Continuous flow, tighter every shift.

RL-tuned process loops

Soft Actor-Critic and Proximal Policy Optimization controllers for continuous loops — refinery catalytic reforming, gasoline blending, fermentation, paper machine wet-end. Control-Informed RL embeds PID set-point tracking inside the deep-RL architecture so engineers retain trust. Validated on published 800,000 t/a catalytic-reforming benchmarks; safety envelope enforced at all times.

Golden-batch control under GMP

Pharma 4.0 PAT integration with AI-driven control loops maintaining the 'golden batch' state. Real-time process-analytical sensors (Raman, NIR, pH, conductivity, dissolved O2) feed the controller; convolutional networks detect anomalous particle aggregation or size drift in real time and adjust before the batch slips off-spec. Every parameter excursion logged for the batch record.

Continuous manufacturing

By 2026 PAT + AI control loops are mature enough that pharma is shifting from batch to continuous manufacturing for relevant SKUs. The platform models the coupled-loop continuous process, flags steady-state excursions, and runs the change-control simulation that lets QA sign off without weeks of paper. Cleaning-and-setup downtime largely disappears.

Quality 4.0

Digital twins of the process — actively controlling parameters, not just monitoring — let operators see what the next batch will look like before it runs. AI-driven compliance becomes a real-time layer rather than a post-batch audit. The platform produces the evidence pack QA needs without bolt-on consultants.

Food & beverage AI + FSMA 204

Computer-vision contamination and foreign-object detection at production speed (98%+ accuracy in 2026 deployments). FSMA 204 Critical Tracking Events captured automatically with traceability lot codes, KDEs, and the 24-hour FDA-retrieval requirement met by default. Allergen-changeover validation through CIP and vision QC.

Refinery + chemical optimisation

Real-time optimisation of catalytic reforming, hydrocracking, and crude blending under feedstock variability — transfer learning + RL composes a controller that adapts to today's feedstock without recomissioning. Documented industry results show measurable margin uplift; the platform attributes uplift back to the controller decisions for accounting.