United Arab Emirates University
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UPCYCLING CARBIDE SLAG WASTE FOR LOW-TEMPERATURE CO2 SEQUESTRATION: DIRECT APPLICATION AND ADSORPTION AFTER CHEMICAL ALTERATION
No full textExcessive carbon dioxide (CO₂) emissions and large-scale industrial waste generation are the results of rapid industrialization and ever-increasing energy demand. The recent trend of repurposing waste materials for pollution prevention is a promising approach to address both these environmental challenges. One such industrial waste residue generated from acetylene gas production is called carbide slag (CS). Its high Ca(OH)₂ content presents a promising opportunity for its utilization in CO₂ sequestration. However, CS waste is primarily studied as a CaO source for the intensive Ca-looping process. Despite the great potential, the high temperature requirements of the Ca-looping process limit its industrial application. Owing to its highly reactive and readily available Ca(OH)₂ content, this dissertation aims to utilize CS waste for CO₂ sequestration at low temperature and pressure conditions with a minimum energy requirement. The research is carried out through an integrated framework where CS waste is (1) employed directly for dry and wet mineral carbonation for CO₂ capture and conversion, and (2) chemically treated to synthesize a regenerable adsorbent for cyclic CO₂ capture. Dry and wet mineral carbonation were studied under varying process conditions. Further, wet mineral carbonation was modelled using response surface methodology (RSM) and assessed for environmental sustainability and the feasibility of large-scale deployment using life cycle analysis (LCA). Experimental results indicate that dry and wet phase direct mineral carbonation of carbide slag occurs spontaneously at ambient conditions with a carbonation efficiency (CE) of 7% and 37%, respectively. A maximum CO₂ capture capacity (CCC) of 12.2 mol CO₂ kg⁻¹ can be achieved via wet mineral carbonation of CS waste with a liquid to solid (L:S) ratio of 0.2, at ~25 °C temperature, and 10 bar pressure. Quadratic RSM models revealed that CCC was mainly influenced by reaction pressure, followed by the L:S ratio. While initial reaction kinetics were majorly influenced by the CO₂ loading rate and reaction pressure. LCA suggests that utilizing the mineral carbonation end product in cement mixing reduces global warming potential by 300% compared to conventional disposal in landfills. Further, the overall CO₂ reduction potential of the CS waste wet mineral carbonation process ranges from 0.1 to 3.5 kg CO₂ avoided per kg CO₂ captured. Cyclic adsorbent was prepared by functionalizing CS-derived hydroxyapatite (CS-HAp) support with tetraethylenepentamine (TEPA) loading at 10-50% dosage. Prepared adsorbent with 30% TEPA loading (CS-HAp-T-30%) exhibited CO₂ adsorption capacity of 3.41 mmol g⁻¹ at ambient conditions. CO₂ adsorption capacity of CS-HAp-T-30% increased with elevation in pressure. Further, the presence of humidity greatly influenced CO₂ adsorption capacity. CS-HAp-T-30% retained ~70% of performance after over ten cycles. Overall, this study demonstrates the potential of CS waste for CO₂ sequestration as a mineral carbonation feedstock and a precursor of high-value adsorbents for cyclic separation. Therefore, utilizing CS waste for CO₂ sequestration offers dual benefits of waste valorization and decarbonization
INTEGRATED OPTIMIZATION AND DATA-DRIVEN MODELING FOR SEAWATER INTRUSION MITIGATION AND PREDICTION
No full textSeawater intrusion (SWI) threatens the reliability of coastal groundwater especially in hyper-arid settings, where climatic stress and pumping accelerate salinization. This dissertation advances two complementary approaches to managing SWI: Part A optimizes mitigation measures, hydraulic (pumping/injection) and physical barriers (cutoff walls, subsurface dams) on benchmark models; Part B predicts SWI in the hyper-arid Fujairah (UAE) coastal aquifer using total dissolved solids (TDS) as a proxy, through machine learning-based models, spatially and dynamically. A bibliometric synthesis first maps the evolution of SWI models and mitigation strategies, identifying gaps that motivate the subsequent methodological developments. Part A employs the classical Henry problem to develop and test mitigation designs independent of site specifics. A FEFLOW–Python optimization workflow derives explicit design correlations for hydraulic barriers, including “negative” barriers that extract brackish water from the dispersion zone. Optimal extraction is near the center-bottom of the wedge; optimal injection depends on rate, with maximum retardation (~61%) when placed at the