Fixed-Bed Antimicrobial Reactor for Wastewater Disinfection
Reactor DesignCatalyst ImmobilizationKineticsCharacterization
The ProblemConventional wastewater disinfection methods produce toxic by-products and struggle with emerging pathogens. A stable, reusable antimicrobial system was needed for continuous-flow operation.
What I DidSynthesized nAg/Kaolin composite via spray deposition and 10-step annealing protocol onto borosilicate glass beads
Designed and built custom continuous-flow fixed-bed reactor
Ran structured experimental campaigns across 288-hour reusability tests
Derived first-order disinfection kinetics using plate cell counting (LB-agar)
Characterized using Raman, TEM-EDX, and FIB-SEM
<2 hrs complete disinfection
kd = 2.56–2.76 cm h⁻¹
288 hrs full reusability
Intact silver integrity post-use
Multiphysics CFD Modeling of Microfluidic Photocatalytic Reactors
COMSOLCFDPhotocatalysisReaction Kinetics
The ProblemPredicting contaminant removal requires coupling fluid dynamics, radiation transport, mass transfer, and reaction kinetics simultaneously — a problem conventional single-physics models cannot solve.
What I DidBuilt 3D COMSOL Multiphysics models coupling four physics simultaneously
Implemented advanced kinetic models (Langmuir-Hinshelwood) with radiation field coupling
Validated models using HPLC/UHPLC and GC-MS experimental data
Ran parametric DOE sweeps on HPC cluster (SLURM)
±5% predictive accuracy
50% fewer experimental iterations
3 peer-reviewed publications
Scale-up guidance for industrial reactors
H₂S-to-Hydrogen Process Design & Techno-Economic Assessment
Aspen PlusTEAScale-UpGas Processing
The ProblemSour natural gas streams containing H₂S are a safety and environmental liability. Converting H₂S to hydrogen offers both economic value and environmental benefit — but requires rigorous process modeling and cost justification.
What I DidBuilt full Aspen Plus steady-state process model for H₂S photocatalytic conversion via NaOH absorption
Performed Pre-FEED level TEA with complete CAPEX/OPEX breakdown and equipment sizing
Conducted sensitivity analysis on feedstock cost, product price, and conversion efficiency
Identified scale-up constraints and retrofit feasibility for existing gas-processing plants
Ran parametric optimization using gPROMS coupled PDE/ODE modeling
+10% process feasibility improvement
Retrofit strategy for existing infrastructure
Reduced corrosion risk
Full TEA investor-ready package
$8M+ Advanced Materials Research Facility — Design, Procurement & Commissioning
Lab InfrastructureProject ManagementQA/QCEHS
The ProblemA 600 m² empty shell needed to become a fully operational, audit-ready advanced materials research facility — 18-month industry benchmark, no margin for equipment downtime.
What I DidLed end-to-end procurement of $8M+ capital equipment across 20+ instruments and 15+ vendors
Co-designed facility layout, utility requirements, HAZOP-style safety review, and EHS compliance framework
Commissioned AFM, Raman, BET, TGA, DSC, DMA, rheometry, UV-Vis, GC-MS, and particle sizing suites
Developed 40+ SOPs and full ISO/IEC 17025 Quality Management System
Trained 80+ researchers and 5 undergraduate interns across all instruments
<7 months (58% faster than 18-month benchmark)
97% uptime across all 20+ instruments in Year 1
5 funded research projects enabled
2 industrial collaborations established
Khalifa University Excellence in Laboratory Safety Award (2024)
AI & ML Models for Global Desalination Cost Prediction
Machine LearningPythonData ScienceDecision Support
The ProblemEarly-stage desalination plant cost estimation is unreliable, slowing investment decisions and project planning for water infrastructure globally.
What I DidBuilt ML regression models (Random Forest, XGBoost, ANN) trained on global desalination plant datasets
Applied Python (scikit-learn, pandas, matplotlib) for full modelling pipeline
Co-developed web-based EPC cost prediction platform for decision-makers
Conducted large-scale data analysis across 5,000+ plant data points from 60+ countries
3–5% prediction accuracy improvement over existing models
5% estimation time reduction
Deployed platform used by industry partners
±5% model accuracy across RF, XGBoost & ANN