  Co-pyrolysis of biomass feedstocks with polymeric wastes is a promising
strategy for improving the quantity and quality parameters of the resulting
liquid fuel. Numerous experimental measurements are typically conducted to find
the optimal operating conditions. However, performing co-pyrolysis experiments
is highly challenging due to the need for costly and lengthy procedures.
Machine learning (ML) provides capabilities to cope with such issues by
leveraging on existing data. This work aims to introduce an evolutionary ML
approach to quantify the (by)products of the biomass-polymer co-pyrolysis
process. A comprehensive dataset covering various biomass-polymer mixtures
under a broad range of process conditions is compiled from the qualified
literature. The database was subjected to statistical analysis and mechanistic
discussion. The input features are constructed using an innovative approach to
reflect the physics of the process. The constructed features are subjected to
principal component analysis to reduce their dimensionality. The obtained
scores are introduced into six ML models. Gaussian process regression model
tuned by particle swarm optimization algorithm presents better prediction
performance (R2 > 0.9, MAE < 0.03, and RMSE < 0.06) than other developed
models. The multi-objective particle swarm optimization algorithm successfully
finds optimal independent parameters.