aquifer bottom near the coast, shifting toward the toe at lower rates. A second benchmark study couples a Python–FEFLOW simulator with machine-learning sensitivity analysis (Random Forest + SHAP) to evaluate physical barriers (cutoff walls, subsurface dams). Under uniform properties, cutoff walls reduce total dissolved salts by up to ~98% and seawater influx by ~76.5%, while subsurface dams achieve ~92% and ~81%, respectively; proximity to the saline boundary can induce stagnation zones and contour distortion, underscoring siting trade-offs. Part B targets the Fujairah coastal aquifer (UAE). Using six hydrogeologic predictors, fifteen algorithms are benchmarked for TDS-based SWI assessment: LightGBM attains R² ≈ 0.957 for prediction, and Gradient Boosting/CatBoost reach ~97.6% accuracy (AUC ≈ 0.999) for classification. Derived empirical equations provide practical screening tools, with hydraulic head and distance from the coast emerging as dominant drivers. Subsequently, a time-dependent deep-learning program forecasts daily TDS from \u3e14,000 records at 16 wells, comparing FFNN, LSTM, Transformer, and a hybrid LSTM-FFNN. After KNN imputation and normalization, an attention-augmented LSTM provides the most reliable temporal forecasts and sustains the highest accuracy across seasonal regimes (including high-stress summer months) and spatial zones (coastal–inland gradients) (MAE ≈ 401 mg/L; R² ≈ 0.983). Building on Parts A and B, future research should (i) translate the benchmark-derived design rules to site-specific, 3D variable-density models that include realistic heterogeneity, anisotropy, tidal forcing, sea-level rise, and temperature/viscosity effects, and validate them via controlled field pilots (instrumented pumping/injection tests, multi-depth EC/TDS logs, and time-lapse geophysics); (ii) couple process models with data assimilation for real-time state estimation and barrier control; (iii) advance prediction by fusing additional drivers (pumping schedules, tides, land use); (iv) develop active-learning strategies to optimize monitoring network design and target new wells/sensors where forecasts are most uncertain; and (v) generalize and transfer the Fujairah-trained pipelines to other hyper-arid coasts via domain adaptation and few-shot fine-tuning, culminating in a “digital twin” for adaptive SWI management
Core-Scale Study of Miscible CO2 Foam–Oil Interactions
No full textFoam is currently the most effective means for gas mobility control in a variety of geo-energy applications (Rossen et al., 2020), including enhanced oil recovery (EOR), carbon capture, utilization, and storage (CCUS), and aquifer/soil remediation. This study investigates the mobility control of miscible CO2 foam in the presence of oil for CCUS. The primary objective is to quantify the impact of oil on CO2 foam behavior under miscible conditions, specifically examining foam stability, strength, and flow regimes as influenced by oil composition and reservoir permeability. While most oils destabilize foam, few studies explore the coarsening mechanisms of CO2 foam in the presence of miscible oils, which is crucial for the successful application of foam in EOR and CCUS under miscible CO2 flood conditions. Three model oils—hexadecane (C₁₆), decane (C₁₀), and a mixture of the two—were co-injected with CO2 and surfactant solution at a fixed velocity ratio into core samples with varying permeabilities. The results show that hexadecane, especially at higher concentrations, significantly enhanced foam apparent viscosity (μₐₚₚ). In core samples with 10 mD permeability, peak foam viscosity reached 64.5 cP for 30 mol% C₁₆, 75 cP for 60 mol%, and 110 cP for 90 mol% C₁₆. For decane, a weaker but still enhancing effect on foam viscosity was observed, with peak values of 28, 43, and 59 cP for 30, 60, and 90 mol%, respectively. Mixed oil produced intermediate results. In high-permeability core samples (650 mD), the 90 mol% C₁₆ concentration increased foam viscosity to around 650 cP, demonstrating the potential of miscible CO2 foam for effective mobility control. In contrast, in low-permeability cores (1.8 mD), the maximum foam viscosity at 90 mol% oil content was about 46 cP, indicating that higher permeability stabilizes foam for the same oil concentration. These findings suggest that miscibility between CO2 and oil enhances foam stability, with higher permeability facilitating more effective mobility control. The experimental approach developed here provides a quantitative framework for understanding the effects of oil composition and permeability on the behavior of miscible CO2 foam, advancing both EOR and CCUS strategies
