ALGAE BIOTECH · IN DEVELOPMENT

Algae biotech
should be easier
to get into.

We started Orr Biologicals because we were shocked by how hard it was to access algae biotechnology. An early interest in biology, followed by college coursework and microbiology, led us here. Now we are developing tools for cultivating algae and studying individual cells — and sharing the science while we work.

RESEARCH CONCEPTS · SIMULATIONS · NO VALIDATED HARDWARE CLAIMS
Illustrative image for the Algaephyte cultivation concept
ALGAEPHYTE · CULTIVATION CONCEPT
CONCEPT IMAGERY

The cultivation system we are working toward.

FIG. 001
ALGAEPHYTE · CONCEPT
ILLUSTRATIVE IMAGE · NOT BUILD EVIDENCE
HOW WE OPERATE. Fail closed, always Testing before claims Research linked to sources Built to be understood

TWO RESEARCH PLATFORMS

Two platforms. Different questions.

Algaephyte explores cultivation.
Cyanoflow explores single cells.

THE INTERACTIVE LAB

Try the model.
See what changes.

Change the light, carbon and airflow to explore a simplified culture model. It illustrates the relationships we want to study in Algaephyte, without claiming that the proposed instrument already works.

Try the living simulation Illustrative model · no measured data
ENVIRONMENT PREVIEW SIMULATION
Light218 µmol
Carbon6.8 g/L
Growth model forecast
72 h ahead
Illustrative curve — not instrument output
The same environment sliders drive the living simulation below.

Living simulation

Biology pushes back.

Change the environment. The model recalculates growth, oxygen stress and useful output. Brightest is not best. More is not always more.

Living Arthrospira filaments under dark-field microscopy
Illustrative optical field · simulated state
Productive regime. Cells are dividing. The twin would hold.
Growth response 52% · oxygen stress 0%
Environment
Light at vessel wall218 μmol
Available inorganic carbon6.8 g/L
Gas exchange58%
Biomass
0.00
g / day
Protein
0.00
g / day
CO₂ fixed
0.0
g / day
72 h biomass forecastnow →
Chamber · live render · SIMULATION

Simulated response from a growth model — illustrative, not measured laboratory data.

Proposed architecture

The whole loop is Algaephyte.

Six proposed layers, from the culture vessel to optional parameter sharing. This is an architecture to develop and test, not a report from deployed instruments.

Algaephyte · design notes

How we plan the control loop

A column of spirulina, six sensor readings, a small model that forecasts growth, a camera that is not allowed to act, and an optional network that shares what vessels learn — never what they see.

The loop, said slowly

The planned inputs are pH, temperature, wall irradiance, optical density at 750 nm, dissolved oxygen and conductivity. We want to use them to estimate biomass, internal nitrogen quota and dissolved inorganic carbon. Droop quota kinetics, Steele light response and Beer-Lambert attenuation are candidate model components; fitting them to real culture data is work still ahead.

The forecast target is seventy-two hours. A local rule-based planner would compare possible changes to carbon supply, nutrients, airflow and light. An optional assistant could explain proposals or accept operator requests, but would not have direct actuator authority. We need to test whether the model remains useful over that forecast horizon.

The proposed safety structure has three layers: fixed limits on mass, duty cycle, pH, temperature and dose frequency; a model check on the proposed action; and independent limits on the ESP32 actuator controller. A watchdog and physical emergency stop are also design requirements. We cannot call these protections reliable until fault testing demonstrates them.

What the TPU is for

Optical density measures light attenuation, not organism identity. A camera could provide a second evidence stream by looking for coils, fragments and unfamiliar shapes. We are considering a quantized vision model on a Coral accelerator, with training and compilation performed separately. A useful classifier needs labeled images and tests against bubbles, debris and organisms it has not seen before.

We have not established inference speed, power draw or contamination-detection accuracy for this proposed setup. Vision should be able to request caution, not authorize a pump. That separation is a design requirement, not an experimental result.

The edge brain

The proposed controller uses a Raspberry Pi 5 for telemetry, the growth model and local policy, with a Coral USB Accelerator for camera inference. A local Qwen assistant is an optional interface idea, not a dependency for control. Resource use, thermal behavior and safe fallback operation all need testing on the assembled hardware.

What the mesh is for

Algaephyte Mesh is a proposed way to share fitted model parameters rather than photos or raw traces. Candidate parameters include μ_max, I_opt, K_s, T_opt and Q_min. MQTT is the planned transport. Whether parameters transfer usefully between vessels and strains must be tested; we are not claiming that a network already exists or speeds up cultivation.

Illustrative control hardware image for the proposed architecture
Illustrative hardware image retained from the site design; not documentation of a completed Algaephyte build.

Bicarbonate deficit calculator

This educational calculator illustrates a mass deficit and an assumed staging rate of 0.5 g/L per hour. That rate is not a validated Algaephyte protocol or a recommendation for your culture. Verify your medium, measurements and procedure before any dosing.

