World's Best Scientists 2026 revealed!
Milad Arabloo

Milad Arabloo

Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
35
Citations
3042
World Ranking
858
National Ranking
20

Engineering and Technology

D-Index
37
Citations
3328
World Ranking
8529
National Ranking
341

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Milad Arabloo is affiliated with PERM Inc. in Canada and has contributions across multiple fields related to engineering and environmental science. Their research portfolio spans diverse subfields including ocean engineering, environmental engineering, water science and technology, computational mechanics, and civil and structural engineering.

Their published work includes studies on enhanced oil recovery techniques, hydrological forecasting using artificial intelligence, water quality monitoring technologies, lattice Boltzmann simulation studies, grouting, rheology and soil mechanics, hydraulic fracturing and reservoir analysis, as well as drilling and well engineering.

Their recent scholarly articles include:

  • Microfluidic study of surfactant flooding of heavy oil in layered porous media containing fractures, 2024, The Canadian Journal of Chemical Engineering
  • Physicochemical properties of nano-enhanced colloidal gas aphron (NCGA)-based fluids, 2020, The European Physical Journal Plus
  • Robust computational modelling of the sodium adsorption ratio using regression analysis and support vector machine, 2020, International Journal of Data Science

Milad Arabloo has published multiple articles in several frequent publication venues such as:

  • International Journal of Data Science
  • The European Physical Journal Plus
  • The Canadian Journal of Chemical Engineering

Their collaboration network includes frequent coauthors:

  • Mohammad Hossein Ghazanfari
  • Alireza Rostami
  • Alibakhsh Kasaeian
  • Khalil Shahbazi
  • Amir Tabzar

The main areas of study of their work consist of:

  • Engineering
  • Environmental Science

The subfields most represented in their research are:

  • Ocean Engineering
  • Environmental Engineering
  • Water Science and Technology
  • Computational Mechanics
  • Civil and Structural Engineering

The core topics covered in Milad Arabloo's publications include:

  • Enhanced Oil Recovery Techniques
  • Hydrological Forecasting Using AI
  • Water Quality Monitoring Technologies
  • Lattice Boltzmann Simulation Studies
  • Grouting, Rheology, and Soil Mechanics
  • Hydraulic Fracturing and Reservoir Analysis
  • Drilling and Well Engineering

Best Publications

  • Intelligent model for prediction of CO2 – Reservoir oil minimum miscibility pressure

    Amin Shokrollahi;Milad Arabloo;Farhad Gharagheizi;Farhad Gharagheizi;Amir H. Mohammadi

  • Implementing radial basis function networks for modeling CO2-reservoir oil minimum miscibility pressure

    Afshin Tatar;Amin Shokrollahi;Mohammad Mesbah;Saeed Rashid

  • Implementation of SVM framework to estimate PVT properties of reservoir oil

    Shahin Rafiee-Taghanaki;Milad Arabloo;Ali Chamkalani;Ali Chamkalani;Mahmood Amani

  • Laccase immobilized manganese ferrite nanoparticle: Synthesis and LSSVM intelligent modeling of decolorization

    Niyaz Mohammad Mahmoodi;Milad Arabloo;Jafar Abdi

  • Efficient estimation of natural gas compressibility factor using a rigorous method

    Amir Fayazi;Milad Arabloo;Amir H. Mohammadi

  • Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO2 flooding

    Arash Kamari;Milad Arabloo;Amin Shokrollahi;Farhad Gharagheizi;Farhad Gharagheizi

  • Application of ANFIS soft computing technique in modeling the CO2 capture with MEA, DEA, and TEA aqueous solutions

    Mohammad M. Ghiasi;Mohammad M. Ghiasi;Milad Arabloo;Amir H. Mohammadi;Amir H. Mohammadi;Tohid Barghi

  • State-of-the-Art Least Square Support Vector Machine Application for Accurate Determination of Natural Gas Viscosity

    Amir Fayazi;Milad Arabloo;Amin Shokrollahi;Mohammad Hadi Zargari

  • Toward a predictive model for estimating dew point pressure in gas condensate systems

    Milad Arabloo;Amin Shokrollahi;Farhad Gharagheizi;Farhad Gharagheizi;Amir H. Mohammadi

  • Application of constrained multi-variable search methods for prediction of PVT properties of crude oil systems

    Milad Arabloo;Mohammad-Amin Amooie;Abdolhossein Hemmati-Sarapardeh;Mohammad-Hossein Ghazanfari

  • Toward an intelligent approach for determination of saturation pressure of crude oil

    Amir Farasat;Amin Shokrollahi;Milad Arabloo;Farhad Gharagheizi

  • Modeling of CO2 solubility in crude oil during carbon dioxide enhanced oil recovery using gene expression programming

    Alireza Rostami;Milad Arabloo;Arash Kamari;Amir H. Mohammadi

  • Wettability modification, interfacial tension and adsorption characteristics of a new surfactant: Implications for enhanced oil recovery

    Milad Arabloo;Mohammad Hossein Ghazanfari;Davood Rashtchian

  • Application of a radial basis function neural network to estimate pressure gradient in water–oil pipelines

    Mohamad A. Halali;Vahid Azari;Milad Arabloo;Amir H. Mohammadi;Amir H. Mohammadi

  • Prediction of natural gas flow through chokes using support vector machine algorithm

    Ibrahim Nejatian;Mojtaba Kanani;Milad Arabloo;Alireza Bahadori

  • Application of soft computing approaches for modeling saturation pressure of reservoir oils

    Roya Talebi;Mohammad M. Ghiasi;Hossein Talebi;Mehrdad Mohammadyian

  • A novel modeling approach to optimize oxygen–steam ratios in coal gasification process

    Milad Arabloo;Alireza Bahadori;Mohammad M. Ghiasi;Moonyong Lee

  • Robust Modeling Approach for Estimation of Compressibility Factor in Retrograde Gas Condensate Systems

    Mohammad M. Ghiasi;Arya Shahdi;Pezhman Barati;Milad Arabloo

  • Toward genetic programming (GP) approach for estimation of hydrocarbon/water interfacial tension

    Alireza Rostami;Hojatollah Ebadi;Milad Arabloo;Mahdi Kalantari Meybodi

  • Accurate estimation of CO2 adsorption on activated carbon with multi-layer feed-forward neural network (MLFNN) algorithm

    Alireza Rostami;Mohammad Amin Anbaz;Hamid Reza Erfani Gahrooei;Milad Arabloo

  • Characterization of Colloidal Gas Aphron-Fluids Produced from a New Plant-Based Surfactant

    Milad Arabloo;Mojtaba Pordel Shahri;Majid Zamani

Frequent Co-Authors

Amir H. Mohammadi
Amir H. Mohammadi University of KwaZulu-Natal
Amin Shokrollahi
Amin Shokrollahi École Polytechnique Fédérale de Lausanne
Alireza Bahadori
Alireza Bahadori Southern Cross University
Farhad Gharagheizi
Farhad Gharagheizi Georgia Institute of Technology
Mohammad Hossein Ghazanfari
Mohammad Hossein Ghazanfari Sharif University of Technology
Moonyong Lee
Moonyong Lee Yeungnam University
Sohrab Zendehboudi
Sohrab Zendehboudi Memorial University of Newfoundland
Niyaz Mohammad Mahmoodi
Niyaz Mohammad Mahmoodi Institute for Color Science and Technology
Ali Abbas
Ali Abbas University of Sydney
Alibakhsh Kasaeian
Alibakhsh Kasaeian University of Tehran

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