prediction of non-wood forest products trade using artificial neural networks

Prediction of Gas Lift Parameters Using Artificial Neural Networks

Non-wood Forest Products

This paper describes an attempt to integrate the assessment of some non-wood forest products (NWFPs) in the framework of a standard forest inventory. The study is restricted mainly to medicinal and aromatic plants, i.e., herbal NWFPs, a group that has economic importance in the hill regions of Nepal. Several specific difficulties are discussed. They arise from the fact that a regular forest inv...

Prediction of Kinematic Viscosity of Petroleum Fractions Using Artificial Neural Networks

In this work, artificial neural network (ANN) was utilized to develop a new model for the prediction of the kinematic viscosity of petroleum fractions. This model was generated as a function of temperature (T), normal boiling point temperature (Tb), and specific gravity (S). In order to develop the new model, different architectures of feed-forward type were examined. Finally, the optimum struc...

Prediction of recovery of gold thiosulfate on activated carbon using artificial neural networks

Since a high toxicity of cyanide which use as a reagent in the gold processing plant, thiosulfate has been recognized as a environmental friendly reagent for leaching of gold from ore. After gold leaching process it's important for recovery of gold from solution using adsorption or extraction methods, One of these methods is activated carbon.The loading of gold from industrial thiosulfate solut...

Estimation of Products Final Price Using Bayesian Analysis Generalized Poisson Model and Artificial Neural Networks

Estimating the final price of products is of great importance. For manufacturing companies proposing a final price is only possible after the design process over. These companies propose an approximate initial price of the required products to the customers for which some of time and money is required. Here using the existing data of already designed transformers and utilizing the bayesian anal...

Prediction of the deformation modulus of rock masses using Artificial Neural Networks and Regression methods

Static deformation modulus is recognized as one of the most important parameters governing the behavior of rock masses. Predictive models for the mechanical properties of rock masses have been used in rock engineering because direct measurement of the properties is difficult due to time and cost constraints. In this method the deformation modulus is estimated indirectly from classification syst...