M. Massinaei

1.4k total citations
32 papers, 1.1k citations indexed

About

M. Massinaei is a scholar working on Water Science and Technology, Mechanical Engineering and Biomedical Engineering. According to data from OpenAlex, M. Massinaei has authored 32 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Water Science and Technology, 26 papers in Mechanical Engineering and 8 papers in Biomedical Engineering. Recurrent topics in M. Massinaei's work include Minerals Flotation and Separation Techniques (29 papers), Mineral Processing and Grinding (24 papers) and Metallurgical Processes and Thermodynamics (8 papers). M. Massinaei is often cited by papers focused on Minerals Flotation and Separation Techniques (29 papers), Mineral Processing and Grinding (24 papers) and Metallurgical Processes and Thermodynamics (8 papers). M. Massinaei collaborates with scholars based in Iran, Malaysia and Chile. M. Massinaei's co-authors include A. Jahedsaravani, Mohammad Hamiruce Marhaban, Nasser Mehrshad, Ali Zeraatkar Moghaddam, Azadeh Amrollahi, Roshanak Rezaei Kalantary, Ali Behnamfard, M. Iqbal Saripan, Susan Sadeghi and M. Noaparast and has published in prestigious journals such as SHILAP Revista de lepidopterología, RSC Advances and Powder Technology.

In The Last Decade

M. Massinaei

32 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
M. Massinaei Iran 20 770 716 233 127 102 32 1.1k
Xianjun Du China 13 325 0.4× 182 0.3× 146 0.6× 75 0.6× 78 0.8× 48 774
Fei He China 18 126 0.2× 490 0.7× 135 0.6× 50 0.4× 107 1.0× 89 997
Xu Gao China 19 357 0.5× 109 0.2× 177 0.8× 139 1.1× 97 1.0× 60 938
Amir Rahimi Iran 24 260 0.3× 880 1.2× 299 1.3× 39 0.3× 57 0.6× 111 1.7k
Claude Bazin Canada 18 342 0.4× 509 0.7× 190 0.8× 41 0.3× 31 0.3× 67 724
Norhaliza Abdul Wahab Malaysia 18 321 0.4× 161 0.2× 86 0.4× 106 0.8× 130 1.3× 118 1.0k
Ling Yu China 12 186 0.2× 138 0.2× 175 0.8× 43 0.3× 45 0.4× 22 524
J. Yianatos Chile 30 1.9k 2.5× 1.7k 2.4× 1.1k 4.8× 99 0.8× 26 0.3× 123 2.3k
Chuqing Cao China 12 490 0.6× 77 0.1× 365 1.6× 74 0.6× 16 0.2× 46 735

Countries citing papers authored by M. Massinaei

Since Specialization
Citations

This map shows the geographic impact of M. Massinaei's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by M. Massinaei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Massinaei more than expected).

Fields of papers citing papers by M. Massinaei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Massinaei. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by M. Massinaei. The network helps show where M. Massinaei may publish in the future.

