Alireza Bahadori mostly deals with Artificial neural network, Particle swarm optimization, Natural gas, Petroleum engineering and Process engineering. His research in the fields of Perceptron overlaps with other disciplines such as Gross domestic product. His biological study spans a wide range of topics, including Shear, Backpropagation and Structural engineering.
His Natural gas study incorporates themes from Volumetric flow rate, Triethylene glycol, Mineralogy, Body orifice and Tight gas. His Petroleum engineering research incorporates elements of Asphaltene, Connectionism, Porous medium and Scaling. While the research belongs to areas of Process engineering, Alireza Bahadori spends his time largely on the problem of Water content, intersecting his research to questions surrounding Sulfur, Calcium, Steam turbine, Chemical engineering and Boiler.
Petroleum engineering, Natural gas, Waste management, Process engineering and Artificial neural network are his primary areas of study. His work carried out in the field of Petroleum engineering brings together such families of science as Viscosity and Fossil fuel, Oil shale, Unconventional oil. His study of Shale oil is a part of Oil shale.
His studies deal with areas such as Hydrate, Clathrate hydrate, Triethylene glycol and Water content as well as Natural gas. His primary area of study in Waste management is in the field of Petrochemical. His Artificial neural network research is multidisciplinary, incorporating perspectives in Particle swarm optimization and Support vector machine.
Alireza Bahadori mainly focuses on Artificial neural network, Petroleum engineering, Piping, Petrochemical and Waste management. Alireza Bahadori combines subjects such as Mean squared error, Particle swarm optimization and Support vector machine with his study of Artificial neural network. His studies in Petroleum engineering integrate themes in fields like Characterization, Fossil fuel and Viscosity.
His Petrochemical study integrates concerns from other disciplines, such as Pipeline transport, Manufacturing engineering and Forensic engineering. His Perceptron research focuses on Diethanolamine and how it connects with Chromatography. In Aqueous solution, Alireza Bahadori works on issues like Solubility, which are connected to Natural gas.
The scientist’s investigation covers issues in Artificial neural network, Solubility, Support vector machine, Natural gas and Perceptron. His Artificial neural network study combines topics from a wide range of disciplines, such as Mean squared error, Particle swarm optimization and Aqueous solution. His Solubility research includes elements of Chromatography, Gene expression programming and Mole fraction, Analytical chemistry.
The Natural gas study combines topics in areas such as Decision tree, Least squares support vector machine, Alkanolamine and Amine gas treating. He works mostly in the field of Least squares support vector machine, limiting it down to concerns involving Pressure drop and, occasionally, Petroleum engineering. His research investigates the connection between Perceptron and topics such as Diethanolamine that intersect with problems in Potassium carbonate.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Global strategies and potentials to curb CO2 emissions in cement industry
Emad Benhelal;Gholamreza Zahedi;Ezzatollah Shamsaei;Alireza Bahadori.
Journal of Cleaner Production (2013)
Natural Gas Processing: Technology and Engineering Design
Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada
Gholamreza Zahedi;Saeed Azizi;Alireza Bahadori;Ali Elkamel.
Asphaltene precipitation and deposition in oil reservoirs –technical aspects, experimental and hybrid neural network predictive tools
Sohrab Zendehboudi;Ali Shafiei;Alireza Bahadori;Lesley A. James.
Chemical Engineering Research & Design (2014)
Implementing radial basis function networks for modeling CO2-reservoir oil minimum miscibility pressure
Afshin Tatar;Amin Shokrollahi;Mohammad Mesbah;Saeed Rashid.
Journal of Natural Gas Science and Engineering (2013)
A review on solar energy utilisation in Australia
Alireza Bahadori;Chikezie Nwaoha.
Renewable & Sustainable Energy Reviews (2013)
Estimation of combustion flue gas acid dew point during heat recovery and efficiency gain
Applied Thermal Engineering (2011)
Estimation of air dew point temperature using computational intelligence schemes
Alireza Baghban;Mohammad Bahadori;Jake Rozyn;Moonyong Lee.
Applied Thermal Engineering (2016)
Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool
Mohammad Ali Ahmadi;Reza Soleimani;Moonyong Lee;Tomoaki Kashiwao.
Thermodynamic investigation of asphaltene precipitation during primary oil production laboratory and smart technique
Sohrab Zendehboudi;Mohammad Ali Ahmadi;Omidreza Mohammadzadeh;Alireza Bahadori.
Industrial & Engineering Chemistry Research (2013)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: