Predictor and optimizer system on selective catalytic reduction of NO in activated carbons based on experiment and computational intelligence technique. Issue 5 (17th January 2020)

• Record Type: Journal Article
• Authors: Yang, Zhen; Song, Kangning; Gu, Xingsheng; Wang, Zhi; Liang, Xiaoyi
• Is Part Of: Engineering computations. Volume 37:Issue 5(2020)
• Page Start: 1737
• Page End: 1756
• Publication Date: 2020-01-17
• Subjects: Predictor and optimizer -- Selective catalytic reduction of nitric oxide -- Spherical activated carbons -- Support vector regression; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-05-2019-0235
• Languages: English
• ISSNs: 0264-4401

Abstract

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.