Abstract: The increased demand and consumption of virgin plastics have led in parallel to growing waste plastics disposed in landfills causing serious hazards towards the environment. In the present study, a Portland cement (PC) was used for the first time as very cheap and commercially available catalyst for the low– temperature pyrolysis of waste polypropylene (WPP) to diesel range pyrolytic oil, utilizing a single – stage semi–batch reactor designed well at appropriate pyrolyzer / catalytic reformer ratio. The thermal decomposition of WPP was studied using a thermogravimetric analysis (TGA). The liquid fuels produced by both catalytic and non– catalytic pyrolysis of WPP at 280°C were investigated by means of gas chromatography – mass spectrometry (GC–MS), Infrared (IR) spectroscopy, and physic–chemical properties of fuels. The PC–catalyzed pyrolysis resulted in remarkably increased liquid and gaseous products, and reduced char yield. Moreover, it significantly prevented the wax production. The results obtained in this work prove that the liquid fuel produced by the PC– catalyzed pyrolysis has nearly similar hydrocarbon composition and functional properties of the commercial grade diesel.Abstract: The increased demand and consumption of virgin plastics have led in parallel to growing waste plastics disposed in landfills causing serious hazards towards the environment. In the present study, a Portland cement (PC) was used for the first time as very cheap and commercially available catalyst for the low– temperature pyrolysis of waste polypropy...Show More
Abstract: Asphaltenes are precipitated and deposited during gas injection and this causes pore throat reduction, permeability reduction and wettability reversal. The result is reduced oil produced thereby leading to sizable revenue loss by field operators. To mitigate or completely prevent the occurrence of this phenomenon, this work has utilised Hybrid Genetic Algorithm Particle Swarm Optimisation-Artificial Neural Network (HGAPSO-ANN) for predicting the amount of asphaltenes deposited in the reservoir during gas injection. A number of methods are available for predicting the amount of asphaltenes deposited but some of them are either too expensive to execute or fraught with errors and deviations. This is due to the nature of asphaltene which is complicated and ambiguous. Some of the methods in existence include correlation with solvent properties, thermodynamic models and recently connectionist models (neural networks). There is however, no publication in the literature on using hybrid algorithms with neural networks to predict asphaltene precipitation during gas injection and this becomes an interesting area of research considering the enormous benefits that would be obtained from a robust hybrid asphaltene precipitation prediction model. The developed model performed well with an AARE of 0.09. This is lower than AARE values reported by Hue et al (2000), Rostami and Manshad (2010), Manshad et al (2015) which were 0.183, 0.153, and 0.121 respectively From the results of the model it can be seen that HGAPSO-ANN is more accurate in predicting asphaltene precipitation than other existing predictive models consulted. This method can therefore, be used as a decision making tool by field operators to set up procedures for the prevention or mitigation of asphaltene precipitation during gas injection. This will help prevent revenue losses and increase profitability of recovering hydrocarbons using gas injection.Abstract: Asphaltenes are precipitated and deposited during gas injection and this causes pore throat reduction, permeability reduction and wettability reversal. The result is reduced oil produced thereby leading to sizable revenue loss by field operators. To mitigate or completely prevent the occurrence of this phenomenon, this work has utilised Hybrid Gene...Show More
