Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization

Sun, Wei; Wang, Caifei; Zhang, Chongchong

doi:10.1016/j.jclepro.2017.06.016

Cited by 117 publications

(51 citation statements)

References 25 publications

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“…The cemented paste backfill (CPB) technology is a promising method for the safe disposal of mine tailings. Moreover, benefits like reclaiming water, stabilising the rock mass and thus increasing mining safety, and increasing ore recovery rate promote the broad application of CPB worldwide [2][3][4][5][6][7][8][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Understanding Cement Hydration of Cemented Paste Backfill: DFT Study of Water Adsorption on Tricalcium Silicate (111) Surface

Liü

et al. 2019

Minerals

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Understanding cement hydration is of crucial importance for the application of cementitious materials, including cemented paste backfill. In this work, the adsorption of a single water molecule on an M3-C 3 S (111) surface is investigated using density functional theory (DFT) calculations. The adsorption energies for 14 starting geometries are calculated and the electronic properties of the reaction are analysed. Two adsorption mechanisms, molecular adsorption and dissociative adsorption, are observed and six adsorption configurations are found. The results indicate that spontaneous dissociative adsorption is energetically favored over molecular adsorption. Electrons are transferred from the surface to the water molecule during adsorption. The density of states (DOS) reveals the bonding mechanisms between water and the surface. This study provides an insight into the adsorption mechanism at an atomic level, and can significantly promote the understanding of cement hydration within such systems.

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Section: Introductionmentioning

confidence: 99%

Understanding Cement Hydration of Cemented Paste Backfill: DFT Study of Water Adsorption on Tricalcium Silicate (111) Surface

Liü

et al. 2019

Minerals

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“…A LEAP model was constructed to predict China's carbon emissions from the electric industry in two different scenarios [9]. Sun et al [10] introduced a hybrid model combined with extreme learning machine and particle swarm optimization method for carbon emissions prediction in Hebei Province. Now many researchers are working on whether the carbon emissions targets can be realized and to explore whether China's reduction targets could be achieved by 2020 under different economic scenarios, Zhu et al [11] deduced the trend of carbon intensity based on economic and energy consumption prediction.…”

Section: The Forecasting Of Carbon Emissionsmentioning

confidence: 99%

An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

Xie

Zhang

et al. 2018

Energies

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Abstract:In this paper, an improved grey model and scenario analysis, GA-GM(1,N) is proposed to forecast the carbon intensity in the Pearl River Delta (PRD) region, one of the most developed regions in China. Moreover, to show the advantage and feasibility of the proposed model, the forecasting results of the GA-GM(1,N) model are compared with that of a single-variable grey model (GM (1,1)) and a multivariable form (GM(1,N)). Data from one sample period (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) are used to develop the models, and data from another sample period (2013)(2014)(2015) are used to test them. The mean absolute percentage error (MAPE) is applied to measure the accuracy of prediction. The results show that, of the three models, GA-GM(1,N) produces the best carbon intensity forecasts, with MAPEs of 0.4-1.4% and 0.04-0.4% in the development and testing periods respectively. This indicates that the optimization of the genetic algorithm is effective. The realization of carbon reduction targets in different cities is also explored by combining grey models with scenario analysis. Only Guangzhou could achieve its reduction target under all scenarios, and it can serve as a reference for other cities. Policy recommendations are provided based on these results.

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“…Comprehensive investigations have been performed in order to obviate the issues related to CO 2 emissions. 19 Zhang et al 20 used a life cycle model for CO 2 emissions contributed to a residential building in China. As suggested, the utilization of thicker insulating layers does not inevitably lead to less CO 2 emission based on life cycle assessment.…”

Section: Introductionmentioning

confidence: 99%

Technological assessment and modeling of energy‐related CO₂ emissions for the G8 countries by using hybrid IWO algorithm based on SVM

Ghazvini

Madvar

Ahmadi

et al. 2020

Energy Science & Engineering

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Recently, energy‐related CO2 emissions are considered as one of the most crucial issues and are promptly augmented due to further urbanization. In this paper, in order to model and calculate yearly CO2 emission, an artificial neural network is used. For the first time, the IWO‐SVM method has been applied in modeling energy‐related CO2 emissions. In this regard, consumption of different energy sources such as renewable energy, natural gas, coal, and oil, and GDP of the G8 countries in various years (from 1990 to 2016) are regarded as input in the present study. For the aim of evaluating the exact ability of the SVM and SVM‐IWO models, the performance of these models in three different modes is carried out on the basis of the number of data in the test and train sections. For this purpose, implementations are split into three categories (a = 80% of the data for the train section and 20% for the test section; b = 70% of the data for the train section and 30% for the test section; and c = 60% of the data for the train section and 40% for the test section). Furthermore, five scenarios were selected on the basis of the number of input parameters and input parameters for achieving the best model. As indicated in the results in all scenarios, the correlation of the model with the hybrid invasive weed algorithm based on SVM is more favorable than that in the support vector machine model, due to better training of the SVM‐IWO model than the SVM model. Moreover, the technological orientations of the G8 countries to mitigate CO2 emissions are determined through patent analysis. While the patents have essential information, investigating the published patent by a country could be helpful for determination of technological orientations. Hence, all published patents by these countries are extracted and deeply analyzed. In the next step, to find out main technological approaches, all patents and their intents have been studied. Eventually, the technological life cycles and trends of each main technology are drawn.

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Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization

Cited by 117 publications

References 25 publications

Understanding Cement Hydration of Cemented Paste Backfill: DFT Study of Water Adsorption on Tricalcium Silicate (111) Surface

Understanding Cement Hydration of Cemented Paste Backfill: DFT Study of Water Adsorption on Tricalcium Silicate (111) Surface

An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

Technological assessment and modeling of energy‐related CO₂ emissions for the G8 countries by using hybrid IWO algorithm based on SVM

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Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization

Cited by 117 publications

References 25 publications

Understanding Cement Hydration of Cemented Paste Backfill: DFT Study of Water Adsorption on Tricalcium Silicate (111) Surface

Understanding Cement Hydration of Cemented Paste Backfill: DFT Study of Water Adsorption on Tricalcium Silicate (111) Surface

An Improved Grey Model and Scenario Analysis for Carbon Intensity Forecasting in the Pearl River Delta Region of China

Technological assessment and modeling of energy‐related CO2 emissions for the G8 countries by using hybrid IWO algorithm based on SVM

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Technological assessment and modeling of energy‐related CO₂ emissions for the G8 countries by using hybrid IWO algorithm based on SVM