An integrated approach for operational knowledge acquisition of refuse incinerators

Refuse incinerator operation poses a tremendous challenge for efficient supervision due to the highly complexity of physical and chemical mechanisms inside the systems. It is difficult to comprehend operational knowledge without thorough study and long-term on site experiments. Fortunately, many sensors are installed in incineration plants and tremendous amounts of raw data about daily practices and system states are recorded to assist operations. Without proper analysis, however, these data are not beneficial to operators. An integrated approach is adopted in the current study using feature selection and data mining techniques. Feature selection was initially applied to cope with the heavy computation burden due to the huge data set. Data dimension can be reduced by discarding redundant information and leaving only relevant features for further analysis. Data mining analysis is then utilized to build two decision tree models based on steam production and NOx emission target attributes. Implicit incinerator system relations, represented by production rules and predicting accuracies, can be acquired from the decision tree models. Such rule-based knowledge is expected to facilitate on-site operations and enhance refuse incinerator efficiency.