Abstract
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.