Interpretable controlled object model of furnace temperature for MSWI process based on a novle linear regression decision tree

The furnace temperature is one of the most critical controlled variables in the municipal solid waste incineration (MSWI) process. The primary challenge of intelligent optimal control is to construct a high-precision and interpretable controlled object model in terms of furnace temperature. To address this problem, this article proposes a novel modeling method, i.e., linear regression decision tree (LRDT), to construct furnace temperature with airflow and grate speed as the inputs. LRDT model consists of (T/2-1) intermediate nodes and T leaves. The intermediate nodes are specified by the mean square error for developing the tree-based model structure. These leaves provide the predicted output by operating the Tikhonov least square method, which can boost the prediction performance. Moreover, LRDT uses leaf prediction under a unique path as the final output, which improves the interpretation of the LRDT-based furnace temperature model. Finally, the proposed method is verified by using actual MSWI process data, and the interpretability of the model is analyzed in detail.