Yes / A detailed steady state FCC riser process model is simulated for the first time with different compressibility (Z) factor correlations using gPROMS software. A 4-lump kinetic model is used where gas oil cracks to form gasoline, coke and gases. The usual practice has been the assumption that the FCC riser gas phase is an ideal gas at every point under any condition (varying C/O ratio, riser diameter, operating temperature and pressure, etc.). This work found that the Z factor varies at every point across the riser height depending on riser operating pressure and temperature, diameter and C/O ratio. It also shows that the magnitude of deviation of a gas phase from ideal gas behaviour can be measured over the riser height. The Z factor correlation of Heidaryan et al. (2010a) is found to be suitable for predicting the Z factor distribution in the riser. / Petroleum Technology Development Fund, Nigeria

Yes / A detailed steady-state catalytic-reforming unit (CRU) reactor process model is simulated in this work, and for the first time, different compressibility Z factor correlations have been applied using gPROMS software. The CRU has been modeled and simulated with the assumption that the gas phase behaves like an ideal gas. This is assumed for the four reactors in series and for different conditions of hydrogen–hydrocarbon ratio (HHR), operating temperature, and pressure. The results show that the Z factor varies at every point along the height of the reactors depending on reaction operating pressure, temperature, and HHR ratio. It also shows that the magnitude of deviation from ideal gas behaviour can be measured over the reactor height. The Z factor correlation of Mahmoud (J Energy Resour Technol Trans ASME 136:012903, 2014) is found to be suitable for predicting the Z factor distribution in the reactors.

The Fluidized Catalytic Cracking (FCC) is known for its ability to convert refinery wastes into useful fuels such as gasoline, diesel and some lighter products such as ethylene and propylene, which are major building blocks for the polyethylene and polypropylene production. It is the most important unit of the refinery. However, changes in quality, nature of crude oil blends feedstock, environmental changes and the desire to obtain higher profitability, lead to many alternative operating conditions of the FCC riser. There are two major reactors in the FCC unit: the riser and the regenerator. The production objective of the riser is the maximisation of gasoline and diesel, but it can also be used to maximise products like propylene, butylene etc. For the regenerator, it is for regeneration of spent or deactivated catalyst. To realise these objectives, mathematical models of the riser, disengage-stripping section, cyclones and regenerator were adopted from the literature and modified, and then used on the gPROMS model builder platform to make a virtual form of the FCC unit. A new parameter estimation technique was developed in this research and used to estimate new kinetic parameters for a new six lumps kinetic model based on an industrial unit. Research outputs have resulted in the following major products’ yields: gasoline (plant; 47.31 wt% and simulation; 48.63 wt%) and diesel (plant; 18.57 wt% and simulation; 18.42 wt%) and this readily validates the new estimation methodology as well as the kinetic parameters estimated. The same methodology was used to estimate kinetic parameters for a new kinetic reaction scheme that considered propylene as a single lump. The yield of propylene was found to be 4.59 wt%, which is consistent with published data. For the first time, a Z-factor correlation analysis was used in the riser simulation to improve the hydrodynamics. It was found that different Z factor correlations predicted different riser operating pressures (90 – 279 kPa) and temperatures as well as the riser products. The Z factor correlation of Heidaryan et al. (2010a) was found to represent the condition of the riser, and depending on the catalyst-to-oil ratio, this ranges from 1.06 at the inlet of the riser to 0.92 at the exit. Optimisation was carried out to maximise gasoline, propylene in the riser and minimise CO2 in the regenerator. An increase of 4.51% gasoline, 8.93 wt.% increase in propylene as a single lump and 5.24 % reduction of carbon dioxide emission were achieved. Finally, varying the riser diameter was found to have very little effect on the yields of the riser products.

