oURspace Repository :: Browsing by Author "Ibrahim, Hussameldin"

Browsing by Author "Ibrahim, Hussameldin"

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Open Access
Adaptive Neuro-Fuzzy Inference Systems (ANFIS) - Based Model Predictive Control (MPC) for Carbon Dioxide Reforming of Methane (CDRM) in a Plug Flow Tubular Reactor for Hydrogen Production
(Faculty of Graduate Studies and Research, University of Regina, 2013-01) Essien, Ememobong Ita; Ibrahim, Hussameldin; Mehrandezh, Mehran; Idem, Raphael; Shirif, Ezeddin; deMontigny, David; Azam, Shahid
The current sources of our energy supply are plagued with many problems, and the impact on the climate is of grave concern. To preserve and sustain our environment, a non-polluting and renewable energy source is required. Hydrogen (H2), when extracted from one of its many sources during carbon dioxide (CO2) capture, is considered a non-polluting, efficient and environmentally sustainable energy source. In this research work, the control of a pilot-scale reformer for the production of hydrogen was studied. Hydrogen was produced through the carbon dioxide reforming of methane (CDRM). This process was used to convert methane (CH4) and carbon dioxide into hydrogen. A high methane conversion was maintained by controlling the temperature in the reformer at the thermodynamically desired level. The control strategy applied to this process was the model predictive control (MPC) based on an adaptive neuro-fuzzy inference system (ANFIS) model. MPC has, among other advantages, the ability to predict the response of the system over a given prediction horizon. Experimental results showed that the ANFIS model was able to accurately replicate the response of the process to changes in temperature. Based on the ANFIS model, an MPC strategy was formulated for the process.
Open Access
Analytical Modeling of Multi-Fractured Horizontal Wells in Heterogeneous Unconventional Reservoirs
(Faculty of Graduate Studies and Research, University of Regina, 2017-08) Zeng, Jie; Zeng, Fanhua; Jia, Na; Yuan, Qingwang; Ibrahim, Hussameldin
Current analytical models for multi-fractured horizontal wells (MFHW) generally neglect reservoir heterogeneity, typical seepage characters of unconventional reservoir, partially penetrating fracture and various fracture damage mechanisms. In this thesis, three linear flow models have been developed to facilitate pressure and rate behavior analysis of shale, tight sand and unconventional reservoir with damaged fractures. These models are validated by comparing with KAPPA Ecrin and are more accurate than previous linear flow models in modeling partially penetrating cases. Field data are analyzed and results prove the reliability of these models. The first model is for heterogeneous shale reservoir with multiple gas transport mechanisms. It subdivides the reservoir into seven parts, namely, two upper/lower regions, two outer regions, two inner regions, and hydraulic fracture region. Fracture interference is simulated by locating a no-flow boundary between two adjacent fractures. The locations of these boundaries are determined based on the boundary’s pressure to satisfy the no-flow assumption. Adsorption/desorption, gas slippage and diffusion effects are included for rigorous modeling of flow in shale. Sensitivity analysis results suggest that larger desorption coefficient causes smaller pressure and its derivative as a larger proportion of gas is desorbed in formation and contributes to productivity. The influences of other parameters, such as matrix II permeability, matrix block size, secondary fracture permeability, hydraulic fracture conductivity, and fracture pattern are also discussed. The second model is for heterogeneous tight sand reservoir with threshold pressure gradient (TPG). The linear flow sub-regions are the same as those of the first model. TPG and pressure drop within the horizontal wellbore are included. Simulation results suggest that TPG affects middle-late time behaviors. Greater TPG results in higher pressure drop and accelerates production decline. But this influence is marginal when TPG is small. Effects of other parameters, such as formation permeability, fracture length, conductivity, and wellbore storage are also investigated. The third model is for heterogeneous reservoir with various fracture damage. In this model, the following possible fracture damage situations are discussed: (1) choked fracture damage (2) partially propped fracture, (3) fracturing fluid leak-off damage, (4) dual or multiple damage effects. Simulation results indicate that choked frature damage influences early-mid time performance. Partially propped section within fracture dominates formation linear flow regime. Only severe matrix impairment near fracture face can have noticeable effects on pressure and rate response. A new parameter, skin factor ratio, is applied to describe the relative magnitude of multiple damage mechanisms. Reservoir heterogeneity and fracture damage make the pressure and rate behaviors deviate significantly from undamaged one but one can distinguish major damage mechanisms even in heterogeneous reservoir.
Open Access
Applicability of Solvent-Assisted Polymer Flooding to Improve Heavy Oil Recovery
(Faculty of Graduate Studies and Research, University of Regina, 2013-03) Vafaei, Venous; Torabi, Farshid; Labropulu, Fotini; Yang, Daoyong; Zeng, Fanhua; Ibrahim, Hussameldin
Recently, one of the non-thermal oil recovery techniques, polymer flooding, has shown its potential for improving heavy oil recovery. This study investigated the potential of combining solvent-based processes and polymer flooding, called Solvent- Assisted Polymer Flooding, to improve the oil recovery from thin heavy oil reservoirs in Western Canada. This was accomplished through a series of carefully designed laboratory experiments and a lab-scale numerical simulation. This study attempted to validate the feasibility of the solvent-assisted polymer flooding method with respect to enhancing heavy oil recovery and the impact of fluid and operational parameters on the performance of this suggested technique in an alternative schematic. Very little attention to combining these techniques has been considered until now, and there are no comprehensive studies on implementing alternative water/solvent/polymer injection schemes and consequently no significant data is available. Over 12 sets of alternative water/solvent/polymer flooding experiments were performed utilizing solvents of pure carbon dioxide, pure propane, and three different mixtures of C3, C1, and CO2. Prior to the tests, a complete phase behaviour (PVT) analysis of the solvents mixtures was conducted using WinPropTM software from Computer Modeling Group (CMG). All experiments were conducted in sandpacks with similar properties utilizing two heavy oil samples. The first heavy oil sample exhibited a viscosity of 825 mPa.s and the second a viscosity of 5,000 mPa.s at a temperature of 27°C. The production trends and recovery factors for each experiment were determined, and the pressure drop data during the tests were collected. Polymer flooding is sometimes operationally difficult in heavy oil reservoirs due to the high injection pressures that can be encountered. The results obtained from this study were promising. Alternating polymer flooding with a hydrocarbon solvent improved the overall recovery factor. Through the injection of solvent, oil near the watered-out pathways was contacted, and the oil viscosity was lowered in these areas. The lower-viscosity oil then could be displaced by subsequent injection of relatively lower-viscosity polymer than would otherwise be required, leading to improved heavy oil recovery under more feasible operational conditions. This technique can create new, reduced oil viscosity pathways through each subsequent cycle of injection, further improving the oil recovery. The study showed that modifying this technique by injecting different types of solvents before polymer injection can significantly improve the potential of this technology in heavy oil reservoirs and the applicability of polymer flooding is not limited to light oil reservoirs. In addition, it can create an opportunity to not only recover more heavy oil, but also perform the flooding under more favourable conditions. Also, implementing a higher operating pressure improved the incremental recovery of the solvent section, as far as the capacity of this study was able to observe. However, the higher operating pressure did not noticeably change the ultimate oil recovery. Higher operating pressure resulted in more noticeable viscosity reduction. Therefore, a greater decrease in the pressure profile of the polymer flooding was observed. Since pilot and field studies are time consuming and relatively expensive, numerical simulation was initially utilized to history match the results obtained in the laboratory experiments. Based on this fact, CMG-STARSTM was used and the discrepancy between recovery factors obtained from the experiments and those of the numerical simulation model were found to be in the range of 3% to 10%. The lighter oil sample model showed less discrepancy.
