**About the Editors**

## **Sebastian Kujawa**

Sebastian Kujawa is employed as an Assistant Professor in the Department of Biosystems Engineering, Poznan University of Life Sciences, Poland. In 2009, he received a PhD degree from ´ the Poznan University of Life Sciences. His scientific activity concerns the applications of computer ´ image analysis and neural modeling in developing methods for condition assessments of dynamic biosystems. Dr. Kujawa is an author of over 50 publications in scientific journals and peer-reviewed conference proceedings. He is a member of the Board of Polish Society for Information Technology in Agriculture (POLSITA). He has served as a Guest Editor of a Special Issue in *Agriculture*: Neural Networks in Agriculture.

## **Gniewko Niedbała**

Gniewko Niedbała is an Associate Professor at the Department of Biosystems Engineering, Poznan University of Life Sciences, Poland. He defended his doctorate in 2006 at the August ´ Cieszkowski Agricultural University of Poznan, and performed his habilitation in 2019 at the Pozna ´ n´ University of Life Sciences. Presently, he is working on using artificial neural networks and machine learning in many aspects of agriculture and forestry. Between 2012 and 2016, he was a Board Member of the National Centre for Research and Development, Poland. He was a Guest Editor for a Special Issue of *Agriculture:* Neural Networks in Agriculture. He presently serves as a Guest Editor in *Agriculture* and *Energies*, is a Member of the Editorial Board in *Agronomy*, and a Member of the Reviewer Board in *Agriculture*, *Water*, and *Land*. In addition, he has authored and coauthored over 90 journal and conference papers and book chapters related to artificial intelligence in agriculture.


**Abstract**

Spark-ignition (SI) engine has a high power density, making it suitable for unmanned aerial vehicles. Normally, gasoline fuel with a high octane number (ON) is used for a spark-ignition engine. However, gasoline fuel is easy to be evaporated and has a low flash point which is unsafe for aviation engines. Kerosene with a high flash point is safer than gasoline. In this chapter, the combustion characteristics of kerosene for a spark-ignition aviation piston engine are analyzed. A three-dimensional (3D) model is setup, and the combustion process of the engine fuelled with kerosene is simulated. Later, the knock limit extension by water injection is evaluated experimentally. The results indicate that water injection can suppress the knock of SI engine with kerosene in some extent and the output power can be improved significantly.

**Keywords:** aviation piston engine, spark ignition, kerosene, knock suppression, water injection

#### **1. Introduction**

Four-stroke spark-ignition (SI) piston engines have advantages of good fuel economy, high power to weight density, low noise, low cost, and easy maintenance, making them suitable for helicopters and unmanned aerial vehicles [1]. However, gasoline is volatile and easy to be ignited, which is not safe as aviation fuel. In contrast, kerosene is safer and has been used widely for airplanes. If gasoline can be replaced by kerosene on a four-stroke SI piston engine, the aviation safety will be improved significantly. Therefore, the combustion characteristics of four-stroke SI piston engine using kerosene as fuel need to be investigated.

The physical properties of kerosene are very different from gasoline. Kerosene has a lower volatility and higher viscosity. Accordingly, the spray penetration, spray velocity, and cone angle are different [2]. As a wide distillation fuel, the chemical properties of kerosene also differ from gasoline. Lots of experiments were conducted to investigate the combustion characteristics of kerosene [3, 4]. For example, the ignition delay time of kerosene was measured as a function of temperature, pressure, and equivalence ratio using a shock tube [5] or laser-induced fluorescence (LIF) imaging [6].

The combustion performance of kerosene has been investigated comprehensively for turbojet and scramjet engines [7, 8]. However, the operation conditions of aviation piston engine deviate from those of turbojets and scramjets significantly. Very few investigations focused on the combustion performance of kerosene for internal combustion engines [9–11]. Fernandes et al. studied the performance of a heavy-duty diesel engine fuelled with JP-8. The torque output and fuel consumption were similar with diesel fuel. However, the injection duration was enlarged to compensate the low fuel density, and the ignition delay time was increased because of a low cetane number. In addition, the emissions of nitrogen oxide (NOx) and particle material (PM) were decreased apparently [12]. Tay et al. developed a reaction mechanism composed of 48 species and 152 reactions for kerosene, and the accuracy was validated in an optical engine [13].

Mass:

Momentum:

Energy:

*∂ ∂t*

Species:

*∂ ∂t*

ð Þþ *ρh ∂ ∂x j*

rate *ε* are expressed by

*ρu*~ *j ∂ε ∂x j*

**93**

*ρu*~ *j ∂k ∂x j*

¼ *∂ ∂x j*

standard wall functions are used.

¼ *∂ ∂x j*

*μt σε* þ *μ* � � *∂ε*

� �

ð Þþ *ρui*

*DOI: http://dx.doi.org/10.5772/intechopen.91938*

*∂ ∂xi*

*ρu jh* � � ¼ *∂p*

ð Þþ *ρYs*

The enthalpy of the mixture can be calculated as

flow in the cylinder. The turbulent eddy viscosity is defined as

*μt σk* þ *μ* � � *∂k*

*∂x j*

� �

*∂ ∂t*

equation of state for gaseous mixture.

*ρuiu j* � � ¼ � *∂p*

> *∂t* þ *∂ ∂xi*

> > *∂ ∂x j*

*∂ρ ∂t* þ *∂ ∂xi*

*Knock Suppression of a Spark-Ignition Aviation Piston Engine Fuelled with Kerosene*

*∂xi* þ *μ ∂ ∂xi*

> *μ Pr ∂h ∂x j* þ *μ Pr*

*ρu jYs* � � ¼ *∂*

*ρ* ¼ *p= RT*X

*h* ¼ X *N*

*s*¼1

*μt* ¼ *Cμρk*2

*∂x j*

The empirical constants for the standard k-ε model are assigned below: C1 = 1.44, C2 = 1.92, Cμ = 0.09, σk = 1.0, and σε = 1.3. A nonslip boundary condition is assumed regarding all solid surfaces of the computational domain, and the

� *C*1 *ε k ρ* ~ *u*00 *i u*00 *j ∂u*~*i ∂x j* þ *μt ρ*2 *∂ρ ∂xi*

The transport equations for the turbulent kinetic energy *k* and the dissipation

� *ρ* ~ *u*00 *i u*00 *j ∂u*~*i ∂x j* � *μt ρ*2 *∂ρ ∂xi*

� �

The above equations can be used in direct numerical simulation of turbulence, but it is difficult for the actual calculation process because the calculation load is extremely large. In this study, *k - ε* model is employed to simulate the turbulent

*∂x j*

ð Þ *Ys=Ms* " #

The density of the fluid is calculated by the following equation according to the

*N*

*s*¼1

ð Þ¼ *ρui* 0*:* (1)

*N*

*:* (3)

þ *ρr*\_*s:* (4)

*:* (5)

*Yshs*ð Þ *T :* (6)

*=ε:* (7)

*∂p ∂xi*

� *C*2*ρ*

*∂p ∂xi* � *ρε*, (8)

*k :* (9)

*ε*2

*s*¼1 *hs ∂Ys ∂x j*

*∂ui ∂x j* þ *∂u j ∂xi* � 2 3 *δij ∂uk ∂xk* � �*:* (2)

