แบบจำลองการอบแห้งพริกไทยด้วยเทคนิคเจ็ตสเปาต์เต็ดเบดโดยใช้ ระบบอนุมานฟัซซี่โครงข่ายปรับตัวได้
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ส.ค. 20, 2018
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เจตสเปาต์เต็ดเบด แบบจำลองคณิตศาสตร์ ระบบอนุมานฟัซซี่โครงข่ายปรับตัวได้ พริกไทย
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บทคัดย่อ
งานวิจัยนี้มีจุดประสงค์เพื่อหาแบบจำลองการอบแห้งพริกไทยด้วยเครื่องอบแห้งแบบเจ็ตสเปาต์เต็ดเบดทำการทดสอบ โดยหาแบบจำลองที่เหมาะสมระหว่างแบบจำลองเอมพิริคัลกับแบบจำลอง ANFIS ทดลองอบแห้งพริกไทยภายใต้อุณหภูมิอบแห้งคือ 70, 80 และ 90°C ความเร็วลมร้อน คือ 20 m/sec และความสูงของชั้นพริกไทย 250 mm โครงสร้าง ANFIS ประกอบด้วยตัวแปรอินพุต คือ อุณหภูมิลมร้อน และเวลาในการอบแห้ง ตัวแปรเอาต์พุต คือ อัตราส่วนความชื้นผลการวิเคราะห์พบว่า แบบจำลอง ANFIS มีความเหมาะสมมากกว่าแบบจำลองเอมพิริกัลโดย แบบจำลอง ANFIS แบบที่ 8 มีความเหมาะสมมากที่สุด โดยมีค่า R2 เท่ากับ 0.9991 และ RMSE เท่ากับ 0.0032 ตามลำดับ ในส่วนของแบบจำลองเอมพิริกัล แบบจำลองของ Page (Page model) มีความเหมาะสมมากที่สุดสำหรับการอบแห้งพริกไย โดยมีค่า R2 เท่ากับ 0.9983 และ RMSE เท่ากับ 0.0067 ตามลำดับ
Article Details
รูปแบบการอ้างอิง
[1]
สถาพรประสาธน์ ก., “แบบจำลองการอบแห้งพริกไทยด้วยเทคนิคเจ็ตสเปาต์เต็ดเบดโดยใช้ ระบบอนุมานฟัซซี่โครงข่ายปรับตัวได้”, sej, ปี 13, ฉบับที่ 1, น. 176–186, ส.ค. 2018.
ประเภทบทความ
บทความวิจัย
ลิขสิทธิ์เป็นของวารสารวิศวกรรมศาสตร์ มหาวิทยาลัยศรีนครินทรวิโรฒ
เอกสารอ้างอิง
[1] S. Torpol, “A study on thin layer drying of pepper using hot air dryer,”M.S. thesis, Dept. Phys., Burapha Univ., Chonburi, Thailand, 2013.
[2] Ministry of Agriculture and Cooperatives. (10 October 2010). Thailand's agricultural trade statistics for 2010 [Online]. Available: http://www.oae.go.th
[3] P. Praneetpolkrang and K. Sathapornprasath, “Application of a combined jet spouted bed and ultrasound waves for drying of agricultural products,” Srinakharinwirot Univ. Eng. J., vol.12, no.1, pp.163-175, Jan. 2017.
[4] C. Ertekin and O. Yaldiz, “Drying of eggplant and selection of suitable thin layer drying model,” J. of Food Eng., vol.21, pp.349-359, Aug. 2004.
[5] S. Suthicharoenpanich, “Paddy drying by two-dimensional spouted bed technique,”M.S. thesis, Dept. Food Eng., King Mongkut's University of Technology Thonburi, Bangkok, Thailand, 1998.
[6] K.B. Mathur and P. Gishler, “”AIChE J., vol.1, pp. 157-164, Jun. 1955.
[7] R.Tapaneyasin, “Study of hydrodynamics and drying characteristics of a jet spouted bed of shrimp,” M.S. thesis, Dept. Food Eng., King Mongkut's University of Technology Thonburi, Bangkok, Thailand, 2003.