Open Access
Application of density functional theory and machine learning in the prediction of efficient catalysts for the oxidative coupling of methane with reduced CO2 production at low temperature
(Faculty of Graduate Studies and Research, University of Regina, 2024-03) Ugwu, Lord Ikechukwu; Ibrahim, Hussameldin; Henni, Amr; Shirif, Ezeddin; Widdifield, Cory M.; Castaño, Pedro
The oxidative coupling of methane (OCM) remains a promising means for the production of ethylene. Though high temperatures of 900 oC and above lead to increased conversion of methane, at higher temperatures, the OCM reaction tends to favour a competing reaction that leads to the production of COx product. This thesis focuses on the generation of catalyst component electronic properties via density functional theory (DFT) and the analysis via machine learning (ML) techniques of the combination of the DFT-computed electronic properties and high-throughput experimental data comprising 12708 data points. The DFT data includes the catalyst components' bandgap, Fermi energy and magnetic moment. Variations of a dataset comprising experimental conditions, experiment performance and electronic catalyst properties were used to build a dataset for data modelling and analysis as well as ML analysis for the prediction of CO2 yield, C2H4 yield C2H6 yield, and CH4 conversion. With the aid of multi-linear regression models, Rh, Pt, Ru and Ir were found to be new catalyst promoters that enhance catalyst performance in OCM, particularly with improved methane conversion. A minimum of 58 new bimetallic combinations and 2784 unique catalytic materials with minimum CH4 conversion at 700 oC of 38.5% were identified and proposed as effective catalysts for OCM reaction, an improvement on the 36% CH4 conversion limit from previous studies. The Rh-Li3VO4/SBA15 and Ru-BaZrO3/SBA15 have been proposed as efficient catalysts for the OCM reaction with a predicted C2y of 30% and 29%, respectively. In comparing predictive model using a dataset containing a combination of catalyst electronic properties, deep neural networks (DNN) configured as deep feed-forward networks with back-propagation, along with random forest regression (RFR), support vector regression (SVR) and extreme gradient boost regression (XGBR), were compared on the basis on their mean-absolute-error, mean-squared-error and coefficient of determination for the prediction of reaction outcomes including ethane yield, ethylene yield, carbon dioxide yield and methane conversion (C2H4y, C2H6y, CO2y, CH4_conv, respectively) and C2y (a combination of C2H4y and C2H6y). The inclusion of electronic properties of the catalyst components into the dataset improved the performance of the models by approximately 10% compared to a dataset with only reaction conditions. RFR models had better accuracy compared to other modeling techniques, with an average R2 of 0.98 for the predictive models of all five reaction outcomes. The mean squared error and mean absolute error of the RFR models were from 0.12 to 9.03 for MSE and 0.21 to 2.02, respectively. The order of performance of the modeling techniques was RFR > XGBR > SVR > DNN. The order of data fit for the labels for the given modeling techniques was C2H6y > C2H4y > C2y > CO2y > CH4_conv. In the analysis of model feature impacts to identify descriptors for catalytic activity in OCM reactions, it was observed that the Fermi energies of the catalyst promoter, its atomic number and the bandgaps of the bimetallic oxide and the catalyst support emerged as effective descriptors. Specifically, in relation to the C2y predictive model, C2y increases with an increase in dataset features, including the number of moles of the alkali/alkali-earth in the metallic oxide, atomic number of the catalyst promoter and Fermi energy of the promoter and just relatively in the case of temperature, suggesting a highly non-linear relationship between C2y and temperature. It, however, reduces with an increase in the bandgap of the active metal oxide and the methane-to-oxygen ratio. Using the RFR, the Fermi energy of the promoter had a 4.31% impact on the model, while its atomic number had 6.24%, the number of moles of the alkali/alkali-earth in the metallic oxide was 13.69%, and temperature was 33.70% on the C2y predictive model. catalysts with active metal oxides with lower bandgap energy and promoters with magnetic moments may not be as effective as OCM reaction catalysts with less ferromagnetic properties and higher bandgap energy.
Open Access
Bismuth Tungstate Photocatalysis for N-Nitrosamines Removal from Amine Washing Wastewater
(Faculty of Graduate Studies and Research, University of Regina, 2021-07) Maddineni, Vasu; Ibrahim, Hussameldin; Henni, Amr; Shirif, Ezeddin; Young, Stephanie
The most practical way of reducing industrial carbon dioxide (CO2) emissions, which is the main contributor to global warming, is post-combustion CO2 capture using amine solvents. A large variety of degradation products are generated during the CO2 capture process. This is mostly due to amines reacting irreversibly with CO2, and other oxygenated compounds in the flue gas. Amine degradation products poses threat to human health and marine life. Amine degradation products like N-nitrosamines are carcinogenic and mutagenic. The formation of carcinogenic and mutagenic N-nitrosamines can obstruct the technology's industrial application. To reduce the impact on human health and marine life, identifying and treating carcinogenic, and mutagenic compounds like N-nitrosamines is extremely important. Photocatalysis, an Advance oxidation process (AOP) which uses UV light source and semiconductor catalysts is studied for degradation of organic and inorganic pollutants. Photocatalysis is used in this study for treating N-Nitrosodiethylamine (NDEA) with strong reactive hydroxyl radicals generated by UV/Visible light source and semiconductor photocatalyst bismuth tungstate (Bi2WO6). The semiconductor photocatalyst Bi2WO6 was studied in pure form and surface modified forms. Surface modification was done by altering hydrothermal synthesis temperature, hard template replication technique with templates such as porous silica KIT-6, transition metal impregnation like silver (Ag), Iron (Fe), Copper (Cu), Lanthanum (La). Various catalyst characterization techniques like Nitrogen adsorption–desorption measurements (BET method) used to determine the surface area, pore size and pore volume, X- ray diffraction (XRD) to determine the crystalline size of catalyst, UV-vis spectroscopy to analyze the light absorption property and band gap energy of the catalyst, scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) to determine the surface morphology and composition of catalyst are used. The operational variables for photocatalytic degradation of NDEA are pH of the solution, catalyst dosing (gram/litre), metal impregnation (%). Stat ease Design expert software Version 13 was used for designing the experiments using a Face centered-central composite design (FC-CCD) in Response Surface Methodology (RSM). Different regression models have been used to fit the experimental responses and evaluated using F- statistic and p- value in Analysis of variance (ANOVA). By removing insignificant terms based on p-value, it was assessed that the quadratic model fits best for experimental responses with all metal impregnated catalyst. Impact of operational variable on photocatalytic degradation was analyzed using three dimensional interactive plots. For photocatalytic degradation of NDEA, pH of the solution is highly significant compared to other variables like catalyst dosing and metal impregnation. Optimization studies for maximum degradation of NDEA with operational variables were studies using RSM. The average degradation efficiency of NDEA is 89.2 % for Fe- Bi2WO6, 87.4% Ag- Bi2WO6, 86.9% for La- Bi2WO6 and 85% for Cu- Bi2WO6
Open Access
Carbon dioxide and ethane solubility in bis tri (fluromethylsulfonyl) imide-based ionic liquids: Selectivity measurements and modelling
(Faculty of Graduate Studies and Research, University of Regina, 2024-05) Quaye, Eric Nii Annang; Henni, Amr; Ibrahim, Hussameldin; Kabir, Golam