*Pr Sc* � 1 � �X

*μ Sc ∂Ys ∂x j* � �

" #

Generally, the octane number (ON) of gasoline is in the range of 70–97, whereas the octane number of kerosene is much lower as only 20–50. Therefore, the knock phenomenon of kerosene is much severe in an SI engine, which will keep the power and economy of the engine in a low level [14]. To control the knock of an SI engine, various methods can be adopted, for example, postponing ignition time, reducing compression ratio, and using antiknock additives. Recently, water injection got wide attentions to control the super knock of gasoline engine [15]. There are basically two methods: port water injection and in-cylinder direct water injection. When port water injection is employed, water is sprayed into the intake manifold. For direct water injection, water is injected directly into the combustion chamber. For port water injection, it is better to install the injector close to the intake valve. The knock suppression was increased for a mass ratio of water over fuel as 0.3 [16]. An experimental study based on a single-cylinder engine indicated that fuel with a lower octane number could be used if port water injection was installed [17]. Kim et al. performed an experiment, and water was sprayed into the cylinder in a pressure of 5 MPa using a gasoline direct injection (GDI) fuel injector. The knock suppression was observed evidently [18]. Wei et al. investigated the influences of water injection quantity on energy efficiency experimentally, and it was found that the energy efficiency maximized with a mass ratio of 0.15 [19].

For the knock suppression of SI aviation piston engine, Anderson et al. had studied the combustion performance of a Rotax 914 engine fuelled with kerosene blends. The octane numbers of the fuel blends were 87 and 70 via blending 100 ON aviation gasoline and JP-8. The brake mean effective pressure reduced evidently when the octane number of the fuel blends diminished. The effect of adjusting the ignition timing was very small for performance improvement [20]. Subsequently, the influences of mass fraction of JP-8 were investigated. The volume ratio of JP-8 changed from 85–27% blending with an 87 ON fuel. To keep a high engine power output, the volume ratio should be decreased accordingly [21]. Later, they used a pre-chamber jet ignition system to increase the flame propagation speed of kerosene, and the fuel octane number was decreased by about 10 [22].

It is critical to find methods that can suppress the knock for an aviation fourstroke SI engine with kerosene. However, very few investigations were performed currently. In this chapter, the knock suppression of an aviation four-stroke SI engine is investigated numerically at first. Then, port water injection is installed, and the experimental results for knock suppression are measured. The improvement of the indicated mean effective pressure (IMEP) is evaluated.

### **2. Mathematical method**

The combustion of SI piston engine is mainly a turbulent flame propagation process. For the flow process in the cylinder, the mass, momentum, energy, and species conservation equations are modeled in the 3D numerical simulation model. *Knock Suppression of a Spark-Ignition Aviation Piston Engine Fuelled with Kerosene DOI: http://dx.doi.org/10.5772/intechopen.91938*

Mass:

aviation piston engine deviate from those of turbojets and scramjets significantly. Very few investigations focused on the combustion performance of kerosene for internal combustion engines [9–11]. Fernandes et al. studied the performance of a heavy-duty diesel engine fuelled with JP-8. The torque output and fuel consumption were similar with diesel fuel. However, the injection duration was enlarged to compensate the low fuel density, and the ignition delay time was increased because of a low cetane number. In addition, the emissions of nitrogen oxide (NOx) and particle material (PM) were decreased apparently [12]. Tay et al. developed a reaction mechanism composed of 48 species and 152 reactions for kerosene, and the

*Numerical and Experimental Studies on Combustion Engines and Vehicles*

Generally, the octane number (ON) of gasoline is in the range of 70–97, whereas the octane number of kerosene is much lower as only 20–50. Therefore, the knock phenomenon of kerosene is much severe in an SI engine, which will keep the power and economy of the engine in a low level [14]. To control the knock of an SI engine, various methods can be adopted, for example, postponing ignition time, reducing compression ratio, and using antiknock additives. Recently, water injection got wide attentions to control the super knock of gasoline engine [15]. There are basically two methods: port water injection and in-cylinder direct water injection. When port water injection is employed, water is sprayed into the intake manifold. For direct water injection, water is injected directly into the combustion chamber. For port water injection, it is better to install the injector close to the intake valve. The knock suppression was increased for a mass ratio of water over fuel as 0.3 [16]. An experimental study based on a single-cylinder engine indicated that fuel with a lower octane number could be used if port water injection was installed [17]. Kim et al. performed an experiment, and water was sprayed into the cylinder in a pressure of 5 MPa using a gasoline direct injection (GDI) fuel injector. The knock suppression was observed evidently [18]. Wei et al. investigated the influences of water injection quantity on energy efficiency experimentally, and it was found that

accuracy was validated in an optical engine [13].

the energy efficiency maximized with a mass ratio of 0.15 [19].

For the knock suppression of SI aviation piston engine, Anderson et al. had studied the combustion performance of a Rotax 914 engine fuelled with kerosene blends. The octane numbers of the fuel blends were 87 and 70 via blending 100 ON aviation gasoline and JP-8. The brake mean effective pressure reduced evidently when the octane number of the fuel blends diminished. The effect of adjusting the ignition timing was very small for performance improvement [20]. Subsequently, the influences of mass fraction of JP-8 were investigated. The volume ratio of JP-8 changed from 85–27% blending with an 87 ON fuel. To keep a high engine power output, the volume ratio should be decreased accordingly [21]. Later, they used a pre-chamber jet ignition system to increase the flame propagation speed of kerosene, and the fuel octane number was decreased by about 10 [22].

It is critical to find methods that can suppress the knock for an aviation fourstroke SI engine with kerosene. However, very few investigations were performed currently. In this chapter, the knock suppression of an aviation four-stroke SI engine is investigated numerically at first. Then, port water injection is installed, and the experimental results for knock suppression are measured. The improve-

The combustion of SI piston engine is mainly a turbulent flame propagation process. For the flow process in the cylinder, the mass, momentum, energy, and species conservation equations are modeled in the 3D numerical simulation model.

ment of the indicated mean effective pressure (IMEP) is evaluated.

**2. Mathematical method**

**92**

$$\frac{\partial \rho}{\partial t} + \frac{\partial}{\partial \mathbf{x}\_i} (\rho u\_i) = \mathbf{0}. \tag{1}$$

Momentum:

$$\frac{\partial}{\partial t}(\rho u\_i) + \frac{\partial}{\partial \mathbf{x}\_i} \left(\rho u\_i u\_j\right) = -\frac{\partial p}{\partial \mathbf{x}\_i} + \mu \frac{\partial}{\partial \mathbf{x}\_i} \left(\frac{\partial u\_i}{\partial \mathbf{x}\_j} + \frac{\partial u\_j}{\partial \mathbf{x}\_i} - \frac{2}{3} \delta\_{\vec{\eta}} \frac{\partial u\_k}{\partial \mathbf{x}\_k}\right). \tag{2}$$

Energy:

$$\frac{\partial}{\partial t}(\rho h) + \frac{\partial}{\partial \mathbf{x}\_j}(\rho u\_j h) = \frac{\partial p}{\partial t} + \frac{\partial}{\partial \mathbf{x}\_i} \left[ \frac{\mu}{Pr} \frac{\partial h}{\partial \mathbf{x}\_j} + \frac{\mu}{Pr} \left( \frac{Pr}{\mathbf{S}c} - \mathbf{1} \right) \sum\_{s=1}^N h\_s \frac{\partial Y\_s}{\partial \mathbf{x}\_j} \right]. \tag{3}$$

