Neural prediction of hydrocarbon degradation profiles developed in a biopile

The biochemical and physical nature of the degradation process in biopile systems is very complex and difficult to describe analytically, thus, neural network modeling and simulation can be of great help. Predictive feedforward neural models (FFNMs) have been commonly used to capture the dynamic phenomena of biological systems by a learning process, but the large number of input/output variables and the vast connectivity of the neural network makes it very time consuming. This paper proposes the use of a recurrent neural network model (RNNM) to predict biodegradation profiles of hydrocarbons contained in an aged polluted soil. The proposed multi-input multi-output RNNM has eight inputs, five outputs, 13 neurons in the hidden layer, and global and local feedbacks. The weight update learning algorithm is a version of dynamic backpropagation. The approximation error for the last epoch of learning is below 1.25% and the total time of learning is about 101 epochs. The learning process is applied to the kinetics of residual hydrocarbons, pH, carbon dioxide, oxygen consumption and moisture obtained with different operational conditions of air flow, and temperature; the kinetics are analyzed at four heights of the columns. The low learning error approximation makes the RNNM interesting to facilitate the study of complex biological processes in a short time.