Abstract
In biomass wastage, carbon is one of the adsorbent materials. Biomass wastage contains complex materials, and pressure, various temperatures, and presence of various chemical components which are subjected to the adsorption of carbon are a tedious task, and it is used in the sustainable waste management system. While screening the biomass wastage management system, prediction of activated carbon’s quality and understanding of the mechanism of adsorption of [Formula: see text] are a complicated task. Many research works have been developed; the main issues are inaccurate and inefficient prediction of carbon available in the various feedstock of biomass wastage. To overcome these issues, this paper proposed gene expression programming (GEP) with [Formula: see text]-nearest neighbour (GEP-KNN). The key advantage of the proposed work shows excellent performance in the prediction of adsorbing carbon and accuracy. The accuracy of the GEP-KNN algorithm with different [Formula: see text] values produced the highest accuracy at [Formula: see text] and [Formula: see text] of 95.12% and 95.67%; the lowest accuracy is [Formula: see text] of 65.34%.
Highlights
In the ecosystem of the globe, one of the main sources is storage of carbon in the terrestrial ecosystem which creates terrestrial biomass [1]
(1) improving efficiency; we proposed gene expression programming (GEP) with K -nearest neighbour (KNN)
The paper has been organized as follows: Section 2describes the review of the literature, Section 3 introduces prediction of CO2 in the biomass wastage system using GEP with KNN, Section 4 discusses the experimented results, and Section 5 concludes the paper with future directions
Summary
In the ecosystem of the globe, one of the main sources is storage of carbon in the terrestrial ecosystem which creates terrestrial biomass [1]. Activated carbon is an adsorbent component of biomass wastage with large adsorption capacity, superior surface reactivity, and high porosity. To. Adsorption Science & Technology overcome these drawbacks, the proposed work GEP-KNN predicts the adsorption of CO2 in the biomass wastage system. Time consumption is low and minimizes the error rate This proposed work fills gaps of existing research work by impacting the interaction of biomass wastage system and implementing the properties of activated carbon using GEP-KNN. The main contribution of this work includes (1) improving efficiency; we proposed GEP with KNN This classifier provides high-quality prediction of CO2 in the biomass wastage system (2) implementing texture properties in biomass wastage and performing the evaluation in the metric measures of the correlation coefficient, RMSE, and bias. The paper has been organized as follows: Section 2describes the review of the literature, Section 3 introduces prediction of CO2 in the biomass wastage system using GEP with KNN, Section 4 discusses the experimented results, and Section 5 concludes the paper with future directions
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