicting a state of the raceway pond using a model, based on the received first data; and, iteratively, receiving further data indicative of one or more properties of the raceway pond from a raceway pond sensing / monitoring apparatus; updating the predicted state of the raceway pond using the model, based on the received further data; and providing an output to a display based on the updated predicted state.

[0029] In a further aspect of the invention, there is provided a system for performing one or more methods of the invention.

[0030] In a further aspect of the invention, there is provided a device for performing one or more methods of the invention.

[0031] In a further aspect of the invention, there is provided a computer program which, when executed by a processor, causes the processor to perform one or more methods of the invention.

[0032] In a further aspect of the invention, there is provided a (non-transitory) computer readable medium having instructions which, when executed by a processor, causes the processor to perform one or more methods of the invention.

[0033] BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 illustrates an example raceway pond according to the invention.

[0035] Figure 2A illustrates an algal cultivation system and depicts a series of connected raceway ponds arranged in stages according to the invention. Figure 2B illustrates a subsection of the algal cultivation system of Figure 2A.

[0036] Figure 3 illustrates a method of culturing algae according to the invention.

[0037] Figure 4A illustrates a first method of culturing algae with a computational algal growth forecasting model according to the invention. Figure 4B illustrates a second method of culturing algae with a computational algal growth forecasting model according to the invention.

[0038] Figure 5 illustrates a system according to the invention (and suitable for implementing the methods of the invention), including various sources of input data for a computational algal growth forecasting model.

[0039] Figure 6 illustrates a system according to the invention (and suitable for implementing the methods of the invention), including various kinds of apparatus to which a control signal may be output when culturing algae to influence one or more operating conditions.

[0040] Figure 7 depicts an exemplary computational algal growth model comprising a plurality of sub-models, according to the invention. Figure 8 illustrates an example of a process for determining a control signal calculated to increase or maintain an algal growth rate using a model according to the invention.

[0041] Figure 9 illustrates iterative updating of predicted states of the raceway pond based on ongoing receipt of fresh data from a raceway pond sensing / monitoring apparatus, according to the invention.

[0042] DETAILED DESCRIPTION OF THE INVENTION

[0043] Raceway ponds

[0044] Land-based mariculture according to the invention comprises culturing algae in at least one raceway pond 100. Preferably the cultivation water for the at least one raceway pond 100 comprises seawater, but other types of water may be used, as discussed herein.

[0045] A typical raceway pond is illustrated in Figure 1. Raceway pond 100 is stadium shaped ( / .e., a rectangle with semicircles at a pair of opposite ends), with a partial divide in the centre of the pond 100 ( / .e., along the longitudinal axis A) to create a circuit ( / .e., channel 120) having two longitudinal channel sections 120a, 120b which are joined at opposite ends of the raceway pond 100 by U- shaped channel sections 120c, 120d. The stadium shape is defined by side wall 102. The partial divide is defined by divider 104. The side wall 102 and divider 104 may be formed of plastic covered and reinforced walls, fencing posts or earthen berms. Water and algae are retained in the raceway pond 100 by the side wall 102 and the base of the pond (not shown).

[0046] At one side of divider 104 (e.g., along a side wall 102) there may be a support structure that serves both as an anchor for a paddlewheel 110, a platform for CO2 sequestering equipment (not shown) and to anchor the divider 104 between the longitudinal channel sections 120a, 120b. Paddlewheel 110 maintains the flow of water and algae around the circuit. The paddlewheel 110 is typically a variable speed paddlewheel.

[0047] At the opposite ends of the raceway pond 100 ( / .e., at the semicircles of the stadium shape), the channel has U-shaped channel sections 120c, 120d. Within the U-shaped channel sections 120c, 120d, there are flow diverters 106 to ensure efficient flow throughout the raceway. Flow diverters 106 act to maintain laminar flow of algae and water around the channel 120, especially at the U- shaped channel sections 120c, 120d.

[0048] The raceway pond 100 further comprises an inlet pipe 108 and a drainpipe 112. The inlet pipe 108 may be connected to a gate-controlled sluice (not shown) for pond intake from either a seawater canal and / or a previous pond. The drainpipe 112 facilitates pond discharge through a second gate- controlled sluice (not shown). The dilution rate of the algae in the raceway pond 100, which is the rate at which water is added to the algae ( / .e., to dilute the algae), is determined by the position of the gate-controlled sluice of the inlet pipe 108 and / or the drainpipe 112. For instance, opening the gate-controlled sluice of the inlet pipe 108 and closing the gate-controlled sluice of the drainpipe 112 increases the dilution rate. The dilution volume is the volume of water added to the already partially filled raceway pond. In one embodiment, raceway pond 100 may be a covered raceway pond, which is a raceway pond 100 covered by a greenhouse (not shown). The primary purpose of the greenhouse is to protect the seed algae from being contaminated by windborne or bird-borne contaminants. Secondly the greenhouse is used to raise the temperature of the algal growth environment; both to increase the algal growth rate, and to inactivate competing or deleterious organisms that might otherwise contaminate the algae or foul the equipment. The greenhouse can also be used to selectively shade or change the illumination colour of the algae to induce a desirable physiological state by altering the wavelength of light. In one specific embodiment, every raceway pond 100 is covered by a greenhouse.

[0049] Alternatively, raceway pond 100 may be an open raceway pond. An open raceway pond has no external cover, and as such is fully exposed to the ambient atmosphere, while a covered raceway pond (which may be fully or partially covered) allows partial or complete control of the temperature and light environment.

[0050] Each raceway pond 100 is able to hold a certain volume of water and algae, depending on the depth, width and length of the raceway pond 100. In the context of the invention, volume of a raceway pond is defined as its capacity ( / .e., the volume of fluid a raceway pond is capable of holding) rather than the volume of fluid actually held in the raceway pond at any given time. An increase in volume of raceway pond 100 is achieved by increased width and / or length of the raceway pond.. In a specific embodiment, raceway pond 100 increases in volume through increased width and length. Depth of the raceway pond 100 cannot be increased as easily since changing the depth affects algal solar irradiation.

[0051] In a specific embodiment, raceway pond 100 has a width (perpendicular to axis A) to length (along axis A) size ratio of between 1 :4 and 1 :12, preferably 1 :8. At this ratio, there is relatively low head loss at each paddlewheel 110 as the water circulates around the bends, favourable economy of the construction materials (straight walls are easier to build than the bends), the reduction of wind influence (wind fetch) as it blows across the pond (to prevent potentially unmixed zones). The width of approximately 30 metres per channel 120 also reduces meandering flow to maintain turbulent flow. Information regarding the application of computational fluid dynamics to raceway pond design can be found in Kusmayadi, 2020.

[0052] In some embodiments, a plurality of raceway ponds 100 may be used in parallel to increase the collective volume of water and algae. The plurality of raceway ponds 100 may form a group. The group may comprise, for example, 2, 4, 6, 8, 10, 12, 14, 16, or 18 ponds. The inlet pipe 108 of each raceway pond 100 of the group may connected to a common seawater canal. Similarly, the drainpipe 112 of each raceway pond 100 of the group may connected to a common outlet.

[0053] In one embodiment, a covered raceway pond (or group of covered raceway ponds) has volume of between 50 I and 15,000,000 I, for example between 50 I and 50,000 I. In a particular embodiment, there is a series of covered raceway ponds, and the volume of covered raceway ponds in the series increases such that there is at least one pond in the series with a volume of (a) 50-1 ,000 I; at least one covered raceway pond in the series with a volume of (b) 100 1-30,000 I; and at least one pond in the series with a volume of (c) 5,000 - 12,000,000 I. In one embodiment, these ponds are linked in a linear fashion, such that there is one pond at each stage in the series. In one embodiment, an open raceway pond (or group of open raceway ponds) has volume of between 1 ,500,000 I and 3,000,000 I, in a further embodiment between 6,000,000 I and 15,000,000 I. In a still further embodiment, the volume of the open raceway ponds in the series increases such that there is at least one open raceway pond in the series with a volume of (a) 360,000 - 720,000 I; at least one pond in the series with a volume of (b) 1 ,500,000 - 3,000,000 I; and at least one pond in the series with a volume of (c) 6,000,000 - 15,000,000 I.

[0054] As mentioned, paddlewheel 110 may be used to maintain the flow of the algae and water around the raceway pond 100. This is energy efficient whilst ensuring thorough mixing and the exposure of the algae to relatively low shear. This enables effective exchange of gases within the ambient air with the algae growth medium. According to the species being cultivated, paddlewheel 100 may be used to achieve a different flow rate of algae and water within a raceway pond based on the physical attributes of the paddlewheel (e.g., number of paddles, size of paddles) and / or the paddlewheel speed ( / .e., its rotational speed). The flow rate may be 0.1-0.5 m / minute, e.g., 0.1-0.4 m / minute or 0.1 -0.3 m / minute. In one embodiment, each raceway pond 100 has one or more paddlewheels 110 depending on the degree of agitation that is required. Preferably, each raceway pond 100 has one paddlewheel 110. The paddlewheel 110 may be positioned at any section of the raceway pond, but is preferably positioned close to the inlet pipe 108 of a raceway pond {e.g., where the rectangle section of the stadium shape transitions to a semicircle).

[0055] In one embodiment, one or more paddlewheels 110 maintain the flow of the algae and water within raceway pond 100 at a rate of about 0.1 -0.5 m / minute, for example 0.1 -0.4 m / minute, or 0.1 -0.3 m / minute, or 0.15 m / minute, 0.2 m / minute, 0.25 m / minute.

[0056] The depth of raceway pond 100 affects algal solar irradiation. Both light intensity and wavelengths are altered by increasing water depth. In general, far red, red and ultraviolet light are absorbed the most rapidly by the water and a blue and green light penetrate the furthest. However, algae in a shallower raceway pond 100 experiences greater light intensity and a greater proportion of red light than algae in a deeper raceway pond 100. In one embodiment, raceway pond 100 is between 0.05 m and 10 m deep, for example, 0.05 m, 0.10 m, 0.20 m, 0.30 m, 0.40 m, 0.50 m, 0.60 m, 0.70 m, 0.80 m, 0.90 m, 1 m, 1.5 m, 2 m, 3 m, or 5 m deep. In one embodiment, a covered raceway pond is 0.1-1 m deep, for example 0.1 m, 0.2 m, 0.3 m, 0.4 m, or 0.5 m deep. In one embodiment, an open raceway pond is 0.25 m or 0.3-1 m deep, for example 0.25 m, 0.3-0.4 m, 0.5 m, 0.75 m or 1 m deep. Preferably an open raceway pond is 1 m deep or less. At this depth there is sufficient outgassing of O2 to help reduce oxidative stress, also at this depth there is sufficient exposure to dissolve atmospheric CO2.

[0057] Raceway ponds known in the art are also usually uniform in depth. However, if the depth of raceway pond 100 is varied along the length of channel 120 e.g., between longitudinal channel sections 120a, 120b), a change in the flow rate results. Furthermore, the exposure of the algae to solar irradiation is varied, with algae in shallower areas of the raceway pond experiencing greater light intensity and a greater proportion of red light than algae in deeper areas. Photosynthetic output can be affected by the light ratio, especially at dawn and dusk, and as discussed herein the depth of the water alters the light wavelength ratio. Thus, in one embodiment, the depth of raceway pond 100 is non-uniform, resulting in a change in algal exposure to light both in terms of light intensity and light wavelength ratio and / or a change in the rate of gas exchange within the non-uniform section of the raceway pond. In one embodiment, the difference between the depth in longitudinal channel sections 120a, 120b is 0.05-1.0 m, for example 0.05 m, 0.10 m, 0.2 m, 0.3 m, 0.4 m, 0.5 m, 0.6 m, 0.7 m, 0.8 m, 0.9 m or 1.0 m. For example, one longitudinal channel section 120a is 0.2-0.5 m deep, while the second longitudinal channel section 120b is 0.8-1.0 m deep.

[0058] As described herein, raceway pond 100 may be lined (not shown). In one embodiment, raceway pond 100 is lined with an impermeable material. If seawater is used to culture the algae, a clay lining may be used to prevent saltwater intrusion onto the land. In one embodiment, a plastic waterproof lining is used instead of, or in addition to, a clay lining. In a particular embodiment, each raceway pond 100 is lined with 10-20 cm of clay and a 2-5 cm, e.g., about 3 cm or specifically 8, 10 or 12 mm, robust synthetic liner such as a black or white geomembrane. A white coating, for instance from titanium oxide, may be provided over the black geomembrane.

[0059] In one embodiment, the colour of the pond liners, or a coating of the pond liners, may be chosen to alter the wavelengths of light received by the algae as light reflects off the base of the ponds. The colour of the liner or coating may selectively decrease exposure of the algae to underwater red (630- 680 nm), far red (700-750 nm) and / or blue (400-450 nm) light. In one embodiment, the liner or coating selectively decreases the exposure to underwater red (630-680 nm), far red (700-750 nm) and / or blue (400-450 nm) light by 10-90%, 20-80%, 30-70%, or 40-60%. For example, the liner or coating may selectively absorb up to 10, 20, 30, 40, 50, 60, 70, 80, 90 or 100% of the incident underwater red (630-680 nm), far red (700-750 nm) and / or blue (400-450 nm) light before the remaining light is reflected through the algal growth environment again. This is achieved by selecting the colour of the pond liner or coating based on the wavelengths that are to be absorbed.

[0060] In a preferred embodiment, the lining or coating is white in order to reflect light off the base of the pond and maximise the light available for photosynthesis and encourage algal growth especially in the low-density cultures and / or shallow cultures less than 30cm deep, where light will penetrate the depth of the medium. Having white lining or coating is also beneficial for remote optical monitoring so that the spectral signature leaving the pond is not altered by the absorption of the liner or coating. In some instances, black liners may be used to deliberately increase the temperature of the cultivation medium.

[0061] Different colours of lining or coating may be used to induce different physiological effects in the algae. For example, the lining or coating may be entirely red, blue or green. Alternatively, a single stage in the sequence of ponds can be coloured blue, for example at the point where the seed algal growth has been synchronised in the initial covered ponds, to reinforce cellular growth synchronisation and increase growth before cell division, just before the cells are introduced into the open growth ponds so that multiple divisions then occur in the growth pond. Similarly, the growth pond can be lined or coated in blue to stimulate the migration of chloroplasts to the outside of the cells, to promote maximum photosynthesis (Kraml & Hermann, 1991 ; Furukawa et al., 1998). As an alternative to coloured lining or coating, the side wall 102, base and other structural features within the pond may be coloured. For instance, the side wall 102, base and other structural features may be white.

