scholarly journals Hybrid Selection Method of Feature Variables and Prediction Modeling for Municipal Solid Waste Incinerator Temperature

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7878
Author(s):  
Jingcheng Guo ◽  
Aijun Yan
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Historical Data ◽  
Fitness Function ◽  
Selection Method ◽  
Waste Incinerator ◽  
Prediction Modeling ◽  
Hybrid Selection

It is difficult to establish an accurate mechanism model for prediction incinerator temperatures due to the comprehensive complexity of the municipal solid waste (MSW) incineration process. In this paper, feature variables of incineration temperature are selected by combining with mutual information (MI), genetic algorithms (GAs) and stochastic configuration networks (SCNs), and the SCN-based incinerator temperature model is obtained simultaneously. Firstly, filter feature selection is realized by calculating the MI value between each feature variable and the incinerator temperature from historical data. Secondly, the fitness function of GAs is defined by the root mean square error of the incinerator temperature obtained by training SCNs, and features obtained by MI methods are searched iteratively to complete the wrapper feature selection, where the SCN-based incinerator temperature prediction model is obtained. Finally, the proposed model is verified by MSW incinerator temperature historical data. The results show that the SCN-based prediction model using the hybrid selection method can better predict the change trend of incinerator temperature, which proves that the SCNs has great development potential in the field of prediction modeling.

scholarly journals Ternary Mixes of Self-Compacting Concrete with Fly Ash and Municipal Solid Waste Incinerator Bottom Ash

2020 ◽  
Vol 11 (1) ◽  
pp. 107
Author(s):  
B. Simões ◽  
P. R. da Silva ◽  
R. V. Silva ◽  
Y. Avila ◽  
J. A. Forero
Keyword(s):  
Fly Ash ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Bottom Ash ◽  
Chloride Diffusion ◽  
Waste Incinerator ◽  
Chloride Diffusion Coefficient ◽  
Municipal Solid Waste Incinerator ◽  
Self Compacting Concrete ◽  
Solid Waste Incinerator

This study aims to evaluate the potential of incorporating fly ash (FA) and municipal solid waste incinerator bottom ash (MIBA) as a partial substitute of cement in the production of self-compacting concrete mixes through an experimental campaign in which four replacement levels (i.e., 10% FA + 20% MIBA, 20% FA + 10% MIBA, 20% FA + 40% MIBA and 40% FA + 20% MIBA, apart from the reference concrete) were considered. Compressive and tensile strengths, Young’s modulus, ultra-sonic pulse velocity, shrinkage, water absorption by immersion, chloride diffusion coefficient and electrical resistivity were evaluated for all concrete mixes. The results showed a considerable decline in both mechanical and durability-related performances of self-compacting concrete with 60% of substitution by MIBA mainly due to the aluminium corrosion chemical reaction. However, workability properties were not significantly affected, exhibiting values similar to those of the control mix.

scholarly journals Seasonal Variation Analysis Method of GHG at Municipal Solid Waste Incinerator

2020 ◽  
Vol 12 (18) ◽  
pp. 7425
Author(s):  
Seongmin Kang ◽  
Joonyoung Roh ◽  
Eui-chan Jeon
Keyword(s):  
Seasonal Variation ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Ghg Emissions ◽  
Co2 Concentration ◽  
Waste Incineration ◽  
Seasonal Effect ◽  
Waste Incinerator ◽  
Waste Composition

The greenhouse gas emissions of the waste incineration sector account for approximately 43% of the total GHG emissions and represent the majority of the CO2 emissions from waste in Korea. Improving the reliability of the GHG inventory of the waste incineration sector is an important aspect for the examination of global GHG emission management according to the Paris Agreement. In this study, we introduced a statistical approach to analyze seasonal changes through analysis of waste composition and CO2 concentration in Municipal Solid Waste incinerators and applied the methodology to one case study facility. The analysis results in the case study showed that there was no seasonal variation in waste composition and CO2 concentrations, except for wood. Wood is classified as biomass, and the GHG emissions caused by biomass incineration are reported separately, indicating that the effect of an MSW incinerator on GHG emissions is not significant. Therefore, the seasonal effect of CO2 concentration or waste composition may not be an impact when calculating GHG emissions from case study facilities’ MSW incinerators. This study proposed an approach for analyzing factors that affect the GHG inventory reliability by analyzing seasonal characteristics and variation through the statistical analysis, which are used for the calculation of the GHG emissions of an MSW incinerator.

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