Modelling and optimization study to improve the filtration performance of fibrous filter

Fibrous filter made up of non-woven material was utilized in many industrial applications for increasing the collection efficiency and the quality factor. But there exists a competing effect among the fibre diameter, filtration efficiency, pressure drop, and sometime type of aerosol (liquid or solid) plays a crucial role in the performance of the fibrous filter. To avoid overdesigning of the filter along with better performance, optimum set of parameters are to be decided before the manufacturing process. In the current effort, the desirability approach and along with the "Response Surface Methodology (RSM)" were considered to optimize filtration efficiency and pressure drop simultaneously. In this perspective, the impact of Filtration velocity (v), Basis weight (φ), Particle diameter (dp), and Packing fraction (α) on filtration efficiency (η) and pressure drop (Pd) was studied. Based on the outcome, the predicted values lie within experimental data through smart agreement. The maximum percentage (%) error was only 3% and 6% filtration efficiency (η) and pressure drop (Pd), which determine the effectiveness of this useful model. The most dominant factor which affects the filtration efficiency (η) was found to be the Basis weight (φ), followed by packing fraction. However, in the case of pressure drop, the most dominant factors were filtration speed followed by the pachining fraction. Moreover, artificial neural network (ANN) models are developed for the prediction of filtration efficiency and pressure drop. The model accuracy has been estimated by calculating "Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2)". Both models show promising results when compared with experimental data with the R2 value of 98.50-99.86. The optimized values of the maximum filtration efficiency and minimum pressure drop simultaneously were obtained for v = 5, φ = 59.60, dp = 52.23, α = 0.24 according to desirability approach.

Energy, exergy, economic, environment, exergo-environment based assessment of amine-based hybrid solvents for natural gas sweetening

Natural gas is the cleanest form of fossil fuel that needs to be purified from CO₂ and H₂S to diminish harmful emissions and provide feasible processing. The conventional chemical and physical solvents used for this purpose have many drawbacks, including corrosion, solvent loss, high energy requirement, and the formation of toxic compounds, which ultimately disrupt the process and affect the environment. Hybrid solvents have lately been researched to cater to these liabilities and enhance process economics. This study screened eight solvents based on CO₂ selectivity viscosity, absorption enthalpy, corrosivity, working capacity, specific heat, and vapor pressure. From the screened solvents, ten cases of hybrid solvents are simulated and optimized on Aspen HYSYS®. Furthermore, 5Es (Energy, Exergy, Economic, Environmental, and Exergy-environmental) analyses were performed on optimized cases, and results were compared with the base case, MEA (30 wt%). The hybrid blend of Sulfolane and MDEA with weight percentages of 6% and 24%, respectively, showed the highest energy savings of 20% concerning the base case. In addition, it offered 93% savings in exergy destruction and 17.26% in the total operating cost of the process. It is also promising to the environment due to reduced entropy sent to the ecosystem and controlled CO₂ emissions. Therefore, the blend of Sulfolane and MDEA is proposed to Supersede the conventional solvent MEA for the natural gas sweetening process.

A molecular simulation study on amine-functionalized silica/polysulfone mixed matrix membrane for mixed gas separation