The urgent need to address carbon dioxide (CO2) emissions, a critical factor in escalating greenhouse gas levels and global warming, underscores the significance of effective capture and reduction methods. This research investigates the absorption of Carbon Dioxide and Ethane in three ionic liquids: 1-Decyl-3-MethylimidazoliumBis (Trifluromethylsulfonyl Imide) [IL1], 1-Hexadecyl-3-Methyl imidazoliumBis (Trifluromethylsulfonyl Imide) [IL2] and Triethytetradecyl Ammonium Bis (Trifluromethylsulfonyl Imide) [IL3]. Solubility measurements of CO2 were conducted from 303.15K to 343.15K and pressures up to 1.5 MPa, utilizing a gravimetric microbalance. The acquired solubility values were correlated with the Peng Robinson (PR) equation of state (EoS) using three mixing rules: Single - van Der Waals one (VDW1), Binary - van Der Waals two (VDW2), and the Wong-Sandler combined with the non-random two-liquid model (WS-NRTL). From the experimental findings, the ILs studied in this research showed the lowest Henry's Law constant (H) i.e. higher solubility of CO2 when compared to all Ionic Liquids in the Literature and even Selexol/ Genesorb 1753, a benchmark physical solvent in Industry. It only appeared less effective than 1-octyl-3-methylimidazolium bis (trifluoromethylsulfonyl)imide, a solvent with high molecular weight and viscosity, making them promising solvents for CO2 removal. [IL2] exhibited the lowest Henry's Law constant (H) i.e. the highest solubility among the studied ionic liquids (ILs) for CO2 absorption, The observed experimental trend wherein [IL1] and [IL3] capture relatively less CO2 than [IL2] up to 1.5 MPa at 30 ℃, 50 ℃, 60 ℃ and 70 ℃, and can be attributed to various factors inherent to the intermolecular structures and properties of these ionic liquids (ILs). Typically, the longer alkyl chains are associated with increased van der Waals interactions and higher solubility for gases like CO2. Thus, the longer alkyl chain in [IL2, 1-Hexa decyl-3-Methyl imidazolium Bis (Trifluromethyl-sulfonyl Imide)] facilitates a more effective CO2 absorption. Additionally, the larger molecular volume and increased surface area of the Hexadecyl-based IL provide more interaction sites for CO2 molecules. Furthermore, Henry’s law constant for Ethane in [IL1] was the highest (lowest solubility) relative to the two other ILs studied. The selectivity of CO2 over C2H6 was obtained and the overall selectivity was in the following order: [IL1] > [IL3]> [IL2]. Comparing this selectivity data to published ILs revealed they are only attractive when used for CO2 capture with no ethane present as in the case of flue gases. Furthermore, insights into the enthalpy and entropy of absorption underscored CO2 stronger interactions and higher solubility than Ethane. These results highlight the significant impact of IL structure on CO2 solubility, confirming the potential of tailored ILs for enhanced carbon capture strategies. In summary, the best ionic liquid amongst the three studied for CO2 capture is therefore [IL2] due to its lowest Henry’s law constant towards CO2 absorption, however [IL1] would be the best ionic liquid provided C2H6 co-absorption is of crucial importance and needs not to be avoided.
Open Access
Catalystic Pyrolysis for the Production of Stable Phenol Rich Bio-Oil from Wood Biomass
(Faculty of Graduate Studies and Research, University of Regina, 2016-09) Kaushik, Priyanka; Ibrahim, Hussameldin; Idem, Raphael; Henni, Amr
Biomass studies over the last two decades shows its use as an alternate source for the production of chemicals and fuels. This can help in reducing our load on the conventional hydrocarbon use and be shared with renewable sources such as forest and agricultural wastes. In Saskatchewan, there is abundant availability of wood waste from the timber industry; hence wood can be used as biomass raw material for the production of bio-chemicals. Pyrolysis is the most promising technology for the conversion of biomass into liquids. In this research, wood pellets obtained from a lumber company were used as the feed for pyrolysis to produce a phenol-rich stable bio-oil. Usually, after the bio-oil production, catalytic cracking is done to produce valuable products. In this research, the aim was to produce phenol-rich bio-oil in single step. The experiments were carried out in three phases; Phase I: Parametric study, Phase II: optimized conditions and best-suited catalyst for stable phenol rich bio-oil and Phase III: kinetic study of the process. Experiments were performed in a packed bed reactor under varying temperatures (400,500, 6000C), acidic catalysts (H-ZSM-5, γ-alumina and silica alumina), feed size (0.71, 0.85, 1, 1.18 and 1.44mm) and catalyst weights (1g and 2g). For the kinetic study, experiments were carried out at varying residence times (0, 30, 60 and 90 minutes). Products were collected and analyzed in three phases: bio-oil, gas and char. Gas Chromatography - Mass Spectroscopy (GC-MS) and online GC equipment was used to analyze bio-oil and gases respectively. Significant in phenol derivatives is observed with the use of acid catalyst along with the reduction of oxygenates or sugars. The reduction in sugar content shows the stabilization of bio-oil as the amount of free radicals is reduced hence polymerization of undesired products can be avoided thus increasing the bio-oil shelf life. Catalyst acidity, strength and number of acid sites showed a significant effect on yield of phenol derivatives in bio-oil. Kinetic study of wood catalytic pyrolysis was performed in a batch reactor; component content data was obtained at increasing time intervals and temperatures. The gases were analyzed with the help of GC and carbon content in char was analyzed by sending samples to the Saskatchewan Research Centre (SRC) in Saskatoon. Using the ultimate analysis of wood and carbon in char, carbon conversion was calculated. The rate of reaction can be expressed by a 2 order kinetic model with an activation energy of 17104.04 J/mol and a preexponential factor of 0.000567/s. Statistical analysis was also carried out using the physical and chemical properties of the catalysts. Using MINITAB, a statistical model for the yield of phenol derivatives was determined for 1g and 2g acidic catalysts. The analysis for second resolution interaction model shows the main effect of pore volume, surface area and number of acid site on phenol derivatives yield. Also, interactions of these factors help to understand the effect on phenol yield. The predicted yield from these models and experimental yield gave overall AAD of 7% that shows a good agreement with the model.
Open Access
Catalysts for Hydrogen Production by the Auto-Thermal Reforming of Glycerol
(Faculty of Graduate Studies and Research, University of Regina, 2013-01) Sabri, Faezeh; Ibrahim, Hussameldin; Idem, Raphael; deMontigny, David; Shirif, Ezeddin; Henni, Amr
Due to a number of major environmental issues, essentially greenhouse gas emissions and fossil fuel reliance, the implications of hydrogen as a promising clean energy carrier have significantly increased. In this respect, the use of bio-renewable feedstock such as glycerol for hydrogen production is becoming important. The majority of natural glycerol is produced as a by-product of the bio-diesel industry. Presently, almost all crude glycerol is refined before its ultimate end use. It is the ultimate goal of the present study to develop an effective catalytic auto-thermal reforming process to convert glycerol into top value bio-based products. Glycerol can be converted to hydrogen-rich streams by steam reforming (SR), partial oxidation (POX), gasification, auto-thermal reforming (ATR), supercritical water reforming (SCWR) or aqueous-phase reforming (APR). Most of the studies have focused on SR processes for producing hydrogen over various costly noble metal-based catalysts. A portfolio of nickel-based catalysts with nominal composition 5% Ni/Ce0.5Zr0.33M0.16O2- [where M is the promoter element(s) selected from Mg, Ca, Y, La, or Gd] was prepared and examined for their catalyst activity for glycerol auto-thermal reforming. The catalysts’ activity was evaluated using a plug flow reactor at atmospheric pressure and in the temperature range of 450°C to 700°C, steam-to-glycerol ratio of 6, 9, and 12, and oxygen-to-glycerol ratio of 0.2, 0.5, and 0.8 at atmospheric pressure in a packed bed tubular reactor (PBTR). The preliminary screening studies were carried out for a time-on-stream (TOS) of 6 hours with sampling intervals of 1 hour. The physicochemical and textural characteristics of the catalysts were investigated by means of a variety of characterization techniques such as temperature-programmed reduction (TPR), temperature-programmed oxidation (TPO), nitrogen (N2) physisorption, hydrogen (H2) chemisorption, UV-Vis diffuse, thermogravimetry analysis (TGA), and X-ray diffraction (XRD). The gaseous products were analyzed by online gas chromatography equipped with a thermal conductivity detector (GC/TCD). The structural, textural, and physicochemical characteristics of the catalysts have been investigated with the help of different bulk and surface characterization techniques. Different ratios of steam/glycerol and oxygen /glycerol were employed to optimize the conditions to achieve the highest conversion as well as H2 selectivity. Based on the above analyses, glycerol conversion and H2 selectivity were calculated. Among all the catalyst formulations screened in the current study, the catalyst formulations prepared with Ca, Y, Mg, La, and Gd exhibit stable and steady activity even at 500°C. The catalyst formulation with Gd as a promoter element performed the best at all the investigated temperatures. Hence, it is a potential candidate for future commercialization and plausible membrane reactor applications. The thermal and catalytic effects on catalytic auto-thermal reforming were identified by performing a number of non-catalytic reaction runs and then comparing the results with the corresponding catalytic reactions.
