Enable participants to deploy AI—from sorting to reprocessing and digital twins—to maximize recycled composite quality and yield while cutting energy, cost, and CO₂e, with traceable, DPP-ready operations.
AI-Based Optimization of Polymer Composite Recycling Processes
Leveraging AI from sorting & separation to reprocessing for sustainability in the composites industry
Skills you will gain:
A three-day, hands-on workshop where you learn to apply AI across the composite-recycling chain—imaging-based sorting, surrogate-model reprocessing optimization, physics-informed AI and digital twins—culminating in pilot-ready tools, an LCA-lite CO₂e calculator, and DPP-compliant traceability.
Aim:
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Build an end-to-end AI pipeline (sorting → reprocessing).
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Create datasets/imaging; train compact classifiers with active learning & uncertainty.
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Optimize via surrogates & multi-objective trade-offs; set KPIs/guardrails.
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Deploy & govern: edge constraints, digital twin, LCA-lite & DPP, MLOps.
What you will learn?
📅 Day 1 – AI-Driven Sorting & Identification
- Mixed composite streams: dataset design, labeling standards, QA criteria
- Imaging stacks: RGB/NIR/HSI selection, illumination & capture setup
- ML pipelines: baselines → compact deep models; active learning & uncertainty
- Edge/line deployment: latency budgets, fail-safes, reject policies
- Hands-on: Build and benchmark a small classifier; set confidence thresholds
📅 Day 2 – Reprocessing Optimization
- Recycling routes & KPIs: yield, fiber-strength retention, resin recovery, energy/cost/CO₂e
- Data-to-decision: tidy historical runs, guardrails, acceptance criteria
- Surrogate modeling & multi-objective optimization (Pareto trade-offs)
- Interpretability & sensitivity (SHAP) to identify governing levers
- Hands-on: Train a surrogate, generate a Pareto set, issue a pilot run card
📅 Day 3 – Physics-Informed AI, Digital Twins & Traceability
- Physics-guided learning (PINNs/data fusion) for sparse, noisy regimes
- Line-level digital twin: soft sensing, guard-railed control, what-if analysis
- Sustainability accounting (LCA-lite) for cost and CO₂e per kg output
- Digital Product Passport readiness: data schema, QR payload, compliance
- MLOps & governance: versioning, drift, retraining cadence, audit trail
- Hands-on: Twin simulations under quality/CO₂e constraints; cost/CO₂e calculator; draft DPP payload
Intended For :
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Process & manufacturing engineers (thermoset/thermoplastic composites)
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Recycling R&D/operations teams (mechanical, chemical, pyro/solvolysis)
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QA/QC analysts & lab technicians (imaging, spectroscopy, materials testing)
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AI/ML engineers & data scientists (vision, edge/line deployment, optimization)
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Sustainability/LCA analysts; EHS/ESG & DPP/traceability leads
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Plant/operations managers; continuous-improvement professionals
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Senior UG/PG students in materials, chemical/mechanical, polymer, or AI/DS
Career Supporting Skills