Tuning the morphology and textural properties of ZSM-5 additive for co-cracking of waste plastics with vacuum gas oil to light olefins.

Typical cracking catalysts, called equilibrium catalyst (E-Cat) are ultra-stable Y (USY) zeolite often used with 15% commercial ZSM-5 zeolite additive (ZSM-5(COM)) to boost olefin yield. In this study, similar additive zeolites with different pore sizes and acidic character were synthesized by rapid ageing of precursor solution and used in the co-cracking of low-density polyethylene (LDPE) and heavy vacuum gas oil (HVGO). Three ZSM-5 zeolites additives with Si/Al ratio 25 (ZSM-5(25)), 50 (ZSM-5(50)) and 75 (ZSM-5(75)) were synthesized and combined with E-Cat to form E-Cat/ZSM-5(25), E-Cat/ZSM-5(50) and E-Cat/ZSM-5(75) respectively. The E-Cat/ZSM-5(50) has slightly better endothermic conversion (cracking) of a mixture of dissolved LDPE and HVGO into H2, C1 to C4 gases and C2-C4 light olefins (total conversion of E-Cat 80.0%, E-Cat/ZSM-5(COM) 75.0% and E-Cat/ZSM-5(50) 83.7% respectively), with different gas, liquid and coke distributions. The E-Cat/ZSM-5(75) has 81% conversion, and highest yield of light olefins (38.4%). Structural (surface area, pore size) and chemical (acid sites) characteristics of the synthetized ZSM-5(75) zeolite explain the observed higher light olefin selectivity by different and competing catalytic routes. The ZSM-5(75) has demonstrated to be a good zeolite additive for converting dissolved plastic in HVGO into light olefins.

Feasibility of the Optimal Design of AI-Based Models Integrated with Ensemble Machine Learning Paradigms for Modeling the Yields of Light Olefins in Crude-to-Chemical Conversions.

The prediction of the yields of light olefins in the direct conversion of crude oil to chemicals requires the development of a robust model that represents the crude-to-chemical conversion processes. This study utilizes artificial intelligence (AI) and machine learning algorithms to develop single and ensemble learning models that predict the yields of ethylene and propylene. Four single-model AI techniques and four ensemble paradigms were developed using experimental data derived from the catalytic cracking experiments of various crude oil fractions in the advanced catalyst evaluation reactor unit. The temperature, feed type, feed conversion, total gas, dry gas, and coke were used as independent variables. Correlation matrix analyses were conducted to filter the input combinations into three different classes (M1, M2, and M3) based on the relationship between dependent and independent variables, and three performance metrics comprising the coefficient of determination (R2), Pearson correlation coefficient (PCC), and mean square error (MSE) were used to evaluate the prediction performance of the developed models in both calibration and validations stages. All four single models have very low R2 and PCC values (as low as 0.07) and very high MSE values (up to 4.92 wt %) for M1 and M2 in both calibration and validation phases. However, the ensemble ML models show R2 and PCC values of 0.99-1 and an MSE value of 0.01 wt % for M1, M2, and M3 input combinations. Therefore, ensemble paradigms improve the performance accuracy of single models by up to 58 and 62% in the calibration and validation phases, respectively. The ensemble paradigms predict with high accuracy the yield of ethylene and propylene in the catalytic cracking of crude oil and its fractions.

Chemical Upcycling of Waste Plastics to High Value-Added Products via Pyrolysis: Current Trends, Future Perspectives, and Techno-Feasibility Analysis.

Chemical upcycling of waste plastics into high-value-added products is one of the most effective, cost-efficient, and environmentally beneficial solutions. Many studies have been published over the past few years on the topic of recycling plastics into usable materials through a process called catalytic pyrolysis. There is a significant research gap that must be bridged in order to use catalytic pyrolysis of waste plastics to produce high-value products. This review focuses on the enhanced catalytic pyrolysis of waste plastics to produce jet fuel, diesel oil, lubricants, aromatic compounds, syngas, and other gases. Moreover, the reaction mechanism, a brief and critical comparison of different catalytic pyrolysis studies, as well as the techno-feasibility analysis of waste plastic pyrolysis and the proposed catalytic plastic pyrolysis setup for commercialization is also covered.

Enhanced light olefins production via n-pentane cracking using modified MFI catalysts.

n-pentane catalytic cracking was studied over a series of MFI zeolites with varying SiO2/Al2O3 ratios (30, 80, 280, 500, and 1500) using a fixed-bed reactor operated at temperature 550-650 °C. Other MFI zeolites (SiO2/Al2O3 = 280) with various crystal morphology and size (such as large crystal and nano size) were also synthesized and tested for n-pentane cracking. The effects of MFI zeolite modification with ammonia and phosphorus on its physiochemical properties and catalytic activity were investigated. Among the parent MFI zeolites, MFI (280) demonstrated high selectivity (51%) towards light olefins (C3 =/C2 = = 0.7) at 650 °C with undesired C1-C4 alkanes (38%). Surface modified MFI (280) zeolites of different crystal size and morphology showed improvement towards propylene selectivity by suppressing undesired reactions. Phosphorous-modified MFI zeolite with a large crystal size was found to improve light olefin selectivity (52.2%) with C3 =/C2 = = ∼1.3 and reduce undesired C1-C4 alkanes (8%) formation due to suppressed strong acidic sites. The characterization and evaluation results for the modified MFI (280) revealed that the incorporation of phosphorous created moderate acidic sites, which were stabilized by some non-framework aluminum species, thereby leading to suppressing the formation of undesired C1-C4 alkanes with improved light olefins selectivity.