Open Access
Catalytic Hydrothermal Liquefaction of Camelina Sativa Residues for the Production of Biogasoline Range Liquid Biofuel
(Faculty of Graduate Studies and Research, University of Regina, 2018-01) Akande, Abayomi; Ibrahim, Hussameldin; Idem, Raphael; Salad Hersi, Osman; Henni, Amr; deMontigny, David; Shirif, Ezeddin; Saberian, Mohammad
Camelina Sativa residue was used as a feedstock to investigate the production of biogasoline from the biomass material by catalyst-assisted hydrothermal liquefaction process. In the new process development, a non-catalytic experimental performance evaluation was conducted as a baseline process to compare and evaluate catalytic performance. Four commercially available catalysts otherwise called precursors were initially tested to set the stage for the performance evaluation of the in-house developed catalysts. The commercial catalysts used were HZSM-5, SiO2-Al2O3, SBA-15 and γ-Al2O3. The performance activities of the commercial catalysts show no sign of improved biomass conversion compared to non-catalytic process, which has a maximum biomass conversion of 72 wt. %. However, of all commercial catalysts used, γ-Al2O3 under subcritical water condition produced the best biogasoline yield performance of 15 wt. %, an improvement over the non-catalytic process which produced 12 wt. % under same condition. The marginal increase was even further improved when 5% cobalt was impregnated on the γ-Al2O3 to form a new bifunctional catalyst, 5Co/γ-Al2O3. In this case, the biogasoline yield increased from 15 wt. % to 23 wt. %. When molecular hydrogen was introduced into the process at pressure 5 MPa hydrogen pressure and 14 MPa process autogenic pressure, 5Co/γ-Al2O3 produced even an improved performance, with biogasoline yield was increase to 28 wt. %. Furthermore, different bifunctional and dual support catalysts were developed with two commercial catalysts as precursors. This development produced new hybrid and synergy catalysts. These catalysts were tested for performance evaluation and the best performing catalyst was 5Co/γ-Al2O3/HZSM-5. The new catalyst under the same experimental conditions gave the best performance, and the biomass conversion increased to 79 wt. %, an improvement over 70-72 wt. % obtained from most catalyst-assisted and non-catalytic cases. Also, the biogasoline yield was 43%, which was the highest obtained in all cases. Addition of nickel as promoter did not add values to the process as the biogasoline yield declined. The best process parameters optimization show that biomass size of 1.0 mm, catalyst metal loading 5%, catalyst weight/biomass weight ratio of 0.2, hydrogen pressure 2 MPa, and retention time 30 minutes are optimum for the best performance. The intrinsic kinetic analysis of this process shows there are two different temperature regimes with different kinetic parameters; this effect was attributed to different factors, one was the ionic product of water that varies exponentially with temperature and second was the heating rate of 5oC/min that was used as a fixed parameter. The low and high temperatures regimes have order of reaction 2 and 1 respectively. The activation energies were 16,420 and 12,627 J/mol respectively and the collision factors were also 0.7603 and 0.1715 s-1 respectively. An overall kinetic parameter shows that the slower high temperature reaction was the rate controlling with collision factor of 0.1970 s-1, activation energy 12,783 J/mol and reaction order 1.0. The parity chart for the experimental and model predicted rate using the overall model gave an average deviation of 6.64%. Two other regression models were developed to estimate the performance of the best selected catalyst for solid biomass conversion and biogasoline range liquid production. The models are given in equation 7.1 and 7.2 respectively. The parity chart developed using results from these models showed an average deviation of 0.99% and 6.53% between the experimental and model derived values. The statistical analysis provided showed relationships between various parameters which could optimize the process. The conclusion of this section would be based on future economic analysis.
Open Access
CO2 emissions reduction through catalytic production & use of fuels derived from biomass
(Faculty of Graduate Studies and Research, University of Regina, 2023-03) Anokye-Poku, Terza; Idem, Raphael; Ibrahim, Hussameldin; Supap, Teeradet; Jia, Na
This study focused on the preparation of environmentally friendly heterogenous catalyst from biomass specifically waste egg shells, cow bones and fish scales for use in the production of biodiesel from waste cooking oil (WCO) which is also a biomass-based waste feedstock collected from households that will be an alternative fuel source and contribute to reducing CO2 emissions into the environment as well as the cost of biodiesel production To begin, the biodiesel feedstock properties needed to be checked for its suitability in making fuel because the feedstock properties were going to impart the properties of biodiesel. The first property of the oil checked was the fatty acid composition. The need for a further purification step apart from filtration was confirmed and other properties of the feedstock such as viscosity, density, acid value and water content of the WCO were also checked. Based on these, the WCO fell in the range within which it could be used in making biodiesel, there was no need for further physical purification steps because the properties of both the crude and purified WCO were very close. In addition, the properties showed that a catalyst basic in nature was suitable for the transesterification process. The next phase involved carrying out reactions with the conventional basic catalyst (KOH) to serve as a baseline for the work to which heterogenous catalytic reactions would be compared. The heterogenous catalysts were then synthesized from waste egg shells (ES), cow bones (CB) and fish scales (FS) separately before bi-blend (CBES, FSCB, FSES) and tri-blend mixtures (M3) of these components were made in a ratio of 1:1 and 1:1:1 respectively and characterized. The focus of the catalyst was on the performance of M3 and how CB, FS and ES contributed to that as well as its performance in comparison to biii blend mixtures that have been the kind of blends prepared in literature for biodiesel catalysts. All seven heterogenous catalysts were utilized in transesterification reaction of the WCO at the same process conditions to evaluate their performance with respect to biodiesel yield. The biodiesel yield of these catalysts followed a trend of decreasing order as follows: M3 > FSES > CBES > ES > FSCB > FS >CB mainly due to the basicity resulting from the type of active components present in these catalysts. Regression analysis was performed to further validate which characteristics affected the performance and it was confirmed that the most important characteristic of the catalysts in this work was basicity. Furthermore, since M3 was the focus and the best performing catalyst amongst the heterogenous catalysts as well, it was compared to the yield of the homogenous reactions that was used as the baseline and a difference of averagely 27% was observed. M3 was then used in reactions to study the effect of the process variables on the biodiesel yield and the optimum conditions were found to be a temperature of 60°C, 6 hours of reaction time, stirring speed of 600 rpm, ethanol-to-oil molar ratio of 15:1 and catalyst concentration of 2wt% of the oil.