IDENTIFYING BARRIERS, CHALLENGES AND OPPORTUNITIES OF IMPLEMENTING RETROFITTING STRATEGIES IN EXISTING BUILDINGS IN THE UAE
No full textThe built environment of the UAE faces sustainability challenges because of its high energy usage and greenhouse gas emissions which require successful retrofitting methods to enhance aging buildings\u27 performance and efficiency. This research investigates the barriers and challenges and opportunities of retrofitting strategy implementation in the UAE to provide direction for policymakers and stakeholders regarding sustainable practices. The research employed a mixed-methods approach through literature reviews and stakeholder surveys and interviews. The study shows that high upfront costs (68% of respondents) and building owner awareness limitations (54%) and fragmented regulations with inconsistent thermal insulation standards across emirates represent major barriers. The survey results show that 46% of participants identified the lack of standardized methodologies as their main obstacle. The research demonstrates how supportive policies together with advanced retrofitting technologies create opportunities to achieve energy savings and environmental benefits despite existing challenges. The study provides a vital framework to handle these obstacles while promoting retrofitting activities in the UAE. The research addresses a vital knowledge deficiency about the UAE by creating customized solutions to boost sustainability in built environments
HYBRID TREATMENT METHOD OF BIOFILTRATION PRECEDED WITH ADVANCED OXIDATION PROCESS FOR THE REMOVAL OF EMERGING CONTAMINANTS FROM THE WASTEWATER TREATMENT PLANT EFFLUENT
No full textThe global demand for potable quality water has been on a continuous rise. Consequently, water reuse is now considered as an alternative to significantly expand supplies of freshwater in communities facing water shortages. Most of the conventional wastewater treatment plants (WWTP) are not efficient in the removal of emerging contaminants from the water, which will cause serious issues to the environment, human and animal life. This research investigated the possibilities of treating wastewater for reuse. First, the feasibility of utilizing a biofiltration system without pre-treatment for removing emerging contaminants from the WWTP effluent was investigated. Two types of biofilter media- activated carbon (AC) and expanded glass (EG) were explored. Second, a hybrid treatment system, in which an advanced oxidation process (AOP) was provided as the pre-treatment to the biofiltration system. The adopted AOP for this study was UV/TiO2 photocatalysis. The conditions adopted for the biofilters were 15 minutes EBCT with a flow rate of 2.9 mL/min, and for the photocatalysis pretreatment, 1 g/L TiO2 dosage, 60 minutes HRT, and 365 nm UV lamps. Solid-phase extraction was performed for the contaminant extraction from the samples, which were analyzed using gas chromatography-mass spectrometry. It was found that 4 emerging contaminants were detected- dibutyl phthalate (DBP), phenothrin (PHN), malathion (MAL), and 9-aminoacridine (9-AA) at μg/L level only. Activated carbon removed 19.2% of TOC; removals increased to 60.2% when preceded by UV/TiO2 photocatalysis. AC was found to perform better than EG biofilters for TOC, DBP, PHN, and MAL removal in both individual and hybrid systems. EG biofilters showed better removal in both biofiltration-only and hybrid systems for the 9-AA than the AC biofilter and AOP-only systems. Both biofilters demonstrated similar nutrient removal and improved performance by the application of AOP pretreatment. In general, this investigation indicates that the integration of AOPs and biofiltration systems has the potential to address both emerging and conventional pollutants, as well as to facilitate water reuse
ENHANCING THE ACCEPTABILITY OF DECISION-MAKING SYSTEM USING XAI CASE: CRIME PROFILING SYSTEM
No full textIn the current world, we need to place more emphasis on how easily interpretable, accurate, and acceptable data analysis results are, given that essential operations in law enforcement, among other sectors, are backed up by the use of complex computing systems. Crime profiling systems that use crime data for profiling encounter major problems because they depend on algorithm-based methods. These methods can be ambiguous and inaccurate, leading to low public acceptability. The study investigates major problems with Complex Crime profiling systems (CPS) because their unexplained algorithms result in system performance issues and public scepticism. XAI provides a solution to handle these problems by adding transparency and accountability to the decision-making process. The XAI usage will help improve and refine the current models.