[8] S. Wachiraphansakul, “Drying of soy residue using sorbent particles in a jet spouted bed,” M.S. thesis, Dept. Food Eng., King Mongkut's University of Technology Thonburi, Bangkok, Thailand 2005.
[9] K. Sathapornprasathand C. Nimmol, “Influences of Far-infrared radiation on the energy efficiency of stepwise jet spouted bed drying system,” KKU Res. J., vol.18, pp.325-343, Mar. 2013.
[10] S. Wachiraphansakul and S. Devahastin, “Drying kinetics and quality of soy residue (okara) dried in a jet spouted bed dryer,”Drying Technol., vol.23, pp.1229-1242, Feb. 2005.
[11] S. Wachiraphansakul and S. Devahastin, “Drying kinetics and quality of okara dried in a jet spouted bed of sorbent particles,”LWT- Food Sci. Technol.,vol.40, pp.207-219, Mar. 2007.
[12] K. Sathapornprasath, “Intermittent batch drying for agricultural products in jet spouted bed drying,” Srinakharinwirot Univ. Eng. J., vol.5, no.1, pp.50-64, Jan. 2010.
[13] P. Waramit,N. Weerayuth, and U. Teeboonma, “Prediction of hot air drying moisture ratio using an empirical model and artificial neural network model,” Srinakharinwirot Univ. Eng. J.,vol. 6 no.1, pp. 39-47, Jan. 2011.
[14] L. Olatomiwa, S. Mekhilef, S. Shamshirband and D. Petkovic, “Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria,” Renewable Sustainable Ener. Rev., Vol.51, pp.1784-1791, Nov. 2015.
[15] M. Hosoz, H.M. Ertuncand H. Bulgurcu, “An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower,” Expert Syst. Appl., Vol.38, pp.14148-14155, Oct. 2011.
[16] S. Soyguder and A. Alli, “ Predicting of fan speed for energy saving in HVAC system based on adaptive network based fuzzy inference system,” Expert Syst. Appl., Vol. 36, pp.8631-8638, May 2009.
[17] A. Hasiloglu, M. Yilmaz, O. Comakli and I. Ekmekci, “Adaptive neuro-fuzzy modeling of transient heat transfer in circular duct air flow,” Int. J. Therm. Sci., Vol.43, pp.1075-1090, Nov. 2004.
[18] S. Lertworasirikul, “Drying kinetics of semi-finished cassava crackers: A comparative study,” LWT, Vol.41, pp.1360-1371, Nov. 2008.
[19] Y. Tana, C. Shuaia, L. Jiaoc and L. Shen, “An adaptive neuro-fuzzy inference system (ANFIS) approach for measuring country sustainability performance,” Environ. Impact Assess. Rev., Vol.65, pp.29-40, Jul. 2017.
[20] L. Virgen-Navarroa, E.J. Herrera-Lópezb, R. Corona-Gonzálezc, E. Arriola-Guevarac and G.M. Guatemala-Morales, “Neuro-fuzzy model based on digital images for the monitoring of coffee bean color during roasting in a spouted bed,” Expert Syst. Appl., Vol.54, pp.162-169, Jul. 2016.
[21] R. Assawarachan, “Microwave Vacuum Drying Kinetics of Dried Spirogyra sp.,” TSAE J., Vol.19(1), pp. 14-24,Jan. 2013.
[22] AOAC (Association of Official Analytical Chemists). Official Methods of Analysis. 17th ed. Association of Official Analytical Chemists. Maryland: Gaithersburg; 2000.
[23] A. Seangsai and S. Jiriwibhakorn, “Power system quality evaluation using adaptive neuro-fuzzy inference systems,” Kasetsart Engi. J. vol. 92, pp. 9-16, Apr. 2014.
[24] T. Madhiyanon, A. Phila and S. Soponronnarit, “Models of fluidized bed drying for thin-layer chopped coconut,” Appl. Therm. Eng., Vol.29, pp.2849-2854, Oct. 2009.
