Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production

*Oluwafemi M. Fadayini, Adekunle A. Obisanya, Gloria O. Ajiboye, Clement Madu,Tajudeen O. Ipaye, Taiwo O. Rabiu, Shola J. Ajayi and Joseph T. Akintola*

## **Abstract**

In this study the process flow diagram for the cement production was simulated using Aspen HYSYS 8.8 software to achieve high energy optimization and optimum cement flow rate by varying the flow rate of calcium oxide and silica in the clinker feed. Central composite Design (C.C.D) of Response Surface Methodology was used to design the ten experiments for the simulation using Design Expert 10.0.3. Energy efficiency optimization is also carried out using Aspen Energy Analyser. The optimum cement flow rate is found from the contour plot and 3D surface plot to be 47.239 tonnes/day at CaO flow rate of 152.346 tonnes/day and the SiO2 flow rate of 56.8241 tonnes/day. The R<sup>2</sup> value of 0.9356 determined from the statistical analysis shows a good significance of the model. The overall utilities in terms of energy are found to be optimised by 81.4% from 6.511 x 10<sup>7</sup> kcal/h actual value of 1.211 x 10<sup>7</sup> kcal/h with 297.4 tonnes/day the carbon emission savings.

**Keywords:** central composite design, optimisation, response surface methodology, cement production, design expert

## **1. Introduction**

Cement is a fine greyish or whitish inorganic, non-metallic powder commonly used as a binding agent in construction materials. It consists of pyroprocessed chemically combined hydraulic cement materials such as calcareous, siliceous, argillaceous and ferriferous [1]. Cement forms paste when mixed with water, which later becomes hard due to cement, mineral hydrate formation when solidified [2]. The various types of cement and their applications such as Portland, Siliceous fly ash, calcareous, slag and Fume silica cement differ by the amount of SiO2, Al2O3, Fe2O3, CaO, MgO, SO3 and other materials such as Na2O and K2O composition [3]. Economic growth and urbanisation have made cement one of the most consumed commodity in world with global annual production increase from 3.3 Gt in 2010 to current 4.1 Gt which is still expected to grow moderately in the next decade due to expected infrastructure development in India and other developing Asian and African countries [4, 5]. Cement production consists of three sections: fuel and raw material processing, production of clinker via pyroprocessing and grinding and

blending of cement clinker nodules with additive materials such as gypsum and anhydrite for different types of cement types [6]. Natural occurring limestone is ground and mixed in required proportion with silicon and aluminium source such as clay and sand and iron-containing compounds to form a homogenous raw mix called raw meal. The raw meal is then pyroproccessed at a high temperature of about 1450 °C in rotary kiln system where it is dried, preheated, calcined and sintered into cement clinker. The pyroprocessing can be dry, wet, semi-dry or semiwet and their selection depends on the moisture content of the raw meal, rotary kiln configuration and energy cost. The wet process is cheaper with a high-quality product but very high energy intensity because of the high moisture content of about 36% in raw meal. The dry process is usually more compact with low operational cost and energy consumption compared with the wet process but with lesser product homogeneity [2]. The clinker produced is further grinded with about 5% gypsum which prevents pre-set and controls the hydration rate of the cement. Other types of cement are produced by blending with hydraulic, pozzolanic or inert materials [7]. Cement production processes are energy-intensive and generate huge greenhouse emissions with the clinker energy intensity of about 3.4 GJ/t in 2018 [4] generating about 4% of the global CO2 emission [8]. Strategies identified to reduce the emissions in cement production include improving heat recovery and energy efficiency [9–11], switching to low carbon source of energy [12], feedstock and material substitute [13–15], reducing the clinker-to-cement ratio [16] and advancing technology innovations such as carbon capture and storage [17, 18]. Cement and concrete technology modelling and simulation have also been used to improve energy efficiency and usage [19–22].

In recent years, Response Surface Methodology has been applied to optimise several chemical processes such as extraction [29], adsorption [23], pharmaceutical wastewater treatment [28], leaching [35]. Studies on cement production optimisation have been carried out on clinker simulation using AspenTech [36], cement raw materials blending using a general nonlinear time-varying model [37], cement grinding using population balance model [6], clinker chemistry and kiln energy efficiency using metaheuristic optimization techniques [38], numerical and computational fluid dynamics study of cement calciner [16]. RSM has been efficient and accurate in studies on cement and concrete technology [39–43]. This study focused on the simulation of an integrated wet cement process flow sheet using Aspen HYSYS and optimisation of the cement production rate at minimum raw material

*Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production*

Aspen HYSYS 8.8 was used for the steady-state simulation of the integrated process flow sheet for the cement production [44]. Within the simulation environment, topological optimization (proper arrangement of equipment) was done to enable very high energy savings or optimization. A pure component such as water, CO2 and air are added as conventional components, while non-conventional components are added as hypothetical components to the HYSYS environment based on their physical properties (molecular weight & density). **Figure 1** shows the block diagram of the production of cement while the HYSYS process flow diagram for the cement production simulation is shown in Figure A1 in the appendix. Limestone is decomposed in the first reactor to give off CO2 as gas, while the produced CaO is the feed to the Section A reactor to react with silicate to form Calcium disilicate. The produced Calcium disilicate reacts further with the unreacted CaO in reactor B to produce Calcium trisilicate. Calcium oxide (CaO) further reacts with Aluminium oxide to produce tricalcium aluminate, another constituent of cement, while the final product component is produced in section C, where CaO reacts with Aluminate and Ferric oxide to produce tetracalciumaluminoferrite. These separate

feed using CCD of response surface methodology.

*DOI: http://dx.doi.org/10.5772/intechopen.95269*

**2. Methodology**

**Figure 1.**

**71**

*Process flow diagram for the production of cement.*

**2.1 Cement production simulation**

Optimization is a mathematical technique used to find the best solution to objective function (s) by maximising the desired variable and minimising the undesired variables under some set of constraints with the sole aim of improving performance and cost [23]. The optimisation technique in cement and concrete studies can be broadly classified as a meta-heuristic approach and statistical experimental design methods [24]. The meta-heuristic approach is an iterative method that intelligently exploits search space at learning strategies. It includes Genetic Algorithm (GAs), Particle swarm optimization (PSO), Harmony Search (HS), Ant Colony Optimization (ACO), Charged System Search (CSS), Big Bang-Big Crunch (BB-BC), Artificial Bee Colony algorithm (ABC), spherical interpolation of the objective function, Colliding bodies optimization (CBO), Vibrating Particles System (VPS), simulated annealing, krill herd (KH), Whale Optimization Algorithm (WOA), hybrid Harmony Search, force method and genetic algorithm, mine and improved mine blast algorithms [24–26] which are modelled from natural and social behaviours as well as physics laws.

Statistical experimental design methods are widely used to obtain desired optimise solution for a set of constraints [27]. Response Surface Methodology (RSM) is a statistical optimisation technique used to model and analyse a process to determine the effect of independent multivariable on the process response and to evaluate the relations between these variables [28]. RSM is based on understanding the topography of the response surface to determine the most appropriate response region [29]. RSM experimental design can be categorised into Box–Behnken Design (BBD), Central Composite Design (CCD), Dohlert design, Mixture response and three-level factorial design [30–32]. The BBD is created from 3 level factorial design [32] and gives quadratic response model with three minimum number of factors requiring three levels of factors (upper, centre, lower) for each factor and specific positioning of design points [33, 34]. The CCD is developed from the 2 factorial design and gives the quadratic response model with five levels for each factor. Hence it is more robust and insensitive to missing data or experimental runs [34].

*Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production DOI: http://dx.doi.org/10.5772/intechopen.95269*

In recent years, Response Surface Methodology has been applied to optimise several chemical processes such as extraction [29], adsorption [23], pharmaceutical wastewater treatment [28], leaching [35]. Studies on cement production optimisation have been carried out on clinker simulation using AspenTech [36], cement raw materials blending using a general nonlinear time-varying model [37], cement grinding using population balance model [6], clinker chemistry and kiln energy efficiency using metaheuristic optimization techniques [38], numerical and computational fluid dynamics study of cement calciner [16]. RSM has been efficient and accurate in studies on cement and concrete technology [39–43]. This study focused on the simulation of an integrated wet cement process flow sheet using Aspen HYSYS and optimisation of the cement production rate at minimum raw material feed using CCD of response surface methodology.

## **2. Methodology**

blending of cement clinker nodules with additive materials such as gypsum and anhydrite for different types of cement types [6]. Natural occurring limestone is ground and mixed in required proportion with silicon and aluminium source such as clay and sand and iron-containing compounds to form a homogenous raw mix called raw meal. The raw meal is then pyroproccessed at a high temperature of about 1450 °C in rotary kiln system where it is dried, preheated, calcined and sintered into cement clinker. The pyroprocessing can be dry, wet, semi-dry or semiwet and their selection depends on the moisture content of the raw meal, rotary kiln configuration and energy cost. The wet process is cheaper with a high-quality product but very high energy intensity because of the high moisture content of about 36% in raw meal. The dry process is usually more compact with low operational cost and energy consumption compared with the wet process but with lesser product homogeneity [2]. The clinker produced is further grinded with about 5% gypsum which prevents pre-set and controls the hydration rate of the cement. Other types of cement are produced by blending with hydraulic, pozzolanic or inert materials [7]. Cement production processes are energy-intensive and generate huge greenhouse emissions with the clinker energy intensity of about 3.4 GJ/t in 2018 [4] generating about 4% of the global CO2 emission [8]. Strategies identified to reduce the emissions in cement production include improving heat recovery and energy efficiency [9–11], switching to low carbon source of energy [12], feedstock and material substitute [13–15], reducing the clinker-to-cement ratio [16] and advancing technology innovations such as carbon capture and storage [17, 18]. Cement and concrete technology modelling and simulation have also been used to improve

*Cement Industry - Optimization, Characterization and Sustainable Application*

Optimization is a mathematical technique used to find the best solution to objective function (s) by maximising the desired variable and minimising the undesired variables under some set of constraints with the sole aim of improving performance and cost [23]. The optimisation technique in cement and concrete studies can be broadly classified as a meta-heuristic approach and statistical experimental design methods [24]. The meta-heuristic approach is an iterative method that intelligently exploits search space at learning strategies. It includes Genetic Algorithm (GAs), Particle swarm optimization (PSO), Harmony Search (HS), Ant Colony Optimization (ACO), Charged System Search (CSS), Big Bang-Big Crunch (BB-BC), Artificial Bee Colony algorithm (ABC), spherical interpolation of the objective function, Colliding bodies optimization (CBO), Vibrating Particles System (VPS), simulated annealing, krill herd (KH), Whale Optimization Algorithm (WOA), hybrid Harmony Search, force method and genetic algorithm, mine and improved mine blast algorithms [24–26] which are modelled from natural and

Statistical experimental design methods are widely used to obtain desired optimise solution for a set of constraints [27]. Response Surface Methodology (RSM) is a statistical optimisation technique used to model and analyse a process to determine the effect of independent multivariable on the process response and to evaluate the relations between these variables [28]. RSM is based on understanding the topography of the response surface to determine the most appropriate response region [29]. RSM experimental design can be categorised into Box–Behnken Design (BBD), Central Composite Design (CCD), Dohlert design, Mixture response and three-level factorial design [30–32]. The BBD is created from 3 level factorial design [32] and gives quadratic response model with three minimum number of factors requiring three levels of factors (upper, centre, lower) for each factor and specific positioning of design points [33, 34]. The CCD is developed from the 2 factorial design and gives the quadratic response model with five levels for each factor. Hence it is more robust and insensitive to missing data or experimental runs [34].

energy efficiency and usage [19–22].

social behaviours as well as physics laws.

**70**

### **2.1 Cement production simulation**

Aspen HYSYS 8.8 was used for the steady-state simulation of the integrated process flow sheet for the cement production [44]. Within the simulation environment, topological optimization (proper arrangement of equipment) was done to enable very high energy savings or optimization. A pure component such as water, CO2 and air are added as conventional components, while non-conventional components are added as hypothetical components to the HYSYS environment based on their physical properties (molecular weight & density). **Figure 1** shows the block diagram of the production of cement while the HYSYS process flow diagram for the cement production simulation is shown in Figure A1 in the appendix. Limestone is decomposed in the first reactor to give off CO2 as gas, while the produced CaO is the feed to the Section A reactor to react with silicate to form Calcium disilicate. The produced Calcium disilicate reacts further with the unreacted CaO in reactor B to produce Calcium trisilicate. Calcium oxide (CaO) further reacts with Aluminium oxide to produce tricalcium aluminate, another constituent of cement, while the final product component is produced in section C, where CaO reacts with Aluminate and Ferric oxide to produce tetracalciumaluminoferrite. These separate

**Figure 1.** *Process flow diagram for the production of cement.*

components produced at a different section of the simulated Kiln are mixed to achieve a matrix compound of the cement product, having over 70% of CaO.

## **2.2 Aspen Hysys simulation**

Aspen Hysys was used for the steady-state simulation of the integrated process flow sheet for the cement production. Within the simulation environment, topological optimization (proper arrangement of equipment) was done to enable very high energy savings or optimization. A pure component such as water, CO2 and air are added as conventional components, while non-conventional components are added as hypothetical components to the HYSYS environment based on their physical properties (molecular weight and density). Based on the process description, the different reactions taking place in each simulated reactor, as presented in the flowchart are:

$$\text{CaCO}\_3 \rightarrow \text{CaO} + \text{CO}\_2(\text{Limestone decomposition}) \tag{1}$$

$$\text{C}\text{CaO} + \text{SiO}\_2 \to \text{Ca}\_2\text{SiO}\_4 \text{ (Section } A\text{)}\tag{2}$$

$$\text{CaO} + \text{Ca}\_2\text{SiO}\_4 \rightarrow \text{Ca}\_3\text{SiO}\_5(\text{Section B}) \tag{3}$$

$$\text{CaO} + \text{Al}\_2\text{O}\_3 \rightarrow \text{Ca}\_3\text{Al}\_2\text{O}\_6 \text{ (Section C)}\tag{4}$$

$$4CaO + Al\_2O\_3 + Fe\_2O\_3 \to Ca\_4Al\_2Fe\_2O\_{10} \text{(Section D)}\tag{5}$$

The various products in the various sections of the process reactors are; Tricalcium silicate (Ca2SiO4) which is responsible for early strength and the initial set of the cement; Dicalcium silicate (Ca3SiO5) which increases the strength as it age; Tricalcium aluminate (Ca3Al2O6) which contributes to the concrete strength development in the first few days but least desirable due to its reactiveness with sulphate containing soils and water; Tetracaliumaluminoferrite (Ca4Al2Fe2O10) which reduces clinkering temperature. The equipment design parameters employed in this work are provided in **Table 1**.

