مدل‌سازی اثر توام پلاسمای سرد و توان فراصوت بر خشک شدن دانه‌های زیره سبز در یک خشک‌کن هوای گرم با استفاده از شبکه عصبی مصنوعی

ماشین های کشاورزی

دوره 15، شماره 1 - شماره پیاپی 35، فروردین 1404، صفحه 1-22   

نوع مقاله: مقاله پژوهشی لاتین

شناسه دیجیتال (DOI): 10.22067/jam.2024.85744.1209

نویسندگان

مسلم نامجو^{* 1}؛ مهدی مرادی²؛ محمد امین نعمت اللهی²؛ حسین گلبخشی³

¹گروه مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه جیرفت، جیرفت، ایران

²گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران

³گروه مکانیک، دانشگاه جیرفت، جیرفت، ایران

چکیده

این مطالعه به‌منظور بررسی اثر زمان پلاسمای سرد (CPt) و توان امواج فراصوت (USp) بر خشک شدن دانه زیره سبز در یک خشک‌کن هوای گرم انجام شد. در این راستا، از یک دستگاه تولید پلاسمای سرد و یک خشک‌کن هیبریدی هوای گرم- فراصوت در مقیاس آزمایشگاهی استفاده شد و روش‌های خشک کردن به گونه‌ای برنامه‌ریزی شد که اثرات CPt و USp در خشک کردن دانه‌ها به‌صورت منفرد یا ترکیبی دخالت داشته باشند. زمان‌های مختلف پیش‌تیمار پلاسمای سرد (15 و 30 ثانیه)، توان‌های امواج فراصوت (60، 120 و 180 وات) و دمای هوای خشک شدن (30، 35 و 40 درجه سانتی‌گراد) برای مطالعه تغییرات زمان خشک کردن، ضریب نفوذپذیری مؤثر رطوبت، مصرف انرژی، تغییر رنگ کل، نیروی گسیختگی بذر زیره سبز انجام گرفت. از سه شبکه عصبی مصنوعی معروف شامل شبکه عصبی مبتنی بر موجک (WNN)، پرسپترون چندلایه (MLPNNs)، تابع پایه شعاعی (RBFNNs) و تحلیل رگرسیون چندگانه درجه دوم (MQR) برای مدل‌سازی ورودی‌های مذکور و پارامترهای خشک‌کردن استفاده شد. بر اساس نتایج مدل‌سازی، بهترین برازش خطی بین داده‌های تجربی و مقادیر پیش‌بینی‌شده توسط مدل‌سازی شبکه عصبی WNN با حداکثر R²،0.92 و 0.83 به‌ترتیب برای داده‌های آموزش و تست به‌دست آمد.

