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This study evaluates the occurrence of cashew plant, extraction processes and composition of cashew nut shell liquid CNSL , modification and conversion processes, as well as environmental impact and controls of the liquid as petrochemical feedstock. The goal of this study is to bring the alternative usage of CNSL to the limelight and to mitigate the serious problems posed by the depleting petroleum reserves.Cashew nut shell CNS was obtained fresh from the wild, pulverized and then extracted by pyrolysis and solvent extraction methods using n-hexane. The liquid obtained was characterized. Experimental results corroborated the fact that CNSL contains mainly phenolic compounds such as cardanol, cardol, anacardic acid andmethyl cardol. CNSL obtained by pyrolysis has a higher density with less moisture content, while solvent-extracted CNSL had higher contents of cardol and anacardic acid.CNSL offers innumerable applications due to the phenolic nature of its constituents, with enshrined features for transformation into high-value specialty chemicals.Varied CNSL composition with varying extraction processes are a vantage opportunity with multiple application potentials as a valuable petrochemical feedstock.

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This study was carried out to examine the thermo-chemical decomposition of postconsumer poly(ethylene terephthalate) (PET) in alkaline solution of butan-1-ol. The effect of various process parameters such as reactor temperature, time and sodium hydroxide concentration on the degree of PET degradation and products yield were studied and it was found that the decomposition of PET was essentially complete in an hour, with terephthalic acid and ethylene glycol being the main products. A kinetic study of the process showed that the alkaline solvolysis of PET is a second order reaction.

Artificial Neural Network (ANN) based models were developed for predicting viscosity and wax deposition potentials of petroleum reservoir fluids as a preliminary measure to address the problem of loss of production associated with wax deposition. Several ANN architectures were trained using supervised paradigms for viscosity modeling and unsupervised paradigms for wax deposition potentials. Input to the models is temperature, pressure and viscosity data of the reservoirs. Five Nigerian crude oil and gas condensate reservoir data were used to validate the models. ANN competitive layer wax deposition model developed in this work excellently identified crude oil and gas condensate potential to deposit wax in upstream and downstream facilities compared to classical regression technique (CRT) based mathematical model. The inherent problems of tubing and pipeline blockage by wax deposits would be minimized by the application of the predicting models during well development stage prior to production Introduction Paraffin crude oil problems have been identified practically in every oil producing country around the world in a trend similar to global oil occurrences. Paraffin deposition causes a loss of billions of dollars per year worldwide due to the enormous cost of prevention and remediation, reduced or deferred production, well shut-in, pipeline replacements and/or abandonment, equipment failures, extra horse power requirements, and increased manpower needs. In Nigeria, pipelines have been known to wax up beyond recovery. Production tubing has also been known to wax up necessitating frequent wax cutting, using scrapers conveyed by wireline, which is an expensive practice. The problem is becoming more severe because production is predominantly offshore, where it is more difficult to pressurize the system at intermediate points thus repeatedly leading to loss of flow-lines and abandonment of wells.