Research on Drilling Fluid Flocculation Identification Method Based on Improved Resnet50 Model
DOI:
https://doi.org/10.54097/p9tawv90Keywords:
Image classification; flocculation identification; deep learning; attention mechanism; multidimensional dynamic convolution.Abstract
Aiming at the problems of low accuracy and high labor cost of traditional methods for identifying the flocculation state of drilling fluid waste, this paper proposes a method for identifying the flocculation state of drilling fluid waste based on the improved ResNet50 model. By building a flocculation acquisition system to obtain image data under different flocculation states and carrying out data enhancement, the original ResNet50 model is used as the base model, based on which the GAM global attention mechanism is introduced to enhance the ability to extract useful information in the flocculation image, and combined with the dimensional dynamic convolution of the ODConv to strengthen the network's generalization ability and adaptability, to achieve intelligent identification of flocculation state of the drilling fluid waste liquid. Experiments are conducted on 6935 flocculated image datasets, and the results show that the H-ResNet50 model designed in this paper achieves classification accuracies of 99.57% and 99.16% on the training and test sets, respectively, which are higher than the classification accuracies of the original ResNet50 model and other common neural network models. Overall, H-ResNet50 can efficiently and correctly identify the flocculation status of drilling fluid waste liquid, which provides the possibility of moving towards intelligent flocculation treatment of drilling fluid waste liquid.
Downloads
References
[1] Davoodi, S.; Al-Shargabi, M.; Wood, D. A.; Rukavishnikov, V. S.; Minaev, K. M. Synthetic polymers: A review of applications in drilling fluids. Pet. Sci. 2024, 21 (1), 475-518, Review. DOI: https://doi.org/10.1016/j.petsci.2023.08.015
[2] Vryzas, Z.; Kelessidis, V. C. Nano-Based Drilling Fluids: A Review. Energies 2017, 10 (4), 34, Review. DOI: https://doi.org/10.3390/en10040540
[3] Sayindla, S.; Lund, B.; Ytrehus, J. D.; Saasen, A. Hole-cleaning performance comparison of oil-based and water-based drilling fluids. J. Pet. Sci. Eng. 2017, 159, 49-57, Article. DOI: https://doi.org/10.1016/j.petrol.2017.08.069
[4] Yao, R. G.; Jiang, G. C.; Li, W.; Deng, T. Q.; Zhang, H. X. Effect of water-based drilling fluid components on filter cake structure. Powder Technol. 2014, 262, 51-61, Article. DOI: https://doi.org/10.1016/j.powtec.2014.04.060
[5] Kania, D.; Yunus, R.; Omar, R.; Rashid, S. A.; Jan, B. M. A review of biolubricants in drilling fluids: Recent research, performance, and applications. J. Pet. Sci. Eng. 2015, 135, 177-184, Review. DOI: https://doi.org/10.1016/j.petrol.2015.09.021
[6] Pereira, L. B.; Sad, C. M. S.; Castro, E. V. R.; Filgueiras, P. R.; Lacerda, V. Environmental impacts related to drilling fluid waste and treatment methods: A critical review. Fuel 2022, 310, 13, Review. DOI: https://doi.org/10.1016/j.fuel.2021.122301
[7] Zhao, X. Y.; Li, D. Q.; Zhu, H. M.; Ma, J. Y.; An, Y. X. Advanced developments in environmentally friendly lubricants for water-based drilling fluid: a review. RSC Adv. 2022, 12 (35), 22853-22868, Review. DOI: https://doi.org/10.1039/D2RA03888A
[8] Xie, S. X.; Jiang, G. C.; Chen, M.; Li, Z. Y.; Mao, H.; Zhang, M.; Li, Y. Y. Treatment Technology for Waste Drilling Fluids in Environmental Sensitivity Areas. Energy Sources Part A-Recovery Util. Environ. Eff. 2015, 37 (8), 817-824, Article. DOI: https://doi.org/10.1080/15567036.2011.594858
[9] Zhang, X. Q. STUDY ON RECENT ADVANCES OF WASTE DRILLING FLUID TREATMENT. Fresenius Environ. Bull. 2018, 27 (6), 4460-4468, Article.