کلیدواژه‌ها

پلاسمای سرد؛ خشک کردن؛ دانه‌های زیره سبز؛ شبکه عصبی مصنوعی؛ فراصوت

مراجع

1. Aghbashlo, M., Kianmehr, M. H., & Arabhosseini, A. (2008). Energy and exergy analyses of thin-layer drying of potato slices in a semi-industrial continuous band dryer. Drying Technology, 26(12), 1501-1508. https://doi.org/10.1080/07373930802426861
2. Akpinar, E. K., Midilli, A., & Bicer, Y. (2005). Energy and exergy of potato drying process via cyclone type dryer. Energy Conversion and Management, 46(15-16), 2530-2552. https://doi.org/10.1016/j.enconman.2004.10.019
3. Amini, G., Salehi, F., & Rasouli, M. (2021). Drying kinetics of basil seed mucilage in an infrared dryer: Application of GA-ANN and ANFIS for the prediction of drying time and moisture ratio. Journal of Food Processing and Preservation, 45(3), e15258. https://doi.org/10.1111/jfpp.15258
4. Bai, J.-W., Xiao, H.-W., Ma, H.-L., & Zhou, C.-S. (2018). Artificial Neural Network Modeling of Drying Kinetics and Color Changes of Ginkgo Biloba Seeds during Microwave Drying Process. Journal of Food Quality, 2018, 3278595. https://doi.org/10.1155/2018/3278595
5. Barreiro, P., Steinmetz, V., & Ruiz-Altisent, M. (1997). Neural bruise prediction models for fruit handling and machinery evaluation. Computers and Electronics in Agriculture, 18(2-3), 91-103. https://doi.org/10.1016/S0168-1699(97)00040-1
6. Bhaskaran, P. E., Chennippan, M., & Subramaniam, T. (2020). Future prediction & estimation of faults occurrences in oil pipelines by using data clustering with time series forecasting. Journal of Loss Prevention in the Process Industries, 66, 104203. https://doi.org/10.1016/j.jlp.2020.104203
7. Chatzilia, T., Kaderides, K., & Goula, A. M. (2023). Drying of peaches by a combination of convective and microwave methods. Journal of Food Process Engineering, 46(4), e14296. https://doi.org/10.1111/jfpe.14296
8. Dhurve, P., Tarafdar, A., & Arora, V. K. (2021). Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics. Journal of Food Quality, 2021, 7739732. https://doi.org/10.1155/2021/7739732
9. Dibagar, N., Kowalski, S. J., Chayjan, R. A., & Figiel, A. (2020). Accelerated convective drying of sunflower seeds by high-power ultrasound: Experimental assessment and optimization approach. Food and Bioproducts Processing, 123, 42-59. https://doi.org/10.1016/j.fbp.2020.03.002
10. Dotto, G. L., Souza, T. B., Simoes, M. R., Morejon, C. F., & Moreira, M. F. P. (2017). Diffusive‐convective model considering the shrinkage applied for drying of pears (pyrus). Journal of Food Process Engineering, 40(4), e12503. https://doi.org/10.1111/jfpe.12503
11. Gong, C., Liao, M., Zhang, H., Xu, Y., Miao, Y., & Jiao, S. (2020). Investigation of hot air–assisted radio frequency as a final-stage drying of pre-dried carrot cubes. Food and Bioprocess Technology, 13(3), 419-429. https://doi.org/10.1007/s11947-020-02560-0
12. Guiné, R. P., Barroca, M. J., Gonçalves, F. J., Alves, M., Oliveira, S., & Mendes, M. (2015). Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. Food Chemistry, 168, 454-459. https://doi.org/10.1016/j.foodchem.2014.07.022
13. Guo, Y.-R., An, Y.-M., Jia, Y.-X., & Xu, J.-G. (2018). Effect of drying methods on chemical composition and biological activity of essential oil from cumin (Cuminum cyminum). Journal of Essential Oil Bearing Plants, 21(5), 1295-1302. https://doi.org/10.1080/0972060X.2018.1509873
14. Habibi, S., & Nematollahi, M. (2019). Position and mass identification in nanotube mass sensor using neural networks. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 233(15), 5377-5387. https://doi.org/10.1177/0954406219846929
15. Izli, N., & Polat, A. (2019). Freeze and convective drying of quince (Cydonia oblonga): Effects on drying kinetics and quality attributes. Heat and Mass Transfer, 55, 1317-1326. https://doi.org/10.1007/s00231-018-02667-6
16. Kalathingal, M. S. H., Basak, S., & Mitra, J. (2020). Artificial neural network modeling and genetic algorithm optimization of process parameters in fluidized bed drying of green tea leaves. Journal of Food Process Engineering, 43(1), e13128. https://doi.org/10.1111/jfpe.13128
17. Kaveh, M., Abbaspour Gilandeh, Y., Amiri Chayjan, R., & Mohammadigol, R. (2019). Comparison of Mathematical Modeling, Artificial Neural Networks and Fuzzy Logic for Predicting the Moisture Ratio of Garlic and Shallot in a Fluidized Bed Dryer. Journal of Agricultural Machinery, 9(1), 99-112. https://doi.org/10.22067/jam.v9i1.66231
18. Kaveh, M., Chayjan, R. A., & Khezri, B. (2018). Modeling drying properties of pistachio nuts, squash and cantaloupe seeds under fixed and fluidized bed using data-driven models and artificial neural networks. International Journal of Food Engineering, 14(1), 10-23. https://doi.org/10.1515/ijfe-2017-0248
