Isothermal hot air drying behavior of municipal sewage sludge briquettes coupled with lignite additive

Moisture distribution in sewage sludge was considered as the essential of thermal drying. Some methods were given in literatures to test the moisture distribution, but there was no standard method to determine the critical water content between different kinds of water. The municipal sewage sludge was dried by hot air in this work. Based on the drying curve, the derivative of drying rate with respect to dry basis moisture content was brought out to analyze the moisture distribution in sewage sludge. Results show that this method can easily determine the free water, interstitial water, surface water and bound water with a high accuracy. The present work can provide new insight to determine the moisture distribution in sewage sludge, which was still lacking in the literatures.

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Empirical Model and Artificial Neural Network Model Approach for Air Dried Sheets (ADS) Rubber

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.622-623.69 ◽

2012 ◽

Vol 622-623 ◽

pp. 69-74

Author(s):

T. Ninchuewong ◽

S. Tirawanichakul ◽

Y. Tirawanichakul

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Moisture Content ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Hot Air Drying ◽

Air Drying ◽

Rubber Sheet ◽

Hot Air ◽

Drying Behavior

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.

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Drying Behaviour and Change in Colour and Textural Properties of Mushroom During Drying

International Journal of Food Engineering ◽

10.1515/1556-3758.1696 ◽

2012 ◽

Vol 8 (1) ◽

Cited By ~ 1

Author(s):

Tarsem Chand Mittal ◽

Sajeev Rattan Sharma ◽

Jarnail Singh Muker ◽

Satish Kumar Gupta

Keyword(s):

Textural Properties ◽

Microwave Drying ◽

Drying Methods ◽

Button Mushroom ◽

Hot Air Drying ◽

Air Drying ◽

Hot Air ◽

Drying Behavior ◽

L Value ◽

Drying Behaviour

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..

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