Ali Jahedsaravani was born in Birjand, Iran, in 1985. He graduated from SAMPAD High School (national organization for development of exceptional talents) in Birjand in September 2003. He received the B.S. degree in electrical engineering from Azad University, Birjand branch, Iran, in February 2008. He pursued his M.S. degree at Azad University, Gonabad branch, Iran in June 2011. He completed his Ph.D. degrees in in control and automation engineering from the University Putra Malaysia (UPM) Serdang, Malaysia, in 2014.
His research interests lie in the area of Digital image processing, Machine learning, Deep learning and Fuzzy control systems. He has collaborated actively with researchers in several other disciplines of computer and mining science. In the mineral processing field, he has worked on designing a machine vision system for monitoring of flotation circuits. He has also investigated different modelling and control techniques of flotation systems.
RESEARCH INTERESTS
Machine learning, Deep learning, Fuzzy control. Digital Image processing, Flotation process
Recognition of process conditions of a coal column flotation circuit using computer vision and machine learning M. Massinaei, A. Jahedsaravani, H. Mohseni International Journal of Coal Preparation and Utilization, 2022 It is a well-established fact that the flotation performance is reflected in the structure of the froth surface. A machine vision is able to extract the froth features and present them to the plant operators or the process control system. In this communication, computer vision and machine learning techniques are integrated for recognition of process conditions of a coal column flotation circuit. An industrial flotation column is operated under various process conditions. The metallurgical parameters (combustible recovery and concentrate ash content) are measured and the froth visual (bubble size, froth velocity, and color) and textural (energy, entropy, contrast, homogeneity, and correlation) features are extracted by a machine vision system. The principle component analysis (PCA) is applied to reduce the input space. The relationship between the froth characteristics and the metallurgical parameters is modeled using different intelligent algorithms and a predictive model is built. The froth images are classified based on the froth features using the K-means data-clustering algorithm. The predictive and classification models are eventually integrated to diagnose the process conditions of the flotation column.
Application of Image Processing and Adaptive Neuro-fuzzy System for Estimation of the Metallurgical Parameters of a Flotation Process A. Jahedsaravani, M. Massinaei, M. H. Marhaban Chemical Engineering Communications, 2016 It is a well-known fact in the literature and practice that flotation froth features are closely related to process conditions and performance. The authors have already developed some reliable algorithms for measurement of the froth surface visual parameters such as bubble size distribution, froth color, velocity and stability. Furthermore, the metallurgical parameters of a laboratory flotation cell were successfully predicted from the extracted froth features. In this research study, the fuzzy c-mean clustering technique is utilized to classify the froth images (collected under different process conditions) based on the extracted visual characteristics. The classification of the images is actually necessary to determine the ideal froth structure and the target set-points for a machine vision control system. The results show that the captured froth images are well-classified into five categorizes on the basis of the extracted features. The correlation between the visual properties of froth (in different classes) and the metallurgical parameters is discussed and modeled by the adaptive neuro-fuzzy inference system (ANFIS). The promising results illustrate that the performance of the existing batch flotation system can be satisfactorily estimated from the measured froth characteristics. Therefore, the outputs from the current machine vision system can be inputted to a process control system.