Inputs are expressed as NaHCO₃ equivalents. Sodium carbonate and sodium bicarbonate are not interchangeable. A calculated deficit alone does not establish a safe dose.

The sensor stack

Six signals, one culture.

The proposed sensor suite measures water chemistry, light and optical density. No single reading can establish culture health, and the combined interpretation will need calibration and testing.

The digital twin

Predicting growth with Droop and Steele

We plan to use nutrient-quota and light-response equations to evaluate proposed actions. Forecast accuracy, dosing limits and biological outcomes still need validation.

Droop: the quota inside the cell

Droop's cell-quota kinetics decouple uptake from growth: cells store nitrogen, and division depends on that internal quota rather than the concentration in the medium. That single idea explains why a starved culture keeps dividing after you feed it, and why over-dosing nitrate buys you nothing but bacteria.

Steele: light has an optimum, then a cliff

Steele's curve handles light — growth rises to an optimum irradiance and falls again under photoinhibition. Brightest is not best: past the optimum, the outer shell of the culture bleaches, oxygen supersaturates, and photosynthesis starts to poison itself. The twin treats irradiance as a control surface, not a schedule.

Beer-Lambert: a dense culture is a stack of dark jackets

Beer-Lambert gives every radial shell of the vessel its own light climate, so the outer millimetre of a dense culture can be photoinhibited while the axis sits below compensation. A single wall sensor cannot see this. The twin can, because it integrates Steele's curve through the radial shells.

Alkalinity, not pH

Most growers chase pH because pH is the number a ten-dollar probe will give you. In an alkaline Arthrospira medium the bicarbonate/carbonate pool is simultaneously the inorganic carbon supply, the overnight buffer, and the reason almost nothing else can live in the vessel. pH is what that pool looks like from the outside. The twin treats dissolved inorganic carbon as a state variable, not a setpoint.

Dissolved oxygen: when photosynthesis poisons itself

As dissolved oxygen climbs through the afternoon, photosynthesis starts to supersaturate — the culture poisons itself. The first proposal is usually more air. The second is a modest dim. The third, if you have been stubborn about air, is a siesta. Oxygen stress is a state variable in the twin, not a footnote.

Autonomy with limits

Intelligence proposes. Hardware decides.

An uncertain proposal should not move a pump. The design calls for three independent checks and a physical stop. Until tested, these are safety requirements, not guarantees.

A request such as "raise alkalinity to target" leaves out essential information: the mass, rate, current chemistry and equipment limits. Our design must reject incomplete requests rather than guess. We plan to test malformed proposals, stale sensor values, lost communications and stuck outputs before trusting automatic dosing.

Algaephyte Mesh

Federated learning without sending a pixel

Our proposed mesh would share fitted growth-model parameters, not photos, raw sensor traces or locations. Participation would be optional. The privacy boundary and usefulness of shared parameters both need verification.

algaephyte/{id}/lwt — last will, retained, the obituary.
algaephyte/{id}/twin — the parameter vector, retained.
algaephyte/{id}/health — a coarse status (ok, hold, human), not a dump.
algaephyte/strain/{name}/aggregate — optional, broker-side, still just parameters.

The transport design uses MQTT QoS 1, authenticated connections and TLS for remote traffic. Small payloads would suit limited connections. A last-will message can indicate a lost connection, but cannot establish whether a culture is healthy or a pump is working.

The proposed sharing concerns the growth model, not on-device camera training. We need to establish how to handle outliers, incompatible strains and malicious inputs. Local cultivation should not depend on the broker; disconnect tests will need to confirm that.

Illustrative hardware image for the Algaephyte concept

Local control by design

The control loop should not need the cloud.

The proposed architecture separates the growth model, camera analysis and actuator controller. Our goal is local operation with independent hardware limits and a physical stop. Disconnect and fault testing must demonstrate that behavior before we claim it.

Inspect the hardware stack

Scale without fantasy

One vessel is small. Reliability multiplies.

Illustrative calculation: 18 L × 0.13 g/L/day
0.00 g dry biomass / day · assumed, not measured

This number is arithmetic, not a result from our equipment: an assumed productivity of 0.13 g/L/day multiplied by a proposed 18 L volume gives 2.34 g/day. It does not demonstrate yield, food safety, carbon removal or scale-up performance. Those claims require measured runs, documented conditions and independent checks.

Development record

What we need to test next.

These are open design questions, not dated records of experiments we have completed.

When it goes wrong

Failure modes to plan for

Sensor drift, dry pumps, contamination and loss of power are risks the design needs to handle. These notes describe checks to investigate, not failures observed on deployed Algaephyte units.

Questions, answered plainly

FAQ

Research questions · collaboration

Talk to us about the project

Algaephyte and Cyanoflow are in development, not available for purchase or deployment. Tell us what you want to study, which access barriers you face or how you could help test the designs. This form opens your email client; nothing is sent until you send the message.

CULTIVATION GUIDES · DESIGN NOTES

Read the science behind the project.

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The access gap is why we started.
Understanding the biology should not require owning a lab.

See the whole system

FROM THE BENCH

The build, as it happens.

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