Improving the estimation accuracy of confined vapor-liquid equilibria by fine-tuning the pure component parameter in the PC-SAFT equation of state.

We propose a thermodynamic model that combines the Young-Laplace equation and perturbed chain-statistical associating fluid theory (PC-SAFT) equation of state to estimate capillary condensation pressure in microporous and mesoporous sorbents. We adjust the PC-SAFT dispersion-energy parameter when the pore size becomes comparable to the molecular dimension. This modelling framework is applied to diverse systems containing associating and non-associating gases, various sorbents, and a wide range of temperatures. Our simulation results show that under extreme confinement, a higher value of the dispersion-energy parameter (ε) is required. Furthermore, using the experimental saturation pressure data for 18 different associating and non-associating confined fluids, we find that the shift in the PC-SAFT dispersion energy correlates with the ratio of the sorbent mean pore size to the PC-SAFT segment size (rp/σ). By fitting to the capillary condensation data, the relative deviation between the confined and bulk PC-SAFT dispersion energy parameter is only 0.1% at rp/σ = 15; however, this deviation starts to increase exponentially as rp/σ decreases. For a sorbent with large pores, when rp/σ > 15, the capillary condensation pressure results from our model are similar to the predictions from the Kelvin equation. Using a dataset containing 235 saturation pressure data points composed of 18 pure gases and 4 binary mixtures, the overall AARD% from our model is 12.26%, which verifies the good accuracy of our model. Because the mean sorbent pore radius (rp), the PC-SAFT energy parameter (ε), and segment size (σ) are known a priori, our model estimates the corrected energy parameter for small pores and, thus, extends its applicability.

Optimization of biological sulfide removal in a CSTR bioreactor.

In this study, biological sulfide removal from natural gas in a continuous bioreactor is investigated for estimation of the optimal operational parameters. According to the carried out reactions, sulfide can be converted to elemental sulfur, sulfate, thiosulfate, and polysulfide, of which elemental sulfur is the desired product. A mathematical model is developed and was used for investigation of the effect of various parameters on elemental sulfur selectivity. The results of the simulation show that elemental sulfur selectivity is a function of dissolved oxygen, sulfide load, pH, and concentration of bacteria. Optimal parameter values are calculated for maximum elemental sulfur selectivity by using genetic algorithm as an adaptive heuristic search. In the optimal conditions, 87.76% of sulfide loaded to the bioreactor is converted to elemental sulfur.