[10] Galloux, J.; Chekli, L.; Phuntsho, S.; Tijing, L. D.; Jeong, S.; Zhao, Y. X.; Gao, B. Y.; Park, S. H.; Shon, H. K. Coagulation performance and floc characteristics of polytitanium tetrachloride and titanium tetrachloride compared with ferric chloride for coal mining wastewater treatment. Sep. Purif. Technol. 2015, 152, 94-100, Article. DOI: https://doi.org/10.1016/j.seppur.2015.08.009
[11] Ofori, P.; Nguyen, A. V.; Firth, B.; McNally, C.; Ozdemir, O. Shear-induced floc structure changes for enhanced dewatering of coal preparation plant tailings. Chem. Eng. J. 2011, 172 (2-3), 914-923, Article. DOI: https://doi.org/10.1016/j.cej.2011.06.082
[12] Selomulya, C.; Liao, J. Y. H.; Bickert, G.; Amal, R. Micro-properties of coal aggregates: Implications on hyperbaric filtration performance for coal dewatering. International Journal of Mineral Processing 2006, 80 (2), 189-197. DOI: https://doi.org/10.1016/j.minpro.2006.04.004
[13] Shen, X. T.; Maa, J. P. Y. A camera and image processing system for floc size distributions of suspended particles. Mar. Geol. 2016, 376, 132-146, Article. DOI: 10.1016/j.margeo.2016.03.009. DOI: https://doi.org/10.1016/j.margeo.2016.03.009
[14] Avnimelech, Y.; Ritvo, G.; Meijer, L. E.; Kochba, M. Water content, organic carbon and dry bulk density in flooded sediments. Aquacultural Engineering 2001, 25 (1), 25-33. DOI: https://doi.org/10.1016/S0144-8609(01)00068-1
[15] Bale, A. J.; Morris, A. W. In situ measurement of particle size in estuarine waters. Estuarine, Coastal and Shelf Science 1987, 24 (2), 253-263. DOI: https://doi.org/10.1016/0272-7714(87)90068-0. DOI: https://doi.org/10.1016/0272-7714(87)90068-0
[16] Eshel, G.; Levy, G. J.; Mingelgrin, U.; Singer, M. J. Critical Evaluation of the Use of Laser Diffraction for Particle-Size Distribution Analysis. Soil Science Society of America Journal 2004, 68 (3), 736-743. DOI: https://doi.org/10.2136/sssaj2004.7360
[17] Furukawa, Y.; Watkins, J. L. Effect of Organic Matter on the Flocculation of Colloidal Montmorillonite: A Modeling Approach. J. Coast. Res. 2012, 28 (3), 726-737, Article. DOI: https://doi.org/10.2112/JCOASTRES-D-11-00128.1
[18] Ren, P. F.; Nan, J.; Zhang, X. R.; Zheng, K. Analysis of floc morphology in a continuous-flow flocculation and sedimentation reactor. J. Environ. Sci. 2017, 52, 268-275, Article. DOI: https://doi.org/10.1016/j.jes.2016.04.007
[19] Benson, T.; French, J. R. InSiPID: A new low-cost instrument for in situ particle size measurements in estuarine and coastal waters. Journal of Sea Research 2007, 58 (3), 167-188. DOI: https://doi.org/10.1016/j.seares.2007.04.003
[20] Chowdhury, S.; Karanfil, T. Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities. Chemosphere 2024, 356, 141958, Review. DOI: https://doi.org/10.1016/j.chemosphere.2024.141958
[21] Zhu, G. C.; Lin, J. L.; Fang, H. Q.; Yuan, F.; Li, X. S.; Yuan, C.; Hursthouse, A. S. A flocculation tensor to monitor water quality using a deep learning model. Environ. Chem. Lett. 2022, 20 (6), 3405-3414, Article. DOI: https://doi.org/10.1007/s10311-022-01524-8
[22] Peng, S.; Guo, Y.; Wang, J. H.; Wang, Y.; Zhang, W. H.; Zhou, X.; Jiang, L. F.; Lai, B. The coagulation-precipitation turbidity prediction model for precision drug delivery system based on deep learning and machine vision. J. Environ. Chem. Eng. 2024, 12 (2), 13, Article. DOI: https://doi.org/10.1016/j.jece.2024.112211
[23] Nielsen, R. F.; Kermani, N. A.; Freiesleben, L. L.; Gernaey, K. V.; Mansouri, S. S. Novel strategies for predictive particle monitoring and control using advanced image analysis. In 29th European Symposium on Computer-Aided Process Engineering (ESCAPE), Eindhoven, NETHERLANDS, Jun 16-19, 2019; Elsevier Science Bv: AMSTERDAM, 2019; Vol. 46, pp 1435-1440. DOI: https://doi.org/10.1016/B978-0-12-818634-3.50240-X
[24] He, K. M.; Zhang, X. Y.; Ren, S. Q.; Sun, J.; Ieee. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, Jun 27-30, 2016; Ieee: NEW YORK, 2016; pp 770-778. DOI: https://doi.org/10.1109/CVPR.2016.90
[25] Liu, Y.; Shao, Z.; Hoffmann, N. Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv preprint arXiv:2112.05561 2021.
[26] Hu, J.; Shen, L.; Sun, G.; Ieee. Squeeze-and-Excitation Networks. In 31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, Jun 18-23, 2018; Ieee: NEW YORK, 2018; pp 7132-7141. DOI: https://doi.org/10.1109/CVPR.2018.00745
[27] Woo, S. H.; Park, J.; Lee, J. Y.; Kweon, I. S. CBAM: Convolutional Block Attention Module. In 15th European Conference on Computer Vision (ECCV), Munich, GERMANY, Sep 08-14, 2018; Springer International Publishing Ag: CHAM, 2018; Vol. 11211, pp 3-19. DOI: https://doi.org/10.1007/978-3-030-01234-2_1
[28] Li, C.; Zhou, A.; Yao, A. Omni-dimensional dynamic convolution. arXiv preprint arXiv:2209.07947 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Academic Journal of Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
