Moisture Content Prediction in the Switchgrass (Panicum virgatum) Drying Process Using Artificial Neural Networks

This article proposes two artificial neural network (ANN)-based models to characterize the switchgrass drying process: The first one models processes with constant air temperature and relative humidity and the second one models processes with variable air conditions and rainfall. The two ANN-based models proposed estimated the moisture content (MC) as a function of temperature, relative humidity, previous MC, time, and precipitation information. The first ANN-based model describes MC evolution data more accurately than six mathematical empirical equations typically proposed in the literature. The second ANN-based model estimated the MC with a correlation coefficient greater than 98.8%.

[1]  Jaime Gómez Gil,et al.  Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks , 2012, Sensors.

[2]  B. V. Conger,et al.  Agrobacterium‐Mediated Genetic Transformation of Switchgrass , 2002 .

[3]  R. Pitt,et al.  The rewetting of partially dried grass swaths by rain: part 1, lumped and distributed models of moisture fluctuation , 1990 .

[4]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[5]  L. Diamante,et al.  Mathematical modelling of the thin layer solar drying of sweet potato slices , 1993 .

[6]  Cengiz Yildiz,et al.  Original paper: The prediction of seedy grape drying rate using a neural network method , 2011 .

[7]  Maryam Nikzad,et al.  Modeling of tomato drying using artificial neural network , 2007 .

[8]  J. Wang,et al.  A SINGLE-LAYER MODEL FOR FAR-INFRARED RADIATION DRYING OF ONION SLICES , 2002 .

[9]  Koksal Erenturk,et al.  Comparison of genetic algorithm and neural network approaches for the drying process of carrot , 2007 .

[10]  Carmen Rosselló,et al.  Dehydration of aloe vera: simulation of drying curves and evaluation of functional properties , 2000 .

[11]  R. W. McClendon,et al.  Artificial neural networks for automated year-round temperature prediction , 2009 .

[12]  D. D. Wolf,et al.  Switchgrass as a sustainable bioenergy crop , 1996 .

[13]  Seyed Mohammad Ali Razavi,et al.  Application of Fractal Theory for Prediction of Shrinkage of Dried Kiwifruit Using Artificial Neural Network and Genetic Algorithm , 2011 .

[14]  Andrew L. Nevai,et al.  Plant population growth and competition in a light gradient: a mathematical model of canopy partitioning. , 2007, Journal of theoretical biology.

[15]  S. M. Henderson,et al.  Progress in Developing the Thin Layer Drying Equation , 1974 .

[16]  M. Islam,et al.  An Artificial Neural Network Model for Prediction of Drying Rates , 2003 .

[17]  E. van Elderen,et al.  Field drying of hay and wheat , 1969 .

[18]  W. K. Lewis.  The Rate of Drying of Solid Materials. , 1921 .

[19]  A. Masoni,et al.  Effect of irrigation and nitrogen fertilization on biomass yield and efficiency of energy use in crop production of Miscanthus , 1999 .

[20]  Edward M. Rubin,et al.  Genomics of cellulosic biofuels , 2008, Nature.

[21]  Arun S. Mujumdar,et al.  USE OF NEURAL NETWORK TO PREDICT INDUSTRIAL DRYER PERFORMANCE , 1993 .

[22]  R. Singh,et al.  SINGLE LAYER DRYING EQUATION FOR ROUGH RICE. , 1978 .

[23]  Dan Cornford,et al.  A Bayesian state space modelling approach to probabilistic quantitative precipitation forecasting , 2004 .

[24]  Fernando Porté-Agel,et al.  Turbulent Flow Inside and Above a Wind Farm: A Wind-Tunnel Study , 2011 .

[25]  E. P. Holland,et al.  Modelling with uncertainty: Introducing a probabilistic framework to predict animal population dynamics , 2009 .

[26]  W. Kaminski,et al.  PRESENTATION OF DRYING KINETICS IN A FLUIDIZED BED BY MEANS OF RADIAL BASIS FUNCTIONS , 1997 .

[27]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .

[28]  I. T. Toğrul,et al.  Modelling of thin layer drying kinetics of some fruits under open-air sun drying process , 2004 .

[29]  Ajith Abraham,et al.  An ensemble of neural networks for weather forecasting , 2004, Neural Computing & Applications.

[30]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[31]  F. Recknagel,et al.  Artificial neural network approach for modelling and prediction of algal blooms , 1997 .

[32]  D. M. Brown,et al.  Estimating forage yield and quality changes during field drying for hay. 2. Model of forage drying , 1995 .

[33]  Qin Zhang,et al.  AN OPTIMIZATION OF INTERMITTENT CORN DRYING IN A LABORATORY SCALE THIN LAYER DRYER , 1991 .