Evaluating the engineering design and computational analysis: a case study of Machai micro-hydropower plant, Mardan, Pakistan

Authors

DOI:

https://doi.org/10.47264/idea.ajset/4.1.5

Keywords:

Hydropower plant, Kaplan turbine, Power canal, Manning coefficient, Remote areas, Hydraulic parameters, Hydroelectric power, Power generation, Renewable energy

Abstract

Hydroelectric power generation is one of the most significant and dependable renewable energy sources for Pakistan. With a few minor adjustments and a range of design options, micro hydropower plants may be built on an existing canal to restore or boost irrigation water delivery and generate electricity by choosing the appropriate turbine. The design of the Machai hydropower plant was examined in this study using the Manning equation to establish the power canal's design specifications. The Manning roughness coefficient is calculated to find the section factor. Hydraulic mean depth, top width, height, area, and channel width may all be determined using the section factor. Based on the outcomes of the TURBNPRO program, the Kaplan turbine installed in the powerhouse was selected. To evaluate the stability of embankments, SLIDE software was employed. The study concludes that micro-hydropower is a technically viable and environmentally friendly method of producing electricity in remote areas. Particularly for isolated off-grid locations, Machai and other micro-hydropower devices provide a cost-effective and ecologically friendly energy alternative. Compared to diesel generators or coal-fired power plants, micro-hydropower is a more environmentally beneficial choice since it emits no greenhouse gases when in operation.

References

Bandyopadhyay, O., Biswas, A., & Bhattacharya, B. B. (2016a). Classification of long-bone fractures based on digital-geometric analysis of X-ray images. Pattern Recognition and Image Analysis, 26, 742–757. https://doi.org/10.1134/S1054661816040027

Bandyopadhyay, O., Biswas, A., & Bhattacharya, B. B. (2016b). Long-bone fracture detection in digital X-ray images based on digital-geometric techniques. Computer Methods and Programs in Biomedicine, 123, 2–14. https://doi.org/10.1016/j.cmpb.2015.09.013

Bhangare, Y., Rajeswari, K., & Game, P. S. (2024). Wrist bone fracture classification using least entropy combiner for ensemble learning. Journal of Engineering Science & Technology Review, 17(3), 45–51. https://doi.org/10.25103/jestr.173.06

Brown, J. P., Engelke, K., Keaveny, T. M., Chines, A., Chapurlat, R., Foldes, A. J., ... & Libanati, C. (2021). Romosozumab improves lumbar spine bone mass and bone strength parameters relative to alendronate in postmenopausal women: results from the Active?Controlled Fracture Study in Postmenopausal Women with Osteoporosis at High Risk (ARCH) trial. Journal of Bone and Mineral Research, 36(11), 2139-2152.

Dell’Osa, A. H., Felice, C. J., & Simini, F. (2019). Bioimpedance and bone fracture detection. Journal of Physics: Conference Series, 1272. https://doi.org/10.1088/1742-6596/1272/1/012010

Deokar, N. D., & Thakur, A. G. (2016). Design, development and analysis of femur bone by using rapid prototyping. International Journal of Engineering Development and Research, 4(3), 881–886. https://rjwave.org/IJEDR/papers/IJEDR1603142

Deshmukh, S., Zalte, S., Vaidya, S., & Tangade, P. (2015). Bone fracture detection using image processing in Matlab. International Journal of Advent Research in Computer and Electronics (IJARCE), 15–19.

Devi, M. S., Aruna, R., Almufti, S., Punitha, P., & Kumar, R. L. (2024). Bone feature quantisation and systematised attention gate UNet-based deep learning framework for bone fracture classification. Intelligent Data Analysis, (Preprint), 1–29. https://content.iospress.com/articles/intelligent-data-analysis/ida240431

Edward, C. P., & Hepzibah, H. (2015). A robust approach for detection of the type of fracture from x-ray images. International Journal of Advanced Research in Computer and Communication Engineering, 4(3), 479–482.

Ganesan, P., Sivakumar, S., & Sundar, S. (2015). A Comparative study on MMDBM classifier incorporating various sorting procedure. Indian Journal of Science and Technology, 8(9), 868. https://doi.org/10.17485/ijst/2015/v8i9/53064

Gonzalez, C. I., Melin, P., Castro, J. R., Mendoza, O., & Castillo, O. (2016). An improved sobel edge detection method based on generalised type-2 fuzzy logic. Soft Computing, 20, 773–784. https://doi.org/10.1007/s00500-014-1541-0

Hareendranathan, A. R., Tripathi, A., Panicker, M. R., Zhang, J., Boora, N., & Jaremko, J. (2023). Deep learning approach for automatic wrist fracture detection using ultrasound bone probability maps. SN Comprehensive Clinical Medicine, 5(1), 276.