The flow rate of the major raw materials for the production of cement in the clinkering reactor as depicted by Eqns. (6–12) are carefully chosen based on the standard provided by Winter N. B. [45]. The Chemical parameters based on the oxide composition are very useful in describing clinker characteristics. The following parameters are widely used.

a. *Lime Saturation Factor (LSF):* is the measure of the ratio of alite to belite in the clinker. It is estimated by the ratio of CaO to the sum of other three main oxides SiO2, Fe2O3 and Al2O3. The equation is given by:

$$LSF = \frac{\text{CaO}}{2.8 \text{SiO}\_2 + 1.2Al\_2O\_3 + 0.65 \text{FeO}\_2} \tag{6}$$

Based on the experimental design for the simulated cement production process. The flow rate of Al2O3 and Fe2O3 are 15 tonnes/day and 10 tonnes/day respectively. The low level and high levels of SiO2 are found to be 50 tonnes/day and 60 tonnes/ day respectively. Hence, the SR values are the high and low value of the SiO2 flow

A high silicate ratio means that more calcium silicates are present in the clinker and less aluminate and ferrite. SR is typical, between 2.0 and 3.0. The SR values of

c. *Aluminate Ratio (AR):* This is the ratio of aluminate and ferrite phases in the clinker. AR value ranges between 1–4 in Portland clinkers. The flow rate of Al2O3 and Fe2O3 used in the process simulation are 15 tonnes/day and 10 tonnes/ day respectively. The equation governing the AR of the oxide is given by

> *AR* <sup>¼</sup> *Al*2*O*<sup>3</sup> *Fe*2*O*<sup>3</sup>

<sup>15</sup> <sup>þ</sup> <sup>10</sup> <sup>¼</sup> <sup>2</sup>*:*<sup>0</sup> (9)

<sup>15</sup> <sup>þ</sup> <sup>10</sup> <sup>¼</sup> <sup>2</sup>*:*<sup>4</sup> (10)

(11)

*SR* <sup>¼</sup> <sup>50</sup>

*SR* <sup>¼</sup> <sup>60</sup>

2.0 and 2.4 fall within an acceptable range of 2.0 and 3.0.

**Simple separator Delta P Stream fractions**

*DOI: http://dx.doi.org/10.5772/intechopen.95269*

(bar)

Top Temp. (°C)

Reactors Delta P Vessel volume

Cooler 0.000 655 2.691 x0 10<sup>7</sup>

Exchanger Delta P

Component Splitter

CaCO3 decomp reactor

*Equipment design parameter.*

**Table 1.**

**73**

0.000 Solids in

vapour

*Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production*

Liquid in bottoms

Bottom Temp. (°C)

> (m<sup>3</sup> )

Solids in liquid 0.0100

Delta T (°C) Duty (kcal/h)

Splitter 1 30 30 1 1 2.054 x 105 Splitter 2 1252 1252 1 1 1.825 x 104 Splitter 3 1252 1252 1 1 1.923 x 104

Section A reactor 0.0000 50.00 50.00 25.00 7.6209 x 10<sup>6</sup> Section B reactor 0.0000 50.00 50.00 25.00 2.6131 x 10<sup>6</sup> Section C reactor 0.0000 50.00 50.00 25.00 4.5634 x 10<sup>5</sup> Section D reactor 0.0000 50.00 50.00 25.00 7.4721 x 105

0.0100

0.0100

Top Pressure (bar)

Liquid level (%)

0.0000 0.0000 50.00 25.00 2.6764 x 10<sup>7</sup>

Bottom Pressure (bar)

Liquid volume (m3 )

Duty

Duty (kcal/h)

rate are calculated as follows:

$$LSF = \frac{190}{2.8(60) + 1.2(15) + 0.65(10)} = 0.98\tag{7}$$

LSF values in clinkers range between 0.92–0.98. The LSF value of 0.98 falls within an acceptable range.

b. *Silica Ratio (SR):* This is also known as Silica Modulus. The expression of SR is given as:

$$SR = \frac{SiO\_2}{Al\_2O\_3 + Fe\_2O\_3} \tag{8}$$


*Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production DOI: http://dx.doi.org/10.5772/intechopen.95269*

## **Table 1.**

components produced at a different section of the simulated Kiln are mixed to achieve a matrix compound of the cement product, having over 70% of CaO.

*Cement Industry - Optimization, Characterization and Sustainable Application*

Aspen Hysys was used for the steady-state simulation of the integrated process flow sheet for the cement production. Within the simulation environment, topological optimization (proper arrangement of equipment) was done to enable very high energy savings or optimization. A pure component such as water, CO2 and air are added as conventional components, while non-conventional components are added as hypothetical components to the HYSYS environment based on their physical properties (molecular weight and density). Based on the process description, the different reactions taking place in each simulated reactor, as presented in the flowchart are:

*CaCO*<sup>3</sup> ! *CaO* þ *CO*2ð Þ *Limestone decomposition* (1)

4*CaO* þ *Al*2*O*<sup>3</sup> þ *Fe*2*O*<sup>3</sup> ! *Ca*4*Al*2*Fe*2*O*10ð Þ *Section D* (5)

The various products in the various sections of the process reactors are; Tricalcium silicate (Ca2SiO4) which is responsible for early strength and the initial set of the cement; Dicalcium silicate (Ca3SiO5) which increases the strength as it age; Tricalcium aluminate (Ca3Al2O6) which contributes to the concrete strength development in the first few days but least desirable due to its reactiveness with sulphate containing soils and water; Tetracaliumaluminoferrite (Ca4Al2Fe2O10) which reduces clinkering temperature. The equipment design parameters employed

The flow rate of the major raw materials for the production of cement in the clinkering reactor as depicted by Eqns. (6–12) are carefully chosen based on the standard provided by Winter N. B. [45]. The Chemical parameters based on the oxide composition are very useful in describing clinker characteristics. The follow-

a. *Lime Saturation Factor (LSF):* is the measure of the ratio of alite to belite in the clinker. It is estimated by the ratio of CaO to the sum of other three main

2*:*8*SiO*<sup>2</sup> þ 1*:*2*Al*2*O*<sup>3</sup> þ 0*:*65*Fe*2*O*<sup>3</sup>

LSF values in clinkers range between 0.92–0.98. The LSF value of 0.98 falls

*SR* <sup>¼</sup> *SiO*<sup>2</sup>

b. *Silica Ratio (SR):* This is also known as Silica Modulus. The expression of SR is

*Al*2*O*<sup>3</sup> þ *Fe*2*O*<sup>3</sup>

<sup>2</sup>*:*8 60 ð Þþ <sup>1</sup>*:*2 15 ð Þþ <sup>0</sup>*:*65 10 ð Þ <sup>¼</sup> <sup>0</sup>*:*<sup>98</sup> (7)

(6)

(8)

oxides SiO2, Fe2O3 and Al2O3. The equation is given by:

*LSF* <sup>¼</sup> *CaO*

*LSF* <sup>¼</sup> <sup>190</sup>

2*CaO* þ *SiO*<sup>2</sup> ! *Ca*2*SiO*<sup>4</sup> ð Þ *Section A* (2) *CaO* þ *Ca*2*SiO*<sup>4</sup> ! *Ca*3*SiO*5ð Þ *Section B* (3) 3*CaO* þ *Al*2*O*<sup>3</sup> ! *Ca*3*Al*2*O*<sup>6</sup> ð Þ *Section C* (4)

