مدلسازي سينتيك خشك كردن گلبرگ زعفران با استفاده از روشهاي مختلف خشك كردن بستر سيال و نيمه سيال

Modeling the Drying Kinetics of Saffron Petals Using Different Methods of Fluid and Semi-fluid Substrate Drying

در ﻓﺮآﯾﻨﺪ ﺗﻮﻟﯿﺪ زﻋﻔﺮان،ﮔﻠﺒﺮگ ﻫﺎ ﺑﻪ ﻋﻨﻮان ﺿﺎﯾﻌﺎت دور رﯾﺨﺘﻪ ﻣﯽ ﺷﻮﻧﺪ. ﺑﻪ ﻃﻮري ﮐﻪ ﺳﺎﻻﻧﻪ در اﯾﺮان رﻗﻤﯽ ﻣﻌﺎدل 7257625ﮐﯿﻠﻮﮔﺮم ﮔﻠﺒﺮگ زﻋﻔﺮان ﺑﻪ ﻋﻨﻮان ﻣﺤﺼﻮل ﻓﺮﻋﯽ ﺑﺪﺳﺖ ﻣﯽ آﯾﺪ. ﺑﻨﺎﺑﺮاﯾﻦ ﯾﺎﻓﺘﻦ راه ﺣﻠﯽ ﺑﺮاي ﺑﺎزﯾﺎﻓﺖ اﯾﻦ ﺣﺠﻢ ﻋﻈﯿﻢ ﺿﺎﯾﻌﺎت از اﻫﻤﯿﺖ ﺑﺎﻻﯾﯽ ﺑﺮﺧﻮردارﻣﯽ ﺑﺎﺷﺪ. اﯾﻦ ﭘﮋوﻫﺶ ﺑﺎ ﻫﺪف ﺑﺮرﺳﯽ و ﺗﻌﯿﯿﻦ ﻣﻨﺎﺳﺐ ﺗﺮﯾﻦ ﻣﺪل ﺳﯿﻨﯿﺘﯿﮑﯽ ﻧﯿﻤﻪ ﺗﺠﺮﺑﯽ ﺑﺮاي ﺗﻮﺻﯿﻒ ﻓﺮآﯾﻨﺪ ﺧﺸﮏ ﮐﺮدن ﮔﻠﺒﺮگ زﻋﻔﺮان ﺑﺎ اﺳﺘﻔﺎده از دو روش ﺑﺴﺘﺮ ﻧﯿﻤﻪ ﺳﯿﺎل و ﺑﺴﺘﺮ ﺳﯿﺎل در دﻣﺎﻫﺎي 45 و 55 درﺟﻪ ﺳﺎﻧﺘﯽ ﮔﺮاد اﻧﺠﺎم ﺷﺪ. ﺑﺪﯾﻦ ﻣﻨﻈﻮر اﺑﺘﺪا ﯾﮏ ﺧﺸﮏ ﮐﻦ ﺑﺴﺘﺮ ﺳﯿﺎل آزﻣﺎﯾﺸﮕﺎﻫﯽ ﺑﺎ ﻗﺎﺑﻠﯿﺖ ﺗﻨﻈﯿﻢ درﺟﻪ ﺣﺮارت و ﺳﺮﻋﺖ ﺟﺮﯾﺎن ﻫﻮا وﻫﻤﭽﻨﯿﻦ داراي ﻗﺎﺑﻠﯿﺖ ﺑﺮﻧﺎﻣﻪ رﯾﺰي ﺟﻬﺖ ﺗﻮزﯾﻦ ﻧﻤﻮﻧﻪ ﻫﺎ در ﻓﻮاﺻﻞ زﻣﺎﻧﯽ ﻣﺨﺘﻠﻒ ﺑﺎ دﻗﺖ ﺑﺴﯿﺎر ﺑﺎﻻ ﺳﺎﺧﺘﻪ ﺷﺪ و ﺳﭙﺲ آزﻣﺎﯾﺶ ﺗﺠﺮﺑﯽ ﺧﺸﮏ ﮐﺮدن ﮔﻠﺒﺮگ زﻋﻔﺮان در دﻣﺎﻫﺎي )45 و 55 درﺟﻪ ﺳﺎﻧﺘﯽ ﮔﺮاد ( و ﺳﺮﻋﺖ ﻫﻮاي ﺧﺸﮏ ﮐﻦ ﺑﺴﺘﺮ ﻧﯿﻤﻪ ﺳﯿﺎل و ﺳﯿﺎل )0/7 و 1/7 ﻣﺘﺮ ﺑﺮ ﺛﺎﻧﯿﻪ( اﻧﺠﺎم و رﻓﺘﺎر ﺧﺸﮏ ﺷﺪن ﺑﺮ اﺳﺎس داده ﻫﺎى آزﻣﺎﯾﺸﮕﺎﻫﻰ ﻣﻮرد ﺑﺮازش ﻗﺮارﮔﺮﻓﺖ. ﻧﺘﺎﯾﺞ ﻧﺸﺎن داد زﻣﺎن ﻻزم ﺟﻬﺖ ﺧﺸﮏ ﺷﺪن ﮔﻠﺒﺮگ زﻋﻔﺮان ﺑﺎ اﻓﺰاﯾﺶ دﻣﺎي ﻫﻮاي ﺧﺸﮏ ﮐﺮدن از 45 ﺑﻪ 55درﺟﻪ ﺳﺎﻧﺘﯽ ﮔﺮاد و ﺳﺮﻋﺖ ﺟﺮﯾﺎن ﻫﻮا از 0/7ﺑﻪ 1/7 ﻣﺘﺮ ﺑﺮ ﺛﺎﻧﯿﻪ، ﮐﺎﻫﺶ ﯾﺎﻓﺖ. ﺑﻪ ﻃﻮري ﮐﻪ ﻓﺮآﯾﻨﺪ ﺧﺸﮏ ﮐﺮدن ﮔﻠﺒﺮگ زﻋﻔﺮان در دﻣﺎي 45 درﺟﻪ ﺳﺎﻧﺘﯽﮔﺮاد ﺑﺎ ﺳﺮﻋﺖ ﺳﯿﺎل 1/7 ﻣﺘﺮ ﺑﺮ ﺛﺎﻧﯿﻪ ﻃﯽ زﻣﺎن 60 دﻗﯿﻘﻪ و ﺑﺎ ﺳﺮﻋﺖ ﺳﯿﺎل 0/7 ﻣﺘﺮ ﺑﺮ ﺛﺎﻧﯿﻪ ﻃﯽ زﻣﺎن 96 دﻗﯿﻘﻪ و در دﻣﺎي 