The opportunity axis
The opportunity is not “AI for its own sake.” The opportunity is to turn a variable, slow, manually adjusted bioprocess into a predictable, measurable, auditable production system.
1. Waste becomes value
Grape pomace is fibre-rich and polyphenol-rich. Controlled fermentation can turn an underused by-product into high-value functional ingredients.
2. Time becomes capacity
Reducing cycle time from roughly 45 days toward 10–15 days increases effective capacity without building new tanks.
3. Data becomes control
Historical and pilot data become the ground truth for soft sensors, digital twins, and closed-loop control decisions.
Full science section
A plain-language and technical explanation of what is happening in the fermentation process, why the measurements matter, and how the AI control layer uses them.
What fermentation is doing here
Fermentation is a controlled biological conversion process. Microbial and enzymatic activity transforms grape pomace chemistry over time. The production goal is not simply to “let it ferment,” but to reach target acidity, quality, and consistency as quickly and reliably as possible.
Industrial fermentation is sensitive to oxygen transfer, temperature, substrate availability, moisture, pH, and mixing. Small differences early in the batch can compound into different final acidity, yield, and cycle time.
Why grape pomace is hard
Pomace is biologically rich but variable. Feedstock lots can differ by grape variety, harvest timing, moisture, skin/seed composition, sugar, phenolics, and microbial load. That variability is why a static recipe or manual checklist cannot always hold the process in the optimum zone.
TA: titratable acidity
TA is a key process-progress and quality KPI. It represents the acid content measured by titration. In this project, TA is treated as a central endpoint and state variable: the system needs to know where the batch is now, where it is heading, and when it will cross target.
dTA/dt = instantaneous acidification rate
ETA-to-target ≈ (TA_target − TA_now) / predicted dTA/dt
pH versus acidity
pH measures hydrogen ion activity, while TA measures total titratable acid capacity. They are related but not interchangeable. A process can have similar pH but different TA depending on buffering and acid composition. That is why AI soft sensors should learn from multiple inputs rather than relying on pH alone.
Oxygen transfer and kLa
For aerobic or oxygen-influenced fermentation, cells and reactions depend on oxygen delivery. The oxygen transfer coefficient kLa summarizes how effectively oxygen moves from bubbles/headspace into the liquid/solid matrix.
where OTR = oxygen transfer rate,
C* = saturation concentration,
C = current dissolved oxygen concentration
Oxygen uptake rate: OUR
OUR estimates how quickly the biological system consumes oxygen. A mismatch between oxygen transfer and oxygen demand can create stress, slow conversion, or quality drift.
If gap < 0: risk of oxygen limitation
If gap ≫ 0: possible energy waste / over-aeration
Temperature and kinetics
Temperature changes reaction rates and biological activity. Too low can slow fermentation; too high can stress organisms, shift pathways, or damage quality. The controller’s job is to manage temperature as a dynamic input, not a fixed background condition.
Small temperature shifts can materially change reaction velocity.
Mixing and power
Mixing affects mass transfer, heat distribution, and homogeneity. More mixing is not always better: it may improve oxygen transfer but also consume energy or create shear. The control problem is a trade-off between biological performance and energy efficiency.
N = impeller speed, D = impeller diameter
Full science & algorithm documentation
The complete technical reference: fermentation kinetics, governing equations, dataset schema, data-cleaning algorithm, model fits, soft-sensor derivation, and a plain-English "Othertist" brief for non-technical reviewers.
System architecture
The AI layer supervises the existing PLC/SCADA environment. It predicts, recommends, validates, and eventually updates setpoints inside safety and quality limits; it does not remove existing interlocks or operator authority.
Soft sensor
Predicts unmeasured state variables such as TA, yield trajectory, and fermentation progress.
Digital twin
Combines mechanistic fermentation equations with ML residual correction.
MPC
Chooses setpoints that optimize future outcomes under constraints.
Anomaly detection
Flags sensor drift, contamination risks, unexpected kinetics, and actuator issues.
Evidence pack: raw spreadsheet + analysis
The uploaded spreadsheet is converted into cleaned tables and run-level metrics, then used to demonstrate how fermentation evidence becomes model-ready data.
What the current evidence shows
The workbook contains run sheets with time, acidity, pH, Brix, ethanol, temperature, aeration, and volume measurements. The analysis transforms those sheets into long-format observations and run-level summaries.
