ABSTRACT
In this work, in situ polymerization of modified sol-gel silica in a polyether sulfone matrix is presented to control the interfacial defects in organic-inorganic composite membranes. Polyether sulfone polymer and modified silica are used as organic and inorganic components of mixed matrix membranes (MMM). The membranes were prepared with different loadings (2, 4, 6, and 8 wt.%) of modified and unmodified silica. The synthesized membranes were characterized using Field emission electron scanning microscopy, energy dispersive X-ray, Fourier transform infrared spectroscopy, thermogravimetric analyzer, and differential scanning calorimetry. The performance of the membranes was evaluated using a permeation cell set up at a relatively higher-pressure range (5-30 bar). The membranes appear to display ideal morphology with uniform distribution of particles, defect-free structure, and absence of interfacial defects such as voids and particle accumulations. Additionally, the CO2/CH4 selectivity of the membrane increased with the increase in the modified silica content. Further comparison of the performance indicates that PES/modified silica MMMs show a promising feature of commercially attractive membranes. Therefore, tailoring the interfacial morphology of the membrane results in enhanced properties and improved CO2 separation performance.
ABSTRACT
Ionic liquids (ILs) show remarkable performance in enhancing the naphthenic acid extraction efficiency and decreasing the extraction time. However, the ultrasonic-assisted IL-based extraction of naphthenic acid is merely addressed previously. Therefore, this study investigated the impact of essential ultrasonic parameters, including amplitude and time, on naphthenic acid extraction using different ILs, and the system was optimized for maximum extraction. The IL 1,8-diazobicyclo[5.4.0]-undec-7-ene (DBU) with thiocyanate anions revealed the highest efficiency in extracting naphthenic acid from a model oil (dodecane) at optimized conditions, and the experimental liquid-liquid equilibrium data were obtained at atmospheric pressure for the mixture of dodecane, [DBU], thiocyanate, and naphthenic acid. In addition, the influence of the chain length of the cation (hexyl, octyl, or decyl) on the extraction efficiency was also evaluated by determining the distribution coefficients, and the conductor-like screening model for real solvents (COSMO-RS) study was carried out at infinite dilution. It was found that [DBU-Dec] [SCN] gives the best extraction efficiency and has a distribution coefficient of 9.2707 and a performance index of 49.48. Based on these values, ILs can be ordered as follows: [DBU-Dec] [SCN] > [DBU-Oct][SCN] > [DBU-Hex][SCN] in the decreasing order of performance index 49.48, 41.58, and 28.13. Moreover, non-random two liquid and Margules thermodynamic models were employed to investigate the interaction parameters between the components. Both models showed excellent agreement with the experimental results and could successfully be used for ultrasonic-assisted IL extraction of naphthenic acid.
ABSTRACT
Membranes for carbon capture have improved significantly with various promoters such as amines and fillers that enhance their overall permeance and selectivity toward a certain particular gas. They require nominal energy input and can achieve bulk separations with lower capital investment. The results of an experiment-based membrane study can be suitably extended for techno-economic analysis and simulation studies, if its process parameters are interconnected to various membrane performance indicators such as permeance for different gases and their selectivity. The conventional modelling approaches for membranes cannot interconnect desired values into a single model. Therefore, such models can be suitably applicable to a particular parameter but would fail for another process parameter. With the help of artificial neural networks, the current study connects the concentrations of various membrane materials (polymer, amine, and filler) and the partial pressures of carbon dioxide and methane to simultaneously correlate three desired outputs in a single model: CO2 permeance, CH4 permeance, and CO2/CH4 selectivity. These parameters help predict membrane performance and guide secondary parameters such as membrane life, efficiency, and product purity. The model results agree with the experimental values for a selected membrane, with an average absolute relative error of 6.1%, 4.2%, and 3.2% for CO2 permeance, CH4 permeance, and CO2/CH4 selectivity, respectively. The results indicate that the model can predict values at other membrane development conditions.
ABSTRACT
The chemical process industry has become the backbone of the global economy. The complexities of chemical process systems have been increased in the last two decades due to online sensor technology, plant-wide automation, and computerized measurement devices. Principal component analysis (PCA) and signed directed graph (SDG) are some of the quantitative and qualitative monitoring techniques that have been widely applied for chemical fault detection and diagnosis (FDD). The conventional PCA-SDG algorithm is a single-scale FDD representation origin, which cannot effectively solve multiple FDD representation origins. The multiscale PCA-SDG wavelet-based monitoring technique has potential because it easily distinguishes between deterministic and stochastic characteristics. This study uses multiscale PCA-SDG to detect, diagnose the root cause and identify the fault propagation path. The proposed method is applied to a continuous stirred tank reactor system to validate its effectiveness. The propagation route of most process failures is detected, identified, and diagnosed, which is well-aligned with the fault description, demonstrating a satisfactory performance of the suggested technique for monitoring the process failures.