The resulting models will offer better interpretability and accuracy, with human acceptance accordingly at the core. Evaluating criminal investigations with and without XAI integration differs from the blended methods approach. Through a blended-methods approach combining quantitative evaluation and qualitative analysis, the research assesses the effectiveness and stakeholder perceptions of crime profiling systems with and without XAI integration.
Similarly, the main research focal areas are an exploration of the extent to which the use of XAI contributed to the issues of transparency, bias in CPS systems and evaluation of the corresponding datasets of the stakeholders to determine the extent to which XAI-enhanced systems could be trusted. Lastly, an assessment of XAI\u27s performance in the system\u27s success in mitigating biases and enhancing the findings will be communicated through the above research. It will instil confidence in accepting XAI technologies for criminal profiling systems. Thus, decisions made by law enforcement agencies will be more transparent, accurate, and acceptable in the long run
EXTREMISM GOVERNANCE IN THE UNITED ARAB EMIRATES: A CASE STUDY USING QUALITATIVE POLICY FRAMEWORK
No full textThis thesis investigates how the United Arab Emirates governs extremism using various models of governance and Gareth Morgan’s metaphorical organizational models. This research goes beyond a focus on security and looks at extremism as a governance and policy problem related to social cohesion, institutional trust, and legitimacy. The research utilizes a qualitative case study design through analyzing federal laws and national strategies, as well as institutional frameworks to understand how authority, coordination and meaning are organized in the UAE’s governance of extremism.
The results show that the UAE’s governance of extremism is characterized as a hybrid governance model with hierarchical authority, network collaboration and good governance at play. Hierarchical authority is evident through federal laws such as Federal Law No. 7 of 2014 on Combating Terrorism Crimes, while institutions such as Hedayah and Sawab Centre illustrate collaborative and adaptive governance. Using Morgan’s metaphors, we see that the machine and domination metaphors represent legal components of governance, while culture, organism and brain metaphors depict preventive and institutional approaches, balancing coercion and adaptive logics in the governance of extremism.
Ultimately, the study finds that the UAE’s governance of extremism is simultaneously centralized and adaptive, a layered system that provides stability through control, while allowing inclusivity via education, tolerance, and rehabilitation. While successful in achieving security and cohesion, the UAE may face challenges of inclusivity and bottom-up engagement. This study adds to the academic and policy debate, while providing a theorized context-specific model of extremism governance rooted in Emirati cultural, religious and institutional realities that could be utilized for comparative studies across the region
SUPPORTING STUDENTS’ PROGRESS AND ACADEMIC ATTAINMENT: THE ROLE OF ARTIFICIAL INTELLIGENCE IN INCLUSIVE EDUCATION IN UAE SCHOOLS
No full textThis thesis examines the role of artificial intelligence (AI) in strengthening student progress tracking and advancing inclusive education in United Arab Emirates (UAE) schools. Aligned with UAE Vision 2071 and the AI Strategy 2031, it addresses gaps in traditional systems such as delayed interventions, teacher workload, and lack of real-time feedback. Three research questions guided the study: What challenges do teachers face in tracking student progress? How can AI support effective monitoring? What is its impact on inclusion?A convergent mixed-methods design was applied. A bilingual (Arabic–English) questionnaire produced 211 responses from educators across the Emirates, complemented by twelve semi-structured interviews with teachers, inclusion leaders, and IT specialists. Ethical standards of voluntary participation and confidentiality were upheld. Reliability was strong (α \u3e .96) and validity confirmed through expert review and triangulation.Results show broad confidence in AI for progress tracking, with monitoring items averaging above 4.80 on a 5-point scale, particularly early identification of struggling learners (M = 4.87). Benefits for inclusive education were also highly rated (≈ 4.78–4.81), while challenges such as infrastructure, uneven readiness, and teacher resistance were moderate (≈ 3.60–3.98). Thematic analysis revealed six domains: tracking challenges, infrastructure, teacher readiness, AI benefits for monitoring and inclusion, and ethical considerations.The study concludes that AI offers significant potential for real-time, personalized, and equitable learning, provided that infrastructure, training, and governance are prioritized. Policy and practice recommendations are offered to support sustainable adoption
REHABILITATION OF REINFORCED CONCRETE COLUMNS PRE-DAMAGED BY CORROSION USING ADVANCED COMPOSITE MATERIALS
