A knowledge transfer online stochastic configuration network-based prediction model for furnace temperature in a municipal solid waste incineration process

To alleviate the concept drift of an offline prediction model for the furnace temperature in a municipal solid waste incineration (MSWI) process caused by changes in working conditions, a prediction method for the furnace temperature based on a knowledge transfer online stochastic configuration network is proposed in this work. The proposed method includes offline learning and online learning. First, a stochastic configuration network is utilized to construct the offline furnace temperature prediction model, and a knowledge transfer method is employed to update the model under new operating conditions in the offline learning stage. Then, the updated model is used as the initial state of online modeling. Second, the recursive solution of the model output weights is presented to adapt to the dynamic change in incineration conditions, and a direction forgetting mechanism is utilized to enhance the result of the prediction model for the furnace temperature under nonpersistence of excitation in the online stage. Finally, to further verify the proposed online modeling method, the real historical data of an MSWI plant are utilized to finish the comparative experiments. The experimental results with the other methods show that the proposed prediction method for the furnace temperature presents a smaller error. Hence, the proposed method can reduce the influence of working conditions on the accuracy of furnace temperature prediction models.