Data-based modeling of an industrial flotation column using classic and intelligent machine learning algorithms M Zarie, A Jahedsaravani, M Massinaei Canadian Metallurgical Quarterly 64 (4), 2287-2295 , 2025 2025 Citations: 1
Measurement of bubble size and froth velocity using convolutional neural networks A Jahedsaravani, M Massinaei, M Zarie Minerals Engineering 204, 108400 , 2023 2023 Citations: 24
Improving the contrast of images with various brightness ranges Using wavelet transform M Zarie, A Jahed Saravani, F Sadeghi Almaloo, J Ranjbar Aerospace Defense 2 (3), 1-15 , 2023 2023
Prediction of froth flotation performance using convolutional neural networks A Jahedsaravani, M Massinaei, M Zarie Mining, Metallurgy & Exploration 40 (3), 923-937 , 2023 2023 Citations: 14
Automatic tracking of aerial targets using digital image processing A Jahedsaravani, M Zarie, J Ranjbar Aerospace Defense 2 (1), 98-113 , 2023 2023
Recognition and Tracking of Aerial Targets Using Convolutional Neural Network A Jahedsaravani Radar 10 (2), 1-16 , 2023 2023
Calculation of the Combined Threat of Air Targets Using Neuro-Fuzzy Systems H Mohseni, M Najafzadeh, M Zarei, AJ Saravani, S Zare Radar 9 (2), 69-78 , 2022 2022 Citations: 1
Recognition of process conditions of a coal column flotation circuit using computer vision and machine learning M Massinaei, A Jahedsaravani, H Mohseni International Journal of Coal Preparation and Utilization 42 (7), 2204-2218 , 2022 2022 Citations: 27
Flotation froth image classification using convolutional neural networks M Zarie, A Jahedsaravani, M Massinaei Minerals Engineering 155, 106443 , 2020 2020 Citations: 134
Application of froth images classification and clustering based on visual features in flotation cell performance A Jahedsaravani, M Massinaei, J Khalilpour Journal of Mineral Resources Engineering 4 (2), 133-146 , 2019 2019 Citations: 2
Machine vision based monitoring and analysis of a coal column flotation circuit M Massinaei, A Jahedsaravani, E Taheri, J Khalilpour Powder Technology 343, 330-341 , 2019 2019 Citations: 104
An image segmentation algorithm for measurement of flotation froth bubble size distributions A Jahedsaravani, M Massinaei, MH Marhaban Measurement 111, 29-37 , 2017 2017 Citations: 98
Development of a machine vision system for real-time monitoring and control of batch flotation process A Jahedsaravani, M Massinaei, MH Marhaban International Journal of Mineral Processing 167, 16-26 , 2017 2017 Citations: 44
Application of image processing and adaptive neuro-fuzzy system for estimation of the metallurgical parameters of a flotation process A Jahedsaravani, M Massinaei, MH Marhaban Chemical Engineering Communications 203 (10), 1395-1402 , 2016 2016 Citations: 27
Application of statistical and intelligent techniques for modeling of metallurgical performance of a batch flotation process A Jahedsaravani, MH Marhaban, M Massinaei Chemical Engineering Communications 203 (2), 151-160 , 2016 2016 Citations: 38
Froth-based modeling and control of a batch flotation process A Jahedsaravani, MH Marhaban, M Massinaei, MI Saripan, SBM Noor International Journal of Mineral Processing 146, 90-96 , 2016 2016 Citations: 65
Modeling and Control of an Industrial Flotation Column using Fuzzy Logic A Jahedsaravani, M Massinaei, N Mehrshad First International Conference on New Research Achievements in Electrical … , 2016 2016
Automatic Control of Flotation Process Using Computer Vision A Jahedsaravani University Putra Malaysia , 2015 2015
Prediction of the metallurgical performances of a batch flotation system by image analysis and neural networks A Jahedsaravani, MH Marhaban, M Massinaei Minerals Engineering 69, 137-145 , 2014 2014 Citations: 160
Fuzzy-based modeling and control of an industrial flotation column A Jahedsaravani, N Mehrshad, M Massinaei Chemical Engineering Communications 201 (7), 896-908 , 2014 2014 Citations: 30
MOST CITED SCHOLAR PUBLICATIONS
