Hot-air drying behavior and fragmentation characteristic of single lignite particle

Fuel ◽  
2019 ◽  
Vol 247 ◽  
pp. 209-216 ◽  
Author(s):  
Mingqiang Gao ◽  
Keji Wan ◽  
Zhenyong Miao ◽  
Qiongqiong He ◽  
Pengchao Ji ◽  
...  
2012 ◽  
Vol 622-623 ◽  
pp. 69-74
Author(s):  
T. Ninchuewong ◽  
S. Tirawanichakul ◽  
Y. Tirawanichakul

The objective of this research was to predict drying behavior of hot air drying using an empirical model (EM) and an artificial neural network model (ANN). Rubber sheet with initial moisture content ranging of 23-40% dry-basis was dried by temperature ranging of 40-70°C and air flow rate of 0.7 m/s. The desired final moisture content was set at 0.15% dry-basis. The results showed that drying rate of rubber sheet dried with hot air convection was faster than conventional natural aeration. The EM and ANN were simulated to describe the drying behavior of products. Furthermore, prediction results between EM and ANN were compared with the experimental data. In this research, it was obviously found that ANN can describe the drying behavior effectively. Additionally, it was also found that predicted results of Multilayer feed forward Levenberg-Maqurdt’s Back-propagation ANN were good agreement with the experimental results compared to those results of EM. It is the optimum architecture for prediction the evolution of moisture transfer for hot air drying.


Author(s):  
Tarsem Chand Mittal ◽  
Sajeev Rattan Sharma ◽  
Jarnail Singh Muker ◽  
Satish Kumar Gupta

Button mushroom in the form of whole and slices were dried using convective hot air drying and microwave drying methods. Main objectives were to study the drying behavior and change in colour and textural properties. To get moisture content of 0.08 g/g, hot air drying at 600C took 463 minutes and 350 minutes for whole and sliced mushroom respectively whereas these times were just 9 minutes and 8.5 minutes when the microwave oven was run at 60% of its maximum power (1350 W). The convective hot air drying process can be put into two falling rate periods. The decrease in brightness (indicated by L-value) in dried samples was about 44% as compared to the fresh ones. The variation within the differently dried samples was not much. Hardness was lowest (<2>N) in fresh samples and was highest (>5.5 N) in microwave dried samples with hot air dried samples in between. For most of the samples, the springiness were between 0.4 and 0.6 except for hot air dried sliced samples where it was 0.9. Except in hot air dried samples, the change in cohesiveness was not much. Adhesiveness was found in fresh mushroom only..


2021 ◽  
Vol 18 (118) ◽  
pp. 297-311
Author(s):  
Bijan Askari ◽  
Mahdi Kashaninejad ◽  
Aman mohammad Ziaiifar ◽  
Ebrahim Esmaeelzade ◽  
◽  
...  

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1309
Author(s):  
Muhammad Heikal Ismail ◽  
Hii Ching Lik ◽  
Winny Routray ◽  
Meng Wai Woo

Fresh rice noodle was usually coated in a large amount of oil to avoid stickiness and extend the shelf life. Pre-treatment has been applied to reduce the quantity of oil in rice noodle. In this research, the pre-treatment and temperature effect on the rice noodle quality subjected to hot air drying, heat pump drying, and freeze drying was investigated. Texture, color, oil content, and starch gelatinization of the dried noodle was further evaluated. Results revealed that there were significant differences (p < 0.05%) in texture, color, oil content, and starch gelatinization in rice noodle subjected to pre-treatment. Furthermore, the texture, color, oil content, and starch gelatinization demonstrated a significant difference (p < 0.05%) in freeze drying rather than hot air drying and heat pump drying. The findings indicate that the qualitative features of the dehydrated noodle are synergistic to pretreatment and drying temperature. Despite superior quality shown by freeze drying, the hierarchical scoring has proven that rice noodle undergoing hot air drying at 30 °C to produce comparable quality attributes. The hierarchical scoring can be a useful tool in quality determination for the food industry.


Meat Science ◽  
2021 ◽  
pp. 108638
Author(s):  
Shuo Shi ◽  
Jia Feng ◽  
Geer An ◽  
Baohua Kong ◽  
Hui Wang ◽  
...  

Author(s):  
Kritsada Puangsuwan ◽  
Saysunee Jumrat ◽  
Jirapond Muangprathub ◽  
Teerask Punvichai ◽  
Seppo Karrila ◽  
...  
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