Species:

$$\frac{\partial}{\partial t}(\rho Y\_s) + \frac{\partial}{\partial \mathbf{x}\_j} \left(\rho u\_j Y\_s\right) = \frac{\partial}{\partial \mathbf{x}\_j} \left(\frac{\mu}{\text{Sc}} \frac{\partial Y\_s}{\partial \mathbf{x}\_j}\right) + \rho \dot{r}\_s. \tag{4}$$

The density of the fluid is calculated by the following equation according to the equation of state for gaseous mixture.

$$\rho = p / \left[ \text{RT} \sum\_{s=1}^{N} (Y\_s / \mathcal{M}\_s) \right]. \tag{5}$$

The enthalpy of the mixture can be calculated as

$$h = \sum\_{s=1}^{N} Y\_s h\_s(T). \tag{6}$$

The above equations can be used in direct numerical simulation of turbulence, but it is difficult for the actual calculation process because the calculation load is extremely large. In this study, *k - ε* model is employed to simulate the turbulent flow in the cylinder. The turbulent eddy viscosity is defined as

$$
\mu\_t = \mathbb{C}\_{\mu} \overline{\rho} k^2 / \varepsilon. \tag{7}
$$

The transport equations for the turbulent kinetic energy *k* and the dissipation rate *ε* are expressed by

$$
\overline{\rho}\tilde{u}\_{j}\frac{\partial k}{\partial \mathbf{x}\_{j}} = \frac{\partial}{\partial \mathbf{x}\_{j}} \left[ \left( \frac{\mu\_{t}}{\sigma\_{k}} + \mu \right) \frac{\partial k}{\partial \mathbf{x}\_{j}} \right] - \overline{\rho}u\_{i}^{\tilde{\nu}}u\_{j}^{\prime\prime} \frac{\partial \tilde{u}\_{i}}{\partial \mathbf{x}\_{j}} - \frac{\mu\_{t}}{\overline{\rho}^{2}} \frac{\partial \overline{\rho}}{\partial \mathbf{x}\_{i}} \frac{\partial \overline{\overline{p}}}{\partial \mathbf{x}\_{i}} - \overline{\rho}\varepsilon,\tag{8}
$$

$$\overline{\rho}\ddot{u}\_{j}\frac{\partial\varepsilon}{\partial\mathbf{x}\_{j}} = \frac{\partial}{\partial\mathbf{x}\_{j}}\left[\left(\frac{\mu\_{t}}{\sigma\_{\varepsilon}} + \mu\right)\frac{\partial\varepsilon}{\partial\mathbf{x}\_{j}}\right] - C\_{1}\frac{\varepsilon}{k}\left[\overline{\rho}u\_{i}^{\prime\prime}u\_{j}^{\prime\prime}\frac{\partial\bar{u}\_{i}}{\partial\mathbf{x}\_{j}} + \frac{\mu\_{t}}{\overline{\rho}^{2}}\frac{\partial\overline{\rho}}{\partial\mathbf{x}\_{i}}\frac{\partial\overline{p}}{\partial\mathbf{x}\_{i}}\right] - C\_{2}\overline{\rho}\frac{\varepsilon^{2}}{k}.\tag{9}$$

The empirical constants for the standard k-ε model are assigned below: C1 = 1.44, C2 = 1.92, Cμ = 0.09, σk = 1.0, and σε = 1.3. A nonslip boundary condition is assumed regarding all solid surfaces of the computational domain, and the standard wall functions are used.

The chemical time scales are assumed being much smaller than the turbulent ones. Therefore, the coherent flame model is used for the turbulent premixed combustion of the kerosene. The mean turbulent reaction rate is defined as

$$
\rho \dot{r}\_{\text{f\u}} = -\rho\_{\text{fr}} \mathbf{y}\_{\text{fu}\,\text{fr}} \mathbf{S}\_L \Sigma,\tag{10}
$$

where

*By* ¼ *Yvs* � *Yv*∞ 1 � *Yvs*

*Knock Suppression of a Spark-Ignition Aviation Piston Engine Fuelled with Kerosene*

The convective heat flux supplied from the gas to the droplet is denoted by

The KH-RT model is used for the breakup process of the droplets. The wave-

1 þ 0*:*45*Oh*0*:*5 � � 1 þ 0*:*4*T*0*:*7 � �

*g*

1 þ 0*:*87*We*1*:*67

� ��0*:*5 0*:*34 þ 0*:*38*We*1*:*5

*τa* ¼ 3*:*726*C*2*r*

Rayleigh-Taylor disturbances are in continuous competition with Kelvin-Helmholtz surface waves. The RT mechanism is caused by quick deceleration of the droplets, resulting in a growth of surface waves. When the droplet diameter is larger than the wavelength of the critical disturbance wave, the process of droplet breakage caused by RT must be considered. The fastest growing frequency *Ωt* and

> 2 3 ffiffiffiffiffi 3*σ* p

> > r

Λ*t* ¼ *C*4

τ*t* ¼ *C*5

The ATKIM model is used for the ignition process in the cylinder, which considers the charge stratification, the available electrical energy, the heat losses to the

The AnB model is used for the prediction of autoignition of the air-fuel mixture. The autoignition delay time of the combustible air-fuel mixture can be calculated as

100 � �3*:*4017

where *ON* is the octane number of kerosene and the parameters *A*, *n*, and *B* are

*p*�*ne B*

s

*Kt* ¼

spark plug, and the influences of turbulence on the early flame kernel.

*τd* ¼ *A ON*

calibrated by the measured data of the in-cylinder pressure.

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi *gt ρd* � *ρc* j j 3*σ*

> *π Kt*

1 Ω*t*

*gt ρd* � *ρc* j j1*:*5 *ρd* þ *ρc*

length *Λ* and wave growth rate *Ω* of the fastest growing wave are

Λ ¼ 9*:*02*r*

Ω ¼ *ρdr*3 *σ*

Ω*t* ¼

The corresponding wave length and breakup time are

Then, the breakup time is calculated as

*DOI: http://dx.doi.org/10.5772/intechopen.91938*

wave number *Kt* are

**95**

*:* (17)

*Q*\_ ¼ *DdπλNu T*ð Þ ∞ � *Ts :* (18)

� �0*:*6 *:* (19)

ΛΩ *:* (21)

, (22)

*:* (23)

, (24)

*:* (25)

*T*, (26)

*g* ð Þ 1 þ *Oh* 1 þ 1*:*4*T*0*:*6 � � *:* (20)

where *SL* is the mean laminar burning velocity and *Σ* is the flame surface density. The transport equation for the flame surface density is

$$\frac{\partial \Sigma}{\partial t} + \frac{\partial}{\partial \mathbf{x}\_j} \left( \overline{\mathbf{u}}\_j \Sigma \right) - \frac{\partial}{\partial \mathbf{x}\_j} \left( \frac{\nu\_t}{\sigma\_2} \frac{\Sigma}{\partial \mathbf{x}\_j} \right) = \mathbf{S}\_\mathbf{g} - \mathbf{S}\_a + \mathbf{S}\_{LAM},\tag{11}$$

where *σ*Σ is the turbulent Schmidt number, *vt* is the turbulent kinematic viscosity, *Sg* is the product of the turbulent strain rate and the flame surface, *Sa* is the annihilation of flame surface, and *SLAM* is the contribution of laminar combustion.

