In order to determine the relationships among temperature, moisture content, and microbial activity during
biosolids composting, well-controlled composting experiments were conducted using 2-factor factorial design with six
temperatures (22.C, 29.C, 36.C, 43.C, 50.C, and 57.C) and five moisture contents (30%, 40%, 50%, 60%, and 70%). The
microbial activity was indicated by O2 uptake rate (mg gdry matter
-1 h-1) and cumulative O2 uptake (mg gdry matter
-1) over time.
An artificial neural network (ANN) model to predict microbial activity was developed using 8760 data patterns. After
experimentation with different ANN architectures (i.e., standard backpropagation, Jordan, and Ward) and model parameter
settings (i.e., data partitioning strategy, number of hidden nodes, learning rate, and momentum), the best prediction model
was a combined Ward network for both O2 uptake rate and cumulative O2 uptake. An additional composting experiment at
34.C with five moisture contents was conducted, and the corresponding 1460 data patterns were used for model evaluation.
The model evaluation with 34.C data showed good prediction results for both O2 uptake rate (r2 = 0.928, MAE = 0.08 mg
g-1 h -1) and cumulative O2 uptake (r2 = 0.986, MAE = 5.28 mg g -1).