**2.2 Aspen Hysys simulation**

in this work are provided in **Table 1**.

ing parameters are widely used.

within an acceptable range.

given as:

**72**

*Equipment design parameter.*

Based on the experimental design for the simulated cement production process. The flow rate of Al2O3 and Fe2O3 are 15 tonnes/day and 10 tonnes/day respectively. The low level and high levels of SiO2 are found to be 50 tonnes/day and 60 tonnes/ day respectively. Hence, the SR values are the high and low value of the SiO2 flow rate are calculated as follows:

$$\text{SR} = \frac{\text{50}}{\text{15} + \text{10}} = \text{2.0} \tag{9}$$

$$\text{SR} = \frac{60}{15 + 10} = 2.4 \tag{10}$$

A high silicate ratio means that more calcium silicates are present in the clinker and less aluminate and ferrite. SR is typical, between 2.0 and 3.0. The SR values of 2.0 and 2.4 fall within an acceptable range of 2.0 and 3.0.

c. *Aluminate Ratio (AR):* This is the ratio of aluminate and ferrite phases in the clinker. AR value ranges between 1–4 in Portland clinkers. The flow rate of Al2O3 and Fe2O3 used in the process simulation are 15 tonnes/day and 10 tonnes/ day respectively. The equation governing the AR of the oxide is given by

$$AR = \frac{Al\_2O\_3}{Fe\_2O\_3} \tag{11}$$

*Cement Industry - Optimization, Characterization and Sustainable Application*

$$AR = \frac{15}{10} = 1.5\tag{12}$$

The mass flow and corresponding clinker quality parameters are presented in **Table 2**.

### **2.3 Multivariate design of experiment**

The central composite design of response surface methodology was used to analyse the effect of CaO and SiO2 on cement production rate. The total number of experimental runs (N) required for *n* independent variables and *nc* number of replica centre points is given by Eq. 13

$$N = 2^{\mathfrak{n}} + 2\mathfrak{n} + \mathfrak{n}\_{\mathfrak{c}} \tag{13}$$

value of 95% confidence level was used to evaluate the significance of the model

*Design of Experiment using central composite (C.C.D) Design of Response Surface Methodology (R.S.M).*

**Factor Name Units Low High -α +α** *A* Flow rate of CaO tonnes/day 135 190 123.609 201.391 *B* Flow rate of SiO2 tonnes/day 50 60 47.9289 62.0711

Lower axial point - α *Xmin*

Upper axial point α *Xmax*

Centre point 0 ð Þ *Xmax* þ *Xmin =*2

*Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production*

Lower level - 1 ½ð Þ *Xmax* þ *Xmin =*2� � ½ � *α*ð Þ *Xmax* � *Xmin =*2

Upper level 1 ½ð Þ *Xmax* þ *Xmin =*2� þ ½ � *α*ð Þ *Xmax* � *Xmin =*2

**Code Mathematical relationship**

All simulations were done in duplicate and the experimental design were gener-

**Coded levels Actual values The flow rate of cement A B A B HYSYS Simulation C.C.D Model**

ated by the Central Composite Design (C.C.D) of the Design-Expert Software, which resulted in a total of 10 experimental (simulation) runs and the results of the

 1.000 1.000 190 60 46.6 45.6674 �1.000 �1.000 135 50 30.74 31.9851 0.000 0.000 162.5 55 46.04 46.04 0.000 �1.414 162.5 47.9289 30.57 31.271 1.000 �1.000 190 50 44.3 42.1551 �1.414 0.000 123.609 55 46.04 43.4867 0.000 0.000 162.5 55 46.04 46.04 �1.000 1.000 135 60 46.6 49.0574 1.414 0.000 201.391 55 46.04 48.2808 0.000 1.414 162.5 62.0711 46.84 45.8265

**Run No. Factors Response**

terms and coefficients.

**Table 3.**

**Table 4.**

**Table 5.**

**75**

**3. Results and discussion**

**3.1 Simulation and optimisation of cement flow**

*Relationship between the variable values and their assigned codes.*

*DOI: http://dx.doi.org/10.5772/intechopen.95269*

experiments (simulations) are shown in **Table 5**.

*Simulation and predicted results from central composite design (C.C.D).*

Design Expert 10.0.3 software was used to generate the experimental design from the ten experimental runs to study the combined effect of two variables on the response. For two variables factor in the experiments; four factorial points (*2<sup>n</sup>* ), four axial points (*2n*) and two replicates at the central points (*nc*) at distance α = 1.414 from the centre were used for the CCD design. A polynomial empirical model was developed from the ten experimental runs to correlate the response with the independent variables. The mathematical expression can be expressed as:

$$Y = \beta\_o + \sum\_{i=1}^{n} \beta\_i X\_i + \sum\_{i=1}^{n} \beta\_{ii} X\_i^2 + \sum\_{1 \le i \le j}^{n} \beta\_{ji} X\_i X\_j + \varepsilon \tag{14}$$

Where *Y* = Predicted response, *β<sup>o</sup>* = constant coefficient, *β<sup>i</sup>* = Linear coefficient, *βii* = Quadratic, *βij* = Interaction coefficients and ε = model random error, *n* the number of variable factors, *Xi* and *X <sup>j</sup>* are the coded values of the variable parameters [35].

The response generated function distance from the centre *<sup>α</sup>* <sup>¼</sup> <sup>2</sup>*<sup>n</sup><sup>=</sup>*4. The codes are calculated as a function of the range the factors as shown in **Table 3**.

The central composite experimental design for the synthesis of cement via simulation is depicted in **Table 4**. The mass flow rate of CaO and SiO2 measured in tonnes/day are the independent variables or predictors which are studied for their effect on the response variable (cement flow rate) at a constant Al2O3 and Fe2O3 flow rates.

#### **2.4 Model fitting and statistical analysis**

The interaction between the variables and the response data as well as the statistical parameters were analysed graphically by analysis of variance (ANOVA) in the Design-Expert software. Regression analysis, significance, F-test, surface and contour plots of the response were also generated from the software. A probability


**Table 2.**

*Raw material mass flow and clinker quality parameter.*

*Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production DOI: http://dx.doi.org/10.5772/intechopen.95269*


**Table 3.**

*AR* <sup>¼</sup> <sup>15</sup>

*Cement Industry - Optimization, Characterization and Sustainable Application*

**Table 2**.

parameters [35].

flow rates.

**Table 2.**

**74**

**2.3 Multivariate design of experiment**

replica centre points is given by Eq. 13

*<sup>Y</sup>* <sup>¼</sup> *<sup>β</sup><sup>o</sup>* <sup>þ</sup>X*<sup>n</sup>*

**2.4 Model fitting and statistical analysis**

*Raw material mass flow and clinker quality parameter.*

*i*¼1

The mass flow and corresponding clinker quality parameters are presented in

The central composite design of response surface methodology was used to analyse the effect of CaO and SiO2 on cement production rate. The total number of experimental runs (N) required for *n* independent variables and *nc* number of

Design Expert 10.0.3 software was used to generate the experimental design from the ten experimental runs to study the combined effect of two variables on the response. For two variables factor in the experiments; four factorial points (*2<sup>n</sup>*

four axial points (*2n*) and two replicates at the central points (*nc*) at distance α = 1.414 from the centre were used for the CCD design. A polynomial empirical model was developed from the ten experimental runs to correlate the response with the independent variables. The mathematical expression can be expressed as:

*<sup>β</sup>iXi* <sup>þ</sup>X*<sup>n</sup>*

are calculated as a function of the range the factors as shown in **Table 3**.

