a life cycle modeling framework for greenhouse gas emissions of cement industry

Peer-review under responsibility of the Organizing Committee of ICAE2014 doi: 10.1016/j.egypro.2014.12.267 The 6th International Conference on Applied Energy – ICAE2014 A Life Cycle Mo

Energy Procedia 61 ( 2014 ) 2649 – 2653

ScienceDirect

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

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

© 2014 The Authors Published by Elsevier Ltd

Selection and/or peer-review under responsibility of ICAE

Keywords: Greenhouse Gas, Cement Production, Life Cycle Perspective, Dynamic Modeling

1 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

© 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

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 CO2

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

2 Methodology

2.1 System boundary

The objective function is to minimize the system emissions that are associated with various energy-supply options, technology alternatives along with energy flows from energy-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

Describe the state or the conditions of the system The state equation is formulated as L

L: Level t Level t  dt  dt * Inflow t  dt  Outflow t  dt (1)

Where: Level t is the value of the state variable at t;

t dt

Level  is the value of the state variable at (t-dt);

dt is the step length of time;

t dt

Inflow  is the input rate at dt;

t dt

Outflow  is the output rate at dt

(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 1 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

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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

3 Carbon emission prediction

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

4 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)

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[3] Hashimoto S, Fujita T, Geng Y, et al Realizing CO2 Emission Reduction through Industrial Symbiosis: A Cement

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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

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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

[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. .. 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... 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