47. Multilayer optimization: calculations and simulations in indoor systems

Multilayer optimization represents one of the points of maximum complexity-and maximum potential-in indoor vertical farming. When stacking multiple growing layers, any design error multiplies; conversely, any well-calculated improvement scales exponentially.
In this article we address the logic, calculations, and simulations required to design and optimize high-performance multilayer systems.

1. What "multilayer optimization" really means

Optimizing a multilayer system means more than just adding layers. It means ensuring that each layer receives:

• the same level of useful light (PPFD)

• the same quality of air and CO₂

• the same thermal stability

• comparable root conditions

In the absence of optimization, upper layers become more productive and lower layers progressively less efficient, with average yields plummeting.

2. The critical parameters to model

In a professional multilayer system, parameters are not managed "by feel." They must be modeled and simulated.

Lighting

• PPFD target per layer

• uniformity (% standard deviation)

• light spillover between layers

• LED efficiency decay with temperature

Thermal

• heat buildup per layer

• vertical gradient (top vs. bottom)

• active/passive dissipation capacity

Airflow

• air velocity (m/s)

• hourly changes per layer

• dead zones and stagnation

• CO₂ distribution.

Crop density

• number of plants/m² per layer

• leaf area (LAI)

• light competition

3. Basic calculations (simplified but real)

3.1 PPFD calculation per layer

Simplified formula:

But in multilayer it has to be corrected for:

• reflections

• absorption of the upper layer

• actual LED-canopy distance

Typical error: use the same setpoint for all layers → guaranteed failure.

3.2 Cumulative thermal load.

Each layer adds:

In stacks of 4-6 layers, the thermal load is not linear, but cumulative upward.
Without CFD simulation, the top layer becomes systematically off target.

3.3 Minimum effective airflow

Real operating range:

• < 0.2 m/s → insufficient gas exchange

• 0.6 m/s → mechanical stress and dehydration

Each layer must be in the optimal window, not the system average.

4. Simulations: what to really simulate (and what not)

Useful simulations

• CFD for multilayer airflow

• transient thermal simulations

• real PPFD distribution

• maximum load scenarios (summer, full density)

Useless simulations

• "theoretical" growth without real feedbacks

• static models without variability

• simulations not validated by field data

Simulation is to reduce errors, not to replace reality.

5. Typical errors in multilayer systems

1. Replicating the same setup on all layers

2. Ignoring thermal stratification

3. Underestimating the effect of moisture between layers

4. Not recalibrating light and airflow as biomass changes

5. Designing "in plan" without thinking "in volume"

6. Correct approach: design → simulation → data → correction

An efficient multilayer system always follows this cycle:

1. Initial engineering design

2. Prior simulation

3. Actual measurement by layer

4. Dynamic correction

5. Standardization only after validation

Skipping any of these steps means building an unstable system.

7. Multilayer and automation: the real multiplier

Manual multilayer optimization does not scale.
It needs:

• independent control per layer

• distributed sensors

• separate actuators

• dynamic compensation logic

Only then does multilayer become a competitive advantage and not an operational problem.

Tomato+ develops multilayer indoor growing systems based on intelligent control, advanced simulation, and real growth data.
Each layer is managed as an independent system, but optimized as part of a whole.
Automation, artificial intelligence and engineering design are at the heart of our vision.
Growing better is not about adding complexity, but governing it.
This is the Tomato+ approach.

Conclusion

Multilayer optimization is an engineering discipline, not a layout exercise.
Those who approach it with calculations, simulations and real data achieve higher yields, stable costs and consistent quality.
Those who improvise it accumulate inefficiencies at every level.

Thank you for reading this article. Keep following us to discover new content on hydroponics, vertical farming and smart agriculture.

Tomato+ Team