The traditional approach of using the existing models is generally adopted in many dryer designs. However, difficulties arise in situations where no good phy-sical model of the process is available, or the conditions are too diverse to use the existing models. It is also important to have a model to predict off-design performance of the dryers. Computer simulation can be used to address part of the problem if an accurate model is available, but when optimization is required, computer simulations too can be time consuming and expensive.
Recently, artificial neural networks have been used in some industrial pro-cesses to overcome this deficiency (HUANG and MUJUMDAR, 1993). Neural networks have the advantages of using the inherent ability of classification and regression to produce an output from a given set of inputs, without having an explicit mathematical model of the system under consideration which can be highly non-linear. HUANG and MUJUMDAR (1993) used neural network in the pre-diction of Yankee dryer performance. A Yankee dryer is a combined convec-tion/conduction dryer for thin paper webs to produce tissue paper in a single pass through the dryer. The parameters that determine the operation of a Yan-kee dryer are numerous. These include the YanYan-kee dryer geometric dimensions, its material thermal properties, paper speed, air temperature and velocity within the hood, drying kinetics, paper thickness and properties, etc. They found that the simulation results can be used to train a given neural net, which can then be used to predict dryer performance where understanding of the total process is lacking or incomplete. While the literature does not report the use of this techni-que for agricultural products, the potential for applying the same to agricultural products can be explored in the future.
MANEVAL et al. (1991) applied nuclear magnetic resonance (NMR) imaging to the study of moisture transport in a capillary porous material during drying.
Using this technique they measured moisture content at the surface of the sam-ple. They used these data to explain the phenomena of constant- and
falling-© Lavoisier – La photocopie non autorisée est un délit
rate period of drying. Although this is an expensive technique it may be tried to determine the EMC-ERH relationship in grains. The advantage of this technique is that it measures the moisture content without the need to measure the mass of the sample.
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