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Your password has been reset. Please set a new password to continue.
Verify reactor ownership to reset your password
Step 1: Read Verification
Enter the current SP value for
Step 2: Write Verification
Change the SP value for to a different value (>10% change), then enter the new value below.
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Your temporary password:
You must change this password on your next login.
Select devices to register
Your bioreactor needs to be connected to the AI cloud service. Download and run the setup wizard on the computer running D2MS.
Download Setup Wizard (54 MB)D:\D2MS Native\OAJ\D2MS_Gateway\TunnelSetup.exe and select "Run as administrator"Requirements: Windows 10/11 (64-bit), Administrator privileges
您的生物反应器需要连接到AI云服务。请在运行D2MS的电脑上下载并运行安装向导。
下载安装向导 (54 MB)D:\D2MS Native\OAJ\D2MS_Gateway\TunnelSetup.exe 选择"以管理员身份运行"系统要求： Windows 10/11（64位），管理员权限
Need help? Email: support@tjxbio.com 需要帮助？邮箱：support@tjxbio.com
Register your bioreactor
Ownership Verification - Step 1 of 2
Please check the current Set Point (SP) value for:
--
Find this value in your D2MS Software and enter it below.
Ownership Verification - Step 2 of 2
Now, please change the Set Point (SP) for:
--
to a different value (must differ by more than 10%), then enter the new value below.
After changing the value in D2MS Software, enter it here.
Don't need write access to the reactor?
Verification Successful!
Create a password and agree to the terms to complete registration.
End User License Agreement
Key Points:
| Parameter | PV | SP | Source | AUTO | PROFILE |
|---|
PDFs and documents ingested into Mia's knowledge base. Hidden documents are kept on disk but excluded from AI search.
Based on current configuration, you need:
Copy this reactor's config to another reactor, or load config from another reactor.
Click "Refresh" to see the model architecture based on current configuration.
Predictor not configured
Please configure the predictor first by selecting input features and output parameter.
Choose a run and time range to use for model training.
No predictions available. Configure and train the model first.
Each training creates a new model version. You can load a previous version to use for predictions.
Loading model history...
Compare model predictions against actual historical data to evaluate accuracy.
Previously run control loops. Click "Run" to start a loop with the same parameters.
All triggers (active and inactive). View fire history and manage triggers.
Historical experiments with high-frequency data logging. View graphs, download data, or delete experiments.
Runs marked as golden candidates in D2MS Runs. Select which ones to use for training.
Select reference runs in the Reference Runs tab, then open this tab to see available features.
Available after loading features above.
Offline parameters monitored via sparse envelope (z-score comparison at sample times).
Train a frozen model on the selected reference run(s). Configure features and architecture in the Configure tab. The short-horizon predictor (auto-retune) can be used alone, but the golden batch model requires a trained short-horizon predictor for Mia to evaluate short-term impacts.
Score a historical run against the golden reference to see how well it matches.
Rules used by the L6 Supervisor to automatically correct parameter deviations.
Typical and safe operating ranges for each parameter.
| Parameter | Typical Range | Safe Range | Control Method | MV |
|---|
PID tuning for controllable manipulated variables.
| Parameter (PV) | MV | Kp | Deadband | Max Change/Cycle |
|---|
L6 supervisor threshold checks for autonomous monitoring.
| Name | Parameter | Check Type | Threshold | Severity | Phase |
|---|
Select a genome-scale metabolic model to activate for this session. When active, Mia can run FBA simulations using real-time reactor data.
View and manage parameter aliases for cross-system name matching (PLC ↔ D2MS ↔ Predictor).
No bioreactors available.
Contact your administrator to request access.
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New users are created with observer role (read-only access). You can promote them later.
Register a new bioreactor by verifying ownership through SP parameter source address.
No configuration selected.
Ownership Verification
Please enter the Source Address for the parameter:
--
Find this value in your D2MS Software, under Control:SP Source Specifications.
✓ Verification Successful!
The bioreactor has been verified and added to the system.
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Executable analysis methods (Python modules). These are dynamically loaded and can be updated without server restart.
Formula documentation describing calculation steps. For reference and review.
User-defined virtual sensors that compute derived values from process data. Results can be used in golden batch, RNN prediction, and automated control.
Define expected ranges to validate results
The Titer Predictor uses a recurrent neural network (GRU/LSTM) to forecast future values based on historical process data. The model learns patterns from your bioreactor's time-series data and predicts values at configurable time horizons.
The minimum raw data points needed for training is calculated as:
Example: With Sequence=60, Max Horizon=480, Min Sequences=100, Avg Window=30s:
Downsampled = 60 + 480 + 100 = 640 points
Raw = 640 × 6 = 3,840 points (~53 hours at 5-second intervals)
| Input Features | Process variables (PVs) and setpoints (SPs) used as inputs to the model. Select parameters that influence your target output. |
| Output Parameter | The target variable to predict (e.g., titer, biomass). This is what the model learns to forecast. |
| Output as Input (AR) | Autoregressive mode - uses past output values as additional input features. Lag steps controls how many past values to include. |
| Model Type | GRU - Faster training, fewer parameters. LSTM - Better for very long sequences, more memory. |
| Avg Window (sec) | Downsamples training data by averaging points. Higher = less data, faster training. 30s (6x reduction) is a good default. |
| Min Sequences | Minimum training sequences required. Lower = less data needed but potentially less accurate. 50-200 typical. Directly affects data requirements. |
| Auto-Retrain (min) | Automatically retrain the model at this interval. Set to 0 to disable. Uses "Quick Train Epochs" for faster training. |
| Sequence (min) | How many minutes of history the model sees at each step. Longer = more context but more data needed. 60 min is typical. |
| Hidden Size | Number of neurons in each recurrent layer. Larger = more capacity but slower. 64-256 typical. |
| Layers | Number of stacked recurrent layers. 1-2 usually sufficient. More layers = more complex patterns but harder to train. |
| Dropout | Regularization to prevent overfitting. 0.1-0.3 typical. Higher if you see overfitting. |
| Learning Rate | How fast the model learns. 0.001 is a good default. Lower if training is unstable. |
| Batch Size | Samples processed before updating weights. 16-64 typical. Larger = faster but uses more memory. |
| Max Epochs | Maximum training iterations for manual "Train Now". Early stopping may end training sooner. |
| Quick Train Epochs | Epochs used for auto-retrain (fewer for faster background training). |
| Early Stop Patience | Stop training if loss doesn't improve for this many epochs. Saves time and prevents overfitting. |
| Horizons (min) | Comma-separated list of future times to predict. E.g., "60, 120, 240" predicts 1h, 2h, 4h ahead. Max horizon affects data requirements! |
| Log Predictions | Which horizons to log to the data history for later analysis and charting. |
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Generate an API token to connect OpenClaw to this bioreactor. One token per reactor.
Copy now - it won't be shown again.
1. Unzip into OpenClaw workspace:
cd ~/.openclaw/workspace/skills && unzip ~/Downloads/mia.skill -d mia
2. Install mcporter & register Mia:
npm i -g mcporter mcporter config add mia --url /mcp --header "Authorization: Bearer <token>"
3. Enable exec in ~/.openclaw/openclaw.json:
"tools": { "profile": "full" }
4. Restart gateway, then ask: "List bioreactors from Mia"
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| Reactor | User | Preview | Status | |
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