O fator de compressibilidade (Z) de gás natural é utilizado em vários cálculos na engenharia de petróleo (avaliação de formações, perda de carga em tubulações, gradiente de pressão em poços de gás, cálculos de balanço de massa, medição de gás, compressão e processamento de gás). As fontes mais comuns de valores de Z são medições experimentais, caras e demoradas. Essa propriedade também é estimada por correlações empíricas, modelos baseados no princípio dos estados correspondentes ou equações de estado (EOS). Foram avaliadas as capacidades das EOS de Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), Patel-Teja (PT), Patel-Teja-Valderrama (PTV), Schmidt-Wenzel (SW), Lawal-Lake-Silberberg (LLS) e AGA-8 para previsão desta propriedade em aproximadamente 2200 pontos de dados experimentais. Estes pontos foram divididos em quatro grupos: Grupo 1 (Presença de frações C7+, Grupo 2 (temperaturas inferiores a 258,15 K), Grupo 3 (pressões superiores a 10000 kPa) e Grupo 4 (pressões inferiores a 10000 kPa). Os cálculos utilizando as equações de estado sob diferentes esquemas de previsão de coeficientes binários de interação foram cuidadosamente investigados. Os resultados sugerem que a EOS AGA-8 apresenta os menores erros para pressões de até 70000 kPa. Entretanto, observou-se uma tendência de aumento nos desvios médios absolutos em função das concentrações de CO2 e H2S. As EOS PTV e a EOS SW são capazes de predizer o fator de compressibilidade (Z) com desvios médios absolutos entre os valores calculados e experimentais com precisão satisfatória para a maioria das aplicações, para uma variada faixa de temperatura e pressão. Este estudo também apresenta uma avaliação de 224 métodos de cálculo de Z onde foram utilizadas 8 correlações combinadas com 4 regras de mistura para estimativa de temperaturas e pressões pseudorreduzidas das amostras, junto com 7 métodos de caracterização das propriedades críticas da fração C7+, quando presente na composição do gás. Em função dos resultados são sugeridas, para diferentes tipos de sistemas, as melhores combinações de correlações com regras de mistura capazes de predizer fatores de compressibilidade (Z) com os menores erros absolutos médios relativos / The compressibility factor (Z-factor) of natural gases is necessary in many petroleum engineering calculations (evaluation of formation, pressure drop through a pipe, pressure gradient in gas wells, material balance calculations, gas metering, gas compression and processing). The most common sources of Z-factor values are experimental measurements, which are expensive and time consuming. This property is also estimated from empirical correlations, corresponding state models or equations of state (EOS) when experimental data is unavailable. Capabilities of Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), Patel-Teja (PT), Patel-Teja-Valderrama (PTV), Schmidt-Wenzel (SW), Lawal-Lake-Silberberg (LLS) and AGA-8 to predict this property of 2200 data points under various schemes of binary interaction numbers are thoroughly investigated. This database was split on four groups: Group 1 (presence of hydrocarbon plus fraction - C7+), Group 2 (temperatures lower than 258.15 K), Group 3 (pressures higher than 10000 kPa) e Group 4 (pressures lower than 10000 kPa). The best results were obtained from EOS AGA-8 for pressures up to 70000 kPa. However, an increasing trend in average absolute deviations was observed as a function of CO2 e H2S concentrations. EOS PTV and EOS SW are capable to predict the compressibility factor (Z) with average absolute deviation between calculated and experimental values with satisfactory accuracy for most applications for a wide range of temperature and pressure. This study also presents an evaluation of 224 possible methods of calculating the gas compressibility factor, based on 8 correlations and corresponding state models, combined with 4 mixing rule that predict the pseudo-reduced pressure and temperatures of the mixture, that were combined with 7 methods of characterizing the plus fraction critical properties when present in the gas composition. Results suggest for different systems conditions, the best correlation and mixing rule combination capable of predicting Z-factor with the least average absolute relative error