No full textReinforced concrete (RC) columns exposed to aggressive environments are highly susceptible to corrosion-induced deterioration, resulting in significant reductions in load-carrying capacity. This research investigates the structural performance of RC circular short columns with varying levels of corrosion damage and evaluates the effectiveness of two composite-based repair techniques, namely carbon fabric-reinforced cementitious matrix (C-FRCM) and carbon fiber-reinforced polymer (C-FRP) composites, combined with concrete cover replacement. The study aims to establish these methods as practical solutions for rehabilitating corrosion-damaged columns under concentric and eccentric loading conditions. The experimental program included 30 RC column specimens tested in two phases. Phase I involved thirteen undamaged columns subjected to eccentricity-to-depth ratios (e/h) ranging from 0.0 to 0.3, eight of which were strengthened using one or two C-FRCM layers. Phase II examined seventeen corroded columns with accelerated corrosion in longitudinal bars (up to 27%) and steel ties (up to 45%), tested under the same e/h ratio range. Six corroded columns were tested without repair, while eleven were repaired using two layers of either C-FRCM or C-FRP composite wraps in the hoop direction before testing. Phase I experimental results indicated that columns with two C-FRCM layers achieved up to 39% load capacity gain under eccentric loading compared to 17% under concentric loading, whereas single-layer strengthening provided minimal improvement due to insufficient confinement and premature fabric–mortar debonding. Phase II experimental results showed that corrosion reduced load capacity by up to 41% under concentric loading and by an average of 17% under eccentric loading, with the effect diminishing at higher eccentricities. Both repair systems restored original capacity, with C-FRP providing superior load capacity enhancement (80%–167%) compared to C-FRCM (49%–86%), attributed to better confinement efficiency. While premature debonding limited C-FRCM performance, it contributed to improved ductility through gradual post-peak degradation. A mechanics-based analytical model was developed and validated against experimental results and literature data to predict column capacity before and after repair. The model accounts for corrosion effects, material nonlinearities, and the interaction between internal steel tie confinement and external composite wrapping. The analytical model aligns well with experimental results and supports the use of advanced composites in the practical rehabilitation of corrosion-damaged RC columns, confirming its reliability and applicability as a simple, accurate tool for structural evaluation and retrofit design. The model also generated P–M interaction diagrams that reasonably reflected the experimental trends, validating its applicability as a robust tool for structural assessment, strengthening, and retrofit design
TOWARDS AN EMIRATI LEGISLATIVE FRAMEWORK FOR THE RECOGNITION OF LEGAL PERSONHOOD FOR ROBOTS
Full text linkwith the significant advancements in the fields of artificial intelligence and robotics, these technologies have had a direct impact on various aspects of daily life. Intelligent robots, which have the ability to self-learn and make decisions, are no longer just technological tools; they have become entities playing vital roles in many sectors such as healthcare, industry, security, and government services. As the reliance on these technologies increases, complex legal and ethical questions have arisen, including: How can the relationship between humans and robots be regulated? Can robots be granted legal personhood to hold them accountable for their actions? The United Arab Emirates is considered a global example in adopting modern technology, as it has developed a comprehensive strategy for artificial intelligence aimed at enhancing innovation and achieving sustainable development. However, integrating these technologies requires the establishment of legal frameworks that align with their complexities, especially since robots are evolving into independent entities capable of making decisions that may have positive or negative effects on society. In this context, the concept of legal personhood for robots is considered an innovative solution to address a range of issues such as civil liability, intellectual property rights, and data protection. This study focuses on examining the suitability of applying this concept within the legal framework in the UAE, reviewing international experiences, technical and ethical challenges, and existing legislation, while offering practical recommendations aimed at enhancing the UAE\u27s position as a global leader in regulating artificial intelligence and robotics. The aim of this study is to explore the various aspects of this topic comprehensively, with a focus on how to strike a balance between promoting innovation and ensuring the sustainability of social and legal values that contribute to protecting individuals and society as a whole