[0062] Series of raceway ponds

[0063] In one embodiment, the land-based mariculture of the invention comprises culturing algae in a series of connected raceway ponds, arranged in stages. Each of the raceway ponds in the series of connected raceway ponds may be based on raceway pond 100 of Figure 1.

[0064] An example of an algal cultivation system 200 comprising a series of connected raceway ponds 210 arranged in stages 210A-230E is illustrated in Figure 2A. Figure 2B shows subsystem 200’ of system 200 in further detail. The stages 210A-230E of raceway ponds are connected in such a way so as to allow water and algae to pass directly between raceway ponds 100 in successive stages of the series. However, the connection between the stages 210A-230E of raceway ponds 100 can be closed and each stage 210A-230E of raceway ponds can be an isolated growth environment. The flow of water and algae between successive stages in the series is unidirectional, i.e., the passage of algae and water through the connected series of raceway ponds is one-way and algae and water are not re-circulated.

[0065] In particular, in Figures 2A and 2B, there are three stages of covered raceway ponds, 210A, 210B and 210C and two stages of open raceway ponds 210D, 210E. However, other numbers of stages of covered raceway ponds and open raceway ponds may be used. In one specific embodiment of the invention the series of connected raceway ponds 210 comprises firstly, one or more stages of covered raceway ponds 210A-210C and secondly, one or more stages of open raceway ponds 210D-210E. The designations of “firstly” one or more stages of covered raceway ponds 210A-210C and “secondly” one or more stages of open raceway ponds 210D-210E indicate that within the series of connected ponds 210, the stages comprising covered raceway ponds 210A-210C will always come before the stages comprising open raceway ponds 210D-210E. Put another way, an open raceway 210A-210C pond will never be succeeded by a covered raceway pond 210D-210E.

[0066] Each stage 210A-210E of the series of connected raceway ponds 210 may comprise one or more raceway ponds 100. Stages having a plurality (or group) of raceway ponds 100 use these ponds in parallel to increase the collective volume of water and algae that is throughput. The system 200 in Figures 2A and 2B, there are 16 ponds in each stage 210A-210E. However, the number of ponds does not have to be the same for each stage, as discussed further herein.

[0067] In addition to the series of connected raceway ponds 210, system 200 comprises an intake pipeline 202 to transport water to the system, typically seawater from the ocean. Intake pipeline 202 feeds intake canal 208 which provides each of the raceway ponds in the series of connected raceway ponds 210 with water. Each raceway pond 100 has a connection to supply canal 208 at its respective inlet pipe 108. The supply canal 208 may be elevated so that gravity can be used to transport water to each of the raceway ponds 100 via the inlet pipe 108. The elevated supply canal filled from intake pipeline 202 with high-rate, low-head pumps. System 200 also comprises a harvest canal 212. Each raceway pond 100 in at least the final stage of the series of connected raceway ponds 210 has a connection to harvest canal 212 at its respective drainpipe 112. In stages other than the final stage, each raceway pond 100 is connected to the next stage of raceway ponds via its respective drainpipe 112. The harvest canal 212 leads to a harvesting building 214, where the algae are collected. The spent water is discharged through discharge pipeline 216. The discharge pipeline is at low elevation so that gravity moves water out of the harvesting building 214. In embodiments where the water is seawater, the intake pipeline 202 is upstream and as far needed from the discharge pipeline 216 to avoid reuptake of already spent seawater.

[0068] Successive dilution in a semi-continuous cultivation manner, which maintains a low algal cell density, is beneficial for maintaining algae in the exponential growth phase.

[0069] Successive dilution can be achieved in two ways. In the first way dilution is achieved by increasing the collective volume of the raceway pond(s) 100 in each stage 210A-210E of the series. This increase in collective volume can be achieved by increasing the volume of the individual raceway ponds in each successive stage 210A-210E of the series and / or by increasing the number of raceway ponds in each successive stage 210A-210E of the series. Thus, at each successive stage 210A-210E in the series of raceway ponds 210 of the present invention, each individual raceway pond has a volume greater than the volume of the individual raceway ponds in the preceding stage of the series; and / or each raceway pond is immediately succeeded by a greater number of raceway ponds, wherein the collective volume of the raceway ponds in any given stage exceeds the collective volume of the raceway ponds of the preceding stage.

[0070] In the second way, dilution is achieved by increasing the volume of water in discrete steps to a maximum volume within a raceway pond 100, before the water and algae are transferred to the subsequent larger pond(s). For example, each pond may be initially filled to a first volume having a first depth {e.g., 0.25 m) where the cells complete a growth cycle. When it is time to increase (e.g., double) the volume of the water, to enable the cells to grow at a low standing stock with natural nutrients, the volume of water of the same pond is increased to a second volume having a second depth {e.g., 0.5 m). After the second growth cycle is complete within that pond, the total volume of the two division stages and the water is transferred to the next larger, subsequent pond. In other embodiments, the pond is filled in subsequent stages to 0.25 m, 0.5 m, 0.75 m and 1 m depth in four sequential ‘within-pond’ dilutions before it is transferred to the next larger pond.

[0071] This ‘stacking’ of ponds is possible because of the relatively low standing stock (or low cellular concentration) of the algae in comparison to other cultivation systems. In other commercial algal growth systems (where cells are grown at a density resulting between 1 ,000 - 2,000 mg Chi a rrr3), the high cell density results in gas exchange limitations and self-shading. However, in this method, neither of these are critical concerns, because the cell densities are significantly lower for the first 12-16 growth cycles resulting in 50, 100, 200, 300, 400, 500 mg Chi a rrr3). At these Chi a concentrations the ponds can be run without cell shading and the natural capacity of the seawater to absorb and buffer gases as well as exchange gases with the atmosphere enables cell growth.

[0072] When algae and water are transferred from a raceway pond (covered or open) in one stage of the series to one or more raceway ponds (covered or open) in the next stage of the series, either the algae and water are transferred to a single raceway pond with a greater volume, or the algae and water are divided between a number of raceway ponds with a larger collective volume. Each transfer of the algae and water from one stage of the series to the next stage in the series thus involves dilution of the algae or is preceded by dilution of the algae in the current stage of ponds.

[0073] In one embodiment, when the algae and water from one stage in the series (e.g., stage 210A) is used to seed the raceway pond or raceway ponds of the next stage (e.g., stage 210B), it is transferred to the raceway pond or raceway ponds of larger volume in the next stage of the series. Water is added to, or is already present in, the raceway ponds to be seeded, such that the final volume of fluid within the raceway ponds after seeding is equal to its capacity. This seeding step results in the algae being diluted and a low algal cell density can thus be maintained. Maintaining this constant low cell density of algae prevents the problems associated with traditional high-density algal culture, such as quorum sensing, biofilm formation and other (often unpredictable) algal stress responses.

[0074] In an alternative embodiment, the algae are diluted prior to being transferred to the next stage of ponds. In this embodiment, fresh seawater is added to the current stage of ponds to dilute the algae.

[0075] The successive dilution of the algae at each seeding step should not be taken to mean that the algal cell density at each stage in the series is successively reduced. Since the algae multiply rapidly, despite the successive dilutions at each seeding step the approximate cell density of algae in each stage of raceway ponds 210A-210E going through the series may increase, decrease or remain the same.

[0076] In a specific embodiment, there are at least two stages of covered raceway ponds in the series of connected raceway ponds. For example, there may be 2, 3, 4, 5, 6, 7, 8, 9, 10, 15 or 20 stages of covered raceway ponds. In one embodiment, there are 2-10 stages of covered raceway ponds, 4-8 stages of covered raceway ponds, or 5 stages of covered raceway ponds. In a preferred embodiment, the covered raceway ponds are preferably greenhouse covered and are connected in linear succession, wherein there is one covered raceway pond at each stage in the series of covered raceway ponds. In this embodiment, each covered raceway pond is at least 2 times, for example 2 to 5 times, the volume of the covered raceway pond of the previous stage in the series. In a specific embodiment, each covered raceway pond is 2, 3, 4, or 5 times volume of the covered raceway pond of the previous stage in the series. In a preferred embodiment, each covered raceway pond is 5 times volume of the covered raceway pond of the previous stage in the series.

[0077] In a further specific embodiment, there are at least two stages of open raceway ponds in the series of connected raceway ponds. For example, there may be 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20 or 25 stages of open raceway ponds. In one embodiment, there are 2-10 stages of open raceway ponds, 4-6 stages of open raceway ponds, or 5 stages of open raceway ponds. In one specific embodiment, there are more open raceway ponds than closed raceway ponds in the series.

[0078] In some embodiments, the number of open raceway ponds in each stage is the same for each successive stage in the series. For example, the system 200 in Figure 2A and 2B has 16 ponds in the first stage of covered raceway ponds 210A, 16 ponds in the second stage of covered raceway ponds 210B which are larger than the ponds in the first stage of covered raceway ponds 210A, 16 ponds in the third stage of covered raceway ponds 210C which are larger than the ponds in the second stage of covered raceway ponds 21 OB. There are also 16 ponds in the first stage of open raceway ponds 210D (fourth stage overall) which are larger than the ponds in the third stage of covered raceway ponds 210C, and 16 ponds in the second stage of open raceway ponds 210E (fifth stage overall), which are larger than the ponds in the first stage of open raceway ponds 210E. Each subsequent pond has at least twice the capacity of the previous pond to hold the entirety of the volume of the previous pond and the equivalent volume of unused seawater.

[0079] In at least one embodiment, the number of raceway ponds in each stage is one (i.e. , the pond series is linear). Preferably, each successive raceway pond in such a linear series increases in volume.

[0080] In a particular embodiment, the number of open raceway ponds in each stage increases at each successive stage in the series. Advantageously, this enables a large number of final-stage raceway ponds to be harvested (e.g., more than one hundred), all of which are fed from a significantly smaller number of upstream greenhouse ponds. In this embodiment, each open raceway pond is connected to two or more open raceway ponds in the next stage in the series, and the collective volume of each of the two or more open raceway pond exceeds the volume of the raceway pond in the preceding stage. Therefore, in this particular embodiment, the algae and water in one open raceway pond is diluted into two or more open raceway ponds when the algae and water are transferred between stages in the series.

[0081] In at least one embodiment, a single standardised pond size can be used for every pond, with the volume increase between successive stage being accommodated solely by increasing the number of ponds in each successive stage of the series of ponds. In some embodiments, a finite number of modular pond sizes can be reused to provide the volume increase. In one embodiment, 2 modular pond sizes are used for the open (outdoor) ponds and 3 modular pond sizes are used for the covered (greenhouse) ponds. This has been found to provide a suitable compromise between modularity and adequate pond mixing properties.

[0082] In a specific embodiment, the number of open raceway ponds in each stage of the series doubles. Therefore, in this embodiment the number of raceway ponds at each stage in the series increases exponentially. For example, the number of open raceway ponds at each stage increases as follows: 1 , 2, 4, 8, 16, 32, 64, 128. In this embodiment, as the number of open raceway ponds at each stage increases, so does the volume of each individual raceway pond. In a preferred embodiment, the volume of the individual open raceway ponds in one stage is at least two times, preferably five times, the volume of the individual open raceway ponds in the previous stage. In an alternative embodiment, the volume of the individual open raceway ponds at each stage in the series remains the same, although the collective volume of the raceway ponds increases with each stage as the number of raceway ponds increases.

[0083] The algae and water may be transferred between raceway ponds in successive stages of the series without any external force, for example it may be transferred under the influence of gravity. However, the algae and water will, on occasion when the local topography does not permit the use of gravity transfers, be pumped from one raceway pond to another, using any suitable pumping means that does not shear the cells. Figure 2A shows a particular layout for a series of connected raceway ponds 210. The advantage of this layout is that it efficiently hugs the coast along the edge of an ocean and enables each raceway pond to have at least one contact with the intake canal 208 and a discharge into the next larger and lower pond. This layout enables the water transfer within the entire pond system to rely on gravity feeds and requires a minimum of piping, while enabling easier maintenance of the ponds. This layout also takes advantage of the frequently encountered natural gradient along coastlines where distance from the shore commonly results in a slight increase in elevation.

[0084] Seed ponds and photobioreactors

[0085] The method of the invention, as depicted in Figure 3, comprises the step of culturing the algae, referred to herein as the culturing phase 310.

[0086] In one embodiment, the first stage 210A of covered raceway ponds is seeded with algae cultivated in a photobioreactor (PBR) 204 or a seed pond (not shown). The amount of algae that is used for seeding the initial pond at first stage 210A is referred to as the inoculum density.

[0087] A PBR 204 achieves a highly controlled environment within the reactor to maintain an uncontaminated stock culture. In order to prevent contamination with competing organisms, bacterial and viral infection, or predatory organisms that reduce the yield or availability of the seed algae, PBR 204 preferably uses sand and membrane filtered, pre-treated and decontaminated seawater. The exchange of gases is carefully controlled, for example by sparging or bubbling air or CO2 into the reactor, and removing excess O2. The addition of nutrients and the removal of waste products is also carefully controlled. Preferably, PBR 204 operates in a clean or sterile environment.

[0088] A seed pond (not shown) is an open or closed pond, preferably a closed raceway pond, in which the growth conditions for the algae can be controlled. The seed pond is used to grow a population of algae sufficient to seed the first stage 210A of covered raceway ponds.

[0089] In one embodiment, the first stage 210A of covered raceway ponds is seeded with algae from a PBR 204 or seed pond in the early morning, e.g., from one hour before to two hours after dawn to enable the algae to exploit their new growth environment, right after they have divided in the predawn hours, for example 1-2 hours before dawn. In a particular embodiment, the first stage 210A of covered raceway ponds is seeded between 1-2 hours before and 1-2 hours after dawn, e.g., between 1 hour before and 2 hours after dawn, or in a specific embodiment when dawn is at 6 AM, the seeding occurs between the hours of 5 AM to 8 AM.

[0090] Algae

[0091] In some embodiments, a single species of algae is cultured. In some embodiments, more than one species of algae is cultured.

[0092] In some embodiments, the algae comprise diazotrophic phytoplankton.

[0093] In some embodiments, the algae comprise assemblages of diazotrophic phytoplankton with other organisms. In some embodiments, the algae comprise diatom-diazotroph assemblages (DDAs).

[0094] In some embodiments, the algae comprise diatoms.

[0095] In some embodiments, the algae comprise bloom-forming algae.

[0096] In some embodiments, the algae comprise r-strategist algae.

[0097] Diazotrophic phytoplankton

[0098] In some embodiments, the algae that are cultured in the method of the invention comprise diazotrophic phytoplankton.

[0099] As used herein, “diazotrophic phytoplankton” refers to marine and freshwater nitrogen-fixing photosynthetic algae. Preferably, the diazotrophic phytoplankton are marine diazotrophic phytoplankton.