O fator de compressibilidade (Z) de gás natural é utilizado em vários cálculos na engenharia de petróleo (avaliação de formações, perda de carga em tubulações, gradiente de pressão em poços de gás, cálculos de balanço de massa, medição de gás, compressão e processamento de gás). As fontes mais comuns de valores de Z são medições experimentais, caras e demoradas. Essa propriedade também é estimada por correlações empíricas, modelos baseados no princípio dos estados correspondentes ou equações de estado (EOS). Foram avaliadas as capacidades das EOS de Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), Patel-Teja (PT), Patel-Teja-Valderrama (PTV), Schmidt-Wenzel (SW), Lawal-Lake-Silberberg (LLS) e AGA-8 para previsão desta propriedade em aproximadamente 2200 pontos de dados experimentais. Estes pontos foram divididos em quatro grupos: Grupo 1 (Presença de frações C7+, Grupo 2 (temperaturas inferiores a 258,15 K), Grupo 3 (pressões superiores a 10000 kPa) e Grupo 4 (pressões inferiores a 10000 kPa). Os cálculos utilizando as equações de estado sob diferentes esquemas de previsão de coeficientes binários de interação foram cuidadosamente investigados. Os resultados sugerem que a EOS AGA-8 apresenta os menores erros para pressões de até 70000 kPa. Entretanto, observou-se uma tendência de aumento nos desvios médios absolutos em função das concentrações de CO2 e H2S. As EOS PTV e a EOS SW são capazes de predizer o fator de compressibilidade (Z) com desvios médios absolutos entre os valores calculados e experimentais com precisão satisfatória para a maioria das aplicações, para uma variada faixa de temperatura e pressão. Este estudo também apresenta uma avaliação de 224 métodos de cálculo de Z onde foram utilizadas 8 correlações combinadas com 4 regras de mistura para estimativa de temperaturas e pressões pseudorreduzidas das amostras, junto com 7 métodos de caracterização das propriedades críticas da fração C7+, quando presente na composição do gás. Em função dos resultados são sugeridas, para diferentes tipos de sistemas, as melhores combinações de correlações com regras de mistura capazes de predizer fatores de compressibilidade (Z) com os menores erros absolutos médios relativos / The compressibility factor (Z-factor) of natural gases is necessary in many petroleum engineering calculations (evaluation of formation, pressure drop through a pipe, pressure gradient in gas wells, material balance calculations, gas metering, gas compression and processing). The most common sources of Z-factor values are experimental measurements, which are expensive and time consuming. This property is also estimated from empirical correlations, corresponding state models or equations of state (EOS) when experimental data is unavailable. Capabilities of Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), Patel-Teja (PT), Patel-Teja-Valderrama (PTV), Schmidt-Wenzel (SW), Lawal-Lake-Silberberg (LLS) and AGA-8 to predict this property of 2200 data points under various schemes of binary interaction numbers are thoroughly investigated. This database was split on four groups: Group 1 (presence of hydrocarbon plus fraction - C7+), Group 2 (temperatures lower than 258.15 K), Group 3 (pressures higher than 10000 kPa) e Group 4 (pressures lower than 10000 kPa). The best results were obtained from EOS AGA-8 for pressures up to 70000 kPa. However, an increasing trend in average absolute deviations was observed as a function of CO2 e H2S concentrations. EOS PTV and EOS SW are capable to predict the compressibility factor (Z) with average absolute deviation between calculated and experimental values with satisfactory accuracy for most applications for a wide range of temperature and pressure. This study also presents an evaluation of 224 possible methods of calculating the gas compressibility factor, based on 8 correlations and corresponding state models, combined with 4 mixing rule that predict the pseudo-reduced pressure and temperatures of the mixture, that were combined with 7 methods of characterizing the plus fraction critical properties when present in the gas composition. Results suggest for different systems conditions, the best correlation and mixing rule combination capable of predicting Z-factor with the least average absolute relative error

The labyrinth seal is a non-contact annular type sealing device used to reduce the internal leakage of the working fluid which is caused by the pressure difference between each stage in a turbomachine. Reducing the leakage mass flow rate of the working fluid through the labyrinth seal is desirable because it improves the efficiency of the turbomachine. The carry-over coefficient, based on the divergence angle of the jet, changed with flow parameters with fixed seal geometry while earlier models expressed the carry-over coefficient solely as a function of seal geometry. For both compressible and incompressible flows, the Reynolds number based on clearance was the only flow parameter which could influence the carry-over coefficient. In the case of incompressible flow based on the simulations for various seal geometries and operating conditions, for a given Reynolds number, the carry-over coefficient strongly depended on radial clearance to tooth width ratio. Moreover, in general, the lower the Reynolds number, the larger is the divergence angle of the jet and this results in a smaller carry-over coefficient at lower Reynolds numbers. However, during transition from laminar to turbulent, the carry-over coefficient reduced initially and once the Reynolds number attained a critical value, the carry-over coefficient increased again. In the case of compressible flow, the carry-over coefficient had been slightly increased if radial clearance to tooth width ratio and radial clearance to tooth pitch ratio were increased. Further, the carry-over coefficient did not considerably change if only radial clearance to tooth width ratio was decreased. The discharge coefficient for compressible and incompressible flows depended only on the Reynolds number based on clearance. The discharge coefficient of the tooth in a single cavity labyrinth seal was equivalent to that in a multiple tooth labyrinth seal indicating that flow downstream had negligible effect on the discharge coefficient. In particular, for compressible fluid under certain flow and seal geometric conditions, the discharge coefficient did not increase with an increase in the Reynolds number. It was correlated to the pressure ratio, Pr. Moreover, it was also related to the fact that the flow of the fluid through the constriction became compressible and the flow eventually became choked. At low pressure ratios (less than 0.7), Saikishan’s incompressible model deviated from CFD simulation results. Hence, the effects of compressibility became significant and both the carry-over coefficient compressibility factor and the discharge coefficient compressibility factor needed to be considered and included into the leakage model. The carry-over coefficient compressibility factor, phi, had two linear relationships with positive and negative slopes regarding the pressure ratios. This result was not associated with the seal geometry because the seal geometry ratios for each instance were located within the nearly same ranges. Further, the phi-Pr relationship was independent of the number of teeth regardless of single and multiple cavity labyrinth seals. The discharge coefficient compressibility factor, psi, was a linear relationship with pressure ratios across the tooth as Saikishan predicted. However, in certain flow and seal geometric conditions, Saikishan’s model needed to be modified for the deviation appearing when the pressure ratios were decreased. Hence, a modified psi-Pr relationship including Saikishan’s model was presented in order to compensate for the deviation between the simulations and his model.