# Management of Plastic Wastes through Recent Advanced Pyrolysis Processes
## 3 Recent Advances in Fuel Production from Plastic Pyrolysis
Finally, when the inlet velocity of the fluidizing gas was increased, this resulted in an incomplete consumption of the intermediates and a low yield of liquid products. The MAP system presented a highly efficient design to model the pyrolysis of plastic waste to produce pyrolysis oil as a source of energy. This system provides a more energy-efficient and lower-temperature process compared to conventional fluidized-bed reactors. **Pyrolysis Machine Learning:** This study [47] modeled the effect of input variables or parameters on the non-catalytic pyrolysis process of plastic waste into valuable fuel and chemicals. The algorithms of the Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM), and Gaussian process (GP) were compared to identify the most accurate model for this process. The experimental data needed to develop the model were collected from 93 reviewed studies related to the scope of the study. The plastics included in this study were PS, PE, PP, PVC, and PET. The variables tested were the feedstock properties, the feed intake capacity, reaction temperature, and vapor residence time. The results showed that the DT algorithm predicted the response with the best accuracy among the other algorithms, with an R\({}^{2}\) value of 0.984. The ANN algorithm also showed a relatively high accuracy, with an R\({}^{2}\) value of 0.926. However, the GP and SVM algorithms were not applicable for predicting the liquid product yield in this study. The fitensemble (i.e., applying the formula to fit the model to the predictor and response data) mode of DT also performed excellent results in predicting the mass and energy balances of the process. The DT model was also able to generalize the process, making it highly efficient for this type of predictive modeling. It was also observed that the data set with the elemental composition of plastic achieved higher accuracy in predicting the liquid yield than the sample with the plastic. This observation allows for accurate predictions even when specific plastic-type data are unavailable, relying instead on easily obtainable elemental analysis. This study highlights the potential of machine learning in improving the predictability and control of the pyrolysis process of plastic waste into useful products, thereby addressing its environmental challenges. **Pyrolysis-effect of temperature and residence time:** [PERSON] et al. [48] studied the effect of reactor temperature and residence time on the quality of liquid oil produced from low-temperature pyrolysis of a plastic mixture. Two types of samples were tested in this study: a virgin mixture of PP, HDPE, and PS, and real plastic waste collected from a recycling facility. The real-waste sample was pyrolyzed in a batch reactor conducting a 2\({}^{2}\) factorial design to optimize the production of liquid oil using values in the range of 430-460 \({}^{\circ}\)C, and 45-60 min for temperature and residence time, respectively. The highest liquid yield of 36.8% was achieved at the central point of the experimental design at 445 \({}^{\circ}\)C and 53 min. The results of the experiments also showed the importance of the temperature variable over residence time in the pyrolysis process. Furthermore, three mixtures of virgin plastics in different ratios were also pyrolyzed to determine which type of plastic generates the highest pyrolysis oil. The experiments were carried out at a temperature of 430 \({}^{\circ}\)C and a residence time of 60 min. The PS sample produced the highest oil with a yield of 85.2%. Also, a mixture of 25 wt.% PP + 75 wt.% HDPE produced a high yield of 83%. Analyzing the obtained oil, it was found to contain high amounts of aromatic hydrocarbons and phenol and its derivatives. Moreover, the oil obtained from different experiments had an HHV of around 40 MJ kg\({}^{-1}\), matching that of commercial fuel. The oil was also less viscous and lighter, with a similar toxicity profile to commercial fuels.