*i*¼1

*βii* = Quadratic, *βij* = Interaction coefficients and ε = model random error, *n* the number of variable factors, *Xi* and *X <sup>j</sup>* are the coded values of the variable

*βiiXi*

Where *Y* = Predicted response, *β<sup>o</sup>* = constant coefficient, *β<sup>i</sup>* = Linear coefficient,

The response generated function distance from the centre *<sup>α</sup>* <sup>¼</sup> <sup>2</sup>*<sup>n</sup><sup>=</sup>*4. The codes

The central composite experimental design for the synthesis of cement via simulation is depicted in **Table 4**. The mass flow rate of CaO and SiO2 measured in tonnes/day are the independent variables or predictors which are studied for their effect on the response variable (cement flow rate) at a constant Al2O3 and Fe2O3

The interaction between the variables and the response data as well as the statistical parameters were analysed graphically by analysis of variance (ANOVA) in the Design-Expert software. Regression analysis, significance, F-test, surface and contour plots of the response were also generated from the software. A probability

High level 190 60 15 10 0.98 2.4 1.5 Low level 135 50 15 10 0.82 2.0 1.5

**Mass flow (tonnes/day) Clinker quality parameter CaO SiO2 Al2O3 Fe2O3 LSF SR AR**

<sup>2</sup> <sup>þ</sup> <sup>X</sup>*<sup>n</sup>* 1≤*i*≤*j*

<sup>10</sup> <sup>¼</sup> <sup>1</sup>*:*<sup>5</sup> (12)

*<sup>N</sup>* <sup>¼</sup> <sup>2</sup>*<sup>n</sup>* <sup>þ</sup> <sup>2</sup>*<sup>n</sup>* <sup>þ</sup> *nc* (13)

),

*βijXiX <sup>j</sup>* þ *ε* (14)

*Relationship between the variable values and their assigned codes.*


### **Table 4.**

*Design of Experiment using central composite (C.C.D) Design of Response Surface Methodology (R.S.M).*

value of 95% confidence level was used to evaluate the significance of the model terms and coefficients.

## **3. Results and discussion**

## **3.1 Simulation and optimisation of cement flow**

All simulations were done in duplicate and the experimental design were generated by the Central Composite Design (C.C.D) of the Design-Expert Software, which resulted in a total of 10 experimental (simulation) runs and the results of the experiments (simulations) are shown in **Table 5**.


**Table 5.** *Simulation and predicted results from central composite design (C.C.D).*

The change in mean response in cement flow per unit increase in variable occurs when other predictors area kept constant and is estimated by the coefficient of estimation and is presented **Table 6**.

The empirical quadratic equation for the optimal cement product rate as a function of CaO and SiO2 mass flow in coded form as derived from **Table 7** was obtained according to the CCD and is given in Eq. 15

$$\mathbf{C} = 4\mathbf{6}.04 + \mathbf{1.70A} + \mathbf{5.15B} - \mathbf{3.39AB} - \mathbf{0.078A^2} - \mathbf{3.75B^2} \tag{15}$$

strong signal for optimisation. Hence, this indicates the two predictors (flow rate of CaO and flow rate of SiO2) could predict the flow rate of cement, thus the model equation, contour plot and 3D surface plot could be used to predict the response

**Parameter Values** R-Squared 0.9356 Adj R-Squared 0.8896 Pred R-Squared 0.5422 Mean 43.69 Std. Dev. 1.93 C.V. % 4.42 Adeq Precision 13.554

*Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production*

The contour plot which shows the possible relationship between the CaO, SiO2 and cement product mass flow is presented in **Figure 2**. The darker red regions indicate higher C (response) values. Here, the optimum flow rate of cement is found from the isolines to be 47.748 tonnes/day at a flow rate of 152.346 tonnes/day

The three-dimensional (3D) response surface plots obtained from the model equation using Design Expert 10.03 is depicted in **Figure 3**. This depicts the effect

*Contour surface plot showing the effects of the flow rate of CaO and flow rate of SiO2 on the flow rate of cement.*

(flow rate of the cement).

of CaO and 56.8241 tonnes/day SiO2.

*Statistical information for the statistical model for cement flow rate.*

*DOI: http://dx.doi.org/10.5772/intechopen.95269*

**3.2 Surface plots**

**Figure 2.**

**77**

**Table 8.**

The test of the significance and the adequacy of the model and its coefficients lack fitness which was based on F-value or P-value at 95% confidence level was tested from analysis of variance (ANOVA) and the result is presented in **Table 7**. The result shows that the model at an F value of 20.35 and a very low P-value of 0.0005 indicates that the statistical regression model was significant. The result also shows that *A*, *B*, *AB* and *B*<sup>2</sup> are significant terms.

The regression statistical analysis is summarised in **Table 8**. The R squared value of 0.9356 is in good agreement with the adjusted R-square value of 0.8896, showing a good fit of the model, as the closer the R squared value to 1.00, the more significant the model. The adequate precision of 13.55 indicates the low noise level and a


#### **Table 6.**

*Coefficient estimation for cement flow rate in terms of coded factors.*


#### **Table 7.**

*ANOVA results for the statistical model for cement flow rate.*

*Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production DOI: http://dx.doi.org/10.5772/intechopen.95269*


**Table 8.**

The change in mean response in cement flow per unit increase in variable occurs

*<sup>C</sup>* <sup>¼</sup> <sup>46</sup>*:*<sup>04</sup> <sup>þ</sup> <sup>1</sup>*:*70*<sup>A</sup>* <sup>þ</sup> <sup>5</sup>*:*15*<sup>B</sup>* � <sup>3</sup>*:*39*AB* � <sup>0</sup>*:*078*A*<sup>2</sup> � <sup>3</sup>*:*75*B*<sup>2</sup> (15)

when other predictors area kept constant and is estimated by the coefficient of

*Cement Industry - Optimization, Characterization and Sustainable Application*

The empirical quadratic equation for the optimal cement product rate as a function of CaO and SiO2 mass flow in coded form as derived from **Table 7** was

The test of the significance and the adequacy of the model and its coefficients lack fitness which was based on F-value or P-value at 95% confidence level was tested from analysis of variance (ANOVA) and the result is presented in **Table 7**. The result shows that the model at an F value of 20.35 and a very low P-value of 0.0005 indicates that the statistical regression model was significant. The result also

The regression statistical analysis is summarised in **Table 8**. The R squared value of 0.9356 is in good agreement with the adjusted R-square value of 0.8896, showing a good fit of the model, as the closer the R squared value to 1.00, the more significant the model. The adequate precision of 13.55 indicates the low noise level and a

Factor Estimate df Error Low High VIF

*A*-Flowrate of CaO 1.70 1 0.68 0.080 3.31 1.00 *B*-Flow rate of Silica 5.15 1 0.68 3.53 6.76 1.00 *AB* �3.39 1 0.97 �5.67 �1.11 1.00 *A*<sup>2</sup> �0.078 1 0.73 �1.81 1.65 1.02 *B*<sup>2</sup> �3.75 1 0.73 �5.48 �2.01 1.02

**Source Sum of squares df Mean F value p-value (Prob > F)** *Model* 379.61 5 75.92 20.35 0.0005 *A 22.98 1 22.98 6.16 0.0421 B 211.86 1 211.86 56.78 0.0001 AB 45.97 1 45.97 12.32 0.0099 A*<sup>2</sup> *0.042 1 0.042 0.011 0.9180 B*<sup>2</sup> *97.60 1 97.60 26.16 0.0014*

Intercept 46.04 1 0.86 44.00 48.08

**Coefficient Standard 95% CI 95% CI**

estimation and is presented **Table 6**.

obtained according to the CCD and is given in Eq. 15

shows that *A*, *B*, *AB* and *B*<sup>2</sup> are significant terms.