The laminar flame speed can be computed as

$$\mathbf{S}\_{L} = \mathbf{S}\_{L0} \left( \mathbf{1} - \mathbf{2}. \mathbf{1} \mathbf{y}\_{\mathrm{EGR}} \right) \left( \frac{T\_{fr}}{T\_{ref}} \right)^{a\_1} \left( \frac{p}{p\_{ref}} \right)^{a\_2},\tag{12}$$

where *Tref* and *pref* are the reference values of the standard state and *a*1 and *a*2 are fuel-dependent parameters.

The water injected from the water injector forms small droplets and mixes with the gases in the cylinder. The spray model needs to consider the droplet movement, fragmentation, evaporation, and wall impingement. Considering the movement resistance and buoyancy of droplets in the gases, the following equation for the droplet velocity is obtained.

$$\frac{du\_{id}}{dt} = \frac{3}{4} \mathbf{C}\_{\rm D} \frac{\rho\_{\rm g}}{\rho\_{\rm d}} \frac{\mathbf{1}}{D\_{\rm d}} |u\_{\rm g} - u\_{d}| \left( u\_{\rm ig} - u\_{id} \right) + \left( \mathbf{1} - \frac{\rho\_{\rm g}}{\rho\_{\rm d}} \right) \mathbf{g}\_{i},\tag{13}$$

where *uid* is the particle velocity vector, *uig* is the domain fluid velocity, *CD* is the drag coefficient, *Dd* is the particle diameter, *ρg* and *ρd* are the densities of the gas and the droplet, and *gi* is the gravitational acceleration vector.

The instantaneous droplet position vector can be determined by

$$\frac{d\boldsymbol{\omega}\_{id}}{dt} = \boldsymbol{\mu}\_{id}.\tag{14}$$

The evaporation process of the droplet is described by

$$m\_d c\_{p,d} \frac{dT\_d}{dt} = \dot{Q} \left( 1 + L \frac{\dot{f}\_w}{\dot{q}\_s} \right). \tag{15}$$

Assuming the Lewis number is unity, the flux ratio can be written as

$$\frac{\dot{f}\_{\text{vs}}}{\dot{q}\_{\text{s}}} = \frac{-B\_{\text{y}}}{h\_{\text{os}} - h\_{\text{s}} - \left(h\_{\text{vs}} - h\_{\text{gc}}\right)\left(Y\_{\text{voo}} - Y\_{\text{vs}}\right)},\tag{16}$$

*Knock Suppression of a Spark-Ignition Aviation Piston Engine Fuelled with Kerosene DOI: http://dx.doi.org/10.5772/intechopen.91938*

where

The chemical time scales are assumed being much smaller than the turbulent ones. Therefore, the coherent flame model is used for the turbulent premixed combustion of the kerosene. The mean turbulent reaction rate is defined as

where *SL* is the mean laminar burning velocity and *Σ* is the flame surface density.

*vt σ*Σ Σ *∂x j* � �

� � *Tfr*

*ug* � *ud* � � �

The instantaneous droplet position vector can be determined by

*dTd*

Assuming the Lewis number is unity, the flux ratio can be written as

¼ �*By h*∞ � *hs* � *hvs* � *hgs*

*dt* ¼ *Q*\_ 1 þ *L*

*dxid*

the droplet, and *gi* is the gravitational acceleration vector.

The evaporation process of the droplet is described by

*mdcp*,*d*

\_ *f vs q*\_ *s*

**94**

where *σ*Σ is the turbulent Schmidt number, *vt* is the turbulent kinematic viscosity, *Sg* is the product of the turbulent strain rate and the flame surface, *Sa* is the annihilation of flame surface, and *SLAM* is the contribution of laminar combustion.

> *Tref* !*a*1

� *uig* � *uid*

where *uid* is the particle velocity vector, *uig* is the domain fluid velocity, *CD* is the drag coefficient, *Dd* is the particle diameter, *ρg* and *ρd* are the densities of the gas and

� � þ 1 � *ρg*

\_ *f vs q*\_ *s*

!

� �ð Þ *Yv*∞ � *Yvs*

*ρd* � �

*dt* ¼ *uid:* (14)

*gi*

*:* (15)

, (16)

, (13)

where *Tref* and *pref* are the reference values of the standard state and *a*1 and *a*2 are

The water injected from the water injector forms small droplets and mixes with the gases in the cylinder. The spray model needs to consider the droplet movement, fragmentation, evaporation, and wall impingement. Considering the movement resistance and buoyancy of droplets in the gases, the following equation for the

The transport equation for the flame surface density is

*Numerical and Experimental Studies on Combustion Engines and Vehicles*

*u j*Σ � � � *∂*

*SL* ¼ *SL*0 1 � 2*:*1*yEGR*

The laminar flame speed can be computed as

*∂x j*

∂Σ *∂t* þ *∂ ∂x j*

fuel-dependent parameters.

droplet velocity is obtained.

*duid dt* ¼ 3 4 *CD ρg ρd* 1 *Dd*

*ρr*\_*fu* ¼ �*ρfryfu*,*frSL*Σ, (10)

*p pref*

!*a*2

¼ *Sg* � *Sa* þ *SLAM*, (11)

, (12)

$$B\_{\mathcal{Y}} = \frac{Y\_{\nu\nu} - Y\_{\nu\infty}}{1 - Y\_{\nu\nu}}.\tag{17}$$

The convective heat flux supplied from the gas to the droplet is denoted by

$$
\dot{Q} = D\_d \pi \lambda \text{Nu}(T\_\Leftrightarrow - T\_s). \tag{18}
$$

The KH-RT model is used for the breakup process of the droplets. The wavelength *Λ* and wave growth rate *Ω* of the fastest growing wave are

$$\Lambda = 9.02r \frac{\left(1 + 0.45 \Omega h^{0.5}\right) \left(1 + 0.47^{0.7}\right)}{\left(1 + 0.87 \, W e\_{\text{g}}^{1.67}\right)^{0.6}}.\tag{19}$$

$$
\Omega = \left(\frac{\rho\_d r^3}{\sigma}\right)^{-0.5} \frac{\mathbf{0}.34 + \mathbf{0}.38 \mathbf{W} \mathbf{e}\_\mathbf{g}^{1.5}}{(1 + \mathcal{O}h)\left(1 + \mathbf{1}.4T^{0.6}\right)}.\tag{20}
$$

Then, the breakup time is calculated as

$$
\pi\_d = \frac{3.726C\_2r}{\Lambda \Omega}.\tag{21}
$$

Rayleigh-Taylor disturbances are in continuous competition with Kelvin-Helmholtz surface waves. The RT mechanism is caused by quick deceleration of the droplets, resulting in a growth of surface waves. When the droplet diameter is larger than the wavelength of the critical disturbance wave, the process of droplet breakage caused by RT must be considered. The fastest growing frequency *Ωt* and wave number *Kt* are

$$\mathfrak{Q}\_{t} = \sqrt{\frac{2}{3\sqrt{3\sigma}} \frac{\mathbf{g}\_{t} |\rho\_{d} - \rho\_{c}|^{1.5}}{\rho\_{d} + \rho\_{c}}},\tag{22}$$

$$K\_t = \sqrt{\frac{\mathbf{g}\_t |\rho\_d - \rho\_c|}{\mathbf{3}\sigma}}. \tag{23}$$

The corresponding wave length and breakup time are

$$
\Lambda\_l = \mathcal{C}\_4 \frac{\pi}{K\_l},
\tag{24}
$$

$$
\pi\_t = C\_5 \frac{1}{\Omega\_t}.\tag{25}
$$

The ATKIM model is used for the ignition process in the cylinder, which considers the charge stratification, the available electrical energy, the heat losses to the spark plug, and the influences of turbulence on the early flame kernel.