[0100] In some embodiments, the diazotrophic phytoplankton belong to the Cyanobacteria phylum. Accordingly, in some embodiments the diazotrophic phytoplankton comprise cyanobacteria.

[0101] In preferred embodiments, the diazotrophic phytoplankton is Trichodesmium sp. In some embodiments, the diazotrophic phytoplankton is one or more of T. erythraeum, T. contortum, T. hildebrandtii, T. radians, T. tenue, and T. thiebautii. In some embodiments, the diazotrophic phytoplankton is T. erythraeum.

[0102] In some embodiments, the diazotrophic phytoplankton is Crocosphaera sp., optionally C. watsonii.

[0103] In some embodiments, the diazotrophic phytoplankton is Calothrix sp., optionally C. adscendens, C. atricha, C. braunii, C. breviarticulata, C. caespitora, C. confervicola, C. Crustacea, C. donnelli, C. elenkinii, C. epiphytica, C. fusca, C. juliana, C. parasitica, C. parietina, C. pilosa, C. pulvinata, C. scopulorum, C. scytonemicola, C. simulans, C. solitaria, C. stagnalis, C. stellaris, and C. thermalis.

[0104] The inventors have found that culturing diazotrophic phytoplankton in land-based mariculture e.g., in a raceway pond) under oligotrophic conditions results in especially efficient carbon sequestration. Oligotrophic conditions are nutrient poor conditions. For example, oligotrophic conditions may comprise low concentrations of bioavailable nitrogen and phosphorus. These oligotrophic conditions are preferably present within a raceway pond or a series of connected raceway ponds.

[0105] In some instances, the oligotrophic conditions comprise less than 40 pM e.g., 20 pM) nitrogen. Accordingly, in some embodiments of the invention, the algae comprise diazotrophic phytoplankton, and nitrogen-containing acid (e.g.,

[0106] HNO3) is added in the first algal culture phase to a final concentration of less than 40 pM e.g., less than 20 pM). GB 2207837.2, to Brilliant Planet Limited, describes culturing diazotrophic phytoplankton and / or diatom-diazotroph assemblages (DDAs) in land-based mariculture under oligotrophic conditions.

[0107] GB 2212805.2, also to Brilliant Planet Limited, describes methods that involve the use of nutrient mineral acids to improve de-acidification of the mariculture.

[0108] DDAs

[0109] In some embodiments, the algae that are cultured in the method of the invention comprise “diatom-diazotroph assemblages (DDAs)”.

[0110] As used herein, “diatom-diazotroph assemblages (DDAs)” refers to symbioses between diatoms and diazotrophic prokaryotes. In these associations, the diazotrophic prokaryote captures or ‘fixes’ atmospheric N2 and makes it bioavailable to the diatom symbiont.

[0111] In some embodiments, the DDAs comprise diazotrophic cyanobacteria.

[0112] Diazotrophic cyanobacteria like Richelia, Calothrix and other unicellular species similar in morphology to free-living diazotroph Crocosphaera, thrive by forming symbiotic relationships with diatoms like Hemiaulus, Rhizosolenia, Chaetoceros and Climacodium (Mutalipassi et al., 2021 ; Hilton 2014). Some of these associations, like Calothrix or the novel unicellular cyanobacteria Candidatus Atelocyanobacterium thalassa (UCYN-A) (Tuo et al., 2017) associate with unicellular algae epiphytically (or on the outside of cells). Other diazotrophic symbionts are intracellular such as Richelia.

[0113] In some embodiments, the DDAs comprise marine diazotrophic cyanobacteria. In some embodiments, the marine diazotrophic cyanobacteria are selected from Richelia sp., Calothrix sp., Crocosphaera sp. And Candidatus Atelocynaobacterium Thalassa. In some embodiments, the marine diazotrophic cyanobacteria are Richelia sp. In some embodiments, the marine diazotrophic cyanobacteria are Calothrix sp. In some embodiments, the marine diazotrophic cyanobacteria are Crocosphaera sp. In some embodiments, the marine diazotrophic cyanobacteria are Candidatus Atelocynaobacterium Thalassa.

[0114] In some embodiments, the DDAs comprise Richelia sp., optionally R. intracellularis.

[0115] In some embodiments, the DDAs comprise Calothrix sp., optionally one or more of C. adscendens, C. atricha, C. braunii, C. breviarticulata, C. caespitora, C. confervicola, C. Crustacea, C. donnelli, C. elenkinii, C. epiphytica, C. fusca, C. juliana, C. parasitica, C. parietina, C. pilosa, C. pulvinata, C. scopulorum, C. scytonemicola, C. simulans, C. solitaria, C. stagnalis, C. stellaris, and C. thermalis.

[0116] In some embodiments, the DDAs comprise Crocosphaera sp., optionally C. watsonii.

[0117] In some embodiments, the DDAs comprise one or more of the following diatoms: Hemiaulus sp., Skeletonema sp., Rhizosolenia sp., Climacodium sp. and Chaetoceros sp.

[0118] In some embodiments, the DDAs comprise Hemiaulus sp., optionally H. hauckii, H. indicus, H. membranaceus, or H. sinensis. In some embodiments, the DDAs comprise Skeletonema sp., optionally S. barbadense, S. costatum, S. cylindraceum, S. mediterraneum, S. punctatum, S. tropicum, or S. pseudocostatum.

[0119] In some embodiments, the DDAs comprise Rhizosolenia sp., optionally R. alata, R. acuminata, R. antarctica, R. antennata, R. bergonii, R. clevei, R. curvata, R. cylindrus, R. delicatula, R. minima, R. pugens, R. robusta, R. rothii, R. stricta, or R. styliformis.

[0120] In some embodiments, the DDAs comprise Climacodium sp., optionally C. biconcavum or C. frauenfeldianum.

[0121] In some embodiments, the DDAs comprise Chaetoceros sp., optionally C. socialis, C. debilis, C. curvisetus, C. muelleri, C. calcitrans, or C. didymus.

[0122] In some embodiments, the DDAs comprise an assemblage of marine diazotrophic cyanobacteria and diatoms. In some embodiments, the marine diazotrophic cyanobacteria are selected from Richelia sp., Calothrix sp., Crocosphaera sp. And Candidatus Atelocynaobacterium Thalassa, and the diatoms are selected from Hemiaulus sp., Skeletonema sp., Rhizosolenia sp., Climacodium sp. and Chaetoceros sp.

[0123] In some embodiments, the DDAs comprise an assemblage of Richelia sp. And Hemiaulas sp.

[0124] The inventors have found that culturing DDAs in land-based mariculture {e.g., in a raceway pond) under oligotrophic conditions results in especially efficient carbon sequestration. Oligotrophic conditions are nutrient poor conditions. For example, oligotrophic conditions may comprise low concentrations of bioavailable nitrogen and phosphorus. These oligotrophic conditions are preferably present within a raceway pond or a series of connected raceway ponds.

[0125] In some instances, the oligotrophic conditions comprise less than 20 pM {e.g., 10 pM) phosphorus and less than 40 pM e.g., 20 pM) nitrogen. Accordingly, in some embodiments of the invention, the algae comprise DDAs, and nutrient mineral acids are added to a total concentration of less than 60 pM {e.g., less than 30 pM). In some such embodiments, phosphorus-containing acid {e.g., H3PO4) is added in the first algal culture phase to a final concentration of less than 20 pM {e.g., less than 10 pM). In some such embodiments, nitrogen-containing acid {e.g., HNO3) is added in the first algal culture phase to a final concentration of less than 40 pM {e.g., less than 20 pM).

[0126] Diatoms

[0127] In some embodiments, the algae that are cultured in the method of the invention comprise diatoms.

[0128] In some embodiments, the diatoms are fast-growing diatoms.

[0129] In some embodiments, the diatoms are bloom-forming diatoms. For example, diatoms that have (i) the ability to grow exponentially or (ii) a cell division rate that exceeds one division per day. In some embodiments, the diatoms are selected from Skeletonema sp., Chaetoceros sp., Thalassiosira sp., Coscinodiscus sp., Navicula sp., Synedra sp. and Nitzschia sp.

[0130] In some embodiments, the diatoms are Skeletonema sp., optionally S. barbadense, S. costatum, S. cylindraceum, S. mediterraneum, S. punctatum, S. tropicum, or S. pseudocostatum.

[0131] In some embodiments, the diatoms are Chaetoceros sp., optionally C. socialis, C. debilis, C. curvisetus, C. muelleri, C. calcitrans, or C. didymus.

[0132] In some embodiments, the diatoms are Thalassiosira sp., optionally T. pseudonana, or T. symmetrica.

[0133] In some embodiments, the diatoms are Coscinodiscus sp., optionally C. wailesii.

[0134] In some embodiments, the diatoms are Navicula sp., optionally N. pelliculosa, N. incerta, N. oblonga, N. salinicola, N. ramosissima, N. minima, N. cryptocephala, or N. trivialis.

[0135] In some embodiments, the diatoms are Synedra sp., optionally S. capitata, S. famelica, S. radians, S. rumpens, or S. ulna.

[0136] In some embodiments, the diatoms are Nitzschia sp., optionally N. frigida, N. acicularis, N. amphibia, or N. angustata.

[0137] In some embodiments, the diatoms are Rhopalodiaceae sp. optionally S. Epithemia pelagica.

[0138] In some embodiments, the algae that are cultured in the method of the invention comprise bloom-forming algae.

[0139] In some embodiments, the bloom-forming algae are Dunaliella sp., optionally D. salina.

[0140] In some embodiments, the bloom-forming algae are Chlorella sp., optionally C. vulgaris.

[0141] In some embodiments, the bloom-forming algae are Spirulina sp.

[0142] In some embodiments, the bloom-forming algae are Cryptomonas sp., optionally C. ovate.

[0143] In some embodiments, the bloom-forming algae are Nannochlorpsis sp., optionally N. gaditana or N. oculate.

[0144] In some embodiments, the bloom-forming algae are Tetraselmis sp.

[0145] In some embodiments, the bloom-forming algae are Isochyrsis sp., optionally / . galbana.

[0146] In some embodiments, the bloom-forming algae are Rhodomonas sp., optionally R. minuta. In some embodiments, the bloom-forming algae are Pyramimonas sp.

[0147] In some embodiments, the bloom-forming algae are Microcystis sp., optionally M. aeruginosa.

[0148] In some embodiments, the bloom-forming algae are Gymnodinium sp., optionally G. nagasakiense.

[0149] In some embodiments, the bloom-forming algae are Chaetoceros sp., optionally C. socialis, C. debilis, C. curvisetus, C. muelleri, C. calcitrans, C. didymus, or C. convolutus.

[0150] In some embodiments, the bloom-forming algae are Gonyaulax sp., optionally G. polygramma.

[0151] R-strateqist algae

[0152] In ecology, r-strategist organisms are characterised by their high growth rates in less-crowded ecological niches. They are considered opportunistic organisms and thrive in more unstable and unpredictable environments due to their ability to reproduce rapidly.

[0153] Conversely, K-strategist organisms are characterised by a much lower growth rate in an ecological niche which is close to or at the carrying capacity for that organism. They exist in an equilibrium and thrive in more stable or predictable environments, competing for limited resources.

[0154] The method of the invention described herein represents a controlled, stable and predictable environment in which algae are cultured. Algae often fit the description of r-strategist organisms, displaying rapid growth rates in often unstable and unpredictable environments.

[0155] Therefore, it is surprising to find that algae grow particularly well in the tightly controlled, highly predictable conditions of the method of the invention described herein.

[0156] In some embodiments, the algae that are cultured in the method of the invention comprise r-strategist algae.

[0157] In some embodiments, the r-strategist algae are cultured in a K-strategist environment.

[0158] In some embodiments, the r-strategist algae are cultured under K-strategist conditions.

[0159] In some embodiments, K-strategist conditions comprise one or more of a stable temperature, a stable salinity level, a stable humidity and a stable duration of sunlight.

[0160] Algal flow management and operating conditions

[0161] Algae may be cultivated in seawater, hypersaline water, desalination brine, brackish water, wastewater or freshwater. The choice of water for the culture medium will depend on the algae being grown. Algae will be grown in water that replicates their natural growth environment. Once the seawater has circulated through at least one raceway pond 100 (e.g., the series of connected raceway ponds 210), it is cleaned of algae and returned to the warmer ocean surface water, down-current at a distance from the intake to avoid intake of water that has already been used for cultivation of the algae.

[0162] In one embodiment, algae are first cultivated in at least one stage of covered raceway ponds (210A- 210C). The covered raceway ponds (210A-210C) allow for the control of the algae growth environment. The water used to fill the covered raceway ponds (210A-210C) may be filtered or otherwise treated to remove competing and deleterious organisms before being introduced into the covered raceway ponds.

[0163] In one embodiment, greenhouses 206 are used to cover the raceway ponds 100 and as a result the algae environment is maintained at a higher than ambient temperature. In this embodiment, the temperature within the covered raceway ponds is between 18 °C and 32 °C, for example between 28 °C and 34 °C. This will substantially inactivate organisms acclimated to temperatures of 14°C - 18°C when they are pumped from depth off-shore. Advantageously, covered raceway ponds are less susceptible to contamination, either by bacteria or viruses, or by potentially competing organisms. The relatively controlled environment of the covered raceway pond promotes the algae transitioning into the exponential growth phase. Passive and / or active ventilation may be used in greenhouse 206 to regulate the temperature. As passive ventilation, greenhouse 206 may have walls that have a mesh netting on the inside and are movable outside which can be shut to retain more heat inside the greenhouse (e.g., in winter) or moved to allow free air moment (e.g., in summer). For the active ventilation, if a certain temperature threshold is exceeded, vents turn on to remove heat from greenhouse 206.

[0164] As the algal cellular density increases, the algae are successively diluted, preferably by being transferred between stages in the series of covered raceway ponds (to covered raceway ponds of successively larger volume (e.g., from stage 210A to stage 210B, from stage 210B to 210C). This successive dilution maintains a relatively low cell density of algae, for example between 100,000 cells / ml and 2,000,000 cells / ml, for example about 350,000 cells / ml. Each transfer of the algae to seed a covered raceway pond in the next stage of the series ( / .e., the successive dilution of the algae) can be timed to match the growth rate of the algae, or cellular resource requirements. In one embodiment, algae reside ( / .e., have a residence time) in each stage of covered raceway ponds 210A, 210B, 210C for2 hours to 10 days, for example 2 hours, 3, hours, 5 hours, 12 hours, 24 hours, 36 hours, 2 days, 3 days or 5 days. In one embodiment, the algae reside in each covered raceway pond 210A, 210B, 210C for two days, before being transferred to a raceway pond of larger volume. In one embodiment, algae remain in a covered raceway pond 210A, 210B, 20C for a length of time sufficient for the algae cellular population to at least double, for example 2 hours, 4 hours, 12 hours, 24 hours, 36 hours or 48 hours. In a specific embodiment, algae remain in a covered raceway pond for 24 hours before being transferred to the next stage in the series.