*Coefficient estimation for cement flow rate in terms of coded factors.*

*Residual* 26.12 7 3.73 *Lack of Fit 26.12 3 8.71 Pure Error 0.000 4 0.000*

*ANOVA results for the statistical model for cement flow rate.*

Cor. Total 405.73 12

**Table 6.**

**Table 7.**

**76**

*Statistical information for the statistical model for cement flow rate.*

strong signal for optimisation. Hence, this indicates the two predictors (flow rate of CaO and flow rate of SiO2) could predict the flow rate of cement, thus the model equation, contour plot and 3D surface plot could be used to predict the response (flow rate of the cement).

## **3.2 Surface plots**

The contour plot which shows the possible relationship between the CaO, SiO2 and cement product mass flow is presented in **Figure 2**. The darker red regions indicate higher C (response) values. Here, the optimum flow rate of cement is found from the isolines to be 47.748 tonnes/day at a flow rate of 152.346 tonnes/day of CaO and 56.8241 tonnes/day SiO2.

The three-dimensional (3D) response surface plots obtained from the model equation using Design Expert 10.03 is depicted in **Figure 3**. This depicts the effect

**Figure 2.** *Contour surface plot showing the effects of the flow rate of CaO and flow rate of SiO2 on the flow rate of cement.*

**4. Conclusion**

**Figure 5.**

**5. Recommendations**

cement production could be improved:

high tech cement equipment and parts.

transportation system.

limestone is needed.

**79**

Process flow diagram for the cement production was simulated to achieve high energy optimization and optimum cement flow rate by minimising the flow rate of the feed (CaO and SiO2). Central composite Design (C.C.D) of Response Surface Methodology used to design the experiment for the simulation using Design Expert 10.0.3. The optimum cement flow rate is found from surface and contour plots to be 47.239 tonnes/day at CaO flow rate of 152.346 tonnes/day and SiO2 flow rate of 56.8241 tonnes/day. The R squared value of 0.9356 determined from the statistical analysis shows a very high significance of the model. Energy efficiency optimization is also carried out using Aspen Energy Analyser. The overall utilities in terms of energy are found to be optimised by 81.4 % from 6.511 x 10<sup>7</sup> kcal/hactual value to

Further work could be performed on fault identification and diagnosis of the process plant. Incorporated with an automated plant to guarantee the safety of workers, reduce environmental problems and increase yield to sustain production improvement.

This research work sought to recommend the following concerns in which

1.Research and development (R&D) in the cement production, processing and utilisation should be encouraged. This will play a vital role in the construction industry, operation and maintenance of efficient road network and effective

2.Automation of cement and kiln sections of the cement production is recommended

3.Optimization of the cement production can be tailored into the fabrication of

4.Optimization of the limestone crusher to quantify the amount of crushed

5.Looking into future the results obtained in this research will open up several important possibilities in the cement production at optimum conditions. This will have a multiplier effect on infrastructural amenities development.

1.211 x 10<sup>7</sup> kcal/h with 297.4 tonnes/day the carbon emission savings.

*Energy savings at optimum feed (CaO and SiO2) rate and product (cement) flow rate.*

*Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production*

*DOI: http://dx.doi.org/10.5772/intechopen.95269*

#### **Figure 3.**

*Response surface plot of the effects of CaO and SiO2 mass flow on cement production.*

of the flow rate of CaO, the flow rate of SiO2 on the flow rate of cement. The flow rate of cement was observed to increase with an increase in the flow rate of CaO. Conversely, increasing the flow rate of SiO2 did not increase the flow rate of cement. Hence, the major predictor in cement production in the clinkering section is the flow rate of CaO.

The optimization plot of the cement output is shown in **Figure 4**. The optimum cement flow rate of 47.239 tonnes/day is found to be at CaO flow rate of 152.346 tonnes/day and SiO2 flow rate of 56.8241 tonnes/day.

## **3.3 Energy optimization**

Aspen Energy Analyser was used to determine the percentage of energy savings based on converged steady-state simulation of the process flow sheet in **Figure 1**. The total energy savings as a function of process utilities and carbon emissions are present in **Figure 5**.

The overall utilities in terms of energy are found to be optimised from the actual value of 6.511 x 10<sup>7</sup> kcal/h to 1.211 x 10<sup>7</sup> kcal/h and indicating available energy savings of 5.3 x 107 kcal/h, with overall energy savings of 81.40% which also correspond to 297.4 tonnes/day carbon emission reduction.

#### **Figure 4.**

*Optimization plot showing the effects of the flow rate of CaO and flow rate of SiO2 on the flow rate of cement.*

*Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production DOI: http://dx.doi.org/10.5772/intechopen.95269*

**Figure 5.**

of the flow rate of CaO, the flow rate of SiO2 on the flow rate of cement. The flow rate of cement was observed to increase with an increase in the flow rate of CaO. Conversely, increasing the flow rate of SiO2 did not increase the flow rate of cement. Hence, the major predictor in cement production in the clinkering section

*Response surface plot of the effects of CaO and SiO2 mass flow on cement production.*

*Cement Industry - Optimization, Characterization and Sustainable Application*

The optimization plot of the cement output is shown in **Figure 4**. The optimum cement flow rate of 47.239 tonnes/day is found to be at CaO flow rate of 152.346

Aspen Energy Analyser was used to determine the percentage of energy savings based on converged steady-state simulation of the process flow sheet in **Figure 1**. The total energy savings as a function of process utilities and carbon emissions are

The overall utilities in terms of energy are found to be optimised from the actual

value of 6.511 x 10<sup>7</sup> kcal/h to 1.211 x 10<sup>7</sup> kcal/h and indicating available energy savings of 5.3 x 107 kcal/h, with overall energy savings of 81.40% which also

*Optimization plot showing the effects of the flow rate of CaO and flow rate of SiO2 on the flow rate of cement.*

is the flow rate of CaO.

**Figure 3.**

**3.3 Energy optimization**

present in **Figure 5**.

**Figure 4.**

**78**

tonnes/day and SiO2 flow rate of 56.8241 tonnes/day.

correspond to 297.4 tonnes/day carbon emission reduction.

