Estimating arsenic concentration in compost production using Ann Model

Abstract

Arsenic concentration is one of the most important concerns in compost production. Measuring arsenic concentration is time consuming and expensive process for compost companies. This study has been developed to predict arsenic concentration in compost, based on input materials and outside temperature, using an Artificial Neural Network model (ANN) in Christchurch, New Zealand. The final ANN model developed was based on monthly input of kerbside collections, food wastes, river wastes, and average air temperature for the last eight years. Comparing actual and predicted energy usage showed that the model could be fitted to arsenic concentration and accounted for around 94% and 97% of the variance for training and validation data, respectively.