55 درﺟﻪ ﺳﺎﻧﺘﯽﮔﺮاد ﺑﺎ ﺳﺮﻋﺖ ﺳﯿﺎل 1/7 ﻣﺘﺮ ﺑﺮ ﺛﺎﻧﯿﻪ ﻃﯽ زﻣﺎن30 دﻗﯿﻘﻪ و ﺑﺎ ﺳﺮﻋﺖ 0/7 ﻣﺘﺮ ﺑﺮ ﺛﺎﻧﯿﻪ ﻣﺘﺮ ﺑﺮ ﺛﺎﻧﯿﻪ ﻃﯽ زﻣﺎن 36 دﻗﯿﻘﻪ ﺑﻪ ﭘﺎﯾﺎن رﺳﯿﺪ. ﺑﻪ ﮔﻮﻧﻪ اي ﮐﻪ ﺑﯿﺸﺘﺮﯾﻦ زﻣﺎن ﺧﺸﮏ ﮐﺮدن ﻣﺮﺑﻮط ﺑﻪ ﺟﺮﯾﺎن ﻫﻮاي 0/7 ﻣﺘﺮ ﺑﺮﺛﺎﻧﯿﻪ در دﻣﺎي 45 درﺟﻪ ﺳﺎﻧﺘﯽ ﮔﺮاد و ﮐﻤﺘﺮﯾﻦ زﻣﺎن ﻣﺮﺑﻮط ﺑﻪ ﺟﺮﯾﺎن ﻫﻮاي 1/7 ﻣﺘﺮ ﺑﺮ ﺛﺎﻧﯿﻪ در دﻣﺎي 55 درﺟﻪ ﺳﺎﻧﺘﯽ ﮔﺮاد ﻣﯽ ﺑﺎﺷﺪ. ﺑﺮرﺳﯽ ﻣﯿﺰان دﻓﻊ رﻃﻮﺑﺖ ﮔﻠﺒﺮگ زﻋﻔﺮان ﻃﯽ ﺧﺸﮏ ﮐﺮدن ﻧﺸﺎن داد ﭘﺎراﻣﺘﺮ ﺿﺮﯾﺐ ﻧﻔﻮذ ﻣﻮﺛﺮ رﻃﻮﺑﺖ از 10 -10 × 9/548 ﺗﺎ 10 -10 × 9/548 ﻣﺘﺮ ﻣﺮﺑﻊ در روش ﺑﺴﺘﺮ ﻧﯿﻤﻪ ﺳﯿﺎل و 10-9 × 1/963 ﺗﺎ 10 -9 × 1/114 ﻣﺘﺮ ﻣﺮﺑﻊ ﺑﺮ ﺛﺎﻧﯿﻪ در روش ﺑﺴﺘﺮ ﺳﯿﺎل، اﻧﺮژي ﻓﻌﺎل ﺳﺎزي از56/83 ﮐﯿﻠﻮژول ﺑﺮ ﻣﻮل در ﺑﺴﺘﺮ ﺳﯿﺎل ﺗﺎ 78/78ﮐﯿﻠﻮژول ﺑﺮ ﻣﻮل در ﺑﺴﺘﺮ ﻧﯿﻤﻪ ﺳﯿﺎل ﻣﺘﻐﯿﺮ ﺑﻮد. در ﺑﺮازش داده ﻫﺎ ﺑﺎ 11 ﻣﺪل ﻧﯿﻤﻪ ﺗﺠﺮﺑﯽ ﻣﺸﺨﺺ ﺷﺪ ﺑﺮاي ﺑﺮازش داده ﻫﺎي آزﻣﺎﯾﺸﮕﺎﻫﯽ، ﻣﺪل ﻫﺎي دو ﺟﻤﻠﻪ اي، ﻟﮕﺎرﯾﺘﻤﯽ و اﺻﻼح ﺷﺪه ﻫﻨﺪرﺳﻮن و ﭘﺎﺑﯿﺲ ﺑﺎ داﺷﺘﻦ ﺑﺎﻻﺗﺮﯾﻦ ﺿﺮﯾﺐ ﺗﺒﯿﯿﻦ و ﮐﻤﺘﺮﯾﻦ رﯾﺸﻪ ﻣﺘﻮﺳﻂ ﺧﻄﺎي داده ﻫﺎ و ﻣﺠﻤﻮع ﻣﺮﺑﻌﺎت ﺧﻄﺎ، ﻣﺪل ﻫﺎي ﻣﻨﺎﺳﺐ ﺑﻮدﻧﺪ.

In the process of producing saffron, petals are discarded as waste materials. As a result, in Iran, annually, 7257625 kg saffron petals are obtained as a sub-product. So finding a solution for recycling this huge amounts of waste is very important.This study conducted to find the most appropriate models for saffron petal dehydration with the use of semi-fluidized drier (SFD) and fluidized bed drier (FBD). س For this purpose, a fluidized bed drier was designed to measure the temperature and air flow velocity, and