Predictor and optimizer system on selective catalytic reduction of NO in activated carbons based on experiment and computational intelligence technique

Abstract

Purpose Nitrogen oxides (NOx) have been considered as primarily responsible for many serious environmental problems. Removing NO is the key task to remove NOx hazards. To clarify, NO removal process for pitch-based spherical-activated carbons (PSACs), an online prediction and optimization technique in real-time based on support vector machine algorithm in regression (support vector regression [SVR]) is discussed. The purpose of this paper is to develop a predictor and optimizer system on selective catalytic reduction of NO (SCRN) using experimental data and data-driven SVR intelligence methods. Design/methodology/approach Predictor and optimizer using developed SVR have been proposed. To modify the training efficiency of SVR, the authors especially customize batch normalization and k-fold cross-validation techniques according to the unique characteristics of PSACs model. Findings The results present that SVR provides a property regression model since it can linkage linear and non-linear process and property relationships in few experimental data sets. Also, the integrated normalization and k-fold cross-validation show a satisfying improvement and results for SVR optimization. The predicted results of predictor and optimizer in single and double factor systems are in excellent agreement with the experimental data. Originality/value SCRN-PO for predicting and optimization SCRN problems is developed by data-driven methods. The outperformed SCRN-PO system is used to predict multiple-factors property parameters and obtain optimum technological parameters in real-time. Also, experiment duration is greatly shortened.