Open Access
Comparative Assessment, Parametric Sensitivity, Economics, and Modeling of Novel 1,5-diamino-2-methylpentane Based Amine Solvent Blend for CO2 Capture From Large Industrial Sources
(Faculty of Graduate Studies and Research, University of Regina, 2018-11) Nwaoha, Chikezie Ndubuisi; Tontiwachwuthikul, Paitoon; Idem, Raphael; Ibrahim, Hussameldin; Torabi, Farshid; Raina-Fulton, Renata; Mahinpey, Nader
This research investigates the development of novel 1,5-diamino-2-methylpentane (DA2MP) based amine solvent blend for CO2 capture from large industrial sources. The lab-scale absorber and desorber pilot plant (2 inch by 42 inch each) were used to investigate the comparative CO2 capture analysis of the novel 2 kmol/m3 AMP-(1 to 3) kmol/m3 DA2MP blend to 5 kmol/m3 MEA and 2 kmol/m3 AMP-1 kmol/m3 PZ blend for the power plants (8 vol.% CO2 and 15.1 vol.% CO2), and lime kiln (30 vol.% CO2) industries. For the water-gas shift process plant (50 vol.% CO2) the CO2 capture performance of novel 3 kmol/m3 MDEA-(0.5 to 1.5) kmol/m3 DA2MP blend was compared to the benchmark 3 kmol/m3 MDEA-0.5 kmol/m3 PZ. The main key performance indicators (KPIs) are mass transfer coefficients, regeneration energy, CO2 absorption efficiency, CO2 absorption rate, and viscosity and density of the CO2 loaded amine solutions. A nonlinear correlation was developed and compared to the artificial neural network (ANN) for accurate prediction of viscosity and density of AMP-DA2MP and MDEA-DA2MP blends. The effect of the carbon tax, CO2 sales price and a newly developed carbon tax model/correlation on the CO2 capture cost was studied. Results from the KPIs revealed that the 2 kmol/m3 AMP-1.5 kmol/m3 DA2MP is the optimal amine concentration and has superior CO2 capture performance compared to MEA and AMP-PZ blend while 3 kmol/m3 MDEA-1 kmol/m3 DA2MP was optimal and performed better than the MDEA-PZ blend. Parametric sensitivity analysis of 2 kmol/m3 AMP-1.5 kmol/m3 DA2MP showed that the amine flow rate and concentration, and reboiler temperature of integral in optimizing the performance and reducing carbon capture cost. The developed nonlinear correlation accurately predicted the density and viscosity of the amine blends (R2 up to 0.9309), however, the ANN model has a superior predictive accuracy (R2 up to 0.9999). The effect of the CO2 sales price was observed to reduce the CO2 capture cost iii compared to the carbon tax. Also, the proposed carbon tax model reduced the CO2 capture cost compared to the carbon tax from the government. Overall, the novel AMP-DA2MP and MDEA-DA2MP blends are capable of costeffective and energy efficient CO2 capture.
Open Access
A Comparative Study of Short-Term Electric Vehicle Load Forecasting Using Data-Driven Multivariate Probabilistic DeepAR Approach
(Faculty of Graduate Studies and Research, University of Regina, 2021-11) Vahidmohammadi, Aidin; Henni, Amr; Ibrahim, Hussameldin; Shirif, Ezeddin; El Darieby, Mohamed
With the surge of electric vehicles (EVs) and consequently the increase in power consumption, the power grid is facing many new challenges. Charging load forecasting remains one of the key challenges, that if not effectively scheduled, it may result in instability and quality-related issues in power systems. In recent years, numerous load forecasting techniques using machine learning and deep learning were proposed for predictions covering both commercial and household demands. However, there are very few studies that employed these methods to predict EV charging load behavior. This thesis proposes a multivariate RNN-based deep learning framework to forecast the short-term data-driven EV charging loads on two specific datasets for residential and workplace usage. In this research, a few popular deep learning models have been comparatively investigated to evaluate the forecasting performance of the proposed multivariate DeepAR model, a recurrent neural network-based model, as well as its univariate model on the historical charging data with exogenous variables. The 5-tuples input data used in this research include charging start time, duration of charging, charging load, time of use electricity price, and weekdays/weekends that were collected from three different locations and categorized into residential and workplace/parking lot scenarios. The short-term load forecasting algorithm in this study has been utilized multi-step daily horizons as one, three, seven and fifteens days ahead for the prediction window. Numerical results show that the multivariate DeepAR algorithm persists with manifestly higher stability and accuracy over multi-step daily prediction horizons. Its symmetric mean absolute percentage error (SMAPE) and mean absolute scaled error (MASE) are maintained at 1.9% and 4.95%, respectively, and outperform by a significant margin all other investigated deep learning and statistical models on the provided EV historical charging datasets. Eventually, the proposed framework can be further employed to formulate a more complex approach regarding charging load management at charging stations to maximize the load factor as well as balancing and flattening peak loads on the grid system
Open Access
A Comprehensive Evaluation of Off-Gas Emissions From A Catalyst-Aided, Amine-Based, Post Combustion Capture of CO2 From Industrial Exhaust Gas Streams
(Faculty of Graduate Studies and Research, University of Regina, 2021-12) Sai-Obodai, Lois Sandra Naa Oboshie; Idem, Raphael; Ibrahim, Hussameldin; Supap, Teeeradet; Young, Stephanie
The major focus of all CO2 capture technologies is to reduce emission of CO2 which is undoubtedly one of the major greenhouse gases blamed for global warming. It is imperative to ensure that while we aim to capture CO2 to achieve the production of clean energy, other contaminants are not released into our environment, so as not to defeat the main purpose of ensuring the safety of the environment. In the amine-based, catalyst-aided post combustion capture of CO2 technology, amine degradation occurs, and the degradation products are present in both the liquid and gas phases. It is important to know the composition and quantities of these degradation products to optimize the capturing process and ascertain that the capture process will not have a negative impact on the environment. For the amine solvent selection process two single amines: hexamethylenediamine (HMDA), and 2-amino-2-methyl-1-propanol (AMP), two polyethyleneimine (PEI) blends: HMDA/PEI and AMP/PEI and two 2-amino-2-methyl-1-propanol (AMP) blends: DMAE/AMP and BEA/AMP which served as the benchmark amine blend for this study were screened. After screening and comparing these amines based on their absorption and desorption properties, DMAE/AMP involving a tertiary amine and a sterically hindered amine with a total concentration of 4M was selected as the optimal amine solvent for the CO2 capture process in this study. The next phase of the work was to synthesise, characterise and screen two solid basic absorber catalysts; Activated Carbon Spheres (ACS) and a proprietary absorber catalyst (APC) and one solid acid desorber catalyst, a proprietary desorber catalyst (DPC). The screening results for the absorber catalysts showed that the equilibrium loading, and initial absorption rate were enhanced by 5.6% and 33.3 % respectively for ACS and by 3.9% and 29.4% respectively for APC. For the desorber catalyst, DPC, the lean loading was enhanced by 16%, the initial rate of desorption by 25.6% and the heat duty by 20.3%. The last phase of the work was to apply the selected amine solvent, DMAE-AMP, and a catalyst in a pilot plant set-up in two separate runs which lasted for a total of 15 days (360 hours) each. The desorber catalyst was used due to its high yield and direct involvement in helping to reduce the heat duty required for the regeneration process as observed during the screening tests. For each run, the off gas from the absorber column was trapped and analysed using the relevant EPA methods to access the volatile species qualitatively and quantitatively. The data obtained confirmed the presence of ammonia and four different aldehydes namely formaldehyde, acetaldehyde, acrolein and butyraldehyde in all samples taken. Traces of propionaldehyde and crotonaldehyde were observed in some samples of both runs with traces of valeraldehyde observed in some samples of only the run with the desorber catalyst. Ammonia had the highest rate of emission of 0.43ppmV/h and this was recorded in the blank run. The emissions of all observed compounds were higher in the blank run than the run with desorber catalyst under the same conditions. Finally, the emissions of both runs were found to be within the stipulated occupational limits set by the relevant regulatory board, confirming the overall environmental safety of the emissions of the capture process using DMAE/AMP, with and without the selected catalyst.