[25] I. Doymaz, “Convective drying kinetics of strawberry,” Chem. Eng. Process., vol.47, pp.914-919, May 2008.
[26] K. Sacilik and A.K. Elicin, “The thin layer drying characteristics of organic apple slices,” J. Food Eng., vol.73, pp.281-289, Apr. 2006.
[27] C. Srinivasakannan and N. Balasubramaniam, “An experimental and modeling investigation on drying of ragi (Eleusinecorocana) in fluidized bed,” Drying Technol., vol.24, pp.1683-1689, Apr. 2006.
[28] E. K. Akpinar and Y. Bicer, “Mathematical modelling of thin layer drying process of long green pepper in solar dryer and under open sun,” Energy Convers. Manage., vol.49, pp.1367-1375, Jun. 2008.
[29] S. Pusat, M. T. Akkoyunlu, E. Pekel, M. C. Akkoyunlu, C. Özkan and S.S. Kara, “Estimation of coal moisture content in convective drying process using ANFIS,” Fuel Process. Technol., vol.147, pp.12-17, Jun. 2016.
[30] T. Srikhacha, “Short-term prediction in stock price using hybrid optimized recursive Slope filtering, adaptive moving approach and neuro fuzzy adaptive learning,” Ph.D. thesis, Dept. Information Tech., King Mongkut's Institute of Technology North Bangkok, Bangkok, Thailand, 2007.
[2] Ministry of Agriculture and Cooperatives. (10 October 2010). Thailand's agricultural trade statistics for 2010 [Online]. Available: http://www.oae.go.th
[3] P. Praneetpolkrang and K. Sathapornprasath, “Application of a combined jet spouted bed and ultrasound waves for drying of agricultural products,” Srinakharinwirot Univ. Eng. J., vol.12, no.1, pp.163-175, Jan. 2017.
[4] C. Ertekin and O. Yaldiz, “Drying of eggplant and selection of suitable thin layer drying model,” J. of Food Eng., vol.21, pp.349-359, Aug. 2004.
[5] S. Suthicharoenpanich, “Paddy drying by two-dimensional spouted bed technique,”M.S. thesis, Dept. Food Eng., King Mongkut's University of Technology Thonburi, Bangkok, Thailand, 1998.
[6] K.B. Mathur and P. Gishler, “”AIChE J., vol.1, pp. 157-164, Jun. 1955.
[7] R.Tapaneyasin, “Study of hydrodynamics and drying characteristics of a jet spouted bed of shrimp,” M.S. thesis, Dept. Food Eng., King Mongkut's University of Technology Thonburi, Bangkok, Thailand, 2003.
[8] S. Wachiraphansakul, “Drying of soy residue using sorbent particles in a jet spouted bed,” M.S. thesis, Dept. Food Eng., King Mongkut's University of Technology Thonburi, Bangkok, Thailand 2005.
[9] K. Sathapornprasathand C. Nimmol, “Influences of Far-infrared radiation on the energy efficiency of stepwise jet spouted bed drying system,” KKU Res. J., vol.18, pp.325-343, Mar. 2013.
[10] S. Wachiraphansakul and S. Devahastin, “Drying kinetics and quality of soy residue (okara) dried in a jet spouted bed dryer,”Drying Technol., vol.23, pp.1229-1242, Feb. 2005.
[11] S. Wachiraphansakul and S. Devahastin, “Drying kinetics and quality of okara dried in a jet spouted bed of sorbent particles,”LWT- Food Sci. Technol.,vol.40, pp.207-219, Mar. 2007.
[12] K. Sathapornprasath, “Intermittent batch drying for agricultural products in jet spouted bed drying,” Srinakharinwirot Univ. Eng. J., vol.5, no.1, pp.50-64, Jan. 2010.
[13] P. Waramit,N. Weerayuth, and U. Teeboonma, “Prediction of hot air drying moisture ratio using an empirical model and artificial neural network model,” Srinakharinwirot Univ. Eng. J.,vol. 6 no.1, pp. 39-47, Jan. 2011.