The AnB model is used for the prediction of autoignition of the air-fuel mixture. The autoignition delay time of the combustible air-fuel mixture can be calculated as

$$
\pi\_d = A \left(\frac{ON}{100}\right)^{3.4017} p^{-n} e^{\frac{\theta}{\pi}},\tag{26}
$$

where *ON* is the octane number of kerosene and the parameters *A*, *n*, and *B* are calibrated by the measured data of the in-cylinder pressure.


**4.2 Occupational exposure limit values and threshold effects**

OELVs are established to prevent occupational illnesses in general but they are not specific to a health effect or target organ. Thus, even though the TCE and PCE concentrations are below the OELVs, some serious adverse health effects may occur [31]. Furthermore, since the general population is also involved, people, including the occupationally exposed ones, are more effectively protected if the air concentrations are below the toxicological reference values (TRV). These TRVs are exclusively established based on scientific considerations [31]. In this section, we are only discussing TRVs that are protecting populations from threshold effects.

Since there are no Tunisian values, we are checking TRVs that are conceived from French and American agencies. The French values are constructed by the French Agency for Food, Environmental and Occupational Health & Safety (ANSES) [32]. The TCE chronic TRV via inhalation is constructed based on noncarcinogenic renal effect because of a well-established nephrotoxicity mechanism [33]. The TRV is fixed on 3.2 mg/m3 . The mean value of all TCE concentrations is 13.4 times the French TRV, which means that there is a great risk of nephrotoxicity for workers. Among the American TRVs, we can mention, for instance, the ones that are established by the Environmental Protection Agency (US EPA) [34]. The TCE

*Solvents, Ionic Liquids and Solvent Effects*

*ND, not detected; F, dry cleaning facility.*

**Table 3.**

it still is 18% lower than the mean value of TCE concentrations in the workplaces' atmospheres, which confirms its connection with the lowest consumed quantity. Regarding PCE, the personal sampling concentrations are higher than workplaces' atmospheres, particularly in P3 where the PCE level has doubled. Thus, the employees who are carrying personal sampling devices (particularly in F3 and F29) are exposed to greater levels of solvents. This is obviously due to their specific movement patterns where they could be moving closer to the emission sources. This consequently justifies our judgment to use the personal sampling in these job tasks. As we can notice in **Table 3**, almost all TCE concentrations are close to the selected OELV (**Table 2**). These results are quite surprising because all facilities have declared to only use PCE. In fact, we suspected to have found TCE in trace amounts as impurity content. But, these high concentrations are an alarming finding. Thus, even though all PCE concentrations are lower than one-tenth of the OELV (except for both measurements in F29 and for F3 personal sampling level), we believe that urgent corrective actions should be carried out in all facilities.

*Indoor air measured concentrations of TCE and PCE in dry cleaning facilities.*

**Fixed monitoring Personal sampling TCE PCE TCE PCE**

**F mg/m3 ppm mg/m3 ppm mg/m3 ppm mg/m3 ppm** F3 26.35 4.98 12.60 1.89 35.38 6.69 25.08 3.76 F4 40.58 7.67 7.86 1.17 — — — — F5 44.67 8.45 ND ND — — — — F13 42.06 7.95 ND ND 41.60 7.87 — — F19 49.61 9.38 ND ND — — — — F21 43.57 8.24 ND ND — — — — F22 49.72 9.40 ND ND — — — — F28 42.22 7.98 ND ND — — — — F29 41.10 7.77 54.34 8.14 44.06 8.33 65.40 9.80 F30 41.45 7.84 ND ND — — — — F31 48.89 9.25 7.98 1.19 — — — — F35 43.21 8.17 8.47 1.27 — — — — F36 45.86 8.67 ND ND — — — — F39 46.21 8.74 ND ND — — — — F41 43.74 8.27 12.27 1.84 — — — — F43 42.27 7.99 ND ND — — — —

**4.1 Location of dry cleaning facilities in Sfax and residential exposure**

First of all, since there was no list of dry cleaning facilities in Sfax city, we encountered some difficulties during the identification and location steps. That is why, it was decided to conduct a door-to-door canvassing in the study area.

**34**

**4. Discussion**

chronic Reference concentration (RfC) is fixed on 2 10−3 mg/m3 [35] based on both developmental and immune effects. The mean value of all TCE concentrations is 21,385 times the RfC, which means that there is also an enormous risk related to the increase of congenital cardiac malformations and a decrease of thymus weight [36]. This developmental effect could be also alarming because 70% of the workers are women.

Regarding PCE, the chronic French TRV via inhalation is developed based on neurological effects. Among them, the ANSES distinguished the visual alterations as the most sensitive effect toward the lowest exposure levels [30]. The TRV is fixed on 0.4 mg/m3 [30, 32], which is over 60 times the mean value of all PCE concentrations. Since the PCE concentration distribution is highly dispersed, the ratio, and consequently the risk, is even higher (108 times) if we use the upper bound of the 95% confidence interval for mean. As for the RfC, it is set at the tenth of the French TRV (0.04 mg/m3 [37]) based also on neurological effects, notably neurobehavioral effects and color vision impairment induced by neurotoxicity [29]. Thus, the risk of neurotoxicity could be even 10 times higher for workers. It is worth to note that these risks maybe also extrapolated to the general population with lower intensity, but with great significance and likely adverse consequences.

### **4.3 Carcinogenic effects**

Chronic exposure can also induce carcinogenic effects. Thus, there are nonthreshold TRVs as well. Each value is established for a specific tumor site or sites based on a lifetime exposure.

As shown in **Table 1**, according to World Health Organization (WHO) [13, 38], TCE was proved to cause kidney cancer to humans. Indeed, the French carcinogen TRV via inhalation, established by ANSES, is the ERU "excès de risque unitaire," and is equal to 10−6 (μg.m−3 ) −1 [32]. It was constructed based on kidney cancer, specifically the renal cell carcinoma [33].

Yet, other solid scientific evidences indicate other potential carcinogenic effects of TCE. In fact, the US EPA established the Inhalation Unit Risk for three different cancer sites: the hematologic, liver, and kidney cancers. Its value is more protective and equal to 4.1 10−6 (μg/m3 ) −1 [35, 36]. According to the latter value, the lifetime cancer risk, which is defined as the product of multiplying the carcinogen TRV by the exposure concentration, is equal to 17.5 10−2 , when compared with the mean value of all TCE concentrations. This means that, more than 17 additional cases of hematologic, hepatic, and renal cancers are estimated to occur during a lifetime exposure to TCE in a population of 100 people. This value is considerably high even for an occupationally exposed population.

Furthermore, other international agencies have also demonstrated the carcinogenic effect of TCE for other target organs, such as in testes or in lungs [33].

As for PCE, according to WHO [38], it is probably inducing urinary bladder cancer to humans.

Elsewhere, ERU established by ANSES is based on liver cancer (hepatocellular adenoma and carcinoma) [30] and is equal to 2.6 × 10−7 (μg/m3 ) −1 [32]. Additionally, the Inhalation Unit Risk by US EPA has the same value, but only for the hepatic cancer. According to this carcinogen TRV, the lifetime cancer risk is 6.3 × 10−3 . It means that six additional cases of liver cancer are expected to take place during a lifetime exposure in a population of 1000 people. Due to the high variability of PCE concentrations, this value could be even higher (more than 11 cases if the upper bound of the 95% confidence interval for mean is used) in some cases. Although these findings are slightly better than those for the TCE, they are still high and troubling. In fact, the inhalation cancer risk could be worse

**37**

interdicted [7].