Open Access
Computational Fluid Dynamics of High Temperature Shift Reaction of Reformate Gas in Fixed Bed and Membrane Reactors
(Faculty of Graduate Studies and Research, University of Regina, 2020-01) Nnabuo, Nnaemeka Nnamdi; Ibrahim, Hussameldin; Salama, Amagad; Henni, Amr; Xue, Jinkai; El-Darieby, Mohamed
Crude biogas upgrade is required in order to boost its industrial use as it contains impurities such as carbon dioxide, carbon monoxide, hydrogen sulphide and ammonia that limits its value. Renewable hydrogen is a clean, carbon-free energy resource that is obtainable from biogas upgrade via the reforming process. To increase hydrogen yield and CO conversion in reformate gas, the product of reforming, the water gas shift (WGS) reaction is required. The conduction of the WGS reaction in a membrane reactor (MR) is a process intensive technology that not only increases hydrogen yield and purity but also reduces costs due to lesser energy required and lesser number of process units required compared to the traditional WGS approach. The use of a novel catalyst that combats the limitations of the traditional WGS catalyst is necessary to further cut down costs and improve the overall process efficiency. Such a novel catalyst (3Ni5Cu/Ce0.5Zr0.33Ca0.17) was developed in a previous experimental study in our group. To gain insights into the interaction of 3Ni5Cu/Ce0.5Zr0.33Ca0.17 catalyst with fluid transport phenomena taking place inside the reactor, a comprehensive computational fluid dynamics (CFD) model has been developed in this study for WGS reaction of biogas reformate in a reactor with and without the presence of a membrane. The model is comprehensive, as it does not consider non-practical assumptions like isothermal condition, isobaric condition, adiabatic condition, constant fluid properties and ideal feed. The developed CFD model aligned with both literature and experimental results. An average absolute deviation (AAD) of 8.59% and 6.32% was obtained when the CFD model was validated with experimental fixed bed reactor (FBR) data and experimental MR data respectively. Comparison between operating the MR in counter-flow configuration versus co-flow configuration revealed that the counter-flow configuration is marginally better in terms of CO conversion. The advantage of the counter-flow configuration is however more evident with respect to decreasing the exothermic temperature rise within the catalyst bed. Increasing the wall heat transfer coefficient from 0 to 14.6 W/m2/K results in a rise in conversion of 8% and 5% for the MR and FBR respectively. Above 8.6 W/m2/K, the effect on conversion is negligible, amounting to only a 1% increase in conversion for both reactors. Conversion values comparable to equilibrium conversion was attained by increasing either the H2O/CO ratio to 3.4 or the catalyst weight time to 2.95 gcat.h/mol CO. Equilibrium conversion was exceeded when the catalyst weight time is increased to at least 2.04 gcat.h/mol CO and the H2O/CO ratio is as high as 3.4.
Open Access
Depositional and Dewatering Behaviour of Uranium Mill Tailings
(Faculty of Graduate Studies and Research, University of Regina, 2014-08) Bhuiyan, Md. Imteaz Ferdoush; Azam, Shahid; Ng, Tsun Wai Kelvin; Shirif, Ezeddin; Ibrahim, Hussameldin
The Key Lake operation in Saskatchewan, Canada, is the largest uranium mill in the world. This mill process generates tailings that are deposited into an onsite storage area called the Deilmann Tailings Management Facility (DTMF). An effective tailings management scheme requires a clear understanding of slurry behaviour throughout the life-cycle, starting from production thorough the deposition to dewatering in the storage facility. The main objective of this research was to investigate the depositional and dewatering behaviour of uranium mill tailings (4%, 5%, and 6% mill tailings) from the Key Lake operation under laboratory and field conditions. All of the samples exhibited the same trend for yield strength development during the tests for rheological properties. A negligible strength (0.4 kPa) was found to have at 60% solids content (s) followed by a rapid increase thereafter. The settling and segregation tests were performed under different initial solids contents (si). The 4% mill tailings exhibited a lower rate and total amount of settlement than 5% and 6% mill tailings in the settling tests. The initial hydraulic conductivity (ki) decreased by two orders of magnitude (10-2 m/s to 10-4 m/s) with a decrease in initial void ratio (ei) from 16 to 4 (15% < si < 40%) and a decrease in final void ratio (ef) from 8 to 4 (30% < si < 45%) such that 4% mill tailings showed one order of magnitude lower values than the 5% and 6% mill tailings. The corresponding settling potential (SP) decreased ten times (50% to 5%) for 4% mill tailings and four times (60% to 15%) for 5% and 6% mill tailings. The effective stress (σ') increased from 80 Pa to 260 Pa in the settling tests. The average solids content after settling was 35% (20% < s < 42%) for 4% mill tailings, 40% (15% < s < 60%) for 5% mill tailings, and 39% (18% < s < 54%) for 6% mill tailings with a corresponding normalized solids content deviation of ±3%, ±8%, ±6%, respectively. The 4% tailings were less prone to segregation when compared with 5% and 6% tailings. Nevertheless, all materials were essentially non-segregating at 40% initial solids content. The large strain consolidation tests were conducted by using a customized and fabricated consolidation test system. During the tests, the total strains were 31% to 42% for all investigated mill tailings in an effective stress range of 0.3 kPa to 8 kPa. The change in void ratio was higher for 4% mill tailings (Δe = 2.5) than 5% and 6% mill tailings (Δe = 1.3 to 1.7). The lowest measurable effective stress was 0.3 kPa for all investigated mill tailings. The void ratios were found to be 3.8, 3.1, and 3.4 at σ' of 1 kPa and further reduced to 3.3, 2.8, and 3.1 at σ' of 8 kPa for 4%, 5%, and 6% mill tailings. The k values showed an initial scatter before attaining a steady value and were found to range from 10-7 m/s to 10-8 m/s. The test results provided the volume compressibility and hydraulic conductivity relationships for current (4%) and future (5% and 6%) mill tailings. The large strain consolidation behaviour in the DTMF was investigated by analyzing survey data from 1996 to 2008, laboratory testing of the current (4%) mill tailings, and history matching of the deposited tailings using numerical modeling. The numerical modeling results closely approximated the consolidated tailings elevations and effective profiles in the DTMF over the period of 1996 to 2008. The field effective stress values correlated quite well with the modeling results thereby validating the predictions. Overall, the results indicate that the effective stress increased from 0 kPa at the surface to the following values at the DTMF bottom: 200 kPa in 1999, 530 kPa in 2005, and 680 kPa in 2008.