[14] L. Olatomiwa, S. Mekhilef, S. Shamshirband and D. Petkovic, “Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria,” Renewable Sustainable Ener. Rev., Vol.51, pp.1784-1791, Nov. 2015.
[15] M. Hosoz, H.M. Ertuncand H. Bulgurcu, “An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower,” Expert Syst. Appl., Vol.38, pp.14148-14155, Oct. 2011.
[16] S. Soyguder and A. Alli, “ Predicting of fan speed for energy saving in HVAC system based on adaptive network based fuzzy inference system,” Expert Syst. Appl., Vol. 36, pp.8631-8638, May 2009.
[17] A. Hasiloglu, M. Yilmaz, O. Comakli and I. Ekmekci, “Adaptive neuro-fuzzy modeling of transient heat transfer in circular duct air flow,” Int. J. Therm. Sci., Vol.43, pp.1075-1090, Nov. 2004.
[18] S. Lertworasirikul, “Drying kinetics of semi-finished cassava crackers: A comparative study,” LWT, Vol.41, pp.1360-1371, Nov. 2008.
[19] Y. Tana, C. Shuaia, L. Jiaoc and L. Shen, “An adaptive neuro-fuzzy inference system (ANFIS) approach for measuring country sustainability performance,” Environ. Impact Assess. Rev., Vol.65, pp.29-40, Jul. 2017.
[20] L. Virgen-Navarroa, E.J. Herrera-Lópezb, R. Corona-Gonzálezc, E. Arriola-Guevarac and G.M. Guatemala-Morales, “Neuro-fuzzy model based on digital images for the monitoring of coffee bean color during roasting in a spouted bed,” Expert Syst. Appl., Vol.54, pp.162-169, Jul. 2016.
[21] R. Assawarachan, “Microwave Vacuum Drying Kinetics of Dried Spirogyra sp.,” TSAE J., Vol.19(1), pp. 14-24,Jan. 2013.
[22] AOAC (Association of Official Analytical Chemists). Official Methods of Analysis. 17th ed. Association of Official Analytical Chemists. Maryland: Gaithersburg; 2000.
[23] A. Seangsai and S. Jiriwibhakorn, “Power system quality evaluation using adaptive neuro-fuzzy inference systems,” Kasetsart Engi. J. vol. 92, pp. 9-16, Apr. 2014.
[24] T. Madhiyanon, A. Phila and S. Soponronnarit, “Models of fluidized bed drying for thin-layer chopped coconut,” Appl. Therm. Eng., Vol.29, pp.2849-2854, Oct. 2009.
[25] I. Doymaz, “Convective drying kinetics of strawberry,” Chem. Eng. Process., vol.47, pp.914-919, May 2008.
[26] K. Sacilik and A.K. Elicin, “The thin layer drying characteristics of organic apple slices,” J. Food Eng., vol.73, pp.281-289, Apr. 2006.
[27] C. Srinivasakannan and N. Balasubramaniam, “An experimental and modeling investigation on drying of ragi (Eleusinecorocana) in fluidized bed,” Drying Technol., vol.24, pp.1683-1689, Apr. 2006.
[28] E. K. Akpinar and Y. Bicer, “Mathematical modelling of thin layer drying process of long green pepper in solar dryer and under open sun,” Energy Convers. Manage., vol.49, pp.1367-1375, Jun. 2008.
[29] S. Pusat, M. T. Akkoyunlu, E. Pekel, M. C. Akkoyunlu, C. Özkan and S.S. Kara, “Estimation of coal moisture content in convective drying process using ANFIS,” Fuel Process. Technol., vol.147, pp.12-17, Jun. 2016.
[30] T. Srikhacha, “Short-term prediction in stock price using hybrid optimized recursive Slope filtering, adaptive moving approach and neuro fuzzy adaptive learning,” Ph.D. thesis, Dept. Information Tech., King Mongkut's Institute of Technology North Bangkok, Bangkok, Thailand, 2007.
