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Interactive simulator

Cleanroom Air Change Recovery Simulator

Watch in real time how, after a contamination event, ventilation (ACH) purges airborne particles and concentration decays exponentially until the room recovers to the target cleanliness.

Parameters
Air changes ACH
1/h

Room air changes per hour. ACH = supply airflow Q ÷ room volume V.

Effective removal efficiency
%

Effective efficiency, accounting for imperfect mixing, dead zones, and filter bypass.

Initial concentration
count/m3

Airborne particle concentration right after the event (t=0).

Target fraction
%

Target cleanliness as a fraction of the initial concentration. 1% = 100:1 recovery.

Live results
—
ACH (1/h)
—
Recovery time 100:1 (min)
—
Current conc. (count/m³)
—
Recovered (%)
—
Room volume (m³)
—
Airflow (m³/h)
Cleanroom: particles purged by airflow
t = 0.0 min
Blue dots = airborne particles, blue arrows = supply/return airflow. Higher ACH purges particles faster, so concentration decays exponentially. Green frame = recovered to target cleanliness.
Concentration decay curve
Recovery time vs ACH
Model and equations

Assuming perfect mixing, the particle concentration after a contamination event follows first-order exponential decay.

$$C(t)=C_0\,e^{-\frac{\eta\,\mathrm{ACH}}{60}\,t},\qquad \mathrm{ACH}=\frac{Q}{V}$$

The recovery time to reach the target fraction $C_\text{target}/C_0$ is

$$t=-\frac{60}{\eta\,\mathrm{ACH}}\,\ln\!\left(\frac{C_\text{target}}{C_0}\right)$$

where $C_0$ is the initial concentration, $\eta$ the effective removal efficiency, $\mathrm{ACH}$ the air changes per hour [1/h], $Q$ the supply airflow [m³/h], and $V$ the room volume [m³]. The time constant is $\tau=60/(\eta\,\mathrm{ACH})$ [min]. For 100:1 recovery ($C_\text{target}/C_0=0.01$), $t=-\tau\ln(0.01)\approx 4.6\,\tau$.

This simplified model assumes perfect mixing and steady airflow. Unidirectional (laminar) flow, local dead zones, continuous particle sources, and code-specific corrections still need separate checks.

What this simulator shows

In a cleanroom, particles generated by people and processes are continuously replaced (ventilated) with filtered clean air. After a contamination event you can see, with both an animation and live numbers, how the particle concentration falls over time and how many minutes it takes to recover to the target cleanliness.

The room animation on the left draws the supply/return airflow arrows and the airborne particles themselves being purged. The decay curve shows the exponential decay $C(t)=C_0 e^{-\eta\,\mathrm{ACH}\,t/60}$, and the sensitivity curve on the right shows how recovery time changes as ACH varies. It is immediately clear that raising ACH shortens recovery time roughly inversely.

How to read it

Start with "Recovery time 100:1" — the time for concentration to fall to 1% (one hundredth) of its initial value. This is the basic metric of the ISO 14644 recovery test.

On the sensitivity curve (recovery time vs ACH), look for the region where adding more ACH stops shortening recovery time much. Each extra unit of ACH helps most at the low-ACH end and shows diminishing returns at the high-ACH end.

For early design, focus on whether ACH or removal efficiency η controls recovery rather than the absolute value. The two act equivalently through the product η·ACH.