Hasnain, M. A. (2023). Deep learning-based classification of dental disease using X-rays. Journal of Computing & Biomedical Informatics, 5(01), 82–95. https://www.jcbi.org/index.php/Main/article/view/141

Jabbar, J., Hussain, M., Malik, H., Gani, A., Khan, A. H., & Shiraz, M. (2022). Deep learning based classification of wrist cracks from X-ray imaging. CMC-Computers Materials & Continua, 73(1), 1827–1844.

Johnson, B., Alizai, H., & Dempsey, M. (2021). Fast field echo resembling a CT using restricted echo-spacing (FRACTURE): a novel MRI technique with superior bone contrast. Skeletal Radiology, 50, 1705-1713.

Kaur, H. & Jain, A. (2017). Detection of Fractures in Orthopedic X-Ray Images. International Journal of Advanced Research in Computer Science, 8(3), 545–551. https://www.researchgate.net/publication/352438797

Khan, M., Sirdeshmukh, S. P. S. M. A., & Javed, K. (2016). Evaluation of bone fracture in animal model using bio-electrical impedance analysis. Perspectives in Science, 8, 567–569. https://doi.org/10.1016/j.pisc.2016.06.022

Khatik, I. (2017). A study of various bone fracture detection techniques. Int J Eng Comput Sci, 6(5), 21418–21423.

Kishor, K., Sengar, A., Sharma, A., & Gautam, D. (2025). Osteo fracture identification using deep learning techniques. Health Services and Outcomes Research Methodology, 1–40. https://doi.org/10.1007/s10742-025-00340-1

Lanka, S. R., & Yarramalle, S. (2018). Bone fracture identification-a case study based on statistical modelling. International Journal of Advanced Research in Computer Science, 9(1). https://doi.org/1010.26483/ijarcs.v9i1.5326

Muchtar, M. A., Simanjuntak, S. E., Rahmat, R. F., Mawengkang, H., Zarlis, M., Sitompul, O. S., ... & Nasution, T. H. (2018, March). Identification tibia and fibula bone fracture location using scanline algorithm. Journal of Physics: Conference Series, 978, 012043. https://doi.org/10.1088/1742-6596/978/1/012043

Naeem, A., Khan, A. Haider., Ayubi, S., & Malik, H. (2023). Predicting the metastasis ability of prostate cancer using machine learning classifiers. Journal of Computing & Biomedical Informatics, 4(02), 1–7. https://www.jcbi.org/index.php/Main/article/view/139

Nowroozi, A., Salehi, M. A., Shobeiri, P., Agahi, S., Momtazmanesh, S., Kaviani, P., & Kalra, M. K. (2024). Artificial intelligence diagnostic accuracy in fracture detection from plain radiographs and comparing it with clinicians: a systematic review and meta-analysis. Clinical Radiology, 79(8), 579–588. https://doi.org/10.1016/j.crad.2024.04.009

Pal, O. K., Danmusa, N. A., & Yusuff, H. D. (2024, July). bonenet: human bone fractures localising and diagnosing using hybrid neural network. In 2024 10th International Conference on Smart Computing and Communication (ICSCC) (pp. 423–427). https://doi.org/10.1109/ICSCC62041.2024.10690459

Saraswat, A., Tooley, T., & Shrivastav, S. (2024). Osteoarthritis by deep learning approach. In A. Kalam, K. R. Niazi, A. Soni, S. A. Siddiqui, & A. Mundra (eds.), Intelligent computing techniques for smart energy systems. Springer.

Smith, T. O., Daniell, H., Geere, J. A., Toms, A. P., & Hing, C. B. (2012). The diagnostic accuracy of MRI for the detection of partial-and full-thickness rotator cuff tears in adults. Magnetic resonance imaging, 30(3), 336-346.

Windarto, A. P., & Alkairi, P. (2024). Bone fracture classification using convolutional neural network architecture for high-accuracy image classification. International Journal of Electrical & Computer Engineering, 14(6), 2088–8708. https://doi.org/10.11591/ijece.v14i6.pp6466-6477

Xie, Z., Lu, Q., Guo, J., Lin, W., Ge, G., Tang, Y., ... & Wang, W. (2024). Semantic segmentation for tooth cracks using improved DeepLabv3+ model. Heliyon, 10(4). e25892. https://www.cell.com/heliyon/fulltext/S2405-8440(24)01923-6

Published

2025-06-21

How to Cite

Ali , U., Nawaz, A., Mansoor, A. B., Usman, A., Iqbal, M. J., & Ali , F. (2025). Evaluating the engineering design and computational analysis: a case study of Machai micro-hydropower plant, Mardan, Pakistan. Asian Journal of Science, Engineering and Technology (AJSET), 4(1), 69–86. https://doi.org/10.47264/idea.ajset/4.1.5

Issue

Section

Original Research Articles