What is CPNET Recipe Optimization?

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Recipe Optimization uses CPNET’s Rx+ engine to recommend best-next setpoints and ratios for your line—across temperatures, speeds, ratios, and additives—while respecting equipment limits, spec bands, and step sizes. It learns from historical + live data, suggests a batch of diverse candidates, and explains why they work.

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Built for: steel, plastics, construction materials, and other continuous manufacturing where recipes drive yield, quality, and cost.

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How customers use CPNET for Recipe Optimization

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Connect & define objectives

Link historian/MES/quality. Pick the line, product/grade, and targets (e.g., maximize throughput, keep gauge Cpk > 1.33, minimize energy/ton)

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Declare variables & constraints

Mark controllables (with bounds and step sizes) and CTQs/specs (LSL/USL/TAR). Add equipment and change-direction limits.

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Get optimized recipes (batch)

Rx+ proposes 3–5 candidate recipes balancing exploration vs. exploitation, honoring constraints, and minimizing changeover.

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Run, capture, iterate

Execute candidates, auto-capture outcomes, and Rx+ updates predictions, uncertainty, and the next batch. Stop when targets are met with confidence.

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Lock in & standardize

Promote the winning recipe to a saved playbook with guardrails and alerts, so operators can repeat success shift after shift

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Why teams choose CPNET for Recipe Optimization

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Optimize in days, not weeks

Adaptive batches learn quickly from your data—no giant test matrices

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Industrial guardrails

Hard/soft constraints, trust regions, step sizes, and change-direction rules keep suggestions realistic and safe

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Transparent decisions

Every recipe shows predicted impact, 95% interval, feasibility, and top drivers—so engineers and ops trust the change

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From analysis to action

Built-in alerts and operator run sheets turn insights into repeatable execution

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Works with your stack

Uses your existing tags, quality signals, and dashboard patterns.

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Ready to dial in your recipe?

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With CPNET, your team goes from guesswork to guided optimization—and keeps the wins repeatable.

Let’s talk. We’ll show Recipe Optimization on your line, with your data.