Learn Cleanroom Air Change Recovery by dialogue

🙋
After a contamination event, how does the particle concentration fall? Moving ACH changes both the plots and the numbers, so it is a bit confusing.
🎓
Assuming perfect mixing, concentration decays as the exponential $C(t)=C_0 e^{-\eta\,\mathrm{ACH}\,t/60}$. The larger the ACH, the steeper the exponent, so the same target concentration is reached sooner. Read the purge speed in the left animation, the shape of the decay in the centre curve, and the concrete minutes in "Recovery time 100:1" together to build intuition.
🙋
Raising ACH speeds recovery, but how much does effective removal efficiency matter?
🎓
Recovery rate is set by the product η·ACH. So multiplying ACH by 1.2 is mathematically equivalent to multiplying efficiency η by 1.2. In real rooms η drops easily through poor mixing and dead zones, so even with high ACH recovery can lag expectations. Move η in small steps and watch the recovery time to see which one is controlling.
🙋
What is the recovery-time-vs-ACH curve for? The ordinary decay curve seems enough.
🎓
Recovery time is inversely proportional to ACH ($t\propto 1/\mathrm{ACH}$), so at low ACH one extra unit shortens it a lot, while at high ACH it plateaus. This plot shows that diminishing-returns boundary at a glance. Excess ventilation only adds fan power (energy cost), so the curve helps you pick a sufficient-but-not-excessive ACH.
🙋
If recovery time is within the limit, can I accept the condition?
🎓
Treat it as a first pass. In unidirectional (laminar) flow where perfect mixing breaks down, or in steady operation with ongoing particle generation by people, real recovery lags further. Final decisions still need the standard (ISO 14644-3 recovery test), measured data, detailed analysis such as CFD, and filter vendor limits.

Practical use

First-pass estimate of the required ACH for an ISO 14644-1 class (ISO 5–8) and of the recovery-test time.

Narrowing whether ACH or removal efficiency controls recovery before detailed analysis (CFD, measurement).

Teaching or explaining the exponential-decay equation, numbers, and the particle-purge animation under the same inputs.

FAQ

Start with Time constant and Time to target. Then use Particle recovery curve to confirm the assumed state and Ventilation, efficiency, and target to read distribution or bias. Use the main plot to read the controlling trend, including break points that a single result card can hide
Move Air changes ACH alone, then move Effective removal efficiency by a comparable amount and compare the change in Time constant. Recovery rate is set by the product η·ACH, so the two act equivalently in the equation.
Use it for First-pass comparison of design options before review. Instead of trusting a single point, widen the input range and check whether Time constant keeps enough margin before moving to detailed analysis.
This simplified model captures the main relationship only. It assumes perfect mixing and steady airflow, so unidirectional flow, local dead zones, and continuous particle sources need separate checks. Final decisions still require standards, measured data, detailed analysis, and vendor limits.
After a contamination event, it is the time for particle concentration to fall to a target fraction of the initial value (for example 1% = 100:1 recovery). Assuming perfect mixing, t=-(60/(η·ACH))·ln(C_target/C_0), so higher ACH shortens it exponentially.
ACH is the number of room air changes per hour, defined as ACH = Q/V (supply airflow Q in m³/h divided by room volume V in m³). This tool takes ACH directly and also displays room volume and airflow for reference. Stricter ISO 14644-1 classes require higher ACH.

How to Use

  1. Enter Air Changes per Hour (ACH): typical cleanroom values range 20–600 ACH depending on ISO class (ISO 6 ≈ 20 ACH, ISO 5 ≈ 240 ACH, ISO 4 ≈ 600 ACH)
  2. Set removal efficiency (η): accounts for filter bypass and dead zones; enter 0.70–0.95 for HEPA systems
  3. Input initial particle concentration (particles/m³) and target fraction (as fraction of initial, e.g., 1% for 100:1 recovery)
  4. Read the recovery time, current concentration and recovered %, and watch particles being purged by airflow in the animation

Worked Example

ISO Class 6 pharmaceutical cleanroom: ACH = 25, η = 0.82 (HEPA with 18% bypass loss), initial concentration = 500,000 particles/m³, target = 0.05 (50,000 particles/m³). Time constant τ = 60/(25 × 0.82) = 2.93 minutes. Using exponential decay C(t) = C₀ × e^(−t/τ), concentration after 10 min ≈ 27,500 particles/m³; time to reach 5% ≈ 8.8 minutes. Supply airflow Q = ACH × V; for a 50 m³ room at ACH = 25 that is 1,250 m³/h.

Practical Notes

  1. Higher ACH reduces recovery time nonlinearly; doubling ACH from 50 to 100 cuts time constant by 50%, not 25%
  2. Efficiency degradation: filter saturation reduces η by 5–15% over 6–12 months; recalibrate quarterly in critical areas
  3. Dead zones (corners, equipment recesses): actual effective ACH may be 10–20% lower than nominal; use η ≤ 0.75 for conservative estimates
  4. Particle size matters: large particles (>5 µm) settle gravitationally; model applies primarily to 0.3–1 µm aerosols captured by HEPA

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