[0165] Once the algae enter the exponential growth phase, or when sufficient quantities of algae have been cultivated, the algae are transferred to the first stage of open raceway ponds 210D. In one embodiment, the algae and water are transferred in volumes of 1 ,000 I to 3,000,000 I, for example 360,000 I to 720,000 I, into the first stage of open raceway ponds 210D. The transfer of a relatively large bolus of algae is intended to seed the open raceway ponds to populate the growth environment with a large excess of several orders of magnitude of the product algae relative to any surviving organisms that were within the source water used in the open raceway pond thereby establishing a robust population.

[0166] The algae may be diluted ( / .e., the dilution rate or dilution volume may be increased) by introducing additional water into the current stage of ponds (210A-210E), increasing the volume of water contained within that stage. Additional water may be added by opening the sluice gate of the inlet pipe 108 of a raceway pond 100. Alternatively, the algae may be diluted by transferring the algae to the next stage of ponds and mixing the algae with water already present in those ponds.

[0167] Algae in an open raceway pond 210D, 210E are successively diluted by increasing the volume or number of open raceway ponds in each stage of the series (e.g., from stage 210D to 210E). This serial dilution maintains the algae at a low enough cell density to sustain exponential growth. In one embodiment, the algae are successively diluted to maintain a cell density of 50,000 cells / ml to 100,000 cells / ml, for example 200,000 cells / ml to 250,000 cells / ml. This approach is completely different to current methods of cultivating algae in which the algae are grown to artificially high densities often reaching cell densities of over 1 million cells / ml.

[0168] In one embodiment, algae reside ( / .e., have a residence time) in each stage of open raceway ponds 210D, 210E for 2 hours to 5 days, for example 2 hours, 3, hours, 5 hours, 12 hours, 24 hours, 36 hours, 2 days, 3, days or 5 days before being transferred to the next stage of open raceway ponds in the series (e.g., from stage 210D to 210E). In a specific embodiment, algae remain in a stage of open raceway ponds 210D for 48 hours before being transferred to the next stage of raceway ponds 210E in the series. In one embodiment, algae remain in a stage of an open raceway pond 210D, 210E for a length of time sufficient for the algae cellular population to at least double, i.e., for one round of cell division to take place. In a specific embodiment, the algae remain in a stage of an open raceway pond 210D, 210E for a length of time sufficient for one, two, three, four or five rounds of cell division to take place. This may be, for example, 2 hours, 4 hours 12 hours, 24 hours, 36 hours, 48 hours, or 72 hours.

[0169] This successive dilution of algae maintains a low cell density which advantageously maintains the exponential growth phase, thereby increasing productivity. Furthermore, the problems associated with high-density algal culture methods such using traditional raceway ponds and PBRs 204 are avoided.

[0170] With each successive dilution of the algae, water is added to, and / or is already present in, the covered and open raceway ponds in the next stage 210A-210E in the series. Following the transfer of the algae and water from one stage to seed the next stage in the series (e.g., from stage 210A to 210B, from 210B to 210C, from 210C to 210D, from 210D to 210E), the volume of fluid within each raceway pond 100 is equal to the capacity of the raceway pond 100. In one embodiment, the entire volume of water in a raceway pond 100 is replaced every 2 hours to 8 days, or every 4 hours to 8 days, preferably every 24 to 72 hours. In a preferred embodiment, the volume of water passing through the series of connected covered and open raceway ponds 210 in 24 hours is greater than 20-250% of the entire volume of the series of covered and open connected raceway ponds 210, e.g., greater than 30% of the entire volume of the series of covered and open connected raceway ponds, for example greater than 100% for both seed and growth ponds. This exchange rate of water is much higher than in traditional raceway ponds, where the water replacement rate is usually around 0 - 20% in 24 hours, in order to match the growth rate of the cells and rate of evaporation.

[0171] This high volume of water exchange has several advantages. In traditional raceway ponds a high cell density of algae is maintained and any contamination can potentially render the entire raceway pond un-harvestable. However, the series of raceway ponds 210 is inherently resilient, as small degrees of contamination do not matter since all of the contaminants are inevitably washed out of the series of ponds, and none of the product algae is reintroduced into the series of raceway ponds.

[0172] In some instances, it may be beneficial to supplement the algae and water with additional nutrients to promote algal growth rate. Typically, the nutrients added are one or more of: nitrogen, phosphorous, iron and silicate. Nutrients may be added to the intake water and therefore distributed to all raceway ponds. Alternatively, nutrients may be selectively added to individual ponds through dedicated nutrient-dosing pumps. The amount of nutrients added to the intake water or individual ponds determines the nutrient supplementation rate. Nutrient density is determined using one or more nutrient sensors in the intake seawater supply, such as in the pipeline pumping and distributing the local seawater to the ponds. The nutrient sensors may be configured to detect one or more of: nitrogen, phosphorous, iron and silicate.

[0173] In some instances, it may be useful to add mineral acids to the algae and water to balance the pH of the algae and water. Mineral acids may be added to the intake water and therefore distributed to all raceway ponds. Alternatively, mineral acids may be selectively added to individual ponds through dedicated nutrient-dosing pumps. The amount of mineral acids added to the intake water or individual ponds in this way determines the mineral acid addition rate. A pH sensor is used to determine the pH of the algae and water in the raceway ponds.

[0174] Operating conditions of the raceway pond can encompass aspects traditionally regarded as “operating parameters” (also sometimes known as operational parameters), some of which may comprise internal parameters of a control system under the direct influence of a controller and immediately changeable thereby.

[0175] Operating conditions of the raceway pond can encompass aspects traditionally regarded as “operating decisions” (also sometimes known as operating decisions), such as decisions about when to move the algae out of the raceway pond (either for harvesting, or into another raceway pond e.g., a “later” raceway pond). For instance, operating conditions may comprise a frequency for moving algae out of the raceway pond and into another pond or a harvesting shed. Operating conditions may comprise a time for making such a movement of (some or all of) the algae out from one or more raceway ponds - in such cases, adjusting this operating condition entails changing the decision about when to move the algae. Operating conditions may comprise a due date / time for performing maintenance, such that adjusting the operating conditions entails bringing forward or pushing back scheduled maintenance, or changing a frequency for such maintenance.

[0176] Operating conditions of the raceway pond can encompass aspects which are not internal to the control system and / or which are not under the direct influence of a controller and immediately changeable thereby, but which are instead external, or indirectly controlled, or both. For example, the temperature of water in a raceway pond is not a parameter directly and immediately set in hardware or software, but it can nevertheless be adjusted by a controller via appropriate effectors, actuators and / or other hardware (in this case, heating equipment) to reach a desired value.

[0177] In the invention, one or more operating conditions are adjusted. In embodiments, the one or more operating conditions may be any of the operating conditions described herein, including dilution rate, dilution volume, nutrient supplementation rate / amount, acid supplementation rate / amount, paddlewheel speed, inoculum density, mineral acid addition rate, or residence time in the raceway pond after dilution. In some embodiments, other operating conditions such as temperature may be used.

[0178] Operating conditions can also include dilution rates, dilution volumes, and residence times. These may be based on decisions concerning when to move the algae between raceway ponds, when to harvest the algae, and when to perform maintenance. Moving the algae between raceway ponds may be seen as equivalent to diluting the algae.

[0179] In some embodiments, measurements are taken directly from the pond, such as via the nutrient sensors, pH sensor, or sensors measuring other factors such as temperature and pond depth.

[0180] In some embodiments, the operating conditions are adjusted automatically, for instance via appropriate computer logic. As will be appreciated, it is possible to control most of the operating conditions via a processor with appropriate computer logic (e.g., processor 555).

[0181] Algal optical properties

[0182] Algae has unique optical properties which can be used to determine algal growth rate and other characteristics of the algae in accordance with various methods of the invention.

[0183] An optical property of algae that is important for determining algal growth rate is the light absorption spectrum of the algae, specifically the light absorption spectrum across visible wavelengths. The light absorption spectrum of algae is specific to the type of algae and / or the physiological condition of the algae. The higher the light absorption across a unit of depth in water, the higher the biomass (cell count) of algae, Therefore, using in situ measurements of light absorption, as well as depth and biomass, the relationship between the optical properties of algae and the algae biomass can be established.

[0184] The optical properties of the algae may be predetermined. By this, it is meant that the optical properties of the algae are determined prior to the methods of the present disclosure being performed. Typically, the optical properties of the algae are measured in a controlled environment, such as a laboratory environment, to build a library of in vivo light absorption spectra, for various species of algae, under different physiological conditions.

[0185] Remote optical monitoring for algal growth

[0186] GB 2303165.1 describes methods of culturing algae with remote optical monitoring. Aspects of its disclosure are summarised herein. Possible sources of input data indicative of one or more properties of a raceway pond include imaging data (e.g., taken from a satellite or drone passing above the raceway pond) and / or remote optical monitoring of the raceway pond.

[0187] Remote optical monitoring can comprise the following steps. First, image data is received depicting the raceway pond at a first time via remote imaging. Then, a first spectral signature of the raceway pond is determined from the first image data. Image data is received depicting the raceway pond at a second time subsequent to the first time via remote imaging. A second spectral signature of the raceway pond is determined from the second spectral signature. Subsequently, the algal growth rate in the raceway pond is determined based on the difference between the second spectral signature and the first spectra signature, predetermined optical properties of the algae, and physical properties of the raceway pond.

[0188] Remote imaging sensors may be waveband selective sensors and / or hyperspectral sensors (the latter, for example, ranging from ultraviolet wavelengths through to near-infrared wavelengths). In some embodiments, the first image data and the second image data are received from one or more of a satellite, an aircraft, a balloon, and a drone. In certain embodiments, the first image data and the second image data are received from two different satellites. By “different” it is meant two distinct remote imaging platforms, but the two distinct remote imaging platform may be the same type of imaging platform, e.g., two satellites. In such embodiments, each of the first image data and the second image data may be received from two different satellites. In other words, the first image data may originate from more than one distinct image sensor and the second image data may originate from more than one distinct image sensor (typically the same image sensors as the first image data).

[0189] Once algal growth rate is determined, it may be used in the prediction of a state of the raceway pond and / or to inform (and, in some instances as previously discussed, cause automatic adjustment of) one or more operating conditions of the raceway pond. In some embodiments, a higher algal growth rate informs an increase in dilution rate, increase in dilution volume, or decrease in residence time in the raceway pond. In such embodiments, processor 555 may be configured to control the sluice gates at the inlet pipe and the outlet pipe of the raceway pond to achieve a desired dilution rate, dilution volume or residence time of the algae in the raceway pond (e.g., before being moved to the next raceway pond in a series of raceway ponds). In some embodiments, a higher algal growth rate in combination with a lower nutrient density informs an increase in nutrient supplementation rate. Again, processor 555 may be configured to control the nutrient supplementation rate to achieve the desired rate.

[0190] Other useful characteristics besides algal growth can be determined via imaging and / or remote optical monitoring of a raceway pond as an input for predicting a state of the raceway pond and automatically adjusting one or more operating conditions of the raceway pond.

[0191] For example, biomass (or cell count) may be used instead of or in addition to algal growth rate. Productivity may be used instead of or in addition to algal growth rate. The uniformity of the raceway pond may be used instead of or in addition to algal growth rate. One or more colour wavebands of a spectral signature the raceway pond may be used instead of or in addition to algal growth rate. Harvesting the algae

[0192] The method of the invention further comprises the step of harvesting the algae. This step is referred to herein as the harvest phase 320, which is depicted in Figure 3.

[0193] Harvesting of the algae in a raceway pond may be performed once algal growth rate falls below a predetermined threshold. In general, it is desirable to reduce nutrient supplementation rate in the final ponds of a series of raceway ponds, to force the cells down to a metabolic path where they have a higher carbon to nitrogen ratio and carbon content, and to avoid excess nutrients being washed out of the system (e.g., system 200). However, if the algal growth rate falls below the predetermined threshold (e.g., stops growing), then harvest should be performed as soon as possible.

[0194] Alternatively, harvesting of the algae in a raceway pond may be performed once the biomass in the raceway pond has exceeded a predetermined threshold. As discussed elsewhere herein, remote optical monitoring may be used to determine the biomass in the raceway pond. In such embodiments, the predetermined threshold may be set based on the maximum biomass that the raceway pond is capable of holding. For example, the predetermined threshold may be set to at least 70%, 75%, 80%, 85%, 90%, or 95% of the maximum biomass that the raceway pond is capable of holding.

[0195] Alternatively, harvesting of the algae in a raceway pond may be performed once productivity reaches a predetermined threshold. As discussed elsewhere herein, remote optical monitoring may be used to determine the productivity in the raceway pond.

[0196] In some embodiments, the algae are harvested by tangential flow filtration. In some such embodiments, the tangential flow filtration is performed using filters comprising hydrophilic simple weave (irradiated) polyester fabric.

[0197] In some embodiments, the algae are harvested by filtering with a rotary mesh screen. In some such embodiments, the screen comprises a (pore-sized) mesh and a filter comprising hydrophilic (irradiated) polyester fabric.

[0198] In some embodiments, diazotrophic phytoplankton that are chain-forming cyanobacteria, such as Trichodesmium sp. (e.g., Trichodesmium erythraeum), are cultured. In some such embodiments, the cyanobacteria are harvested by tangential flow filtration. In some such embodiments, the tangential flow filtration is performed using filters comprising hydrophilic (irradiated) polyester fabric.

[0199] In some embodiments, diazotrophic phytoplankton that are chain-forming cyanobacteria, such as Trichodesmium sp. (e.g., Trichodesmium erythraeum), are cultured. In some such embodiments, the cyanobacteria are harvested by filtering with a rotary mesh screen. In some such embodiments, the screen comprises a (pore-sized) mesh and a filter comprising hydrophilic (irradiated) polyester fabric.

[0200] In some embodiments, DDAs comprising a chain-forming diatom (e.g., Hemiauluas sp.) are cultured and the algae are harvested by tangential flow filtration. In some such embodiments, the tangential flow filtration is performed using filters comprising hydrophilic (irradiated) polyester fabric. In some embodiments, DDAs comprising a chain-forming diatom (e.g., Hemiauluas sp.) are cultured and the algae are harvested by filtering with a rotary mesh screen. In some such embodiments, the screen comprises a (pore-sized) mesh and a filter comprising hydrophilic (irradiated) polyester fabric.

