Iranian Journal of Chemical Engineering (IJChE)

Abstract The pyrolysis of polyethylene terephthalate (PET) was investigated across a broad range of final temperatures, from 478°C to 640°C, yielding various products. Infrared radiation was employed as the heat source in this study. To minimize experimental deviations, each test under fixed conditions was repeated twice, and the results were compared for consistency. The primary objective was to evaluate the quantitative and qualitative differences in the products generated through this novel heating method, aiming to enhance the understanding of the pyrolysis process and the influence of the heating rate on the final outputs. Pyrolysis was conducted in a vertical tubular reactor housed within an infrared furnace. The average heating rates varied significantly, ranging from approximately 20.61°C/min in the slowest test to 161.12°C/min in the fastest one. The solid residue (char) resulted from PET pyrolysis was analyzed. The yields of 87.5% and 91% were recorded for the slowest and fastest heating rates respectively. These char samples were assessed using Differential Thermal Analysis (DTA) and Thermogravimetric Analysis (TGA) to compare their quantity and quality. The findings revealed that the heating rate during the infrared pyrolysis of PET has a direct correlation with the quantity of valuable products, but an inverse relationship with their quality. 

Abstract This paper presents a machine learning-based approach for accurately predicting pyrolysis product yields. Methods such as Linear Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Support Vector Regression (SVR), Random Forest (RF), and Neural Networks (NN) leverage operating conditions and/or ultimate/proximate analysis data, eliminating the need for reaction kinetics. This innovative approach offers a broader range and higher accuracy of feedstock compared to traditional kinetics-based methods. The KNN model demonstrated superior performance, achieving a correlation coefficient greater than 0.998 and an RMSE of 0.64. These findings provide valuable insights for engineers and practitioners, facilitating the efficient design and operation of pyrolysis units.The selectivity exhibited a notable increase from 2.46 to 5.27. This improvement in selectivity can primarily be attributed to the significantly higher increase in the solubility coefficient of CO₂ compared to that of CH₄.

Abstract Different methods of urban sewage sludge energy recovery such as burning, gasification, pyrolysis and digestion based on the net energy production efficiency, advantages and disadvantages and complexity of these processes have been investigated in this article. The best method for energy production from sludge was selected among different methods according to energy and the amount of the greenhouse gas production. The capacity of the constructed power plant was calculated and investigated economically for each scenario. Quantitative and qualitative information on sludge was required to carry out this research so Ekbatan wastewater treatment sludge was analyzed. The results showed that the sludge of this treatment plant has 5.7% solids, containing 65.7% volatiles and the dry heat value is about 15100 kJ/kg. It was found that the best scenario for sludge energy production in this treatment plant is a digestion process with pure net energy production of 73.2 × 107 kJ/d. The energy recovery in an anaerobic digester can prevent the emission of 16,680 tons of CO2 annually and release about 1,460 tons of CO2 per year. The chemical analysis shows that the selected sludge has a potential production of 25m3 of CH4 for each m3 of sludge. The annual amount of biogas that can be recovered from municipal treatment plant is 836543 m3. On the other hand, the biogas can be used to generate electricity. The power of the plant is about 216.8 kW that with the construction of this power plant, an annual saving of 1.5 million dollars will occur.

Abstract Eventual realization of a hydrogen economy requires cheap and readily available hydrogen sources and a technology to convert them into pure hydrogen in an efficient and sustainable manner. The objective of this paper is the computational investigation of the hydrogen production through thermal decomposition of sour natural gases containing CH 4 and H 2S inside a solar reactor. In this study a solar reactor has been used to absorb solar power and allow it to concentrate in a graphite nozzle in the middle of the reactor and transfer it to a flow reactant thereafter. From an economical standpoint, the main issue is the production of two valuable products, hydrogen (H 2) and a high-value nano-material of carbon. The effects of H 2S to CH 4 feed ratio and reactor temperature on hydrogen, carbon black, S2 and CS 2 formation are investigated. The results show complex behavior in the products because of the difference in CH 4 and H 2S pyrolysis temperatures. It can be seen that for H2S/CH 4>2, the yields of H2 and CS 2 reach a local and global maximum at H2S/CH 4=5, respectively. A comparison performed between the presented results and the results of Towler and Lynn, and Abanades demonstrates a good agreement.