also with the ability of weighing the samples at different intervals with high precision and then. The airflow and temperatures ranges used in SFD and FBD were 0.7-1.7 ms-1 and 45-55 °C, respectively. When the air temperature and airflow respectively increased from 45 to 55°C and 0.7 to 1.7 ms-1, the dehydration time in SFD or FBD decreased significantly. At air temperature of 45°C and similar conditions, the SFD and BFD could dehydrate saffron petals in airflow and drying times of 0.7 ms-1 for 96 min and 1.7 ms-1 for 30 min, respectively. However, at equal temperature of 55°C and airflow of 0.7 ms-1, the petal dehydration time of SFD and FBD changed to 36 and 96 min, respectively. The effective moisture diffusivity coefficient in SFD and FBD systems varied from 9.548 × 10-10to 2.455 × 10- 9m2s-1and from 1.114 × 10-9to 1.963 × 10-9m2s-1, respectively. The calculated activation energy in SFD was ranged from 56.83 to 78.78 KJ/mol. Among eleven semi-experimental models tested for the saffron petals dehydration, only two models including logarithmic and modified form of Henderson and Pabis formula were found appropriate to fit the obtained data and show the actual trend of changing moisture ratio versus drying time of saffron petals. Because these models showed highest determination coefficients, lowest root mean squared and minimumsum-squared errors with the actual data.