[0201] Suitable rotary mesh screens include (simple weave) polyester screens that have a pore size of 10-200 pM, preferably a pore size of 20-120 pM. In some embodiments, these screens have been irradiated. Advantageously, this makes the screen more hydrophilic, which improves the filtration rate. Suitable rotary mesh screens may comprise a surfactant coating, such as SAATIcare Hyphyl™.

[0202] A further example of a suitable rotary mesh screens is monofilament polyester fabric, such as SUPREX (EXTRIS).

[0203] In the context of rotary mesh screens, the thinner the fibre strands, the larger the percentage of open area for filtration, which the inventors have found to be advantageous for harvest filtration of algae. Accordingly, in some embodiments, the average fibre strand diameter in a rotary mesh screen less than 50 pM.

[0204] Accordingly, in some embodiments, the rotary mesh screens have a pore size of 10-200 pM and an average fibre strand diameter of less than 50 pM.

[0205] Accordingly, in some embodiments, the rotary mesh screens have a pore size of 20-120 pM and an average fibre strand diameter of less than 50 pM.

[0206] In some embodiments, the harvested algal slurry (or algal concentrate) is solar dried and buried to sequester carbon in the ground. Accordingly, the harvest phase may further comprise burying the harvested algae for carbon sequestration.

[0207] Maintenance of the raceway ponds

[0208] In one embodiment, the method of the invention further comprises the step of maintaining the raceway ponds.

[0209] Maintenance may be performed upon detection of a contaminant in a raceway pond.

[0210] Contaminants may be present in the environment surrounding the raceway pond, especially arising from environmental perturbations such as rainfall or sandstorms. When the raceway pond is situated in a desert, the most common contaminant is sand.

[0211] Additionally or alternatively, the contaminant may be present oceanic contaminants coming in with fresh seawater.

[0212] When algae is cultured in a series of connected raceway ponds, such ponds typically produce sequential batches of algae as opposed to the steady state continuous harvesting of algae that is traditionally utilised in algal culture. This means that batches of algae can be separated according to need. For example, as the algae pass through the series of connected ponds, trace contamination from the air or other sources may occur, particularly in the open raceway ponds 100. However, the series of connected raceway ponds of the present invention is inherently resilient, because small degrees of contamination do not matter as none of the product algae is reintroduced and all of the natural contaminants, such as competing algae, are eventually washed out of the system 200. Furthermore, in one embodiment the dilution of the algae through the repeat addition of seawater also dilutes any natural contaminants present.

[0213] Raceway ponds in the series are connected to allow algae and water to flow from one raceway pond to another, but each pond can be isolated when required (e.g., once a natural contaminant is detected). Individual raceway ponds in parallel can also be isolated when required. This is particularly useful to allow the ponds to be cleaned to remove sediment or biofilms. Therefore, between batches of algae in different raceway ponds, where algae are transferred every 2, 3, 4, 5, 6, 8, 10, 12, 24, 36 hours, 2 days, 3 days, 5 days apart, a maintenance step can be introduced wherein each raceway pond in the series is sequentially pumped dry, cleaned, for example with truck-mounted rotating brushes, and flushed with water. A maintenance step can be performed once a month, and every several maintenance steps an extra day may be introduced to add a day for drying the ponds. Equipment can be cleaned with 0.0001-0.01% peroxyacetic acid or similar disinfectants such as sodium hypochlorite before it is seeded with algae from the raceway pond in the preceding stage in the series and topped up with fresh water. In this manner, a running cleaning wave can travel through the entire series of connected covered and open raceway ponds (and the harvesting ponds if desired). This may be scheduled according to the prevalence of oceanic contaminants coming in with the fresh seawater or environmental perturbations such as rainfall or sandstorms, and therefore presence of a contaminant.

[0214] Computational algal growth forecasting model

[0215] Figure 4A illustrates a computer-implemented method 400 of culturing algae (in a raceway pond) with a computational algal growth forecasting model according to the invention.

[0216] Reference is now made to both Figure 4A and Figure 5, the latter depicting at least a portion of an algae culturing and modelling system 500 suitable for implementing a method of the invention. In a first step 410 of method 400, input data indicative of one or more properties of the raceway pond is received. The input data may comprise data supplied by a raceway pond sensing / monitoring apparatus, for example a thermometer configured to measure a water temperature 525 in the raceway pond, a relative or absolute encoder device connected to the paddlewheel configured to measure its speed 535, sensing means configured to measure chemical properties such as a pH level and / or one or more nutrient densities within the raceway pond, a turbidity sensor configured to measure the turbidity of water in the raceway pond, a probe / sensor configured to measure the density of algal cells in the raceway pond (either viable cell density or total cell density, and by optical sensing means or otherwise), a thermometer configured to measure an ambient air temperature 505 (e.g., an ambient temperature of air adjacent to the raceway pond), a sensor (e.g., a submersible pressure sensor) configured to measure a local depth of the raceway pond, a photodetector device configured to measure a sensed light level, a source of aerial imaging data 510 e.g., from a drone or satellite, and / or devices for measuring one or more properties of inlet water (such as salinity, pH, nutrient densities, flow rate, temperature, turbidity and the like). Various examples of raceway pond sensing / monitoring apparatus may be disposed within, above or adjacent to the raceway pond. For example, the acidity / alkalinity of water in the raceway pond can be measured using a pH sensor disposed in (the water in) the pond itself, whilst aerial imaging data 510 can be acquired from a drone above the raceway pond. Some kinds of input data may be acquired by more than one sensor type or sensor configuration. For instance, the depth of water in the raceway pond may be determined either using a submersible pressure sensor in the pond or using an electrical circuit disposed on a sidewall of the raceway pond (e.g., comprising one or more moisture sensors). The depth may also be determined using aerial imaging of at least a part of the raceway pond, for example if at least part of a sidewall of the pond is sloped or curved away from the vertical to at least some extent. For any one given property of the raceway pond, there may be several input data sources providing input data indicative of that property.

[0217] At least some of the input data may be remotely supplied data 515. For example, some kinds of input data may be received over a network such as the internet, e.g., via an application programming interface (API). The data 515 may be received in real time. The data 515 may be streamed. Such kinds of remotely supplied data 515 can comprise meteorological data such as wind speed and / or direction data, weather forecast data, oceanic forecast data, sunrise or sunset timing data, solar zenith angle data, cloud cover data, UV light exposure data, light attenuation data such as aerosol data or sea haze data, and / or sunlight intensity data. The meteorological data may comprise “live” meteorological data indicative of the sensed / measured current meteorological properties in one or more geographical areas of interest at the time of the data being generated, sent or received. Additionally or alternatively, the meteorological data may comprise forecast data indicative of calculated, simulated, modelled or predicted future properties in one or more geographical areas of interest at a later time (i.e. , after the time of the data being generated, sent or received). Preferably the meteorological data indicates meteorological properties in a geographical area that includes the raceway pond 100 in question.

[0218] Input data may comprise data 530 from computer 550 itself. In one example, some or all control signals output by computer 550 to at least one apparatus to automatically adjust one or more operating conditions of the raceway pond may be tracked, recorded and / or monitored by computer 550 and form part of subsequent input data indicative of one or more properties of the raceway pond. For instance, the current speed of paddlewheel 110 in raceway pond 100 may be known on the basis of the most recent control signal provided to a controller / actuator thereof, which may avoid the need for sensors or encoders on / around the wheel itself. Additionally or alternatively, data 530 may comprise stored data that has been saved in memory 556. Additionally or alternatively, data 530 may comprise data computed by processor 555, including but not limited to calculated or estimated properties of the raceway pond, and including but not limited to one or more (current and / or future) states of the raceway pond that have been predicted using a model, based on other input data.

[0219] At least some of the input data may be data 520 supplied by a user input. User input may, in the context of the computer-implemented method of the present invention, be supplied by any conventional means for providing user input to a computer 550 among those known in the art, such as a mouse, keyboard, trackpad, touchscreen, joystick, controller, webcam, microphone or any other suitable peripheral or hardware device. The input may be made by the user at the point of executing the method, or may be made in advance (with this input being stored inside a memory 556 of computer 550 for later use, and then retrieved from memory 556 by a processor 555 as the input data to the method).

[0220] Such user-input data 520 may include an identified type of the algae - for example, a user may identify to the computer (by typing on a keyboard, by selecting from a drop-down menu or by otherwise clicking on a graphical user interface) the type of algae being cultured in the raceway pond. This identification advantageously allows improved modelling of algal growth because assumptions about algal cells’ response to pond properties (temperature, pH, nutrient balance, CO2 content etc) can be adjusted based on the known algae type. By “identified type”, what is meant is that a user may identify to computer 550 a general classification of the algae (e.g., marine diazotrophic phytoplankton), or may identify to computer 550 a specific species of algae (e.g., Trichodesmium sp.), or both (or, indeed, neither).

[0221] Another kind of input data 520 which may be supplied by a user input is pond geometry data. This can include a user identification of one or more dimensions of raceway pond 100 such as its width, depth and / or length. In some embodiments, one or more of the raceway pond dimensions may be standardised such that the user need only provide e.g., length and width measurements. Providing at least one pond dimension measurement enables the method of the present invention to account for their effect on algal growth (e.g., length and width both affect the surface area for gas exchange, in particular CO2 exchange; pond depth affects the relative proportion of water exposed to sunlight and associated solar heating, and so forth).

[0222] Additionally, or alternatively, the user input may comprise a volume of water in raceway pond 100, and / or a maximum volume that the pond can hold (i.e., a capacity of the pond). Increasing a pond’s capacity decreases the nutrient density for the same quantity of a given nutrient, for instance. Likewise, increasing a pond's capacity decreases the acidity (increases the pH towards neutrality) for the same quantity of a given mineral acid, for instance.

[0223] Other kinds of input data 520 which may be supplied by user input include: a reflective index and / or a roughness index of the raceway pond’s geomembrane, where one is used (these indices may be relevant to the model, because they influence how, and to what extent, solar energy is imparted to the water via the geomembrane and therefore its effect on the water temperature); the speed 535 of the paddlewheel (which can optionally instead be received via one or more sensors, or tracked on the basis of control signals output by the computer); and / or surface area data for raceway pond 100 (which may be entered directly as an alternative to calculating it from dimension measurements).

[0224] As will be appreciated, at least some of the data discussed above may be derived from sources other than user input - for instance, depth and volume data may be sensor-derived as discussed above; width and length data may be derived from aerial imaging data, and so forth.

[0225] The provision of pond geometry or other physical data relevant to (and / or indicative of properties of) raceway pond 100 can inform the computational modelling step 420 in which a pond state is predicted, leading to more accurate predictions of the raceway pond’s present or future state(s) and better consequent automatic adjustments to the pond’s operating conditions. Consequently, the algal growth rate is increased (or at the very least prevented from decreasing, i.e., it is maintained). It is however not essential that any pond geometry data be provided by user input and the benefits of the present invention may still be realised without such input.

[0226] In some embodiments, step 410 comprises both receiving first input data indicative of one or more properties of raceway pond 100 and receiving second input data indicative of one or more properties pertaining to the environment surrounding the raceway pond. Such second input data may include meteorological data (wind speed / direction, pressure, humidity, sunlight intensity and the like), data indicative of a state of the building (e.g., the greenhouse) in which a covered pond is disposed, oceanic data (e.g., tidal / upwelling data, sea current data and the like), and other relevant properties including those described hereinabove.

[0227] Referring to Figure 4A, in a second step 420, a state of the raceway pond is predicted using a model, based on the input data. The predicted state may be a predicted current internal state of the raceway pond, in the sense that the prediction may concern a relevant “hidden” property or metric that is not being directly measured by sensors or other hardware in / above / adjacent to raceway pond 100, or a property / metric that cannot be directly measured easily (or even at all) in-pond by such hardware. For instance, cell cycle stage and / or quiescence properties (e.g., proportion of algal cells in quiescence, proportion in active mitosis, active division rates, and the like) are a useful property to predict computationally because they have a significant influence on the algal growth and CO2 absorption rate, and can therefore be usefully employed in determining how to adjust operating conditions like dilution rate / volume, residence time, and so forth.

[0228] The predicted state may be a forecast of a future state of the raceway pond, e.g., the state at a predetermined future time, or the state after a specific predetermined amount of time has elapsed. The forecasted future state may comprise one or more “hidden” internal properties of the pond / algae as discussed above. Additionally, or alternatively, the forecasted future state may comprise properties of raceway pond 100 and / or its contents that are readily measurable by sensing equipment (such as water temperature) - forecasting of such measurable properties is still beneficial, because it allows adjustments to be made to operating conditions based on a future state (i.e., a future set of values of properties) before it has manifested in raceway pond 100. The automatic adjustments can thus anticipate the upcoming state of the pond ahead of time and take proactive measures to maintain or increase the growth rate of the algae before it drops or plateaus.

[0229] A “state” as used herein may comprise a plurality of values for properties pertaining to raceway pond 100, its contents and / or its surrounding environment. At least one property that may be predicted in accordance with the present invention may be an algal growth rate in the pond. That is, the predicted state may comprise a predicted growth rate of the algae in raceway pond 100.

[0230] Predicting the state of the raceway pond may be based on one or more of a temperature condition, a light condition, a rate of gas exchange across an air-water interface, a chemical condition of water in the raceway pond or in an inlet used for a dilation operation, or a biological condition of the algae. The behaviour of an algae culture in a raceway pond is influenced by availability of CO2, nutrients and sunlight for photosynthesis, as well as acceptable temperature and pH levels of the water, and the states of algal cells as they progress through the usual cycle of growth and division. Therefore, each of the above conditions are beneficial for making predictions because they allow the algal behaviour to be modelled more accurately. A temperature condition may comprise a temperature of water in the raceway pond, a temperature of water in an inlet used for a dilution operation, an ambient wind speed, an ambient wind direction, and / or an ambient air temperature. A light condition may comprise a solar irradiation rate, a total daily solar exposure, a level of photosynthetically active radiation, a spectral property, and / or a depth value. A light condition may comprise a level of photosynthetically active radiation spectrally corrected to the pond, at a representative pond depth. A chemical condition may comprise a nutrient density, a pond / inlet temperature, a level of salinity, a pH value, a carbon dioxide content, a total dissolved inorganic carbon content, a total alkalinity, and / or a bicarbonate content. These values used in making the prediction may be values for the water in raceway pond 100 itself, or for the water supplied by inlet 108.