*Per- and Trichloroethylene Air Monitoring in Dry Cleaners in the City of Sfax (Tunisia)*

if we consider a total cancer risk due to the cumulative exposure to both solvents,

In Tunisia, chemical risk assessment studies have started in many industrial sectors that are handling solvents, such as adhesive [39] and shoe manufacturing [40] industries. Yet, the occupational exposure in dry cleaning industry is not explored. The exposure assessment was achieved using questionnaires and indoor air measurements. In fact, air samplings were carried out for the first time in the region of Sfax. We consider that this study will help for better understanding the dry cleaning industrial sector in Tunisia, which will lead to further improvement in health risk

All the 33 dry cleaning facilities in Sfax have announced to use PCE as a dry cleaning product; however, due to the high TCE concentrations, we assume the

• The dry cleaning products are contaminated by TCE but in significant amounts, so either the product manufacturers did not inform their customers about TCE or solvent handlers are mixing PCE with TCE to reduce cost since it is cheaper.

• TCE could be coming from a different emission source, as it could be used as a

• Both managers and workers are not aware of the chemical risks and the adverse

It is worth to note that in our study, the air monitoring was conducted during the warm season (in June and the temperature was ~30°C) when the workplaces were relatively highly ventilated; doors and windows were open. However, in winter season, they are rather closed. So, we suppose that the exposure levels will be even

The suggested corrective measures could start with the substitution. It consists

of eliminating the use of the hazardous products by replacing them with less dangerous ones, especially because they are suspected to contain a high amount of TCE or by switching to a different process. Among the substitution processes, there are the (i) wet cleaning that consists of using a mixture of water, detergents, and surfactants whose risks are little or currently unknown, (ii) hydrocarbon dry cleaning that involves solvents that are less volatile than PCE, and (iii) siloxane D5 dry cleaning that are using the latter product as a liquid solvent that is barely volatile. These alternative machines may be quite costly; however, other easier corrective

For the second level of the corrective actions, we highly recommend the implementation of collective protection measures (CPM). In every facility, general mechanical ventilation ought to be installed immediately, with fume extraction systems positioned toward every job task. It should be noted that the polluted air needs to be rejected after purification [7]. In addition, the mechanical ventilation has to be supplied by outdoor fresh air and the reuse of the same air after gas scrubbing is

The third level of the corrective actions involves the mandatory use of PPE in some job tasks. The wearing of protective clothing is advisable for every worker. Elsewhere, we noticed that the loading of solvents in the cleaning machines is generally introduced by manual pouring on the back or through the porthole,

*DOI: http://dx.doi.org/10.5772/intechopen.86725*

assessment studies in this sector.

following:

especially when the target organ is the same.

degreaser in maintenance operations.

higher in poor ventilation conditions.

actions could be carried out.

health effects of both solvents, especially TCE.

**4.4 Health risk assessment and chemical risk prevention**

if we consider a total cancer risk due to the cumulative exposure to both solvents, especially when the target organ is the same.


*4.7. Testing of a Gulf of Maine Dinophysis norvegica Culture for DST Production*

Two new cultured clonal isolates of *D. norvegica* (DNBH-FB4 and DNBH-B3F) were established in culture in May 2018 from surface water collected from Blue Hill Falls, Maine, following the single-cell isolation methods described by [43]. At the time of water collection, the salinity was 30 psu and the water temperature was 12 ◦C. During isolation and maintenance of the culture, *D. norvegica* were fed *Mesodinium rubrum,* which had been previously raised on *Teleaulax amphioxeia* isolated from Japan [44] following the protocols of [45] as modified by [46]. The dinoflagellate, ciliate, and cryptophyte cultures were grown in modified f/6-Si medium [47] and a salinity of 30 at 15 ◦C in dim light (40 µmol photons·m-2·sec-1) under a 14 h:10 h light:dark photocycle.

To assess the toxigenicity of DNBH-FB4 and DNBH-B3F, cells were inoculated into fresh medium, salinity 30 psu, at 400 cells·mL−1 , fed *M. rubrum* at a 1:10 ratio of prey to predator, and monitored

every 3 days for the complete consumption of *M. rubrum* by examining 1 mL subsamples in a Sedgewick-Rafter counting cell at 100x using an Olympus CX31 light microscope (Olympus America, Waltham, MA, USA). Three days after all ciliate prey were consumed, (i.e., during late exponential growth of the dinoflagellate) the culture was harvested for toxin analysis.

The harvested cultures were gently separated into cells (intracellular toxins) and medium (extracellular toxins) using a 10 µm sieve and the components were extracted and analyzed for toxins separately. Cells and medium were bath-sonicated at room temperature for 15 min (Branson 5800 Ultrasonic Cleaner, 5800) and loaded onto an Oasis HLB 60 mg cartridge (Waters Corporation, Millford, MA, USA) that was previously equilibrated with 3 mL of MeOH and 3 mL of GenPure water. The cartridge was then washed with 6 mL of GenPure water, blown dry, and eluted with 1 mL of 100% MeOH into a glass 1.5 mL high recovery LC vial and stored at −20 ◦C until analyzed. A portion of the sample underwent alkaline hydrolysis to enable the quantitation of total DSP toxins (free plus esterified) following [48]. Extracts, original and alkaline hydrolyzed, were analyzed using an Acquity liquid chromatography system coupled with a Xevo mass spectrometer with electrospray ionization (Waters, Milford, MA, USA) following the DSP and PTX2 analytical methods described by [49].

Dihydro-DTX1 was detected using selected reaction monitoring (SRM) in negative ion mode with the transitions *m*/*z* 819.5→255.1, 819.5→819.5, 819.5→151.1, and 819.5→113.1. Quantitation was performed using the former SRM transition; concentrations were calculated using an external DTX1 standard curve with MassLynx 4.1 software (Waters Corporation, Millford, MA, USA). Matrix spikes, final concentration of 12.5 ppb DTX1, were conducted to confirm separation from DTX1. Toxin data are presented as toxin concentration per mL of culture, and free vs. esterified, with the latter being calculated through the subtraction of free toxins from total toxins.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2072-6651/12/9/533/s1, Table S1: Sampling site coordinates for shellfish and phytoplankton samples collected during the 2016 *Dinophysis norvegica* bloom depicted in Figures 1 and 2. Figure S1: MS/MS spectrum of *m*/*z* 819.5 peak detected in the 26–27 min fraction from the bioactivity guided fractionation extract of mussel (*Mytilus edulis*) collected during 2016 *Dinophysis norvegica* bloom in the central coast of the Gulf of Maine, USA; Figure S2: Structure of precursor ions and proposed product ion structures for okadaic acid (OA), dinophysistoxin 1 (DTX1), and dinophysistoxin 2 (DTX2).