Co-authorship network of co-authors of M. Massinaei

This figure shows the co-authorship network connecting the top 25 collaborators of M. Massinaei. A scholar is included among the top collaborators of M. Massinaei based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M. Massinaei. M. Massinaei is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Jahedsaravani, A., et al.. (2025). Data-based modeling of an industrial flotation column using classic and intelligent machine learning algorithms. Canadian Metallurgical Quarterly. 64(4). 2287–2295. 1 indexed citations
2.
Jahedsaravani, A., et al.. (2023). Measurement of bubble size and froth velocity using convolutional neural networks. Minerals Engineering. 204. 108400–108400. 8 indexed citations
3.
Jahedsaravani, A., et al.. (2023). Prediction of Froth Flotation Performance Using Convolutional Neural Networks. Mining Metallurgy & Exploration. 40(3). 923–937. 6 indexed citations
4.
Jahedsaravani, A., et al.. (2020). Flotation froth image classification using convolutional neural networks. Minerals Engineering. 155. 106443–106443. 77 indexed citations
5.
Massinaei, M., A. Jahedsaravani, & Hassan Mohseni. (2020). Recognition of process conditions of a coal column flotation circuit using computer vision and machine learning. International Journal of Coal Preparation and Utilization. 42(7). 2204–2218. 20 indexed citations
6.
Amrollahi, Azadeh, M. Massinaei, & Ali Zeraatkar Moghaddam. (2019). Removal of the residual xanthate from flotation plant tailings using bentonite modified by magnetic nano-particles. Minerals Engineering. 134. 142–155. 72 indexed citations
7.
Massinaei, M., et al.. (2018). Machine vision based monitoring and analysis of a coal column flotation circuit. Powder Technology. 343. 330–341. 78 indexed citations
8.
Jahedsaravani, A., M. Massinaei, & Mohammad Hamiruce Marhaban. (2016). Application of Image Processing and Adaptive Neuro-fuzzy System for Estimation of the Metallurgical Parameters of a Flotation Process. Chemical Engineering Communications. 203(10). 1395–1402. 20 indexed citations
9.
Massinaei, M.. (2015). Estimation of metallurgical parameters of flotation process from froth visual features. SHILAP Revista de lepidopterología. 3 indexed citations
10.
Sadeghi, Susan, Ali Zeraatkar Moghaddam, & M. Massinaei. (2015). Novel tunable composites based on bentonite and modified tragacanth gum for removal of acid dyes from aqueous solutions. RSC Advances. 5(69). 55731–55745. 35 indexed citations
11.
Jahedsaravani, A., Mohammad Hamiruce Marhaban, M. Massinaei, M. Iqbal Saripan, & Nasser Mehrshad. (2014). Development of a new algorithm for segmentation of flotation froth images. Mining Metallurgy & Exploration. 31(1). 66–72. 30 indexed citations
12.
Shirazi, H., et al.. (2014). Modeling the Relationship between Froth Bubble Size and Flotation Performance Using Image Analysis and Neural Networks. Chemical Engineering Communications. 202(7). 911–919. 45 indexed citations
13.
Jahedsaravani, A., Mohammad Hamiruce Marhaban, & M. Massinaei. (2014). Prediction of the metallurgical performances of a batch flotation system by image analysis and neural networks. Minerals Engineering. 69. 137–145. 112 indexed citations
14.
Mehrshad, Nasser, et al.. (2013). FUZZY-BASED MODELING AND CONTROL OF AN INDUSTRIAL FLOTATION COLUMN. Chemical Engineering Communications. 201(7). 896–908. 18 indexed citations
15.
Massinaei, M., et al.. (2013). USING DATA MINING TO ASSESS AND MODEL THE METALLURGICAL EFFICIENCY OF A COPPER CONCENTRATOR. Chemical Engineering Communications. 201(10). 1314–1326. 11 indexed citations
16.
Mehrshad, Nasser, et al.. (2013). A new approach for froth image segmentation using fuzzy logic. 28. 1–6. 1 indexed citations
17.
Mehrshad, Nasser & M. Massinaei. (2011). New image-processing algorithm for measurement of bubble size distribution from flotation froth images. Mining Metallurgy & Exploration. 28(3). 146–150. 26 indexed citations
18.
Massinaei, M., et al.. (2009). Modeling of bubble surface area flux in an industrial rougher column using artificial neural network and statistical techniques. Minerals Engineering. 23(2). 83–90. 42 indexed citations
19.
Massinaei, M., et al.. (2008). Froth zone characterization of an industrial flotation column in rougher circuit. Minerals Engineering. 22(3). 272–278. 14 indexed citations
20.
Massinaei, M., et al.. (2008). Hydrodynamic and metallurgical characteristics of industrial and pilot columns in rougher circuit. Minerals Engineering. 22(1). 96–99. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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