*Energy savings at optimum feed (CaO and SiO2) rate and product (cement) flow rate.*

## **4. Conclusion**

Process flow diagram for the cement production was simulated to achieve high energy optimization and optimum cement flow rate by minimising the flow rate of the feed (CaO and SiO2). Central composite Design (C.C.D) of Response Surface Methodology used to design the experiment for the simulation using Design Expert 10.0.3. The optimum cement flow rate is found from surface and contour plots to be 47.239 tonnes/day at CaO flow rate of 152.346 tonnes/day and SiO2 flow rate of 56.8241 tonnes/day. The R squared value of 0.9356 determined from the statistical analysis shows a very high significance of the model. Energy efficiency optimization is also carried out using Aspen Energy Analyser. The overall utilities in terms of energy are found to be optimised by 81.4 % from 6.511 x 10<sup>7</sup> kcal/hactual value to 1.211 x 10<sup>7</sup> kcal/h with 297.4 tonnes/day the carbon emission savings.

Further work could be performed on fault identification and diagnosis of the process plant. Incorporated with an automated plant to guarantee the safety of workers, reduce environmental problems and increase yield to sustain production improvement.

## **5. Recommendations**

This research work sought to recommend the following concerns in which cement production could be improved:


*Cement Industry - Optimization, Characterization and Sustainable Application*

## **Conflict of interest**

There is no conflict of interest associated with this work.

## **Appendix**

**References**

protection agency, 1995.

1999. doi: 10.2172/751775.

[3] S. P. Dunuweera and R. M. G. Rajapakse, "Cement Types,

[4] IEA, "Cement," Paris, 2020. [Online]. Available: https://www.iea.

[5] TERI, "CEMENT INDUSTRY; Trends Report," New Delhi, 2017. [Online]. Available: http://www.teriin. org/library/files/Cement-Industry-Tre

[6] A. Jankovic, W. Valery, and E. Davis, "Cement grinding optimisation," *Miner. Eng.*, vol. 17, no. 11–12, pp. 1075–1081, 2004, doi: 10.1016/j.mineng.2004.06.031.

[7] D. Olsen, S. Goli, D. Faulkner, and A. Mckane, "Opportunities for Energy Efficiency and Demand Response in the California Cement Industry," no. December. Lawrence Berkeley National

[8] J. G. J. Olivier and J. A. H. W. Peters, "Trends in Global CO2 and Total Greenhouse Gas Emissions: Report 2019," The Hague, 2020. [Online]. Available: www.pbl.nl/en.

[9] H. Mikulčić, M. Vujanović, N. Markovska, R. V. Filkoski, M. Ban, and

org/reports/cement.

nds-Report2017.pdf.

Laboratory, 2010.

**81**

Composition, Uses and Advantages of Nanocement, Environmental Impact on Cement Production, and Possible Solutions," *Adv. Mater. Sci. Eng.*, vol. 2018, 2018, doi: 10.1155/2018/4158682.

[1] EPA, "Mineral Products Industry," in *Compilation of air pollutant emission factors. Volume I: stationary point and area sources*, AP-42 5th., Research Triangle Park, NC: U.S. Environmental

*DOI: http://dx.doi.org/10.5772/intechopen.95269*

N. Duić, "CO2 emission reduction in the cement industry," *Chem. Eng. Trans.*, vol. 35, pp. 703–708, 2013, doi: 10.3303/

[10] S. Zhang, H. Ren, W. Zhou, Y. Yu, and C. Chen, "Assessing air pollution abatement co-benefits of energy efficiency improvement in cement industry: A city-level analysis," *J. Clean. Prod.*, vol. 185, pp. 761–771, 2018, doi:

10.1016/j.jclepro.2018.02.293.

[11] M. J. S. Zuberi and M. K. Patel, "Bottom-up analysis of energy efficiency improvement and CO2 emission reduction potentials in the Swiss cement industry," *J. Clean. Prod.*, vol. 142, pp. 4294–4309, 2017, doi: 10.1016/j.jclepro.2016.11.178.

[12] N. Chatziaras, C. S. Psomopoulos, and N. J. Themelis, "Use of wastederived fuels in cement industry: a review," *Manag. Environ. Qual. An Int. J.*, vol. 27, no. 2, pp. 178–193, 2016, doi:

[13] E. Marchetti, "Use of Agricultural Wastes as Supplementary Cementitious Materials," KTH ROYAL INSTITUTE

[14] A. Naqi and J. G. Jang, "Recent progress in green cement technology utilizing low-carbon emission fuels and raw materials: A review," *Sustain.*, vol. 11, no. 2, 2019, doi: 10.3390/su11020537.

[15] R. Maddalena, J. J. Roberts, and A. Hamilton, "Can Portland cement be replaced by low-carbon alternative materials? A study on the thermal properties and carbon emissions of innovative cement," *J. Clean. Prod.*, vol. 186, no. April, pp. 933–942, 2018, doi:

[16] H. Mikulcic, M. Vujanovic, and N. Duic, "Improving the Sustainability of Cement Production by Using Numerical

10.1016/j.jclepro.2018.02.138.

10.1108/MEQ-01-2015-0012.

OF TECHNOLOGY, 2020.

CET1335117.

*Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production*

[2] N. Martin, E. Worrell, and L. Price, "Energy Efficiency and Carbon Dioxide Emissions Reduction Opportunities in the U.S. Cement Industry," Berkeley,

**Figure A1.** *HYSYS process flow simulation diagram for the production of cement.*

## **Author details**

Oluwafemi M. Fadayini<sup>1</sup> \*, Adekunle A. Obisanya<sup>2</sup> , Gloria O. Ajiboye<sup>2</sup> , Clement Madu1 , Tajudeen O. Ipaye<sup>3</sup> , Taiwo O. Rabiu<sup>4</sup> , Shola J. Ajayi<sup>1</sup> and Joseph T. Akintola<sup>1</sup>

1 Department of Chemical Engineering, Lagos State Polytechnic, Ikorodu, Lagos, Nigeria

2 Department of Chemical Engineering, Yaba College of Technology, Yaba, Lagos, Nigeria

3 Department of Civil Engineering, Lagos State Polytechnic, Ikorodu, Lagos, Nigeria

4 Department of Mechanical Engineering, Lagos State Polytechnic, Ikorodu, Lagos, Nigeria

\*Address all correspondence to: olufeday@gmail.com; fadayini.o@mylaspotech.edu.ng

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Simulation and Optimization of an Integrated Process Flow Sheet for Cement Production DOI: http://dx.doi.org/10.5772/intechopen.95269*

## **References**

**Conflict of interest**

**Appendix**

**Author details**

**Figure A1.**

Clement Madu1

Nigeria

Nigeria

Nigeria

**80**

Oluwafemi M. Fadayini<sup>1</sup>

and Joseph T. Akintola<sup>1</sup>

There is no conflict of interest associated with this work.

*Cement Industry - Optimization, Characterization and Sustainable Application*

\*, Adekunle A. Obisanya<sup>2</sup>

1 Department of Chemical Engineering, Lagos State Polytechnic, Ikorodu, Lagos,

2 Department of Chemical Engineering, Yaba College of Technology, Yaba, Lagos,

3 Department of Civil Engineering, Lagos State Polytechnic, Ikorodu, Lagos, Nigeria

4 Department of Mechanical Engineering, Lagos State Polytechnic, Ikorodu, Lagos,

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

, Taiwo O. Rabiu<sup>4</sup>

, Tajudeen O. Ipaye<sup>3</sup>

*HYSYS process flow simulation diagram for the production of cement.*

\*Address all correspondence to: olufeday@gmail.com;

fadayini.o@mylaspotech.edu.ng

provided the original work is properly cited.