19. Khalo ahmadi, A., Roustapour, O. R., & Borghaee, A. M. (2022). Design and Construction of a Cabinet Dryer for Food Waste and Evaluation of its Kinetics and Energy Consumption. Journal of Agricultural Machinery, 12(4), 467-480. https://doi.org/10.22067/jam.2021.69918.1037
20. Khanlari, A., Güler, H. Ö., Tuncer, A. D., Şirin, C., Bilge, Y. C., Yılmaz, Y., & Güngör, A. (2020). Experimental and numerical study of the effect of integrating plus-shaped perforated baffles to solar air collector in drying application. Renewable energy, 145, 1677-1692. https://doi.org/10.1016/j.renene.2019.06.084
21. Lingayat, A., VP, C., & VRK, R. (2021). Drying kinetics of tomato (Solanum lycopersicum) and Brinjal (Solanum melongena) using an indirect type solar dryer and performance parameters of dryer. Heat and Mass Transfer, 57, 853-872. https://doi.org/10.1007/s00231-020-02956-7
22. Liu, Z.-L., Bai, J.-W., Wang, S.-X., Meng, J.-S., Wang, H., Yu, X.-L., ..., & Xiao, H.-W. (2019). Prediction of energy and exergy of mushroom slices drying in hot air impingement dryer by artificial neural network. Drying Technology. https://doi.org/10.1080/07373937.2019.1673328
23. Matlab, S. (2016). Matlab. The MathWorks, Natick, MA.
24. Meerasri, J., & Sothornvit, R. (2022). Artificial neural networks (ANNs) and multiple linear regression (MLR) for prediction of moisture content for coated pineapple cubes. Case Studies in Thermal Engineering, 33, 101942. https://doi.org/10.1016/j.csite.2022.101942
25. Merah, O., Sayed-Ahmad, B., Talou, T., Saad, Z., Cerny, M., Grivot, S., ..., & Hijazi, A. (2020). Biochemical composition of cumin seeds, and biorefining study. Biomolecules, 10(7), 1054. https://doi.org/10.3390/biom10071054
26. Miraei Ashtiani, S.-H., Rafiee, M., Mohebi Morad, M., Khojastehpour, M., Khani, M. R., Rohani, A., ..., & Martynenko, A. (2020). Impact of gliding arc plasma pretreatment on drying efficiency and physicochemical properties of grape. Innovative Food Science & Emerging Technologies, 63, 102381. https://doi.org/10.1016/j.ifset.2020.102381
27. Moghimi, M., Farzaneh, V., & Bakhshabadi, H. (2018). The effect of ultrasound pretreatment on some selected physicochemical properties of black cumin (Nigella Sativa). Nutrire, 43(1), 1-8. https://doi.org/10.1186/s41110-018-0057-4
28. Moosavi, A. A., Nematollahi, M. A., & Rahimi, M. (2021). Predicting water sorptivity coefficient in calcareous soils using a wavelet–neural network hybrid modeling approach. Environmental Earth Sciences, 80, 1-19. https://doi.org/10.1007/s12665-021-09399-2
29. Moradi, M., Ghasemi, J., & Azimi-Nejadian, H. (2021). Energy and Exergy Analysis of Drying Process of Lemon Verbena Leaves in a Solar Dryer. Journal of Agricultural Machinery, 11(2), 423-433. https://doi.org/10.22067/jam.v11i2.85801
30. Namjoo, M., Dibagar, N., Golbakhshi, H., Figiel, A., & Masztalerz, K. (2024). RSM-Based Optimization Analysis for Cold Plasma and Ultrasound-Assisted Drying of Caraway Seed. Foods, 13(19), 3084. Retrieved from: https://www.mdpi.com/2304-8158/13/19/3084
31. Namjoo, M., Golbakhshi, H., Kamandar, M. R., & Beigi, M. (2024). Multi-Objective Investigation and Optimization of Paddy Processing in a Hot Air Dryer. Periodica Polytechnica Chemical Engineering. https://doi.org/10.3311/PPch.24100
32. Namjoo, M., Moradi, M., Dibagar, N., & Niakousari, M. (2022). Cold plasma pretreatment prior to ultrasound-assisted air drying of cumin seeds. Food and Bioprocess Technology, 15(9), 2065-2083. https://doi.org/10.1007/s11947-022-02863-8
33. Namjoo, M., Moradi, M., Niakousari, M., & Karparvarfard, S. H. (2022). Ultrasound-assisted air drying of cumin seeds: modeling and optimization by response surface method. Heat and Mass Transfer. https://doi.org/10.1007/s00231-022-03306-y
34. Nazghelichi, T., Kianmehr, M. H., & Aghbashlo, M. (2010). Thermodynamic analysis of fluidized bed drying of carrot cubes. Energy, 35(12), 4679-4684. https://doi.org/10.1016/j.energy.2010.08.028
35. Nematollahi, M. A., Jamali, B., & Hosseini, M. (2020). Fluid velocity and mass ratio identification of piezoelectric nanotube conveying fluid using inverse analysis. Acta Mechanica, 231(2), 683-700. https://doi.org/10.1007/s00707-019-02633-5
36. Nematollahi, M. A., & Mousavi Khaneghah, A. (2019). Neural network prediction of friction coefficients of rosemary leaves. Journal of Food Process Engineering, 42(6), e13211. https://doi.org/10.1111/jfpe.13211
