Available online at www.sciencedirect.com ScienceDirect Energy Procedia 61 (2014) 2649 – 2653 The 6th International Conference on Applied Energy – ICAE2014 A Life Cycle Modeling Framework for Greenhouse Gas Emissions of Cement Industry Dan Song, Bin Chen* State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China Abstract How to deal with global climate change and alleviate the pressure of carbon mitigation has been an urgent problem for the current social economic development Cement is one of the three main building materials, which provides important support for other related industrials Concerning the emission characteristics during the production procedure, it has a vital significance to analyze the current emission situation and forecast its emission trends However, recent research mainly focused on static analysis of the direct emissions, which is not conductive to decision makers to grasp the emission potential in cement industry This study presented a simulation model to depict the future emission trends based on system dynamics, and aimed to put up with different optimization scenarios in view of current energy conservation and emission reduction targets The results may help decision makers identify the current emission situation and predict precisely the emission trend, thus realizing the emissions targets in view of the whole process of the cement industry in China © Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license ©2014 2014The The Authors Published by Elsevier Ltd (http://creativecommons.org/licenses/by-nc-nd/3.0/) Selection and/or peer-review under responsibility of ICAE Peer-review under responsibility of the Organizing Committee of ICAE2014 Keywords: Greenhouse Gas, Cement Production, Life Cycle Perspective, Dynamic Modeling Introduction As the foundation of national economics and urban development, cement industry provides an irreplaceable and upstream support for the successive development of other related industries For example, the cement industry production in China as largest producer increased from 597 million tons in 2000 to 2.18 billion tons in 2012 due to growing economic demand Moreover, this status will continue in the next few years Thus, it has a particularly significance to account and predict the greenhouse gas emissions associated with its high energy consumption in the production process [1-2] Different from other industries, cement production emits CO2 not only via direct fossil fuel use, but also through the production procedure as indirect emission Therefore, the whole process emission must be considered * Corresponding author Tel.: +86 10 58807368 E-mail address: chenb@bnu.edu.cn 1876-6102 © 2014 The Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the Organizing Committee of ICAE2014 doi:10.1016/j.egypro.2014.12.267 2650 Dan Song and Bin Chen / Energy Procedia 61 (2014) 2649 – 2653 when predicting the trend of greenhouse gas emissions of cement industry So far, many researchers have studied the energy consumption and CO2 emissions of cement industry Hashimoto et al (2010) calculated the total CO2 emission with different scenarios by adopting a life cycle analysis method for Kawasaki Eco-town, asserting that further improvement in GHG emissions reduction and natural resources conservation can be realized through effective material exchanges, not only between companies, but also with the surrounding area [3] Ali et al (2011) summarized the current pollutant mitigation technology in cement industry, concluding that the major techniques are: capture and storage CO2 emissions, reducing clinker/cement ratio by replacing clinker with different of additives and using alternative fuels instead of fossil fuels [4] Xu et al (2012) analyzed the energy consumption and CO emissions in China’s cement industry, and pointed out that the growth of cement output is the most important driving factor [5] Regarding the simulation of emission trends, however, there are very few life-cycle dynamic modelling researches System dynamics (SD) is an approach to investigate the behaviour of complex systems that is originally developed by Jay W Forrester to help managers improve their understanding of industrial processes Current SD studies have been conducted at various scales, especially in industrial sector [6-9] It can also give a time-step simulation for reflecting a clear alteration of the trend of GHG emissions A mathematical model aimed at improving calcining kiln’ production efficiency was built up through optimization of the calciner’s geometry [10] When simulating the future emission trends, some variables such as energy structure, production technology structure, technical progress factor, and optimization scenarios based on material substitution and fuel alternative, were also considered [11-12] This paper presented a framework for depicting the future emission trends based on dynamic modeling, proposing five optimization scenarios in view of current energy conservation situation and emission reduction targets The results may help decision makers identify the current emission situation and predict precisely the emission trend, thus realizing the emissions targets in view of the whole process of the cement industry in China Methodology 2.1 System boundary The objective function is to minimize the system emissions that are associated with various energysupply options, technology alternatives along with energy flows from supply side to demand and policy compensations for GHG emissions The model parameters are determined according to the statistical data of cement industry and the future development goals using the method of Stella The functional unit in simulating GHG emissions’ changing trend for cement industry from 2015 to 2025 is the whole process of cement industry production The manufacturing process is divided into the following parts: large-sized dry cement production process (LDCP), middle-sized dry cement production process (MDCP), small-size dry cement production process (SDCP), traditional shaft kiln (TSK), and JT kiln of semi-dry method (JTK) In addition, the model covers the interactions among demand module, production module, and CO2 emission module 2.2 Modeling structure 2.2.1 Variable and equations According to the modelling procedure, related variables were selected as state variables, rate