2 resultados para Refinery effluents
em Memorial University Research Repository
Resumo:
This research explores Bayesian updating as a tool for estimating parameters probabilistically by dynamic analysis of data sequences. Two distinct Bayesian updating methodologies are assessed. The first approach focuses on Bayesian updating of failure rates for primary events in fault trees. A Poisson Exponentially Moving Average (PEWMA) model is implemnented to carry out Bayesian updating of failure rates for individual primary events in the fault tree. To provide a basis for testing of the PEWMA model, a fault tree is developed based on the Texas City Refinery incident which occurred in 2005. A qualitative fault tree analysis is then carried out to obtain a logical expression for the top event. A dynamic Fault Tree analysis is carried out by evaluating the top event probability at each Bayesian updating step by Monte Carlo sampling from posterior failure rate distributions. It is demonstrated that PEWMA modeling is advantageous over conventional conjugate Poisson-Gamma updating techniques when failure data is collected over long time spans. The second approach focuses on Bayesian updating of parameters in non-linear forward models. Specifically, the technique is applied to the hydrocarbon material balance equation. In order to test the accuracy of the implemented Bayesian updating models, a synthetic data set is developed using the Eclipse reservoir simulator. Both structured grid and MCMC sampling based solution techniques are implemented and are shown to model the synthetic data set with good accuracy. Furthermore, a graphical analysis shows that the implemented MCMC model displays good convergence properties. A case study demonstrates that Likelihood variance affects the rate at which the posterior assimilates information from the measured data sequence. Error in the measured data significantly affects the accuracy of the posterior parameter distributions. Increasing the likelihood variance mitigates random measurement errors, but casuses the overall variance of the posterior to increase. Bayesian updating is shown to be advantageous over deterministic regression techniques as it allows for incorporation of prior belief and full modeling uncertainty over the parameter ranges. As such, the Bayesian approach to estimation of parameters in the material balance equation shows utility for incorporation into reservoir engineering workflows.
Resumo:
Spent hydroprocessing catalysts (HPCs) are solid wastes generated in refinery industries and typically contain various hazardous metals, such as Co, Ni, and Mo. These wastes cannot be discharged into the environment due to strict regulations and require proper treatment to remove the hazardous substances. Various options have been proposed and developed for spent catalysts treatment; however, hydrometallurgical processes are considered efficient, cost-effective and environmentally-friendly methods of metal extraction, and have been widely employed for different metal uptake from aqueous leachates of secondary materials. Although there are a large number of studies on hazardous metal extraction from aqueous solutions of various spent catalysts, little information is available on Co, Ni, and Mo removal from spent NiMo hydroprocessing catalysts. In the current study, a solvent extraction process was applied to the spent HPC to specifically remove Co, Ni, and Mo. The spent HPC is dissolved in an acid solution and then the metals are extracted using three different extractants, two of which were aminebased and one which was a quaternary ammonium salt. The main aim of this study was to develop a hydrometallurgical method to remove, and ultimately be able to recover, Co, Ni, and Mo from the spent HPCs produced at the petrochemical plant in Come By Chance, Newfoundland and Labrador. The specific objectives of the study were: (1) characterization of the spent catalyst and the acidic leachate, (2) identifying the most efficient leaching agent to dissolve the metals from the spent catalyst; (3) development of a solvent extraction procedure using the amine-based extractants Alamine308, Alamine336 and the quaternary ammonium salt, Aliquat336 in toluene to remove Co, Ni, and Mo from the spent catalyst; (4) selection of the best reagent for Co, Ni, and Mo extraction based on the required contact time, required extractant concentration, as well as organic:aqueous ratio; and (5) evaluation of the extraction conditions and optimization of the metal extraction process using the Design Expert® software. For the present study, a Central Composite Design (CCD) method was applied as the main method to design the experiments, evaluate the effect of each parameter, provide a statistical model, and optimize the extraction process. Three parameters were considered as the most significant factors affecting the process efficiency: (i) extractant concentration, (ii) the organic:aqueous ratio, and (iii) contact time. Metal extraction efficiencies were calculated based on ICP analysis of the pre- and post–leachates, and the process optimization was conducted with the aid of the Design Expert® software. The obtained results showed that Alamine308 can be considered to be the most effective and suitable extractant for spent HPC examined in the study. Alamine308 is capable of removing all three metals to the maximum amounts. Aliquat336 was found to be not as effective, especially for Ni extraction; however, it is able to separate all of these metals within the first 10 min, unlike Alamine336, which required more than 35 min to do so. Based on the results of this study, a cost-effective and environmentally-friendly solventextraction process was achieved to remove Co, Ni, and Mo from the spent HPCs in a short amount of time and with the low extractant concentration required. This method can be tested and implemented for other hazardous metals from other secondary materials as well. Further investigation may be required; however, the results of this study can be a guide for future research on similar metal extraction processes.