Open Access
Developing a Multi-Objective Closed-Loop Supply Chain For Meat Distribution Network Considering The Environmental Impact
(Faculty of Graduate Studies and Research, University of Regina, 2018-09) Hassanzadeh Goocdareh, Amir; Henni, Amr; Ibrahim, Hussameldin; Torabi, Farshid
In the past few years, environmental issues have had a critical and an essential role in designing a supply chain network and need to be taken into consideration; many companies and organizations have started employing environmentally sustainable processes in their business and supply networks. On the other hand, the trends show that by the year 2050, the global demand of food may increase as twice as it is now, as the population of the world may reach 9 billion by then. For thousands of years meat has been an important part of human diet as it is recognized as an essential part of an equitable diet by the World Health Organization. Based on what is available in the literature dealing with food supply chain (FSC), one could notice that there is a lack of reverse logistics in this area of study, and closed-loop supply chain (CLSC) design as an integration of forward and reverse logistics has not been paid much attention. This study is conducted as an expansion of the work done by Mohammed and Wang (2017) dealing with a multi-objective closed-loop Green meat supply chain, and an attempt to cover the gap of reverse logistics in their study. To that aim, this work has targeted to design an optimal Green supply chain configuration for meat production network considering both forward and backward (reverse) flow. ɛ-constraint technique, as a classical method for solving multi-objective optimization (MOO) problems, is used to solve the model. The designed CLSC network is modeled by Mixed-Integer Linear Programming. To validate the developed meat supply chain (MSC) network, a real-life case study is provided. Having modeled the case problem, IBM ILOG CPLEX optimization software is used to code it based on the procedure of the proposed method. Computational results are presented, and a complete discussion and sensitivity analysis are performed, which proves that the developed CLSC network is robust enough to be applied to practical CLSC problems, and the performance of the optimization method is quite acceptable to solve such problems.
Open Access
Development and Evaluation of a Hybrid Membrane Bioreactor for Greywater Reclamation
(Faculty of Graduate Studies and Research, University of Regina, 2013-07) Palmarin, Matthew Joseph; Young, Stephanie; Veawab, Amornvadee; Ng, Tsun Wai Kelvin; Ibrahim, Hussameldin
The development and evaluation of novel treatment and sanitation devices are necessary for the advancement of wastewater management practices. Considering future water demands and the limitation of current supplies, water security will likely become a central issue of the 21st century. To address this concern, a greywater reclamation system for non-potable water applications was developed and evaluated during this research. The reclamation system consisted primarily of a hybrid membrane bioreactor (HMBR), a biological treatment system containing both attached and suspended growth processes and membrane separation. A 10 month pilot study using synthetic and real greywater was conducted at the University of Regina Research and Innovation Centre (RIC). The objectives of this study were to: (1) evaluate treatment performance at various mixed liquor suspended solids (MLSS) concentrations and hydraulic retention times (HRT); (2) evaluate treatment performance at high contaminant loads using high strength synthetic greywater; and (3) evaluate treatment performance following long periods of idle operation. Influent and effluent water quality was evaluated in terms of five-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), nitrate, nitrite, ammonia, and turbidity. Removal efficiencies were evaluated at MLSS concentrations ranging from approximately 1000 to 4000 mg/L and at HRTs ranging from 8 to 12 hours. An MLSS = 3000 or 4000 mg/L at an HRT of 8 hours yielded optimal results for the treatment of medium strength greywater. Under these conditions, removal efficiencies as high as 99.2%, 97.8%, 99.8%, 99.9%, 97.9%, and 44.8% were achieved for BOD5, COD, turbidity, ammonia, TP, and TN, respectively. When treating high strength synthetic greywater, high removal efficiencies were observed and residual BOD5 and turbidity were below the Canadian Guidelines for Domestic Reclaimed Water for Use in Toilet and Urinal Flushing. The system showed prompt recovery following one, two, three, and four week periods of idle operation and was capable of producing reusable effluent within two days of resumed flow.
Open Access
Development of a diagnostic approach for early detection and control of amine degradation in an amine-based CO2 capture process
(Faculty of Graduate Studies and Research, University of Regina, 2022-12) Mensah, Ebenezer Kofi; Idem, Raphael; Ibrahim, Hussameldin; Supap, Teeradet; Zeng, Fanhua
Chemical absorption of CO2 with aqueous amine-based solvents is considered as one of the current benchmark post-combustion capture technologies mainly because of its high process efficiency. When amines are used to capture CO2 from flue gases, changes in the physical and chemical properties of the solvent may take place over time. The changes in these properties of the amines are manifestations of physical changes such as the differential evaporation of components of the aqueous amine solvents and chemical changes resulting from degradation of the amines. Therefore, if changes of these properties relative to the original solvent properties are monitored during the capture process, they could be used to determine whether a physical change such as water evaporation has taken place or a chemical change such as degradation has occurred or both. The amine concentration can be restored by adding the right amount of the right component to restore the amine solution to its original form. This work uses a diagnostic approach to determine the occurrence of physical or chemical changes in amine-based solvents by means of simple solvent property measurements as a way of monitoring solvent quality during the CO2 capture process to maximize the capture efficiency and improve the capture performance of amine-based solvents. In the first phase of the work, amine was prepared at different concentrations to closely imitate the physical changes that could take place in a conventional CO2 capture plant as a result of the differential evaporation of the components of the amine. CO2 loading and temperature were also varied to take into account the different conditions that might exist in the capture plant at the time a sample was taken for analysis. The amine properties at these different concentrations and conditions were measured and then compared to the properties of the original or desired solvent. For the chemical change experiment, the amine was synthetically degraded by intentionally adding different concentrations of primary degradation products and measuring the corresponding properties of the degraded amine. The experimental data obtained from the first phase of the work was then used to develop predictive models that were capable of predicting physical or chemical changes or both based on the change in properties of the amine sample. Three predictive models were developed for physical change using MS Excel, Minitab software and Artificial Neural Network (ANN) with MATLAB software. The standard error for the model developed using MS Excel was determined to be 0.097 with R2 value of 0.97 and average absolute deviation (AAD) of 1.56% between model predicted and actual response values of the validation dataset. The model trained using Minitab reserved a fraction of 0.3 of the data for internal testing and returned standard error of 0.09 and 0.11 for training and test set with R2 values of 0.977 and 0.953 respectively. The AAD between model predicted and actual response values of additional test data was determined to be 1.39%. The trained ANN also returned root mean squared error (RMSE) of the training, validation and test dataset as 0.035, 0.046 and 0.068 respectively with R2 value of 0.995 for the test set. AAD for validation and test set was computed to be 0.91% and 1.47% respectively. An optimizable Gaussian Process Regression (GPR) model was developed for chemical change using Regression Learner tool in MATLAB. The trained model with the best hyperparameters returned R2 values of 0.99 and 0.95 for the validation and test set with RMSE of 0.042 and 0.090 respectively. The AAD for the test set parity plot was computed to be 3.45%. The models with satisfactory performance and accuracy were then selected and employed in the development of an interactive graphical user interface (GUI) to make predictions, interpret predicted values for the user and suggest actions that would enable the user offset any disturbance or deviation from set values.