variables, auxiliary variables, and constant The corresponding equations are listed as follows: (1) State variables and equations 2651 Dan Song and Bin Chen / Energy Procedia 61 (2014) 2649 – 2653 Describe the state or the conditions of the system The state equation is formulated as L Level t Level t dt dt * Inflow t dt Outflow t dt Where: Level t is the value of the state variable at t; Level t dt is the value of the state variable at (t-dt); dt is the step length of time; Inflow t dt is the input rate at dt; Outflow t dt is the output rate at dt L: (1) (2) Rate variables Rate variables represent the input and output variables of the state equation, formulated as R In addition, the equations of rate variables were determined by state variables (3) Auxiliary variables and Constant Auxiliary variable were introduced in order to simplify rate variable equation, which were described as A Constant refers to the parameters which were remain unchanged, expressed as C 2.2.2 Structure analysis The whole system and its sub-blocks were depicted in Figure to determine the overall and partial feedback mechanisms The loops and feedbacks were established to show the internal relationships The system embraced relevant social and economic processes, technology, and policy restriction that can affect cement demand *URZWKUDWH RI SRSXODWLRQ $QQXDO LQFUHDVHRI XUEDQ UHVLGHQWLDO DUHD $QQXDO LQFUHDVHRI UXUDO UHVLGHQWLDO DUHD &HPHQW FRQVXPSWLRQ RISHUXQLW DUHD &HPHQW FRQVXPSWLRQ RISHUXQLW DUHD 8UEDQ UHVLGHQFH 5XUDO UHVLGHQFH *'3 &RPPHUFLDO EXLOGLQJ ,QIUDVWUXFW XUH 'RPHVWLF GHPDQGIRU FHPHQW 2WKHU GHPDQG /'&3 $QQXDO LQFUHDVHRI FRPPHUFLDO EXLOGLQJDUHD &HPHQWGHPDQG RILPSRUWDQG H[SRUW &HPHQW SURGXFWLRQ 0'&3 6'&3 76 -7 Fig.1 Structure of the SD model for cement industry 2.2.3 Modeling formulation and testing In this stage, we established a prediction model using the method of Stella and simulating the trends of GHG emission accompanied with validation and verification to guarantee the preciseness Carbon emission prediction 2652 Dan Song and Bin Chen / Energy Procedia 61 (2014) 2649 – 2653 As GHG mitigation was related to social economic development, energy efficiency, demand, policy, environment capacity, and technological progress, we established a dynamical model to simulate the GHG emissions trend during the whole process, in which both direct and indirect CO2 emissions were considered Moreover, five scenarios, including demand reduction, technological progress, material substitution, fuel alternatives, and waste heat power generation, were incorporated to identify the driving forces for cement GHG emission reduction Concluding remarks This paper provided a preliminary framework for simulation the future emission trends based on dynamic modeling and set up a set of optimization scenarios in view of current energy conservation and emission reduction targets of cement production Compared with the proposed scenarios, an optimized way of realizing low carbon development of cement industry was expected to be figured out in the future Acknowledgements This work was supported by the Major Research plan of the National Natural Science Foundation of China (No 91325302), Fund for Creative Research Groups of the National Natural Science Foundation of China (No 51121003), National Natural Science Foundation of China (No 41271543), and Specialized Research Fund for the Doctoral Program of Higher Education of China (No 20130003110027) References [1] Rehan R, Nehdi M Carbon Dioxide Emissions and Climate Change: Policy Implications for the Cement Industry Environmental Science and Policy, 2005; 8(2): 105-114 [2] Josa A, Aguado A, Cardim A, et al Comparative analysis of the life cycle impact assessment of available cement inventories in the EU Cement and Concrete Research, 2007; 37(5): 781-788 [3] Hashimoto S, Fujita T, Geng Y, et al Realizing CO2 Emission Reduction through Industrial Symbiosis: A Cement Production case study for Kawasaki Resources, Conservation and Recycling, 2010; 54(10): 704-710 [4] Ali MB, Saidur R, Hossain MS A Review on Emission Analysis in Cement Industries Renewable and Sustainable Energy Reviews, 2011; 15(5): 2252-2261 [5] Xu JH, Fleiter T, Eichhammer W, et al Energy consumption and CO2 emissions in China’s cement industry: a perspective from LMDI decomposition analysis Energy Policy, 2012; 50: 821–832 [6] Wang QF (2009) System Dynamicals Shanghai, Shanghai University of Finance and Economics Press (In Chinese) [7] Li YP, Huang GH, Chen X Planning Regional Energy System in Association with Greenhouse Gas Mitigation under Uncertainty Applied Energy, 2008; 88(3), 599-611 [8] Chen B, Ju LP, Dai J System Dynamics of Greenhouse Gases Emission in Chongqing City China Population· Resources and Environment, 2012; 22(4): 72-79 (In Chinese) [9] Mikulčić H, Vujanović M, Duić N Reducing the CO2 emissions in Croatian cement industry Applied Energy, 2013; 101: 41-48 [10] Mikulčić H, Vujanović M, Fidaros DK, et al The application of CFD modeling to support the reduction of CO2 emissions in cement industry Energy, 2012; 45(1): 464-473 [11] Anand S, Vrat P, Dahiya RP Application of a System Dynamics Approach for Assessment and Mitigation of CO2 Emissions from the Cement Industry Journal of Environmental Management, 2006; 79: 383-398 [12] Ansari N, Seifi A A system dynamics model for analyzing energy consumption and CO2 emission in Iranian cement industry under various production and export scenarios Energy Policy, 2013; 58: 75-89 Dan Song and Bin Chen / Energy Procedia 61 (2014) 2649 – 2653 Biography Dan Song is a PhD Candidate in School of Environment, Beijing Normal University Her research interests focus on life cycle assessment, urban ecology, and carbon emission accounting 2653 ... Implications for the Cement Industry Environmental Science and Policy, 2005; 8(2): 105-114 [2] Josa A, Aguado A, Cardim A, et al Comparative analysis of the life cycle impact assessment of available... formulated as R In addition, the equations of rate variables were determined by state variables (3) Auxiliary variables and Constant Auxiliary variable were introduced in order to simplify rate... Variable and equations According to the modelling procedure, related variables were selected as state variables, rate variables, auxiliary variables, and constant The corresponding equations are