37. Onwude, D. I., Hashim, N., Abdan, K., Janius, R., & Chen, G. (2018). Investigating the influence of novel drying methods on sweet potato (Ipomoea batatas): Kinetics, energy consumption, color, and microstructure. Journal of Food Process Engineering, 41(4), e12686. https://doi.org/10.1111/jfpe.12686
38. Osloob, F., Moradi, M., & Niakousari, M. (2023). Cold Plasma: A Novel Pretreatment Method for Drying Canola Seeds: Kinetics Study and Superposition Modeling. Journal of Agricultural Machinery, 13(1), 41-53. https://doi.org/10.22067/jam.2022.75630.1096
39. Özkan Karabacak, A. (2019). Effects of different drying methods on drying characteristics, colour and in-vitro bioaccessibility of phenolics and antioxidant capacity of blackthorn pestil (leather). Heat and Mass Transfer, 55, 2739-2750. https://doi.org/10.1007/s00231-019-02729-w
40. Potisate, Y., Phoungchandang, S., & Kerr, W. L. (2014). The effects of predrying treatments and different drying methods on phytochemical compound retention and drying characteristics of Moringa leaves (Moringa oleifera). Drying Technology, 32(16), 1970-1985. https://doi.org/10.1080/07373937.2014.945223
41. Rezaei, S., Behroozi-Khazaei, N., & Darvishi, H. (2021). Modeling of Potato Slice Drying Process in a Microwave Dryer using Artificial Neural Network and Machine Vision. Journal of Agricultural Machinery, 11(2), 263-275. https://doi.org/10.22067/jam.v11i2.78709
42. Saeidirad, M. H., Rohani, A., & Zarifneshat, S. (2013). Predictions of viscoelastic behavior of pomegranate using artificial neural network and Maxwell model. Computers and Electronics in Agriculture, 98, 1-7. https://doi.org/10.1016/j.compag.2013.07.011
43. Safavi, A. A., & Romagnoli, J. A. (1997). Application of wavelet-based neural networks to the modelling and optimisation of an experimental distillation column. Engineering Applications of Artificial Intelligence, 10(3), 301-313. https://doi.org/10.1016/S0952-1976(97)00009-2
44. Saiedirad, M., & Mirsalehi, M. (2010). Prediction of mechanical properties of cumin seed using artificial neural networks. Journal of Texture studies, 41(1), 34-48. https://doi.org/10.1111/j.2042-4588.2009.00119.x
45. Saiedirad, M., Tabatabaeefar, A., Borghei, A., Mirsalehi, M., Badii, F., & Varnamkhasti, M. G. (2008). Effects of moisture content, seed size, loading rate and seed orientation on force and energy required for fracturing cumin seed (Cuminum cyminum) under quasi-static loading. Journal of food engineering, 86(4), 565-572. https://doi.org/10.1016/j.jfoodeng.2007.11.048
46. Shashikanthalu, S. P., Ramireddy, L., & Radhakrishnan, M. (2020). Stimulation of the germination and seedling growth of Cuminum cyminum L. seeds by cold plasma. Journal of Applied Research on Medicinal and Aromatic Plants, 18, 100259. https://doi.org/10.1016/j.jarmap.2020.100259
47. Sun, Q., Zhang, M., & Mujumdar, A. S. (2019). Recent developments of artificial intelligence in drying of fresh food: A review. Critical Reviews in Food Science and Nutrition, 59(14), 2258-2275. https://doi.org/10.1080/10408398.2018.1487171
48. Tabibian, S. A., Labbafi, M., Askari, G. H., Rezaeinezhad, A. R., & Ghomi, H. (2020). Effect of gliding arc discharge plasma pretreatment on drying kinetic, energy consumption and physico-chemical properties of saffron. Journal of food engineering, 270, 109-117. https://doi.org/10.1016/j.jfoodeng.2019.109766
49. Wang, C., Tian, S., & An, X. (2022). The effects of drying parameters on drying characteristics, colorimetric differences, antioxidant components of sliced chinese jujube. Heat and Mass Transfer, 58(9), 1561-1571. https://doi.org/10.1007/s00231-022-03202-5
50. Wang, X., Zhong, J., Han, M., Li, F., Fan, X., & Liu, Y. (2023). Drying characteristics and moisture migration of ultrasound enhanced heat pump drying on carrot. Heat and Mass Transfer, 1-12. https://doi.org/10.1007/s00231-023-03564-1
51. Yogendrasasidhar, D., & Setty, Y. P. (2018). Drying kinetics, exergy and energy analyses of Kodo millet grains and Fenugreek seeds using wall heated fluidized bed dryer. Energy, 151, 799-811. https://doi.org/10.1016/j.energy.2018.03.104
52. Zakeri, V., Naghavi, V., & Safavi, A. A. (2009). Developing real-time wave-net models for non-linear time-varying experimental processes. Computers & Chemical Engineering, 33(8), 1379-1385. https://doi.org/10.1016/j.compchemeng.2009.01.017
53. Zhou, Y.-H., Vidyarthi, S. K., Zhong, C.-S., Zheng, Z.-A., An, Y., Wang, J., ..., & Xiao, H.-W. (2020). Cold plasma enhances drying and color, rehydration ratio and polyphenols of wolfberry via microstructure and ultrastructure alteration. LWT, 134, 110173. https://doi.org/10.1016/j.lwt.2020.110173