Open Access
Development of Criteria for Selection of Components for Formulation of Amine Blends Based on Structure and Activity Relationships of Amines, and Validation of Formulated Blends in a Bench Scale CO2 Capture Pilot Plant
(Faculty of Graduate Studies and Research, University of Regina, 2017-09) Narku-Tetteh, Jessica; Idem, Raphael; Ibrahim, Hussameldin; Supap, Terradet; deMontigny, David; Zeng, Fanhua
Due to modernisation and industrialisation, an increase in the global energy demand is inevitable. Nuclear, fossil fuels, renewables, hydro and biomass are the major sources of energy. However, considering the current energy framework, fossil fuels appear to be the most reliable and stable energy source. As a result, emphasis on the reduction of emissions of carbon dioxide (CO2, a major type of greenhouse gas (GHG)) is very crucial because almost all fossil fuel activities lead to generation of this environmentally harmful GHG. Scientists have shown that the average global temperature has increased by up to about 1 degree over the last century. Thus, if this issue is left unabated, it will have long lasting consequences both on human lives and the environment. Extreme weather conditions, heat waves, sea level rise, wild fires, health problems are glaring repercussions of global warming and climate change. Various strategies such as use of alternative energy, energy conservation or fossil fuel-energy coupled with carbon capture and sequestration (CCS) are all attempts to mitigate this problem. However, CCS stands out to be the anchor technique due to its compatibility with existing energy infrastructure in conjunction with the reliability of fossil fuel-based energy itself. Post combustion capture which uses a regenerable liquid sorbent, appears to be the predominant technology used and has proven to be successful in most industrial applications. However, this technology is still far from being perfect. My thesis research addresses the imperfections and challenges identified with this technique from the context of sorbent formulation. Optimum sorbent performance cannot be achieved with one single amine sorbent. It is therefore essential to develop new amine sorbent systems by blending and combining their individual strengths to achieve an optimum performance sorbent. Most approaches used to solve this problem use indirect means whereas we need the type of studies that will directly link the chemical structure of the amine sorbent to its performance since this will provide the key in unlocking the rationale in selecting the blend components. For this reason, my research objective focuses on coming up with a rational way to use a fundamental chemical structure – activity relationship study to develop and formulate an optimum sorbent blend. This novel blend is validated in a bench scale pilot plant to ensure that the developed criterion leads to a blend that is practical and implementable. The effects of the chemical structure, namely, side chain structure and number of hydroxyl groups in primary, secondary and tertiary amines as well as the alkanol chain length in primary alkanolamines and the alkyl chain length in secondary and tertiary alkanolamines on amin activities such as CO2 absorption and desorption kinetics, equilibrium loading, heat duty, cyclic capacity, heat of absorption and pKa were studied and used to develop rational criteria for selecting components to formulate an optimum amine blend. Based on the criteria, amines that had a combination of high CO2 absorption parameter and high CO2 desorption parameter were selected. Their mixing ratios and concentrations were varied to obtain the best overall performance. The optimum blend was then validated in a bench scale pilot plant and compared with the benchmark 7M MEA-MDEA solvent blend. The role of a solid acid catalyst in aiding CO2 desorption and further enhancing the performance of the developed novel blend was tested and, again, compared to the benchmark 7M MEA-MDEA blend. The results of this study showed that, in comparison with their straight-chain analogues, steric hindrance present in branched-chain alkanolamines resulted in much faster desorption rate, higher equilibrium CO2 loading and cyclic capacity, much lower heat duty for solvent regeneration, but just a slight decrease in CO2 absorption rate. The effect of chain length studies also showed that, longer alkanol chain lengths of primary alkanolamines and longer alkyl chain lengths of secondary and tertiary alkanolamines led to higher equilibrium CO2 loading and pKa. However, the influence of mass transfer limitations on these positive effects resulted in a maximum trend for initial rate of CO2 absorption for secondary and tertiary alkanolamines. On the other hand, the increase in the chain lengths also caused the generation of larger amounts of bicarbonate ions which resulted in higher CO2 desorption rates and cyclic capacity, but lower heat duty. However, the longer chain alkanolamines also had high viscosities which adversely modified their performance by also introducing mass transfer limitations. The developed criteria, in terms of absorption parameter and desorption parameter, resulted in formulating an excellent bi-solvent aqueous amine blend (comprising 2M BEA + 2M AMP), which had an outstanding desorption characteristics/heat duty as well as very good absorption characteristics. In addition, this work developed a new non-trial-and-error procedure to determine the heat of CO2 absorption based on Gibbs-Helmholtz equation. Also, the pilot plant studies showed that the novel blend, 4M BEA-AMP blend showed outstanding performance in absorber efficiency, heat duty and cyclic capacity over the 7M MEA-MDEA blend, implying that it is a good potential solvent for post combustion CO2 capture thereby validating the developed selection criteria that yielded the 2M BEA + 2M AMP blend. The addition of catalyst in the process led to tremendous improvements in all the performance indicators of the two solvent blend systems.
Open Access
Development of Ionic Liquids Functionalized Porous Materials for CO2 Capture Applications
(Faculty of Graduate Studies and Research, University of Regina, 2018-12) Mohamedali, Mohanned Ezzelden; Henni, Amr; Ibrahim, Hussameldin; Shiriff, Ezeddin; Salad Hersi, Osman; Croiset, Eric
The utilization of ionic liquids (ILs) for CO2 capture applications has recently gained considerable attention due to several remarkable properties including a high CO2 solubility, low volatility, and thermal stability. However, the cost and, in some cases, high viscosity of ILs are considered the major challenges for their wide scale applications in CO2 capture. One of the promising approaches to overcome these limitations is to incorporate ILs into solid porous materials also known as supported ILs (SILs) that were shown to enhance performance in various applications such as catalytic esterification reaction, ionic conductivity, and gas separation applications. In this work, the functionalization of various porous materials such as metal organic frameworks (MOFs) and mesoporous silica supports using ILs for applications in CO2 separation is presented. The immobilization of three ILs namely 1‑Butyl-3- methylimidazolium Acetate [bmim][Ac], 1‑Ethyl-3-methylimidazolium Acetate [emim][Ac], and 1‑Propyl-3-methylimidazolium bis(Trifluoromethylsulfonyl)imide [pmim][Tf2N] into different solid sorbents including zeolitic imidazolate framework (ZIF-8), Copper benzene-1,3,5-tricarboxylate (HKUST-1), chromium 1,4- benzenedicarboxylate (MIL-101), mesostructured silica (MCM-41), and (SBA-15) is reported. The synthesis, characterizations and CO2 capture performances of the different ILs-supported solid sorbents is presented to investigate the impacts of the nature of the ILs used, IL loading, and the role of synthesis methods on the physicochemical properties and CO2 capture performance. Thermogravimetric analysis (TGA) experiments were carried out under nitrogen environment to study the thermal stability of the composite materials and to quantify the composition of the impregnated samples. Fourier transform infrared spectroscopy (FTIR) was used to confirm the successful immobilization of ILs into the porous materials. N2 adsorption experiment at 77 K was conducted to evaluate the specific surface area and pore volume distribution of the different composite sorbents, whereas X-ray diffraction (XRD) analysis was carried out to study the influence of ILs incorporation on the crystallinity and phase stability of the solid sorbents. The adsorption isotherms of CO2 and N2 on the pure ILs, the pristine solid supports, and the different composite sorbents selected was evaluated at different temperatures using an intelligent gravimetric analyzer (IGA). The incorporation of acetate-based ILs into ZIF-8 framework was found to substantially enhance the CO2/N2 selectivity and CO2 capacity up to 7 times higher at 0.2 bar and 303 K using ZIF-8 with 30 wt.% [bmim][Ac] loading as compared to the pristine ZIF-8, which was attributed to the chemisorption interaction between CO2 and the carbonyl group on the acetate ILs. Remarkably, the impregnation of 5 wt.% [bmim][Ac] into HKUST-1 exhibited the highest CO2 uptake with double that of the pristine HKUST-1 at 303 K and 0.2 bar. On the other hand, the introduction of higher loadings of [bmim][Ac] and [pmim][Tf2N] into the pores of HKUST-1 did not show any enhancement in the CO2 capacity of the composite sorbents. Furthermore, no improvements in the CO2 uptakes could be achieved for the MIL-101 samples regardless of the synthesis protocol, whereas the MOF-177 incorporated with [emim][Ac], using wet impregnation method, has shown a remarkable enhancement in CO2 capacity in the low pressure region. Findings from this work provide insights into the synthesis, structure, and sorption capacity of these novel composite materials, which could be utilized for the design of sorbents with outstanding properties to meet the environmental challenges.