Polysulfone (PSF) based mixed matrix membranes (MMMs) are one of the most broadly studied polymeric materials used for CO₂/CH₄ separation. The performance of existing PSF membranes encounters a bottleneck for widespread expansion in industrial applications due to the trade-off amongst permeability and selectivity. Membrane performance has been postulated to be enhanced via functionalization of filler at different weight percentages. Nonetheless, the preparation of functionalized MMMs without defects and its empirical study that exhibits improved CO₂/CH₄ separation performance is challenging at an experimental scale that needs prior knowledge of the compatibility between the filler and polymer. Molecular simulation approaches can be used to explore the effect of functionalization on MMM's gas transport properties at an atomic level without the challenges in the experimental study, however, they have received less scrutiny to date. In addition, most of the research has focused on pure gas studies while mixed gas transport properties that reflect real separation in functionalized silica/PSF MMMs are scarcely available. In this work, a molecular simulation computational framework has been developed to investigate the structural, physical properties and gas transport behavior of amine-functionalized silica/PSF-based MMMs. The effect of varying weight percentages (i.e., 15-30 wt.%) of amine-functionalized silica and gas concentrations (i.e., 30% CH₄/CO₂, 50% CH₄/CO₂, and 70% CH₄/CO₂) on physical and gas transport characteristics in amine-functionalized silica/PSF MMMs at 308.15 K and 1 atm has been investigated. Functionalization of silica nanoparticles was found to increase the diffusion and solubility coefficients, leading to an increase in the percentage enhancement of permeability and selectivity for amine-functionalized silica/PSF MMM by 566% and 56%, respectively, compared to silica/PSF-based MMMs at optimal weight percentage of 20 wt.%. The model's permeability differed by 7.1% under mixed gas conditions. The findings of this study could help to improve real CO₂/CH₄ separation in the future design and concept of functionalized MMMs using molecular simulation and empirical modeling strategies.

Exergy, advance exergy, and exergo-environmental based assessment of alkanol amine- and piperazine-based solvents for natural gas purification

Purification of Natural gas is vital for utilizing it as a source of energy harvesting for the world. Amine-based chemical absorption technique is the most utilized in the gas field for the purification of gas that ensures the purity of the sweet gas stream with the elimination of carbon dioxide. However, it is considered an energy-intensive process to deal with considerable energy loss and environmental damage to the ecosystem. Five cases have been developed in this study based on various blends comprising mono and tertiary amines in combination with piperazine with a focus on the use of Aqueous Monodiethanolamine (Aq. MDEA), Aqueous Monoethanolamine (Aq. MEA) and piperazine (Pz) for the CO₂ sequestration from the sour natural gas extracted from the remote location located in the province of Baluchistan in Pakistan. The use of exergy, advanced exergy, and exergo environment for optimizing and selecting a suitable solvent combination that may result in an effective separation process has been proposed. Five cases have been developed based on various blends such as mono and tertiary amines combined with piperazine. From the results of all the studied scenarios, Case IV, based on the combination of Aqueous monoethanolamine and piperazine, provides reduced exergy destruction of 2551.7 KW. It was observed that the maximum removal of CO₂ around 99.87 wt% is achieved in case IV. In addition, advance exergy analysis also highlights that case-IV has a venue of 25% exergy destruction avoidable, which would further enhance its performance. Nevertheless, still, case-IV has 75% exergy destruction unavoidable. The environmental factors show that Case-IV has a reduced exergy destruction factor of 0.96, a highly environmentally benign choice as a solvent with a high value of 1.03, and case-IV has the higher operational stability and higher exergy efficiency with an exergy stability value of 0.40. Therefore, monoethanolamine combined with piperazine to be an effective and efficient solvent blend that could be an energy-effective approach for sweetening the natural gas.

Multiscale Principal Component Analysis-Signed Directed Graph Based Process Monitoring and Fault Diagnosis

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.

A Study on AI‐based Waste Management Strategies for the COVID‐19 Pandemic

COVID‐19 has swept across the globe and disrupted all vectors of social life. Every informed measure must be taken to stop its spread, bring down number of new infections and move to normalization of daily life. Contemporary research has not identified waste management as one of the critical transmission vectors for COVID‐19 virus. However, most underdeveloped countries are facing problems in waste management processes due to the general inadequacy and inability of waste management. In that context, smart intervention will be needed to contain possibility of the COVID‐19 spread due to inadequate waste management. This paper presents a comparative study of the artificial intelligence/machine learning based techniques, and potential applications in the COVID‐19 waste management cycle (WMC). A general integrated solid waste management (ISWM) strategy is mapped for both short‐term and long‐term goals of COVID‐19 WMC, making use of the techniques investigated. By aligning current health/waste‐related guidelines from health organizations and governments worldwide and contemporary, relevant research in area, the challenge of COVID‐19 waste management and, subsequently, slowing the pandemic down may be assisted.