**Author Contributions:** Conceptualization, J.R.D., W.L.S., J.M., and J.L.S.; Investigation, J.R.D., W.L.S., M.D.C., J.M., A.E.H., J.L.S., D.M.K., P.M., C.D.R., C.A.B., and S.K.L.; Methodology, J.R.D., W.L.S., M.D.C., J.L.S., D.M.K., and P.M.; Resources, J.M., A.E.H., B.J.L., and D.W.M.; Supervision, K.K., S.D.A., J.B., and S.K.L.; Writing—original draft, J.R.D., W.L.S., M.D.C., A.E.H. and J.L.S.; Writing—review and editing, J.M., D.M.K., P.M., S.D.A., and S.K.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** Partial support for this research was received from the National Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science Competitive Research, Ecology and Oceanography of Harmful Algal Blooms Program under awards NA17NOS4780184 and NA19NOS4780182 to Juliette Smith (VIMS) and Jonathan Deeds (US FDA), and Prevention, Control, and Mitigation of Harmful Algal Blooms program award NA17NOS4780179 to Stephen Archer. This paper is ECOHAB publication number EC0956.

**Acknowledgments:** The authors would like to express their gratitude to Marta Sanderson and Han Gao (VIMS) for their assistance in culture maintenance and toxin analysis, Satoshi Nagai (National Research Institute of Fisheries Science, Japan) for his sharing of *Mesodinium rubrum* and *Teleaulax amphioxeia* cultures, Stephen Conrad (CFSAN) for assistance with figure preparation, and Claude Mallet (Waters Inc.) and Sabrina Giddings (Biotoxin Metrology, NRC Canada) for their technical assistance.

**Conflicts of Interest:** The authors declare no conflict of interest

## **References**

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5.2.2. Nonrepresentative Training Data

The effects of nonrepresentative training data on consistency-based MCS topology design and on redundancy-based design are evaluated. Consistency-based topology is obtained by Algorithm 1 with parameter *α* = 0 as argued in Proposition 4. Its redundancybased counterpart is obtained by Algorithm 2. To ensure highly redundant information sources in fusion nodes, parameter *η* is set to 0.6, i.e., sources are added to a fusion node if their redundancy is greater than or equal to *η*.

Both design approaches are applied to the Sensorless Drive Diagnosis (SDD) dataset [82,83]—a multi-class classification dataset (The SDD dataset is available for download at the University of California Machine Learning Repository [84]). Nonrepresentative training data are simulated by withholding data of certain classes from the design algorithms creating a situation of epistemic uncertainty.

For the creation of the Sensorless Drive Diagnostics data set, an electromechanical drive was monitored to detect faulty system behaviour. Data comprise features obtained from phase-related motor currents and voltages. Each feature serves as an information source in this evaluation. The dataset is particularly interesting because (i) it contains highly noisy data and (ii) data are often linearly or non-linearly correlated and thus potentially redundant. The SDD dataset contains 11 classes in total, of which class 1 represents healthy system behaviour (henceforth referred to as the normal condition). All other classes represent various fault states, such as gear or bearing damage.

The design algorithms are executed on two subsets of the dataset. First, only data belonging to the normal condition build a reduced training dataset. This reduced set manifests epistemic uncertainty. It is nonrepresentative with regard to the complete behaviour of information sources. For comparison, the second subset is constructed to include all data, i.e., the complete dataset serves as training data.

Regarding the preprocessing steps, the unimodal potential function (31) is trained on the normal condition with parameters *D*l = 2 and *D*r = 2. To regard the noise in the dataset, possibility distributions are modified with reliability parameters ∀*S* ∈ **S** : *rel*(*S*) = 0.9 and *β* = 1. Additionally, memberships are smoothed with a moving average filter using a window size of 5. As the SDD dataset provides data as singular values, the preprocessing steps result in rectangular possibility distributions.

The following behaviour is expected from the topology design approaches, which helps in verifying their output:

The results of Algorithms 1 and 2 are shown in Tables 2 and 3, respectively. Both tables show found fusion nodes for the first layer of the two-layer fusion topology. Fusion nodes are shown both for reduced and complete training data along with redundancy *ρ* (18), range evidence ep (20), and inconsistency evidence ec (19).

The results of Table 2 show that the MCS-based topology meets the expectation regarding fusion node sizes. Furthermore, each set **S**MCS−*α* (*k*),complete is a subset of at least one **S**MCS−*α* (*k*),reduced, e.g., **S**MCS−*α* (1),complete ⊂ **S**MCS−*α* (7),reduced. It is also notable that—especially but not exclusively on reduced data—some sources occur in many fusion nodes.

This relates, for example, to sources 25 and 37. Sources with little informative value are likely to be consistent with other sources because such sources provide possibility distributions, which are likely wide or even close to total ignorance. For sources 25 and 37, it is the case that both provide large possibility distributions covering a significant part of the frame of discernment. Lastly, no fusion node based on complete data is exactly similar to fusion nodes based on reduced data (which is different in the following redundancy-based approach). Fusion nodes differ significantly. This means that nonrepresentative data limits the performance of the consistent-based approach substantially, i.e., because epistemic uncertainty is not considered by Algorithm 1 fusion nodes are inflated with spuriously consistent information sources.

The results of the redundancy-based approach (Table 3) also meet the expectations formulated beforehand, i.e., ∀*k*, ∃*k* : **S***ρ* (*k*) ⊆ **S**MCS−*α* (*k* ) . In contrast to the consistency-based approach, sources with little informative value are not part of fusion nodes (e.g., sources 25 and 37). The computation of the range (22) penalises wide possibility distributions. This is because of the disjunctive fusion prior to computing the position of a set of distributions (21). Sets including information items close to total ignorance are given a position close to 0.5 resulting in low range values and hence low redundancies.

Similar to the consistency approach, the amount of fusion nodes decreases from reduced to complete training data. This shows that the redundancy-based approach is not able to rule out all sets showing spurious redundancy. However, the majority of nodes learned on complete data are exactly similar to nodes on reduced data. This is true for sets {10, 11, 12}, {19, 20, 21, 22, 23, 24}, {31, 32, 33}, {34, 35, 36}, and {46, 47, 48} with {7, 8, 9} coming close. This shows that the redundancy-based approach finds significant sets despite nonrepresentative training data.

**Table 2.** Fusion nodes and their contributing information sources as designed by Algorithm 1 with parameter *α* = 0. Grouped information sources are consistent for all instances of training data (see metric ec (19)). The left side shows fusion nodes found on reduced, highly epistemic uncertain training data, i.e., only data of the class stating normal condition were available. The right side shows nodes found on complete data. Fusion node sets on reduced training data do not meet the required redundancy threshold (i.e., *ρ η*), which is due to the low range-based evidence ep (20). Information sources are numbered as provided by the SDD dataset [82,83]. Fusion nodes with less than two information sources are omitted. In total, 24 fusion nodes were found on reduced data and 28 on complete data.

Therefore, it copes better than the consistency approach in situations with high epistemic uncertainty because the evidence ep (20) quantifies epistemic uncertainty. Nonetheless, it is advisable to update and adapt fusion nodes and topology with newly available data. This reduces risk of nodes with spurious redundancy.

Figure 7 depicts scatter plots of selected information sources to visualise the shortcomings of the consistency-based approach and to show the effects of epistemic uncertainty. Information items may be close to each other—and therefore be consistent—for parts of the training data (see plots (a), (b), and (c)). This is indicated by the fact that the positions of items are clustered in the upper right corners for reduced training data. This does not mean that consistent behaviour carries over to complete data (which is only true for (c)).

**Table 3.** Fusion nodes and their contributing information sources as designed by Algorithm 2 with parameters *α* = 0 and *η* = 0.6. Grouped information sources are consistent for all instances of training data and range over a significant part of the frame of discernment. The left side shows fusion nodes found on reduced, highly epistemic uncertain training data. The right side shows nodes found on complete data. Information sources (features) are numbered as provided by the SDD dataset [82,83]. Fusion nodes with less than two information sources are omitted. In total, 29 fusion nodes were found on reduced data and 31 on complete data.