, Gloria O. Ajiboye<sup>2</sup>

, Shola J. Ajayi<sup>1</sup>

,

[1] EPA, "Mineral Products Industry," in *Compilation of air pollutant emission factors. Volume I: stationary point and area sources*, AP-42 5th., Research Triangle Park, NC: U.S. Environmental protection agency, 1995.

[2] N. Martin, E. Worrell, and L. Price, "Energy Efficiency and Carbon Dioxide Emissions Reduction Opportunities in the U.S. Cement Industry," Berkeley, 1999. doi: 10.2172/751775.

[3] S. P. Dunuweera and R. M. G. Rajapakse, "Cement Types, Composition, Uses and Advantages of Nanocement, Environmental Impact on Cement Production, and Possible Solutions," *Adv. Mater. Sci. Eng.*, vol. 2018, 2018, doi: 10.1155/2018/4158682.

[4] IEA, "Cement," Paris, 2020. [Online]. Available: https://www.iea. org/reports/cement.

[5] TERI, "CEMENT INDUSTRY; Trends Report," New Delhi, 2017. [Online]. Available: http://www.teriin. org/library/files/Cement-Industry-Tre nds-Report2017.pdf.

[6] A. Jankovic, W. Valery, and E. Davis, "Cement grinding optimisation," *Miner. Eng.*, vol. 17, no. 11–12, pp. 1075–1081, 2004, doi: 10.1016/j.mineng.2004.06.031.

[7] D. Olsen, S. Goli, D. Faulkner, and A. Mckane, "Opportunities for Energy Efficiency and Demand Response in the California Cement Industry," no. December. Lawrence Berkeley National Laboratory, 2010.

[8] J. G. J. Olivier and J. A. H. W. Peters, "Trends in Global CO2 and Total Greenhouse Gas Emissions: Report 2019," The Hague, 2020. [Online]. Available: www.pbl.nl/en.

[9] H. Mikulčić, M. Vujanović, N. Markovska, R. V. Filkoski, M. Ban, and N. Duić, "CO2 emission reduction in the cement industry," *Chem. Eng. Trans.*, vol. 35, pp. 703–708, 2013, doi: 10.3303/ CET1335117.

[10] S. Zhang, H. Ren, W. Zhou, Y. Yu, and C. Chen, "Assessing air pollution abatement co-benefits of energy efficiency improvement in cement industry: A city-level analysis," *J. Clean. Prod.*, vol. 185, pp. 761–771, 2018, doi: 10.1016/j.jclepro.2018.02.293.

[11] M. J. S. Zuberi and M. K. Patel, "Bottom-up analysis of energy efficiency improvement and CO2 emission reduction potentials in the Swiss cement industry," *J. Clean. Prod.*, vol. 142, pp. 4294–4309, 2017, doi: 10.1016/j.jclepro.2016.11.178.

[12] N. Chatziaras, C. S. Psomopoulos, and N. J. Themelis, "Use of wastederived fuels in cement industry: a review," *Manag. Environ. Qual. An Int. J.*, vol. 27, no. 2, pp. 178–193, 2016, doi: 10.1108/MEQ-01-2015-0012.

[13] E. Marchetti, "Use of Agricultural Wastes as Supplementary Cementitious Materials," KTH ROYAL INSTITUTE OF TECHNOLOGY, 2020.

[14] A. Naqi and J. G. Jang, "Recent progress in green cement technology utilizing low-carbon emission fuels and raw materials: A review," *Sustain.*, vol. 11, no. 2, 2019, doi: 10.3390/su11020537.

[15] R. Maddalena, J. J. Roberts, and A. Hamilton, "Can Portland cement be replaced by low-carbon alternative materials? A study on the thermal properties and carbon emissions of innovative cement," *J. Clean. Prod.*, vol. 186, no. April, pp. 933–942, 2018, doi: 10.1016/j.jclepro.2018.02.138.

[16] H. Mikulcic, M. Vujanovic, and N. Duic, "Improving the Sustainability of Cement Production by Using Numerical Simulation of Limestone Thermal Degradation and Pulverized Coal Combustion in a Cement Calciner," *J. Clean. Prod.*, vol. 88, pp. 262–271, 2015.

[17] P. Markewitz *et al.*, "Carbon capture for CO2 emission reduction in the cement industry in Germany," *Energies*, vol. 12, no. 12, pp. 1–27, 2019, doi: 10.3390/en12122432.

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[20] H. R. Goshayeshi and F. K. Poor, "Modeling of Rotary Cement Kiln (In Persian)," *Energy Power Eng.*, vol. 8, pp. 23–33, 2016.

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[37] X. Li, H. Yu, and M. Yuan, "Modeling and Optimization of Cement Raw Materials Blending Process," *Math. Probl. Eng.*, vol. 2012, 2012, doi: 10.1155/ 2012/392197.

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[39] G. Cibilakshmi and J. Jegan, " A DOE approach to optimize the strength properties of concrete incorporated with different ratios of PVA fibre and nanoFe 2 O 3," *Adv. Compos. Lett.*, vol. 29, p. 2633366X2091388, 2020, doi: 10.1177/ 2633366x20913882.

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[45] N. Winter, "Understanding Cement," 2005. https://www.understa nding-cement.com/clinker.html (accessed Nov. 02, 2020).

**Chapter 6**

Raw Mix

**Abstract**

**1. Introduction**

**85**

Peculiarities of Portland Cement

Clinker Synthesis in the Presence

Due to the depletion of the raw material base and a technogenic materials addition into a raw mix for the Portland cement clinker synthesis, sulfur and its oxides amount in a raw mix increases. According to literature the Portland cement clinker synthesis in the presence of a sulfur oxides significant amount is difficult. As the content of SO3 in the raw mix increases the amount of C2S increases while C3S and C3A amount decrease. With an equal total content of C2S and C3S in the clinker their ratio C3S/C2S decreases with an increased content of SO3. These factors lead to a deterioration in the Portland cement clinker quality. The clinker formation reactions thermodynamic analysis and some experimental studies allow determining reasons for the Portland cement clinker quality deterioration. It was found that the presence significant amount of a SO3 in the raw mix the synthesis in solid phase of low-basic C4A3 S (ye'elimite) is the thermodynamically preferred rather than highbasic C3A and C4AF. As a result, excess and crystallized free lime inhibits the C3S synthesis through the liquid phase. The experimental studies result helped to develop a methodology for calculating the composition of a raw mix from materials with significant amount of SO3. It allows to reduce the SO3 negative effect on the Portland cement clinker synthesis and to obtain high-quality Portland cement.

**Keywords:** high-sulfate raw material, Portland cement clinker, alite, belite,

Sulfur and its oxides in the form of sulfate and sulfide minerals appear in the raw mixture of Portland cement clinker with the main raw materials for the preparation of clinker, namely clay and carbonate rock as well as additives and fuel. The technogenic origin additives of the metallurgical and heat-power industry, namely slags, fly ashes and oil cokes have especially high concentration of sulfur compounds [1–3]. According to [4] sulfur with calcium oxide forms calcium sulfate CaSO4 under conditions of oxidative burning. Depending on the burning temperature the latter with the alkaline components of the raw mix forms alkaline metal sulfates or with clinker minerals forms sulfospurrit 2(C2S)C S and ye'elimite C4A3 S

brownmillerite, ye'elimite, calculation procedure

and participates in the alkali-sulfate cycle of the furnace.

*Oleg Sheshukov and Michael Mikheenkov*

of a Significant Amount of SO3 in a

## **Chapter 6**