[0231] At least one such temperature, light, chemical or biological condition and / or rate of gas exchange may be calculated from the input data using a sub-model of the model. By “sub-model”, what is meant is a further model that can be used to obtain some kind of component or constituent part of the predicted state and / or one or more of the inputs from which it is generated (or both), based on the input data (or data derived therefrom). A sub-model may itself comprise further sub-models. For example, an input used to predict the state of raceway pond 100 may be calculated by the model using (at least) the predicted level of CO2 dissolved in the pond, which may itself be calculated by a sub-model based on the temperature of water in the pond, which may itself be calculated by a submodel based on a starting water temperature, a volume or rate of dilution and a temperature of inlet water, and so on. Additional details about models, sub-models, state predictions and step 420 will be discussed further below in relation to Figures 7 and 8.

[0232] Reference is now made to both Figure 4A and Figure 6, the latter depicting at least a further portion of an algae culturing and modelling system 500 suitable for implementing a method of the invention. The same reference numeral is used for the systems of Figure 5 and 6, though it shall be appreciated that both systems are merely exemplary systems depicted for illustrative purposes and combinations of system inputs and outputs are generally contemplated beyond what is specifically drawn in these figures. Sources of input for system 500 are omitted from Figure 6 for clarity and brevity (and likewise outputs for system 500 are omitted from Figure 5). In a third step 430, one or more operating conditions of the raceway pond are automatically adjusted, based on the predicted state, by outputting a control signal to at least one apparatus. The control signal is calculated to increase or maintain the growth rate of the algae.

[0233] In certain embodiments, operational parameters which inform (and / or which are informed by) the algal growth rate and / or other characteristics may be implemented automatically by computer 550 and processor 555. To this end, computer 550 may be communicatively coupled to the raceway pond 100, particularly the component of the raceway pond responsible for changing the operational parameter, via a communications network (e.g., the Internet). The communications network may be the same communications network used for receiving input data as described above in relation to Figure 5, though need not necessarily be.

[0234] For instance, to change paddlewheel speed, the computer 550 may be communicatively coupled to an electric motor that causes the paddlewheel 110 to rotate and configured to send an instruction to the electric motor to change the paddlewheel speed. To change the dilution rate, computer 550 may be communicatively coupled to electrically actuated sluice gates 560, 565 at the inlet pipe 108 and the drainpipe 112 respectively. To change the nutrient and / or acid supplementation rate, computer 550 may be communicatively coupled to an electrically actuated gate at an exit of the nutrient and / or acid storage container 580 (e.g., a lower discharge outlet of a hopper). Nutrients supplied in this way may include nitrogen, phosphorous, iron or silicon / silicate. Acids supplied in this way may include mineral acids. Similar electrically controllable components are available for other parts of the raceway pond that change the operational parameters.

[0235] As a further example, to change a water temperature, a dilution rate, a dilution volume, an acidity, an alkalinity, and / or a concentration of a nutrient (or other chemical properties including various concentrations) the computer 550 may be communicatively coupled to secondary dilution means 570. By “secondary”, what is meant is that the dilution means 570 may be means of adding water to the raceway pond other than via the inlet pipe 108. Secondary dilution means 570 may comprise one or more electrically actuated gates, hoppers, pipes, taps, valves, or the like. The water supplied by such means may be any of the kinds of water described in the present application, such as seawater.

[0236] As a further example, to change a water temperature, an ambient air temperature, an ambient relative humidity, and / or an algal growth rate, various heating / cooling means and / or combinations thereof may be used. System 500 may comprise ambient heating means 575. System 500 may comprise ambient cooling means 575. System 500 may comprise in-pond heating means 585. System 500 may comprise in-pond cooling means 585. System 500 may comprise a combined ambient heating / cooling means, or a combined in-pond heating / cooling means. Any suitable combination of the above elements may be used. By “ambient”, what is meant is that the heating / cooling of air in the environment surrounding the raceway pond, rather than water in the raceway pond. This environment may be contained in a structure such as a greenhouse. Ambient heating / cooling means may be external to the pond (i.e., located out-of-pond) but need not necessarily be (since heating or cooling of the water in the pond itself may also have a measurable heating / cooling effect on air in the surround environment).

[0237] Ambient heating means (or combined heating / cooling means) 575 may comprise any suitable heating element, such as electrical heaters, radiators, panels, space heaters, means for reducing shade / increasing solar exposure, or heat pumps. Additionally or alternatively, ambient heating( / cooling) means may comprise means for actuating an air ventilation system, particularly in the case of raceway ponds or portions thereof that are partially or totally enclosed by a greenhouse or other structure (“enclosed” or “covered” ponds) - ambient heating may for instance be achieved by (at least partially) closing a window, panel or vent where the external air temperature is lower than the internal air temperature, or by (at least partially) opening a window, panel or vent where the external air temperature is greater than the internal air temperature.

[0238] Ambient cooling means (or combined heating / cooling means) 575 may comprise any suitable cooling element, such as fans, evaporative (“swamp”) coolers, devices for conditioning / refrigerating air, means for increasing shade / reducing solar exposure, or heat pumps. Additionally or alternatively, ambient (heating / )cooling means may comprise means for actuating an air ventilation system, particularly in the case of raceway ponds or portions thereof that are partially or totally enclosed by a greenhouse or other structure (“enclosed” or “covered” ponds) - ambient cooling may for instance be achieved by (at least partially) opening a window, panel or vent where the external air temperature is lower than the internal air temperature, or by (at least partially) closing a window, panel or vent where the external air temperature is greater than the internal air temperature.

[0239] In-pond heating means (or combined heating / cooling means) 585 may comprise any suitable heating element, such as submersible heaters, floating heaters, electrical heating elements, submersible heaters, heat exchangers or heat pumps. In-pond cooling means (or combined heating / cooling means) 585 may comprise any suitable cooling element, such as aeration systems, evaporative coolers, chillers, refrigeration systems, heat exchangers or heat pumps.

[0240] Where the present application refers to controlling the output of a heating or cooling element, what is meant is that either there may be a heating element whose output is controlled, or there may be a cooling element whose output is controlled, or there may be a combined heating / cooling element (capable of providing either effect via the same single device) whose output is controlled.

[0241] Advantageously, it may be possible to automatically adjust one or more operating conditions of a raceway pond in accordance with the present invention even where the raceway pond in question is an open (i.e. , outdoor, uncovered) pond. Paddlewheel speeds, inlet / outlet sluice gates, nutrient / acid supply, in-pond heating / cooling and secondary dilution can all be very easily controlled by suitable electrical and / or mechanical means which do not require the presence of a greenhouse or other structure, facilitating rapid and low-cost deployment of algal culturing ponds with automated control.

[0242] In some embodiments, for example, the temperature of water in the pond may be controlled by adjusting the timing, the volume, and / or the rate of one or more dilution operations. Operating conditions including water temperature can thereby be indirectly controlled through how much additional water (e.g., seawater) is introduced into the raceway pond and when. In many embodiments, the raceway pond’s internal water temperature (optimised for algae culturing) is higher than that of the surrounding seawater, such that increasing the rate or amount of dilution has the effect of lowering the average temperature of the water in the raceway pond.

[0243] Referring now to Figure 4B, there is depicted a computer-implemented method 440 suitable for use when culturing algae in a raceway pond using a computational algal growth forecasting model according to the invention. In a first step 450, first data indicative of one or more properties of a raceway pond is received. In a second step 460, a state of the raceway pond is predicted using a model, based on the received data. Steps 450 and 460 may be substantially the same as steps 410 and 420 respectively and involve the same or similar sub-steps, features, implementations, means, devices etc as described in relation to steps 410 and 420 hereinabove and hereinbelow. Accordingly, for brevity, further discussion of steps 450 and 460 is omitted except to note that all discussion of step 410 within the present disclosure is equally applicable to step 450 unless otherwise specified, and likewise all discussion of step 420 to step 460 unless otherwise specified.

[0244] In a further step 470, further data indicative of one or more properties of the raceway pond is received from a raceway pond sensing / monitoring apparatus. The sensing / monitoring apparatus may comprise any of the raceway pond sensing / monitoring apparatus mentioned hereinabove, such as apparatus disposed within, above or adjacent to the raceway pond; apparatus configured to provide data regarding a water temperature, a paddlewheel speed, one or more nutrient densities, a pH level, a turbidity level, a cell density, a biomass density an ambient air temperature, a local depth, an inlet water property, a sensed light level, and / or aerial imaging data; remote monitoring apparatus as described hereinabove; and so forth.

[0245] In a further step 480, based on the received further data, the predicted state of the raceway pond is updated using the model (as will be explained in more detail with reference to Figure 9 hereinbelow).

[0246] In one embodiment, the predicted state may have been a predicted “current” state of the raceway pond at a particular moment, and the further data may be new data received in real time (“live” sensing / monitoring data). In such an embodiment, the real time or live data may be used to update the predicted state to a new “current” state (i.e., the state at a subsequent moment, when the previous state / moment are no longer current). Accordingly, the newly received data can be used to keep an ongoing tracked record of the current state of the pond up-to-date and accurate by using computational algal growth modelling in combination with new “fresh” input from relevant raceway pond sensing / monitoring apparatus.

[0247] In another embodiment, the predicted state may have been a prediction made for a specific time in the future, and the updated predicted state may be a predication made for the same specific time. Accordingly, a prediction can be made about what the state of the pond will be at a certain point in the future, and as that point approaches, the prediction can be repeatedly refined and adjusted to account for received further data, in order to provide continual improvements in the prediction’s accuracy.

[0248] In a further step 490, an output is provided to a display based on the updated predicted state. For example, a computer may be configured to output, via the display, information regarding the predicted current state of the raceway pond (e.g., including algal growth information). Additionally or alternatively, a computer may be configured to output, via the display, information regarding one or more predicted future states of the raceway pond (e.g., including algal growth information).

[0249] Outputting the information may comprise outputting a graphical representation e.g., one or more graphs, charts or plots, to display predicted states over time or across different variables, providing an intuitive way for a user to read off the prediction(s). One or more readouts of precise numerical values may be provided. The computer may show, via the display, an animated simulation dynamically illustrating predicted pond states by showing how they may evolve over time or in response to different inputs / outputs. Alerts or notifications may be displayed to inform a user a predicted current or future state of the pond may require attention or action (e.g., if dilution is required in order to maintain or increase the algal growth rate, or alternatively if the algae in one pool is ready to be moved out of the pond, either for harvesting or into another pond). These alerts / notifications me be displayed in any suitable format, e.g., as pop-up messages.

[0250] Customised reports may be generated and displayed / stored to provide detailed summaries of predicted pond states and / or analysis results. The computer may calculate and output, via the display, analytics relating to predicted present / futures state e.g. productivity in the pond, productivity gain / loss, electrical energy required per unit productivity gain, and so forth. Such analysis may make method 440 highly useful both as a research tool and as an industrial control / automation tool (with the computer optionally also sending one or more signals or adjusting parameters as described herein on the basis of the analytics, e.g., by starting to dilute the pond when an optimal cell density level is determined to have been reached.

[0251] In some embodiments, the further data received at step 470 may be used to train the model. That is, the model may be adjusted or updated based on said further data, in order to improve the overall accuracy of the model. Training / updating / refining the model may be based on using the received further data as feedback for determining the accuracy (or inaccuracy) of a previous state prediction made by the model. For instance, if a prediction is made in step 460 concerning some aspect of the state of the raceway pond (e.g., water temperature) at a time t, and sensor data subsequently gathered at time t (e.g., by a digital thermometer located in the pond) indicating a close correspondence between the predicted value and the measured / sensed value, the model parameters may be adjusted only a small amount, or not at all. On the other hand, if a prediction is made in step 460 concerning some aspect of the state of the raceway pond at a time t, and sensor data subsequently gathered at time t (e.g., by a digital thermometer located in the pond) indicating a lack of correspondence (e.g., a divergence or difference) between the predicted value and the measured / sensed value, then the model parameters may be adjusted by a larger amount in order to improve the accuracy of future predictions based on the new sensor feedback.

[0252] Steps 470, 480 and 490 may be iterated indefinitely. Alternatively, these steps may be iterated for a predetermined number or iterations. Alternatively, these steps may be iterated until a termination condition is met (e.g., until a user input is provided causing termination). Method 440 as depicted in 4B will be explained in more detail with reference to Figure 9 hereinbelow.

[0253] Referring now to Figure 7, there is depicted an exemplary algal forecasting model 700 (simplified for brevity and clarity, and intended to provide illustration rather than limitation of the invention). Model 700 receives, handles and calculates various quantities relating to the state of the raceway pond and its surrounding environment. In the illustrated example, at least one quantity calculated by the model pertains directly to the algal growth rate and / or yield of the raceway pond 740. Such quantities may be useful for providing experimental data to be studied and / or to further train / refine the model; they may also be useful for informing users’ decision-making with regard to the raceway pond (e.g., when to open an inlet / outlet, when (and how) to adjust the chemical properties of the pond such as nutrient densities or pH, when (and how) to adjust a paddlewheel speed or when (and how) to adjust a pond temperature; they may also be useful for sending automated control signals to effect changes in these operating conditions in order to maintain or increase the algal growth rate.

[0254] In the illustrated example, a date / time value 710 and a latitude / longitude value 750 may be among the inputs to model 700 (e.g., as remotely supplied data 515, user-input data 520, computer data 530, or data from any other suitable device(s) e.g., GPS tracking and / or timing / clock devices). The model may determine, based (at least in part) on the global position of the raceway pond and the current date and time, properties concerning the current (and / or forecasted future) solar irradiation of the raceway pond. Exemplary properties may include spectral properties, a solar declination angle, a maximum daily photosynthetically active radiation (PAR), seasonal PAR skewness / kurtosis factors, a surface incident PAR, a boundary layer PAR, and so forth.

[0255] In the illustrated example, model 700 may also determine, based (at least in part) on solar irradiation properties 720 and on a heater output 730, one or more temperature properties 760, e.g., temperature properties of water in the raceway pond. Such properties may include e.g. a mean temperature of water in the pond, or a temperature of water at the surface of the pond. In practice, of course, additional properties besides solar irradiation 720 and heater output 730 may also inform the estimated / predicted temperature 760 (such as a sensed ambient air temperature).

[0256] In the illustrated example, model 700 may also determine, based (at least in part) on temperature 760 and a known (e.g., set, preconfigured, sensed or measured) paddlewheel speed parameter 770, a CO2 gas exchange rate at the surface of the raceway pond. In practice, other properties may inform this rate, such as the pond depth, the pond’s CO2 content, and the ambient CO2 content in the surrounding environment.

