Abdulhalim Shah Maulud

Review and selection criteria of classical thermodynamic models for acid gas absorption in aqueous alkanolamines

Abstract The knowledge of vapour-liquid equilibrium (VLE) and thermodynamic properties plays a pivotal role in the process development of absorption systems for acid gas capture in precombustion and postcombustion streams. A large number of thermodynamic modelling approaches for acid gas absorption in aqueous alkanolamine solutions are published in the literature. However, the reviews of these modelling techniques are limited and scattered. Moreover, poor guidelines exist for the selection of an appropriate modelling approach for the VLE prediction of the aforementioned system. Therefore, the current study presents a concise classification and review of classical thermodynamic models for acid gas absorption in aqueous alkanolamine solutions since their inception. The article systematically details the chronological development and highlights the major capabilities and limitations of classical thermodynamic approaches, namely, semiempirical models, activity coefficient models, and equation of state (and equation of state/excess Gibbs energy) models. A graphical comparison of VLE prediction by each classical approach is presented to form a general guideline in the selection of a suitable approach for process development studies. The review precisely discusses the issues, challenges, and future prospects of each classical thermodynamic approach in the context of application, complexity, and development.

Reconciliation of outliers in CO2-alkanolamine-H2O datasets by robust neural network winsorization

It is normal to find at least a few measured values in CO2-alkanolamine-H2O datasets that deviate greatly from the majority of published data, as the data come from different sources. These values, termed as data outliers, are the major source of conflict in modeling, simulation and process development studies. Therefore, removal of data outliers is mandatory. However, available statistical techniques are known to lose information at the boundaries of the system and exhibit substantial deviation from holistic data trend. Hence, an adaptive approach combining artificial neural networks and robust winsorization is presented for identification and reconciliation of data outliers in CO2-alkanolamine-H2O system. The proposed approach flexibly transforms to the nonlinear data distribution and predicts corrected values for data outliers (winsorized values), thus maintaining the information at extremes of the system. The results have been graphically analyzed and show good conformance in treated data, with retention of winsorized values. The proposed method improves the shortcomings of previous statistical approaches and can be potentially extended to other nonlinear experimental datasets in chemical process systems.

Artificial Neural Network for Anomalies Detection in Distillation Column

Early detection of anomalies can assist to avoid major losses in term of product degradation, machines’ damages as well as human health issues. This research aims to use Artificial Neural Network to recognize anomalies in the distillation column. The pilot scale distillation column for the ethanol-water system is selected for the study. Faults are generated by variation in feed rate, feed composition and reboiler duty using Aspen Plus® dynamic simulation. The effect of these faults on process variables i.e. changes in distillate and bottom composition, distillate and bottom temperature, bottom flow rate, and the pressure drop is observed. The network is trained using back propagation algorithm to determine root mean square error (RMSE). Based on RMSE minimization, the (6-8-6) net serves as the best choice for the case studied for efficient fault detection. The presented techniques are general in nature and easily applicable to various other industrial problems.

Effects of Phase Separation Behavior on Morphology and Performance of Polycarbonate Membranes

The phase separation behavior of bisphenol-A-polycarbonate (PC), dissolved in N-methyl-2-pyrrolidone and dichloromethane solvents in coagulant water, was studied by the cloud point method. The respective cloud point data were determined by titration against water at room temperature and the characteristic binodal curves for the ternary systems were plotted. Further, the physical properties such as viscosity, refractive index, and density of the solution were measured. The critical polymer concentrations were determined from the viscosity measurements. PC/NMP and PC/DCM membranes were fabricated by the dry-wet phase inversion technique and characterized for their morphology, structure, and thermal stability using field emission scanning electron microscopy, Fourier transform infrared spectroscopy, and thermogravimetric analysis, respectively. The membranes’ performances were tested for their permeance to CO2, CH4, and N2 gases at 24 ± 0.5 °C with varying feed pressures from 2 to 10 bar. The PC/DCM membranes appeared to be asymmetric dense membrane types with appreciable thermal stability, whereas the PC/NMP membranes were observed to be asymmetric with porous structures exhibiting 4.18% and 9.17% decrease in the initial and maximum degradation temperatures, respectively. The ideal CO2/N2 and CO2/CH4 selectivities of the PC/NMP membrane decreased with the increase in feed pressures, while for the PC/DCM membrane, the average ideal CO2/N2 and CO2/CH4 selectivities were found to be 25.1 ± 0.8 and 21.1 ± 0.6, respectively. Therefore, the PC/DCM membranes with dense morphologies are appropriate for gas separation applications.

A Fugacity Corrected Thermodynamic Framework for Aqueous Alkanolamine Solutions

A generalized thermodynamic framework for correlating the vapor–liquid equilibria of aqueous primary, secondary and tertiary alkanolamine solutions is presented. The model uses Universal Functional Activity Coefficient (UNIFAC) and translated modified Peng–Robinson equation of state to correlate the activity and fugacity effects of the solution, respectively. New UNIFAC binary interaction parameters are reported for aqueous monoethanolamine, diethanolamine and N-methyldiethanolamine solutions for a wide range of temperature, pressure and concentration. The results are in excellent agreement with experimental data.

