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Polyhydroxyalkanoates (PHAs) are biodegradable polyesters produced by microorganisms and represent one of the most promising alternatives to petroleum-derived plastics. However, its commercialization is constrained by high production costs depending on the choices of microbial strain and carbon substrate, yet experimental investigations of new strain–substrate combinations remain resource-intensive. This study presents a substrate-strain-agnostic, robust, hybrid framework, combining a dynamic, mass-balance and kinetic-based mechanistic model (DPM) and machine learning (ML), to predict fermentation performance in terms of cell biomass and PHA concentrations from any strain–substrate combination. The DPM-generated data and experimental time-course profiles spanning sugar-based substrates, oils, and waste carbon resources, including lignocellulose and waste cooking oils, have been used to train/test ML models. The ML models (with 80:20 train:test split) thus built capture inherent dynamic interactions among carbon source depletion, nitrogen limitation, biomass proliferation, and intracellular PHA-copolymer accumulation. Artificial neural network (ANN) and random forest (RF) surrogates predict cell biomass and PHA concentrations from fermentation-state features: time, starting and current substrate and nitrogen concentrations. Both surrogates achieved R² > 0.99 on the overall test set. However, the ANN outperformed the RF on the unseen experimental profiles (R² > 0.95 vs > 0.80). Thus, the ANN model, available as open-source software: https://jhumasadhukhan.github.io/ANN-model-to-PHA-prediction/, is recommended for strain–substrate screening, batch feed-and-nitrogen-limit design, and soft-sensing in digital-twin-enabled fermentation.

Lignocellulosic residues are underutilized carbon resources for circular, inherently climate-negative biopolymer manufacturing. This study presents the first integrated dynamic simulation, life cycle assessment, and techno-economic analysis (DS-LCA-TEA) of polyhydroxyalkanoate (PHA) biocomposite production from lignocellulose. A kinetic and mass transfer-based bioreactor model was developed and calibrated using experimental data for Cupriavidus necator cultivated on lignocellulose-derived sugars, reproducing transient biomass and intracellular PHA accumulation (0.55 w/w PHA/substrate and 0.67 w/w PHA/cell dry weight). Dynamic outputs informed plant-wide mass and energy balances for a 1 kt/y PHAbiocomposite process integrating biomass pretreatment, fermentation, natural deep eutectic solvent-based PHA recovery, fiber-PHA compounding, and end-of-life circularity. The LCA results using ReCiPe (M) (H) show a global warming potential (GWP) of 1.51 kg COâ‚‚e/kg PHA, which shifts to a net GWP of 0.21