[0257] In the illustrated example, model 700 may determine, based (at least in part) on irradiation 720, temperature 760 and CO2 gas exchange rate 780, a measure of the algal growth rate and / or yield of the raceway pond 740. Knowledge of such values enables an improved operational efficiency of the raceway pond in terms of carbon capture, because operational decisions can be made and parameters adjusted on the basis of the actual growth rate or yield from the pond, in order to maintain the highest achievable rate of carbon sequestration. By contrast, human operatives who are not provided with accurate estimates of the algal growth or yield in the pond will often resort to making decisions or adjusting parameters based on the pond’s appearance alone, leading to sub-optimal rates of growth resulting from attempts to make the raceway pond appear e.g., as green or “lush” as possible. In practice, naturally, other properties may also inform estimated / predicted growth rate / yield 740, such as chemical properties (nutrient densities and / or pH) or biological / algal properties (e.g., species or type of algae).

[0258] In some embodiments (including the illustrated example), at least one output determined by model 700 (e.g., growth rate 740) may be used to automatically adjust one or more operating conditions of the raceway pond, based on the prediction / estimate, by outputting a control signal to at least one apparatus calculated to increase / maintain the algal growth rate. For instance, in the illustrated example in Figure 7, the estimated / predicted property 740 may be used either to output a signal to the heater to control the water temperature in the pond, or to output a signal to (a motor coupled to) the paddlewheel to control its speed, or both.

[0259] The control signal may be output in response to determining, based on a prediction of the current pond state, that an adjustment to one or more operating conditions is required immediately in order to maintain / increase growth. The control signal may be output in response to determining, based on a prediction of a future pond state, that an adjustment to one or more operating conditions will be required at a specific future time in order to maintain / increase growth. In one particularly advantageous embodiment, the control signal may be output in response to determining, based on a prediction of a future pond state, that an adjustment to one or more operating conditions is required immediately in order to maintain / increase growth - in this way, the model can be used to predict future events, changes, states and requirements and make decisions / adjustments in a proactive or anticipatory manner (rather than simply reacting to changing conditions after the change has already happened, by which time it may be too late to achieve optimal growth).

[0260] Various means may be used to determine which operating conditions to adjust, which control signals to output and / or which apparatus to send them to (as well as how to adjust the operating conditions, i.e. , the type / direction / magnitude / parameters of the required adjustment). In one example, a process may comprise determining whether a specific aspect of the pond state acts (or which one(s) of a plurality of aspects of the pond state act) as a (present or future) limiting factor on algal growth. For example, in a raceway pond which is receiving an abundance of sunlight, heat and CO2 but which has an inadequate supply of a particular nutrient, the model can be used to determine that the particular nutrient should be added to the pond to increase or maintain a growth rate. As another example, if the prediction reveals a state in which nutrient levels, CO2 and sunlight are all sufficient to sustain a high growth rate but in which the water temperature is suboptimal (i.e., it is a “bottleneck” or limiting factor, such that raising the temperature would raise the growth rate) then a control signal can be sent to appropriate apparatus accordingly. As yet another example, if a current or future state is predicted in which temperature, nutrients, sunlight and CO2 are all adequate but pH is too high and thus limiting algal growth, a control signal can accordingly be sent to add acid to the raceway pond (e.g., a mineral acid) via suitable means as described herein.

[0261] One or more components of model 700 can be regarded as a “sub-model” insofar as these components use measured or predicted values of one or more inputs to estimate / predict values of one or more outputs within the wider context of the overall model. For instance, in the illustrated example, date / time properties 710 and latitude / longitude properties 750 may be provided as inputs to model 700, heater output 730 and paddlewheel speed 770 may be known controlled parameters, but the remaining properties (solar irradiation 720, temperature 760, CO2 gas exchange rate 780 and algal growth rate / yield 740) may be determined using one or more sub-models. To give an example, a “solar irradiation” sub-model for determining solar irradiation properties 720 may be provided as part of model 700, and may provide a means of mapping date / time and latitude / longitude properties (whether known / sensed, or simply predicted / estimated) onto predicted solar irradiation properties for use in the output and / or in subsequent sub-models. Any given sub-model may comprise any suitable representation of the relationship between inputs and outputs, such as a mathematical or computational function, a lookup table, a statistical model (e.g., regression model) or a machine learning model (e.g., artificial neural network, support vector machine, or the like).

[0262] Referring now to Figure 8, there is depicted a process for predicting a state of the raceway pond using a model, based on input data (step 420 in Figure 4A, and steps 460 and 480 in Figure 4B), which can provide a way to determine a suitable adjustment to one or more operating conditions of the raceway pond in order to increase or maintain the algal growth rate.

[0263] In general, a prediction proceeds from a known initial state 830 (here denoted So), representative of the state of the raceway pond at a first time 850, towards a final state representative of the state of the raceway pond at a subsequent time 856. The prediction may optionally also calculate or comprise one or more intermediate states representative of the state of the raceway pond at respective intermediate times 852, 854. In some embodiments (e.g., when the prediction is solely to determine the current state of the raceway pond and properties thereof), initial state 830 may comprise known properties of the raceway pond derived from the results of measurements made / values sensed at a known prior time 850, and subsequent time 856 may be the current time. In other embodiments (which will now be described), first time 850 and initial state 830 are the current time and current pond state, with the model being used to predict what the state of the raceway pond will be at one or more times 852, 854, 856 in the future. In general, one or more predictions can be made using a model. Each prediction may optionally be made based on a different candidate adjustment 800 to one or more operating conditions. For each prediction, the prediction process comprises using a state at a first time (either known / measured or predicted) to determine, using the model, a predicted state at a subsequent time. This may then be repeated any number of times until a state is obtained that is representative of the state of the raceway pond at the specified future time 856. At each step, the first state can e.g., be an input to the model, and the second state can be e.g., an output from the model. The prediction / forecasting / modelling can be done for the purpose of determine one or more specific properties 810 of the pond state at time 856, such as a growth rate or yield of algae in the raceway pond.

[0264] In the specific example shown in Figure 8, three predictions are made using the model, each based on a candidate adjustment to the paddlewheel speed (respectively, keeping it at its current rotational speed of 10 rpm, dropping it to a speed of 5 rpm, and raising it to a rotational speed of 15 rpm). For the first prediction, the model uses initial state 830 (So) to determine a state Si,o of the pond at first intermediate time 852; next, the model uses state Si,o to determine a state 82,0 of the pond at second intermediate time 854; finally, the model uses state 82,0 to determine a state 83,0 of the pond at specified future time 856. For the second prediction, the model uses initial state 830 (So) to determine a state 81,1 of the pond at first intermediate time 852; next, the model uses state 81,1 to determine a state 82,1 of the pond at second intermediate time 854; finally, the model uses state 82,1 to determine a state 83,1 of the pond at specified future time 856. For the third prediction, the model uses initial state 830 (So) to determine a state 81,2 of the pond at first intermediate time 852; next, the model uses state 81,2 to determine a state 82,2 of the pond at second intermediate time 854; finally, the model uses state 82,2 to determine a state 83,2 of the pond at specified future time 856.

[0265] In the illustrated example, each final state 83,0, 83,1, 83,2 provides a different estimated value for property of interest 810 (in this case, growth). This forecasting process can be used generally to determine a suitable selection of an adjustment from among a plurality of candidates, by determining which candidate adjustment 800 is predicted to lead to the optimal value for property 810, and then effecting that adjustment. In the illustrated example, raising the paddlewheel speed to 15 rpm is predicted to result in a growth of 35%, which is greater than any other simulated / modelled / forecast scenario, and so a control signal can be output to the appropriate apparatus (e.g., a motor) to automatically adjust the paddlewheel speed to this value and thereby maximise growth.

[0266] Candidate adjustments 800 can be proposed, generated, or selected in any suitable manner as will be readily apparent to those skilled in the art from the present disclosure, e.g., as a plurality of fixed values for a parameter, as a plurality of fixed adjustments or offsets to the current value of a parameter, by random or stochastic generation of absolute values or relative offsets, and so forth.

[0267] Although the state prediction(s) can be made effectively using only knowledge of the initial state (and optionally any relevant proposed adjustment(s) to the operating condition(s)), in some embodiments the prediction process(es) can further take into account one or more elements of forecast data. The model may use at least one element of forecast data in combination with the state at each time to derive the prediction of the state at the subsequent time. For example, with reference to Figure 8, at initial time 850 (when the raceway pond is known to be in state 830), a set of forecast data Do,o, Do,i , Do, 2 may be available indicating expected conditions at times 850, 852 and 854 respectively. This forecast data can be used with the model to determine the state predictions and ultimately to provide an accurate estimate of the final state to be arrived at for each “branch” (i.e., each prediction).

[0268] Forecast data 840, 842, 844 may include, but need not be limited to, weather forecast data (solar forecast data, humidity forecast data, precipitation forecast data, temperature forecast data, wind forecast data, and so forth), oceanic forecast data (tidal forecast data, salinity forecast data, inlet temperature forecast data, pH forecast data, pollutant forecast data, and so forth), or any relevant forecast data. Forecast data 840, 842, 844 may be received in the form of user-input data 520, in the form of remotely supplied data 515, or any other suitable form.

[0269] As explained above, an adjustment to one or more operating conditions may be determined based on which of a plurality of candidates is predicted to maximise (or minimise) a particular property of the raceway pond. Additionally or alternatively, an adjustment to one or more operating conditions may be determined based on a calculated and / or predicted limiting factor (i.e., a “bottleneck”) to algal growth (or another property of interest). For example, a future state may be predicted for future time 856, wherein the future state comprises a predicted limitation, such as a predicted growth limiting factor. The predicted growth limiting factor may comprise e.g., a predicted light limitation, a predicted temperature limitation, a predicted nutrient limitation, a predicted pH limitation, and / or a predicted CO2 limitation. That is, the model may determine that in the predicted state, the limiting factor or bottleneck preventing a faster rate of algal growth is (merely for example) e.g., a lack of photosynthetically active light, or a lack of spectrally adjusted photosynthetically active light. Advantageously, this can then inform actions taken to maintain or increase growth rate, by adjusting one or more operating conditions relevant to the limiting factor to algal growth. A predicted state may comprise more than one such limitation.

[0270] In the embodiments described so far, a set of forecast data is obtained and used at one specific time in order to generate a “static” prediction or set of predictions (in the sense that a state of the raceway pond is predicted, and this prediction is then not modified at all). However, other embodiments are contemplated in which the system’s predictive power is enhanced by supplementing the state predictions with new sensor data and / or new forecast data to obtain dynamically-updated predictions. By using “fresh” sensor / forecast data as it becomes available, predictions can be modified (or generated afresh) in order to account for changing conditions or trends within the raceway pond and / or within the wider environment in which the raceway pond is contained.

[0271] With reference now to Figure 9, an example of such dynamic updating is depicted. At a first time 950, given a known (or estimated) initial state So, the model is used to predict further states S1 , S2, S3 (the latter being a final state 910 in which the pond is predicted to be at a specific time in the future). However, the prediction may be iteratively / successively refined or updated in response to new information being received at subsequent times 952, 954.

[0272] In some embodiments, new sensor data 960 may be received (e.g., by raceway pond sensing / monitoring apparatus) at second time 952, and / or further new sensor data 962 may similarly be received at third time 954. The new sensor data 960, 962 may cause a re-evaluation of the state(s) predicted by the model to be arrived in by the raceway pond - whereas at first time 950 the pond was predicted to advance from state So through states Si , S2 to state S3, at second time 952 the predicted future states may be updated to a new set of predicted states Si’, S2’, S3’ to reflect the newly acquired information. Likewise, a third time 954 the predicted future states may be updated again to S2”, S3” to reflect the newly acquired information. To give a more concrete real-word example, newly received sensor data may indicate that the pH of seawater inside the raceway pond is in fact substantially higher than had previously been thought or expected, meaning that the algal growth rate will be limited by this particular condition unless measures are taken to address the predicted future imbalance (e.g., addition of mineral acid). This information may be displayed to a user to prompt them to take such action, or may be used to automatically adjust the relevant one or more operating conditions of the raceway pond and thereby increase or maintain the growth rate. The new sensor data may be any appropriate kind of data relating to the raceway pond or its environment as described elsewhere herein.

[0273] In some embodiments, one or more new items or sets of forecast data 943, 944 may be received (e.g., as remotely supplied data 515) at second time 952, and / or further new forecast data 945 may similarly be received at third time 954. The new forecast data 943, 944, 945 may cause a re- evaluation of the state(s) predicted by the model to be arrived in by the raceway pond - whereas at first time 950 the pond was predicted to advance from state SO through states Si , S2 to state S3, at second time 952 the predicted future states may be updated to a new set of predicted states Si’, S2’, S3’ to reflect the newly acquired information. Likewise, a third time 954 the predicted future states may be updated again to S2”, S3” to reflect the newly acquired information. To give a more concrete real-word example, whereas forecast data Do,o, Do,i, Do, 2 received at first time 950 may have indicated a low predicted average temperature and low level of predicted photosynthetically available radiation (in the form of sunlight), the newly received forecast data Du , Di,2 received at second time 952 (and / or the newly received forecast data D2,2 received at third time 954) may indicate a substantially higher expected average temperature and higher level of predicted photosynthetically available radiation, meaning that the new predicted final state 910 for the raceway pond is associated with a significantly higher algal growth rate than it was at initial time 950 (before the new forecast data was received). This information may be displayed to a user to prompt them to take such action, or may be used to automatically adjust the relevant one or more operating conditions of the raceway pond and thereby increase or maintain the growth rate. The new forecast data may be any appropriate kind of data relating to the raceway pond or its environment as described elsewhere herein.

[0274] In some embodiments, predicted states can be dynamically updated based both on i) newly obtained forecast data, and ii) newly obtained sensor data. That said, in other embodiments the predicted states can be dynamically updated based solely on one or the other.

[0275] General

[0276] The term “comprising” encompasses “including” as well as “consisting” e.g., a composition “comprising” X may consist exclusively of X or may include something additional e.g., X + Y.

[0277] The term “about” in relation to a numerical value x is optional and means, for example, x+10%.

[0278] The various steps of the methods may be carried out at the same time or at different times, in the same geographical location or in different geographical locations, e.g., countries, and by the same or different people or entities. Embodiments

[0279] The following numbered embodiments form part of the description:

[0280] 1. A computer-implemented method for culturing algae in a raceway pond, the method comprising: receiving input data indicative of one or more properties of the raceway pond; predicting a state of the raceway pond using a model, based on the input data; and automatically adjusting one or more operating conditions of the raceway pond, based on the predicted state, by outputting a control signal to at least one apparatus; wherein the control signal is calculated to increase or maintain the growth rate of the algae.

[0281] 2. The method of embodiment 1 , wherein the one or more operating conditions comprise one or more of: a temperature of water in the raceway pond, an ambient air temperature, an ambient relative humidity, a dissolved inorganic carbon speciation of the raceway pond, a dilution rate, a dilution volume, a supplementation amount of a nutrient, a supplementation rate of a nutrient, a speed of a paddlewheel disposed within the raceway pond, an inoculum density, a standing stock, cell density, or biomass density of a specific organism, an irradiation rate, a degree of solar exposure, a supplementation amount of an acid, a supplementation rate of an acid, a frequency or a time for moving the algae out of the raceway pond, a frequency or a due date / time for performing maintenance, an alkalinity of the raceway pond, or a residence time in the raceway pond after a dilution operation.

