Predictive modeling of plastic pyrolysis process for the evaluation of activation energy: Explainable artificial intelligence based comprehensive insights.

Pyrolysis, a thermochemical conversion approach of transforming plastic waste to energy has tremendous potential to manage the exponentially increasing plastic waste. However, understanding the process kinetics is fundamental to engineering a sustainable process. Conventional analysis techniques do not provide insights into the influence of characteristics of feedstock on the process kinetics. Present study exemplifies the efficacy of using machine learning for predictive modeling of pyrolysis of waste plastics to understand the complexities of the interrelations of predictor variables and their influence on activation energy. The activation energy for pyrolysis of waste plastics was evaluated using machine learning models namely Random Forest, XGBoost, CatBoost, and AdaBoost regression models. Feature selection based on the multicollinearity of data and hyperparameter tuning of the models utilizing RandomizedSearchCV was conducted. Random forest model outperformed the other models with coefficient of determination (R2) value of 0.941, root mean square error (RMSE) value of 14.69 and mean absolute error (MAE) value of 8.66 for the testing dataset. The explainable artificial intelligence-based feature importance plot and the summary plot of the shapely additive explanations projected fixed carbon content, ash content, conversion value, and carbon content as significant parameters of the model in the order; fixed carbon > carbon > ash content > degree of conversion. Present study highlighted the potential of machine learning as a powerful tool to understand the influence of the characteristics of plastic waste and the degree of conversion on the activation energy of a process that is essential for designing the large-scale operations and future scale-up of the process.

Insights into the degradation of high-density polyethylene microplastics using microbial strains: Effect of process parameters, degradation kinetics and modeling.

The extensive distribution of microplastics and their abundance around the world has raised a global concern because of the lack of proper disposal channels as well as poor knowledge of their implications on human health. Sustainable remediation techniques are required owing to the absence of proper disposal methods. The present study explores the deterioration process of high-density polyethylene (HDPE) microplastics using various microbes along with the kinetics and modeling of the process using multiple non-linear regression models. Ten different microbial strains were used for the degradation of microplastics for a period of 30 days. Effect of process parameters on the degradation process was studied with the selected five microbial strains that presented the best degradation results. The reproducibility and efficacy of the process were tested for an extended period of 90 days. Fourier-transform infrared spectroscopy (FTIR) and field emission-scanning electron microscopy (FE-SEM) were used for the analysis of microplastics. Polymer reduction and half-life were evaluated. Pseudomonas putida achieved the maximum degradation efficiency of 12.07% followed by Rhodococcus ruber (11.36%), Pseudomonas stutzeri (8.28%), Bacillus cereus (8.26%), and Brevibacillus borstelensis (8.02%) after 90 days. Out of 14 models tested, 5 were found capable of modeling the process kinetics and based on simplicity and statistical data, Modified Michaelis-Menten model (F8; R2 = 0.97) was selected as superior to others. This study successfully establishes the potential of bioremediation of microplastics as the viable process.