How this supports the proposal
This data package demonstrates the analysis pipeline required for M01 and later model training: inventory, cleaning, feature extraction, quality flagging, KPI computation, and stakeholder review.
For formal M01 completion, this same pattern must scale to the full three-year CDI dataset across in-scope tanks and data systems.
Run explorer
Chart controls
Interactive modelling sandbox
Use the controls to understand the economics and process dynamics behind the proposal. These are illustrative calculators, not final validated project KPIs.
Facility impact model
The proposal uses a representative 1,000 tonne/year facility and targets cycle-time reduction, yield uplift, failure reduction, and energy savings. Adjusting inputs shows sensitivity.
Acidification trajectory model
A simple process model shows how control quality changes the time to target acidity. Better control raises the effective acidification rate and reduces drift.
Soft-sensor algorithm
- Synchronize sensor and lab data by batch time.
- Engineer rates and lag features.
- Train model to predict TA and yield.
- Validate on held-out batches.
- Deploy to edge for real-time inference.
Digital twin algorithm
- Compute mechanistic estimates: OTR, OUR, kLa, temperature response.
- Estimate residual error with ML.
- Forecast future states under candidate setpoints.
- Compare forecast against biological and quality constraints.
MPC algorithm
- Read current state estimate.
- Generate candidate aeration/mixing/temperature actions.
- Simulate each action through the twin.
- Select lowest-cost safe trajectory.
- Send validated setpoint to PLC/SCADA.
M01: data readiness and baseline establishment
M01 is the foundation for credible before/after measurement. Without a clean baseline, later claims about cycle time, yield, energy, and failure reduction cannot be defended.
Catalogue SCADA, lab, batch, energy, and maintenance sources.
Find gaps, drift, tag changes, and batch-label inconsistencies.
Extract historical telemetry, lab records, metadata, energy records.
Cycle time, yield, failure rate, energy per kg.
CDI and A47 compare; resolve divergences over ±2%.
Acceptance means
Historical package ingests cleanly, data quality is sufficient, baseline KPI calculations converge or divergence is signed, facility access is committed, and formal acceptance is issued.
Open decisions before M01 starts
Yield denominator basis, energy submeter scope, lab data format, data redaction scope, and tank scope must be resolved before baseline work begins.
Proposal workplan and milestones
The project progresses from historical baseline to pilot data, AI model development, control integration, industrial-scale demonstration, and commercialization readiness.
Business case and commercialization
The proposal combines ingredient-product economics with a scalable AI platform model.
B2B ingredient sales
CDI and future platform users can sell more consistent premium fermented ingredients to food manufacturers seeking sustainable, clean-label, ESG-aligned supply.
SaaS licensing
Atomic47 can package the fermentation optimization stack as a configurable platform for processors using related fermentation processes.
Integration services
Large or complex facilities may need paid customization, data engineering, and industrial AI integration support.
Representative ROI from proposal
| Estimated capital investment | CAD $700k–800k |
|---|---|
| Annual operating benefit | ~CAD $288k |
| Payback, 1,000 t/year facility | 2.5–3.0 years |
| Payback, ≥5,000 t/year facilities | <18 months |
Defensible differentiation
Closed-loop supervisory control, industrial PLC/SCADA integration, 40,000 L validation target, RSM-grounded performance targets, proprietary CDI process knowledge, and A47 AI control IP create a combined process-and-platform advantage.
Governance, security, and IP
The project is designed for a high-consequence food-manufacturing environment where data ownership, auditability, and operational boundaries matter.
Data security
AES-256 at rest, TLS 1.3 in transit, RBAC, MFA for admin access, audit logging, and Canadian sovereign cloud/edge infrastructure.
Human oversight
AI operates within validated safety and quality constraints with operator override and existing PLC/SCADA interlocks retained.
IP structure
CDI retains fermentation process and operational data background IP; A47 retains AI models, digital twin, MLOps, and platform foreground IP subject to agreed project use rights.
Downloads and review order
Use this package to brief non-technical stakeholders first, then let technical reviewers inspect the raw analysis and data.
Recommended review order
- Open this site for the story and interactive models.
- Read the audience brief for board-level context.
- Review the full technical analysis.
- Open the raw spreadsheet and derived CSV files.
- Use the proposal text to map claims to milestones.