Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model

We have developed a hybrid machine learning (ML) model for the prediction and optimization of a gliding arc plasma tar reforming process using naphthalene as a model tar compound from biomass gasification. A linear combination of three well-known algorithms, including artificial neural network (ANN), support vector regression (SVR) and decision tree (DT) has been established to deal with the multi-scale and complex plasma tar reforming process. The optimization of the hyper-parameters of each algorithm in the hybrid model has been achieved by using the genetic algorithm (GA), which shows a fairly good agreement between the experimental data and the predicted results from the ML model. The steam-to-carbon (S/C) ratio is found to be the most critical parameter for the conversion with a relative importance of 38%, while the discharge power is the most influential parameter in determining the energy efficiency with a relative importance of 58%. The coupling effects of different processing parameters on the key performance of the plasma reforming process have been evaluated. The optimal processing parameters are identified achieving the maximum tar conversion (67.2%), carbon balance (81.7%) and energy efficiency (7.8 g/kWh) simultaneously when the global desirability index I-2 reaches the highest value of 0.65.

Automated summary

A hybrid machine learning model has been developed for predicting and optimizing a gliding arc plasma tar reforming process using naphthalene as a model tar compound. The model, which combines three algorithms, shows good agreement between experimental data and predicted results. Optimization of hyper-parameters using genetic algorithm reveals the critical parameters for conversion and energy efficiency, leading to identification of optimal processing parameters for achieving maximum tar conversion, carbon balance, and energy efficiency.