What is he best known for?
The fields of study he is best known for:
- Organic chemistry
- Mechanical engineering
- Carbon dioxide
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.
His most cited work include:
- Global strategies and potentials to curb CO2 emissions in cement industry (486 citations)
- Implementing radial basis function networks for modeling CO2-reservoir oil minimum miscibility pressure (97 citations)
- Estimation of air dew point temperature using computational intelligence schemes (89 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Petroleum engineering (21.43%)
- Natural gas (14.08%)
- Waste management (12.65%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial neural network (9.18%)
- Petroleum engineering (21.43%)
- Piping (3.67%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- Prediction of CO2 loading capacities of aqueous solutions ofabsorbents using different computational schemes (54 citations)
- A neural network-based local rainfall prediction system using meteorological data on the Internet (51 citations)
- Toward genetic programming (GP) approach for estimation of hydrocarbon/water interfacial tension (41 citations)
In his most recent research, the most cited papers focused on:
- Organic chemistry
- Mechanical engineering
- Carbon dioxide
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.
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