Comparative Study of Linear Co-Volume Based Mixing Rules for Equation of State/ Excess Gibbs Energy (EOS/GE) Models for CO2 – MEA and CO2 – MDEA Systems

With the advent of Equation of State/ Excess Gibbs Energy (EOS/GE) models, the linear co-volume based mixing rules have gained vast importance for predicting multi-component VLE for polar mixtures. Owing to their inherent ease of calculation and good prediction abilities, these mixing rules have been applied in extension, to a variety of systems especially for CO2-H2O-alkanolamine systems. However, no comparative study is available to select appropriate mixing rule for prediction of thermodynamic properties. In this study, pressure prediction of various linear co-volume mixing rules has been compared for CO2 – MEA and CO2 – MDEA systems, while effects of activity coefficients and process parameters have been kept constant. The infinite pressure mixing rules have heavily under – predicted and approximate zero reference pressure mixing rules have over – predicted, but latter are valid for low and medium pressure ranges. The linear combination of Vidal and Michelsen (LCVM) mixing rule have good predictions at high pressures.

Fault detection in distillation column using NARX neural network

Fault detection in the process industries is one of the most challenging tasks. It requires timely detection of anomalies which are present with noisy measurements of a large number of variable, highly correlated data with complex interactions and fault symptoms. This study proposes the robust fault detection method for the distillation column. Fault detection and diagnosis (FDD) for process monitoring and control has been an effective field of research for two decades. This area has been used widely in sophisticated engineering design applications to ensure the proper functionality and performance diagnosis of advanced and complex technologies. Robust fault detection of the realistic faults in distillation column in dynamic condition has been considered in this study. For early detection of faults, the model is based on nonlinear autoregressive with exogenous input (NARX) network. Tapped delays lines (TDLs) have been used for the input and output sequences. A case study was carried out with three different fault scenarios, i.e., valve sticking at reflux and reboiler, and tray upset. These faults would cause the product degradation. The normal data (no fault) is used for the training of neural network in all three cases. It is shown that the proposed algorithm can be used for the detection of both internal and external faults in the distillation column for dynamic system monitoring and to predict the probability of failure.

VLE Determination of Carbon Dioxide Loaded Aqueous Alkanolamine Mixtures Using Modified Kent Eisenberg Model

Abstract A computationally simple thermodynamic framework has been presented to correlate the vapour-liquid equilibria of carbon dioxide absorption in five representative types of alkanolamine mixtures. The proposed model is an extension of modified Kent Eisenberg model for the carbon dioxide loaded aqueous alkanolamine mixtures. The model parameters are regressed on a large experimental data pool of carbon dioxide solubility in aqueous alkanolamine mixtures. The model is applicable to a wide range of temperature (298–393 K), pressure (0.1–6000 kPa) and alkanolamine concentration (0.3–5 M). The correlated results are compared to the experimental values and found to be in good agreement with the average deviations ranging between 6% and 20%. The model results are comparable to other thermodynamic models.

Economic Optimization of CO2 Capture Process Using MEA-MDEA Mixtures

Amine based solvents are extensively being used for post combustion carbon capture through absorption. Each solvent has its associated benefits and drawbacks. In order to overcome their drawbacks, a number of mixed amine streams have been used. However, this amalgamation step is usually overshadowed by process optimization issues and cost limitations. In this study, Monoethanolamine (MEA) – Methyldiethanolamine (MDEA) is used as the mixed amine-based solvent for removal of carbon dioxide. A simulation model of CO2 removal is developed using Aspen HYSIS to optimize the process. Subsequently, an economic analysis is constructed to evaluate the operating expenditure (OPEX) and capital expenditure (CAPEX) based on the simulation model, followed by sensitivity analysis. It is found that 25 wt% MDEA and 15 wt% MEA is the optimal operating condition that achieve the minimal total cost. Sensitivity analysis reveals that utilities cost affects the total cost significantly, followed by CAPEX. However, the effect of raw material costs on total cost is negligible.

Preparation and Characterization of Blended Composite Membranes

Composite membranes were prepared by incorporating inorganic silica nanoparticles into blends of polysulfone/polyimide (PSF/PI) membranes via sol-gel route. Morphological structures of the developed membranes were carried out by scanning electron microscopy (SEM). Spectroscopic analysis of the hybrid membranes were done by fourier transform infrared spectroscopy (FTIR) analysis. Differential scanning calorimetry (DSC) analysis shows that the glass transition temperature (Tg) increased from 209oC to 238oC in the hybrid membranes followed by thermogravimetric analysis (TGA) that showed significant improvement in thermal stability of the developed membranes.