Prediction of Influential Operational Compost Parameters for Monitoring Composting Process

Abstract

In this study, two influential parameters were selected, pH and composting temperature, to monitor the composting process, and thus a pH prediction model and composting-temperature prediction model were constructed. We used artificial neural network-based multilayer perceptron (ANN-based MLP) to develop two prediction models. To compare the efficiency achieved using ANNs, traditional multiple-linear regression (MLR) was selected as a benchmark. Subsequently, we presented a composting flowchart to simulate real-time composting processes. Test data were collected from 13 experiments that were conducted in an open-air facility. We measured eight attributes: days being composted, pH, composting temperature, moisture content, food waste, mature compost, sawdust, and soil. Comparison of performance of 1- with 3-day-ahead prediction models revealed that the 1-day-ahead forecasts yielded superior values in terms of relative mean absolute error, relative root mean squared error, coefficient of correlation, and coefficient of efficiency than did the 2- and 3-day-ahead forecasts in both the ANN and MLR models. In predicting the maturity of food wastes, absolute time errors of degree of degradation were 0.67 and 1.22 days, respectively, when ANN and MLR models were used in 1-day-ahead prediction, which demonstrates that prediction was more accurate using ANN than using MLR. Thus, ANN-based prediction models can be regarded as being reliable, and proposed composting real-time forecast models can be effectively used in monitoring composting processes.