[0282] 3. The method of embodiment 1 or embodiment 2, wherein automatically adjusting the one or more operating conditions comprises one or more of: controlling the speed of a motor coupled to a paddlewheel, controlling a sluice gate at an inlet pipe of the raceway pond, controlling a sluice gate at an outlet pipe of the raceway pond, controlling a gate at an exit of a nutrient storage container, controlling a gate at an exit of an acid storage container, controlling the start time of a dilution operation, controlling the rate of a dilution operation, controlling the output of a heating or cooling element, controlling the time of a dilution operation to indirectly regulate the daily mean pond temperature, controlling means for adding hydroxide to the raceway pond, or actuating an air ventilation system.

[0283] 4. The method of embodiment 3, wherein automatically adjusting the one or more operating conditions comprises controlling a sluice gate at an inlet pipe of the raceway pond and wherein said inlet pipe is configured to provide water to the raceway pond, optionally wherein the water is seawater.

[0284] 5. The method of embodiment 3 or embodiment 4, wherein automatically adjusting the one or more operating conditions comprises controlling a sluice gate at an outlet pipe of the raceway pond and wherein said outlet pipe leads into another raceway pond or into one or more harvesting screens.

[0285] 6. The method of any one of embodiments 2 to 5, wherein the one or more operating conditions comprise a supplementation amount or supplementation rate of a nutrient, and the nutrient is one or more of nitrogen, phosphorous, iron or silicon.

[0286] 7. The method of any one of embodiments 2 to 6, wherein the one or more operating conditions comprise a supplementation amount or supplementation rate of an acid, and the acid is a mineral acid.

[0287] 8. The method of any one of embodiments 1 to 7, wherein outputting the control signal to the at least one apparatus comprises outputting an electrical signal to an electrically actuated apparatus, and / or wherein the apparatus belongs to an industrial control or SCADA system.

[0288] 9. The method of any one of embodiments 1 to 8, wherein the predicted state is a forecast of a future state of the raceway pond.

[0289] 10. The method of any one of embodiments 1 to 9, wherein the predicted state of the raceway pond comprises a predicted growth rate of the algae in the raceway pond. 11. The method of any one of embodiments 1 to 10, wherein predicting the state of the raceway pond is based on one or more of: a temperature condition, a light condition, a rate of gas exchange across an air-water interface, a chemical condition of water in the raceway pond or in an inlet used for a dilation operation, or a biological condition of the algae.

[0290] 12. The method of embodiment 11 , wherein predicting the state of the raceway pond is based on a temperature condition, and wherein the temperature condition comprises a temperature of water in the raceway pond, a temperature of water in an inlet used for a dilution operation, an ambient wind speed, an ambient wind direction, and / or an ambient air temperature.

[0291] 13. The method of embodiment 11 or embodiment 12, wherein predicting the state of the raceway pond is based on a light condition, and wherein the light condition comprises a solar irradiation rate, a total daily solar exposure, a level of photosynthetically active radiation, a spectral property, and / or a depth value, optionally wherein the light condition comprises a level of photosynthetically active radiation spectrally corrected to the pond, at a representative pond depth.

[0292] 14. The method of any one of embodiments 11 to 13, wherein predicting the state of the raceway pond is based on a chemical condition of water in the raceway pond or in an inlet used for a dilation operation, and wherein the chemical condition comprises a nutrient density, a pond / inlet temperature, a level of salinity, a pH value, a carbon dioxide content, a total dissolved inorganic carbon content, a total alkalinity, and / or a bicarbonate content.

[0293] 15. The method of any one of embodiments 11 to 14, wherein at least one temperature, light, chemical or biological condition and / or rate of gas exchange is calculated from the input data using a sub-model of the model.

[0294] 16. The method of any one of embodiments 1 to 15, wherein the input data comprises data supplied by a raceway pond sensing / monitoring apparatus, optionally wherein either: the raceway pond sensing / monitoring apparatus is disposed within, above or adjacent to the raceway pond; or the input data comprises a water temperature, a paddlewheel speed, one or more nutrient densities, a pH level, a turbidity level, a cell density, a biomass density, an ambient air temperature, a local depth, an inlet water property, a sensed light level, and / or aerial imaging data.

[0295] 17. The method of any one of embodiments 1 to 16, wherein the input data comprises remotely supplied data, optionally wherein the input data comprises wind speed and / or direction data, weather forecast data, oceanic forecast data, sunrise or sunset timing data, solar zenith angle data, cloud cover data, UV light exposure data, light attenuation data such as aerosol data or sea haze data, and / or sunlight intensity data.

[0296] 18. The method of any one of embodiments 1 to 17, wherein the input data comprises data supplied by a user input, optionally wherein the input data comprises an identified type of the algae and / or pond geometry data such as width, depth, length, volume, geomembrane reflective index, geomembrane roughness index, paddlewheel speed or surface area data for the raceway pond.

[0297] 19. The method of any one of embodiments 1 to 18, further comprising receiving second input data indicative of one or more properties pertaining to the environment surrounding the raceway pond.

[0298] 20. A computer-implemented method, comprising: receiving first data indicative of one or more properties of a raceway pond; predicting a state of the raceway pond using a model, based on the received first data; and iteratively: receiving further data indicative of one or more properties of the raceway pond from a raceway pond sensing / monitoring apparatus; updating the predicted state of the raceway pond using the model, based on the received further data; and providing an output to a display based on the updated predicted state.

[0299] 21 . A device comprising a processor and a memory, the memory containing instructions which, when executed by the processor, cause the processor to perform the method of any one of embodiments 1 to 20.

[0300] 22. A non-transitory computer-readable medium comprising computer-readable instructions for causing a computer to perform the method of any one of embodiments 1 to 20.

[0301] 23. A system comprising: a raceway pond for culturing algae, the raceway pond comprising a paddlewheel; at least one raceway pond sensing / monitoring apparatus; and a device according to embodiment 21.

[0302] REFERENCES

[0303] AR6: https: / / www.ipcc.ch / report / sixth-assessment-report-cycle

[0304] Benemann, J.R. & Oswald, W.J. Systems and Economic Analysis of Microalgae Ponds for Conversion of CO2 to Biomass. Final Report to the Pittsburgh Energy Technology Center (1996)

[0305] Furukawa, T., Watanaba, M. and Shihira-lshikawa, I. Green and blue-light-mediated chloroplast migration in the centric diatom Pleurosira laevis Protoplasma, 203;214-220 (1998)

[0306] Hilton J. A., Ecology and Evolution of Diatom Associated Cyanobacteria through genetic analysis, June 2014, PhD Dissertation, UC Santa Cruz

[0307] Kraml, M. and Herrmann, H. Red-blue interaction in Mesotaenium chloroplast movement — blue seems to stabilize the transient memory of the phytochrome signal. Photochem. Photobiol., 53:255- 259 (1991)

[0308] Kusmayadi, A., Suyono E. A., Nagarajan, D., Chang, J.-S. and Yen, H.W., Application of computational fluid dynamics (CFD) on the raceway design for the cultivation of microalgae: a review, 2020, Journal of Industrial Microbiology and Biotechnology, 47(4-5):373-382.

[0309] Mutalipassi M., Riccio G., Mazzella V., Galasso C., Somma E., Chiarore A., de Pascale D. and Zupo V., Symbioses of Cyanobacteria in Marine Environments: Ecological Insights and Biotechnological Perspectives, 2021 , Mar. Drugs 19:227-256

[0310] Tuo S-H., Lee Chen Y-L., Chen H-Y., Chen T-Y., Free-living heterocystous cyanobacteria in the tropical marginal seas of the western North Pacific, 2017, J. Plankton Res. 39(3):404-422

[0311] Weissman, J.C. and Goebel, R.P. Design and Analysis of Pond Systems for the Purpose of Producing Fuels. Solar Energy Research Institute, Golden Colorado (1987)

Claims

CLAIMS1. A computer-implemented method for culturing algae in a raceway pond, the method comprising: receiving input data indicative of one or more properties of the raceway pond; predicting a state of the raceway pond using a model, based on the input data; and automatically adjusting one or more operating conditions of the raceway pond, based on the predicted state, by outputting a control signal to at least one apparatus; wherein the control signal is calculated to increase or maintain the growth rate of the algae.

2. The method of claim 1 , wherein the one or more operating conditions comprise one or more of: a temperature of water in the raceway pond, an ambient air temperature, an ambient relative humidity, a dissolved inorganic carbon speciation of the raceway pond, a dilution rate, a dilution volume, a supplementation amount of a nutrient, a supplementation rate of a nutrient, a speed of a paddlewheel disposed within the raceway pond, an inoculum density, a standing stock, cell density, or biomass density of a specific organism, an irradiation rate, a degree of solar exposure, a supplementation amount of an acid, a supplementation rate of an acid, a frequency or a time for moving the algae out of the raceway pond, a frequency or a due date / time for performing maintenance, an alkalinity of the raceway pond, or a residence time in the raceway pond after a dilution operation.

3. The method of claim 1 or claim 2, wherein automatically adjusting the one or more operating conditions comprises one or more of: controlling the speed of a motor coupled to a paddlewheel, controlling a sluice gate at an inlet pipe of the raceway pond, controlling a sluice gate at an outlet pipe of the raceway pond, controlling a gate at an exit of a nutrient storage container, controlling a gate at an exit of an acid storage container,controlling the start time of a dilution operation, controlling the rate of a dilution operation, controlling the output of a heating or cooling element, controlling the time of a dilution operation to indirectly regulate the daily mean pond temperature, controlling means for adding hydroxide to the raceway pond, or actuating an air ventilation system.

4. The method of claim 3, wherein automatically adjusting the one or more operating conditions comprises controlling a sluice gate at an inlet pipe of the raceway pond and wherein said inlet pipe is configured to provide water to the raceway pond, optionally wherein the water is seawater.

5. The method of claim 3 or claim 4, wherein automatically adjusting the one or more operating conditions comprises controlling a sluice gate at an outlet pipe of the raceway pond and wherein said outlet pipe leads into another raceway pond or into one or more harvesting screens.

6. The method of any one of claims 2 to 5, wherein the one or more operating conditions comprise a supplementation amount or supplementation rate of a nutrient, and the nutrient is one or more of nitrogen, phosphorous, iron or silicon.

7. The method of any one of claims 2 to 6, wherein the one or more operating conditions comprise a supplementation amount or supplementation rate of an acid, and the acid is a mineral acid.

8. The method of any one of claims 1 to 7, wherein outputting the control signal to the at least one apparatus comprises outputting an electrical signal to an electrically actuated apparatus, and / or wherein the apparatus belongs to an industrial control or SCADA system.

9. The method of any one of claims 1 to 8, wherein the predicted state is a forecast of a future state of the raceway pond.

10. The method of any one of claims 1 to 9, wherein the predicted state of the raceway pond comprises a predicted growth rate of the algae in the raceway pond.

11. The method of any one of claims 1 to 10, wherein predicting the state of the raceway pond is based on one or more of: a temperature condition, a light condition,a rate of gas exchange across an air-water interface, a chemical condition of water in the raceway pond or in an inlet used for a dilation operation, or a biological condition of the algae.

12. The method of claim 11 , wherein predicting the state of the raceway pond is based on a temperature condition, and wherein the temperature condition comprises a temperature of water in the raceway pond, a temperature of water in an inlet used for a dilution operation, an ambient wind speed, an ambient wind direction, and / or an ambient air temperature.

13. The method of claim 11 or claim 12, wherein predicting the state of the raceway pond is based on a light condition, and wherein the light condition comprises a solar irradiation rate, a total daily solar exposure, a level of photosynthetically active radiation, a spectral property, and / or a depth value, optionally wherein the light condition comprises a level of photosynthetically active radiation spectrally corrected to the pond, at a representative pond depth.

14. The method of any one of claims 11 to 13, wherein predicting the state of the raceway pond is based on a chemical condition of water in the raceway pond or in an inlet used for a dilation operation, and wherein the chemical condition comprises a nutrient density, a pond / inlet temperature, a level of salinity, a pH value, a carbon dioxide content, a total dissolved inorganic carbon content, a total alkalinity, and / or a bicarbonate content.

15. The method of any one of claims 11 to 14, wherein at least one temperature, light, chemical or biological condition and / or rate of gas exchange is calculated from the input data using a sub-model of the model.

16. The method of any one of claims 1 to 15, wherein the input data comprises data supplied by a raceway pond sensing / monitoring apparatus, optionally wherein either: the raceway pond sensing / monitoring apparatus is disposed within, above or adjacent to the raceway pond; or the input data comprises a water temperature, a paddlewheel speed, one or more nutrient densities, a pH level, a turbidity level, a cell density, a biomass density, an ambient air temperature, a local depth, an inlet water property, a sensed light level, and / or aerial imaging data.

17. The method of any one of claims 1 to 16, wherein the input data comprises remotely supplied data, optionally wherein the input data comprises wind speed and / or direction data, weather forecast data, oceanic forecast data, sunrise or sunset timing data, solar zenith angle data, cloud cover data,UV light exposure data, light attenuation data such as aerosol data or sea haze data, and / or sunlight intensity data.

18. The method of any one of claims 1 to 17, wherein the input data comprises data supplied by a user input, optionally wherein the input data comprises an identified type of the algae and / or pond geometry data such as width, depth, length, volume, geomembrane reflective index, geomembrane roughness index, paddlewheel speed or surface area data for the raceway pond.

19. The method of any one of claims 1 to 18, further comprising receiving second input data indicative of one or more properties pertaining to the environment surrounding the raceway pond.

20. A computer-implemented method, comprising: receiving first data indicative of one or more properties of a raceway pond; predicting a state of the raceway pond using a model, based on the received first data; and iteratively: receiving further data indicative of one or more properties of the raceway pond from a raceway pond sensing / monitoring apparatus; updating the predicted state of the raceway pond using the model, based on the received further data; and providing an output to a display based on the updated predicted state.21 . A device comprising a processor and a memory, the memory containing instructions which, when executed by the processor, cause the processor to perform the method of any one of claims 1 to 20.

22. A non-transitory computer-readable medium comprising computer-readable instructions for causing a computer to perform the method of any one of claims 1 to 20.

23. A system comprising: a raceway pond for culturing algae, the raceway pond comprising a paddlewheel; at least one raceway pond sensing / monitoring apparatus; and a device according to claim 21 .

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