LightRAG empowered multi-objective optimization framework for process parameter control in polyester fiber polymerization

doi:10.1007/s10845-026-02966-5

LightRAG empowered multi-objective optimization framework for process parameter control in polyester fiber polymerization

2026-09-06

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DOI：10.1007/s10845-026-02966-5

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Quality control in polyester fiber polymerization is a multi-objective process-parameter optimization problem complicated by nonlinear parameter-quality coupling and the limited reuse of abnormal-condition knowledge. Existing surrogate-assisted optimization methods typically search within fixed engineering boundaries and lack a mechanism for converting unstructured process knowledge into computable constraints. To address this limitation, this study develops PLMC as a LightRAG empowered multi-objective optimization framework that integrates knowledge-to-constraint transformation, surrogate-based objective evaluation, and knowledge-constrained dual-population co-evolution. Using a polyester polymerization process knowledge graph, PLMC retrieves mechanistic causes and control measures. A domain-adapted large language model then uses this information to generate condition-specific prior knowledge constraints. These constraints are embedded into PLMC-DP, a knowledge-constrained dual-population co-evolution module. The global population explores the engineering feasible space, whereas the knowledge-guided population searches within the prior-constraint region. Candidate solutions are evaluated using a surrogate error compensation-enhanced Kolmogorov–Arnold Network (SEC-KAN). SEC-KAN predicts the effects of key process parameters on intrinsic viscosity, diethylene glycol content, and terminal carboxyl group concentration while correcting local surrogate bias using historical residuals. Under the tested settings, experiments on industrial production-line data show that the complete PLMC framework achieves better Pareto-set quality and convergence stability than the baseline algorithms. The resulting hypervolume (HV) and inverted generational distance (IGD) are 0.8387 and 0.0473, respectively. Stable process-control performance across fully drawn yarn (FDY), partially oriented yarn (POY), and draw textured yarn (DTY) product scenarios indicates the applicability of PLMC to diverse polyester products.

Keywords:

Polyester fiber polymerization

Large language model

Retrieval-augmented generation

Multi-objective optimization

LightRAG empowered multi-objective optimization framework for process parameter control in polyester fiber polymerization

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