A Molecular Simulation Study of Silica/Polysulfone Mixed Matrix Membrane for Mixed Gas Separation

Polysulfone-based mixed matrix membranes (MMMs) incorporated with silica nanoparticles are a new generation material under ongoing research and development for gas separation. However, the attributes of a better-performing MMM cannot be precisely studied under experimental conditions. Thus, it requires an atomistic scale study to elucidate the separation performance of silica/polysulfone MMMs. As most of the research work and empirical models for gas transport properties have been limited to pure gas, a computational framework for molecular simulation is required to study the mixed gas transport properties in silica/polysulfone MMMs to reflect real membrane separation. In this work, Monte Carlo (MC) and molecular dynamics (MD) simulations were employed to study the solubility and diffusivity of CO₂/CH₄ with varying gas concentrations (i.e., 30% CO₂/CH₄, 50% CO₂/CH₄, and 70% CO₂/CH₄) and silica content (i.e., 15–30 wt.%). The accuracy of the simulated structures was validated with published literature, followed by the study of the gas transport properties at 308.15 K and 1 atm. Simulation results concluded an increase in the free volume with an increasing weight percentage of silica. It was also found that pure gas consistently exhibited higher gas transport properties when compared to mixed gas conditions. The results also showed a competitive gas transport performance for mixed gases, which is more apparent when CO₂ increases. In this context, an increment in the permeation was observed for mixed gas with increasing gas concentrations (i.e., 70% CO₂/CH₄ > 50% CO₂/CH₄ > 30% CO₂/CH₄). The diffusivity, solubility, and permeability of the mixed gases were consistently increasing until 25 wt.%, followed by a decrease for 30 wt.% of silica. An empirical model based on a parallel resistance approach was developed by incorporating mathematical formulations for solubility and permeability. The model results were compared with simulation results to quantify the effect of mixed gas transport, which showed an 18% and 15% percentage error for the permeability and solubility, respectively, in comparison to the simulation data. This study provides a basis for future understanding of MMMs using molecular simulations and modeling techniques for mixed gas conditions that demonstrate real membrane separation.

Physical property and gas transport studies of ultrathin polysulfone membrane from 298.15 to 328.15 K and 2 to 50 bar: atomistic molecular simulation and empirical modelling

Elucidation of ultrathin polymeric membrane at the laboratory scale is complicated at different operating conditions due to limitation of instruments to obtain in situ measurement data of membrane physical properties. This is essential since their effects are reversible. In addition, tedious experimental work is required to collect gas transport data at varying operating conditions. Recently, we have proposed a validated Soft Confining Methodology for Ultrathin Films that can be used to simulate ultrathin polysulfone (PSF) membranes upon confinement limited to 308.15 K and 2 bars. In industry application, these ultrathin membranes are operated within 298.15–328.15 K and up to 50 bars. Therefore, our proposed methodology using computational chemistry has been adapted to circumvent limitation in experimental study by simulating ultrathin PSF membranes upon confinement at different operating temperatures (298.15 to 328.15 K) and pressures (2 to 50 bar). The effect of operating parameters towards non-bonded and potential energy, free volume, specific volume and gas transport data (e.g. solubility and diffusivity) for oxygen and nitrogen of the ultrathin films has been simulated and collected using molecular simulation. Our previous empirical equations that have been confined to thickness dependent gas transport properties have been modified to accommodate the effect of operating parameters. The empirical equations are able to provide a good quantitative characterization with R² ≥ 0.99 consistently, and are able to be interpolated to predict gas transport properties within the range of operating conditions. The modified empirical model can be utilized in process optimization studies to determine optimal membrane design for typical membrane specifications and operating parameters used in industrial applications.

Pioneering work to elucidate and model the effect of operating conditions on physical and transport properties of ultrathin membranes.