Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints

Abstract

A data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal component analysis method is employed to reconstruct new variables as the input of the predictive model, reduce the over-fitting of data and improve modeling accuracy. Then, a k-nearest neighbors algorithm is used to classify the samples to distinguish the data by the different operating conditions. Based on the classified data, a least square support vector machine optimized by the differential evolution algorithm is established. Based on the boiler key parameter model, the proposed model attempts to maximize the combustion efficiency under the boiler load constraints, the nitrogen oxide (NOx) emissions constraints and the boundary constraints. The experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) The model predictive control algorithm can effectively control the boiler combustion efficiency, the average errors of simulation are less than 5%. The proposed model predictive control method can improve the quality of production, reduce energy consumption, and lay the foundation for enterprises to achieve high efficiency and low emission.

Modeling NOx Emissions with an Intelligent Combinatorial Algorithm

“…For evaluating the performance of the proposed NOx emission prediction model, we use five performance indices, namely, the mean absolute error (MAE), the root-mean-square error (RMSE), the mean absolute percentage error (MAPE), the coefficient of determination (R 2 ), and the classification accuracy (AC). ose indices are, respectively, defined as follows [16,21]:…”

“…A new hybrid jump particle swarm optimization (PSO) algorithm has been used to tune control parameters of the boiler-turbine unit control [15]. Differential evolution algorithm (DE) has been proposed to optimize LSSVM parameters for different problems [16]. Ant colony optimization (ACO) algorithm has been proposed to select the parameters for SVR to model NOx emission [11].…”

Simulation of electric heating prediction model by internet of things technology and room thermal performance analysis

“…Also, when the electric heater or the execution channel fails, the temperature control requirements of the room cannot be completed. If the communication channel between the management layer, control layer, and field control layer fails, the entire DCS control system will also be paralyzed [9].…”