Prediction of the metallurgical performances of a batch flotation system by image analysis and neural networks A Jahedsaravani, MH Marhaban, M Massinaei Minerals Engineering 69, 137-145 , 2014 2014 Citations: 160
Flotation froth image classification using convolutional neural networks M Zarie, A Jahedsaravani, M Massinaei Minerals Engineering 155, 106443 , 2020 2020 Citations: 134
Machine vision based monitoring and analysis of a coal column flotation circuit M Massinaei, A Jahedsaravani, E Taheri, J Khalilpour Powder Technology 343, 330-341 , 2019 2019 Citations: 104
An image segmentation algorithm for measurement of flotation froth bubble size distributions A Jahedsaravani, M Massinaei, MH Marhaban Measurement 111, 29-37 , 2017 2017 Citations: 98
Froth-based modeling and control of a batch flotation process A Jahedsaravani, MH Marhaban, M Massinaei, MI Saripan, SBM Noor International Journal of Mineral Processing 146, 90-96 , 2016 2016 Citations: 65
Development of a new algorithm for segmentation of flotation froth images A Jahedsaravani, MH Marhaban, M Massinaei, MI Saripan, N Mehrshad Mining, Metallurgy & Exploration 31 (1), 66-72 , 2014 2014 Citations: 49
Development of a machine vision system for real-time monitoring and control of batch flotation process A Jahedsaravani, M Massinaei, MH Marhaban International Journal of Mineral Processing 167, 16-26 , 2017 2017 Citations: 44
Application of statistical and intelligent techniques for modeling of metallurgical performance of a batch flotation process A Jahedsaravani, MH Marhaban, M Massinaei Chemical Engineering Communications 203 (2), 151-160 , 2016 2016 Citations: 38
Fuzzy-based modeling and control of an industrial flotation column A Jahedsaravani, N Mehrshad, M Massinaei Chemical Engineering Communications 201 (7), 896-908 , 2014 2014 Citations: 30
Recognition of process conditions of a coal column flotation circuit using computer vision and machine learning M Massinaei, A Jahedsaravani, H Mohseni International Journal of Coal Preparation and Utilization 42 (7), 2204-2218 , 2022 2022 Citations: 27
Application of image processing and adaptive neuro-fuzzy system for estimation of the metallurgical parameters of a flotation process A Jahedsaravani, M Massinaei, MH Marhaban Chemical Engineering Communications 203 (10), 1395-1402 , 2016 2016 Citations: 27
Measurement of bubble size and froth velocity using convolutional neural networks A Jahedsaravani, M Massinaei, M Zarie Minerals Engineering 204, 108400 , 2023 2023 Citations: 24
Prediction of froth flotation performance using convolutional neural networks A Jahedsaravani, M Massinaei, M Zarie Mining, Metallurgy & Exploration 40 (3), 923-937 , 2023 2023 Citations: 14
Application of froth images classification and clustering based on visual features in flotation cell performance A Jahedsaravani, M Massinaei, J Khalilpour Journal of Mineral Resources Engineering 4 (2), 133-146 , 2019 2019 Citations: 2
Data-based modeling of an industrial flotation column using classic and intelligent machine learning algorithms M Zarie, A Jahedsaravani, M Massinaei Canadian Metallurgical Quarterly 64 (4), 2287-2295 , 2025 2025 Citations: 1
Calculation of the Combined Threat of Air Targets Using Neuro-Fuzzy Systems H Mohseni, M Najafzadeh, M Zarei, AJ Saravani, S Zare Radar 9 (2), 69-78 , 2022 2022 Citations: 1
Improving the contrast of images with various brightness ranges Using wavelet transform M Zarie, A Jahed Saravani, F Sadeghi Almaloo, J Ranjbar Aerospace Defense 2 (3), 1-15 , 2023 2023
Automatic tracking of aerial targets using digital image processing A Jahedsaravani, M Zarie, J Ranjbar Aerospace Defense 2 (1), 98-113 , 2023 2023
Recognition and Tracking of Aerial Targets Using Convolutional Neural Network A Jahedsaravani Radar 10 (2), 1-16 , 2023 2023
Modeling and Control of an Industrial Flotation Column using Fuzzy Logic A Jahedsaravani, M Massinaei, N Mehrshad First International Conference on New Research Achievements in Electrical … , 2016 2016