**Figure 7.** Information items of selected information sources belonging to reduced training data (green) and complete training data (blue). Data belongs to the Sensorless Drive Diagnosis dataset [82,83]. Subplot (**a**) shows information sources (features) {1, 5}, (**b**) {25, 10}, and (**c**) {43, 45}. Each point in the scatter plots represents the position or centre of gravity of a possibility distribution obtained by (21). Possibility distributions of a single pair are plotted below each scatter plot to give an intuition about the size of the distributions. In the case of reduced training data, information sources (**a**) {1, 5} and (**b**) {25, 10} belong to fusion nodes in the consistency-based approach (see Table 2) but not in the redundancy-based approach (see Table 3). Without the additional information provided by the range metric (22), the consistency-based approach considers sources, which result in being inconsistent on complete training data. Sources (**c**) {43, 45} are given as an example in which information items are consistent over the complete training data. Both the consistency-based as well as the redundancybased approach consider {43, 45} in fusion nodes. Note that the scatter plot in (**a**) is zoomed in for better visibility.

#### 5.2.3. Defective Sources

Regarding defective sources, two adaptations to the MCS topology were proposed in this paper. Both adaptations—(i) discounting defective sources and (ii) estimation-fusionbased nodes—were evaluated on data with purposely engineered source defects.

The Typical Sensor Defects (TSD) dataset [21] provides such defective sources (The TSD dataset is available for download at https://zenodo.org/record/56358 (accessed on 9 March 2022)). The TSD dataset contains data of a storage container for hazardous and flammable materials measured, e.g., by temperature, smoke, and gas sensors. The dataset comprises several files, which each include a specific simulated source defect, such as incremental drift or outlier readings. For this evaluation, the files "data\_standard.csv" and "data\_drift\_0\_001.csv" are used.

The first provides unaltered data without defects. The second one contains the same data with the exception that a temperature sensor (feature 15) drifts with 1‰ h−1 of its base value. Regarding preprocessing, the parameters for the unimodal potential function (31) are provided as metadata in the dataset. As data are hardly affected by noise, sources are fully reliable, and no averaging filter is applied. Data are provided with an error margin of ±2% of the sensor's measurement range [21] creating a uniform probability density function. Thus, preprocessing results in triangular possibility distributions.

The fusion topology is learned on unaltered data using the consistency-based approach of Algorithm 1—again with *α* = 0. This creates three fusion nodes on the first layer:

Their fusion results are fused at the final node *fn*(1,2) using MCS fusion (6). For the first layer nodes, the following fusion rules are used and evaluated:

Intermediate and final fusion outputs are computed for each of these fusion rules. The results of the same fusion rule on unaltered (standard) and drifted data are compared regarding their similarity. As similarity measure the possibilistic Jaccard index [32,85]

$$\text{sim}(\mathbf{p}) = \frac{\int\_0^1 \min\left(\pi\_{(k),\text{standard}}^{\text{fu}}(\mu), \pi\_{(k),\text{drift}}^{\text{fu}}(\mu)\right) \text{d}x}{\int\_0^1 \max\left(\pi\_{(k),\text{standard}}^{\text{fu}}(\mu), \pi\_{(k),\text{drift}}^{\text{fu}}(\mu)\right) \text{d}x}. \tag{32}$$

is applied. Similarities *sim* ∈ [0, 1] with *sim* = 1 indicating full similarity. Table 4 lists the minimum, arithmetic mean, and maximum values of the computed similarity values for *fn*(2) and *fn*(1,2). High similarities show robust behaviour against the defective source. As *fn*(1) and *fn*(3) contain no defective sources, they are omitted from the table.

It can be seen from the results that renormalised conjunctive fusion, which is the default rule in MCS fusion, was affected the most by the drifting source. Measures against defective sources are therefore reasonable.

The approach of detecting and discounting by widening inconsistent possibility distributions improved the robustness slightly but not substantially. The ineffectiveness is due to two reasons. First, widening with (25) does shift the fusion result toward reliable sources but does not guarantee that the original fusion result is restored. It is reasonable to assume that parameter *β* has a substantial impact, which needs to be investigated in further works. Second, a drifting possibility distribution may actually drift into other possibility distributions creating a false most consistent subset in the process.

This may lead to situations in which the wrong source is discounted. It is assumed that the risk of this happening decreases with the number of sources in a fusion node. Estimation fusion nodes showed, on the other hand, a significant increase in robustness evidenced by the higher min- and mean-values. Weighted estimation fusion demonstrated the best performance. Due to its averaging nature, estimation fusion reduces the effects of defective sources the better the higher the number of sources.

**Table 4.** Similarity between fusion node outputs on unaltered (standard) dataset and drift affected dataset. The table shows the minimum, arithmetic mean, and maximum of similarities computed on each data instance. The drift affected source belongs to *fn*(2). Therefore, *fn*(1) and *fn*(3) are not explicitly listed. Similarity is increased by proposed countermeasures to defective sources.


5. Recovery of gold from Mongolian gold ore

At present, majority of gold has been recovered from gold and silver ores by means of cyanide process developed at the beginning of twentieth century as schematically shown in Figure 14. In this process, pulverized ores are leached using alkaline cyanide solution to extract gold as gold(I)-cyanide complexes according to the following reaction:

$$4\text{Au} + 8\text{CN}^- + \text{O}\_2 + 2\text{H}\_2\text{O} = 4\text{AuCN}\_2^- + 4\text{OH}^-\tag{3}$$

The extracted gold(I) as anionic species, AuCN2 �, is adsorbed onto activated carbon or strongly basic anion exchange resins, which are termed as CIP and RIP

Figure 14. Flow sheet of conventional gold recovery process from gold ores using alkaline cyanide solutions.

## Gold Recovery Process from Primary and Secondary Resources Using Bioadsorbents DOI: http://dx.doi.org/10.5772/intechopen.84770

processes, respectively. Because it is difficult to desorb the gold adsorbed onto these adsorbents, these are incinerated at high temperature to recover metallic gold. This cyanide process has suffered from some problems as follows:

As alternatives to cyanide leaching, some noncyanide leaching processes such as those using hypochlorous acid, bromine, thiosulfate, and thiourea have been proposed. However, these new processes also suffer from their own drawbacks as follows. Thiourea is known as carcinogen and, additionally, it is expensive and chemically unstable compared to cyanide, while it has a big advantage of much faster dissolution rate of gold than cyanide; that is, it was reported that the dissolution rate of gold using the mixture of 1% thiourea in 0.5% sulfuric acid containing 0.1% ferric ion is over 10-folds faster than that using the mixture of 0.5% sodium cyanide and 0.05% calcium oxide [33].

Following the recovery of gold from spent PCBs, a typical secondary resource, we attempted to apply the bioadsorbents we prepared to noncyanide leach liquor of actual gold ore (one example of typical primary resources). The sample of the ore was kindly donated by Western Mongolian Metals Co. Ltd., Ulaanbaatar, Mongolia. It was fine powder, the particle size of which was around 75–150 μm and the metal contents (mg/g) were as follows: gold 0.046, platinum 0.018, aluminum 0.694, iron 64.75, cobalt 0.008, nickel 0.040, copper 0.779, and zinc 0.069.

In the present work, the recovery of gold from the abovementioned gold ore was investigated by means of leaching using acidothiourea solution consisting of 0.1 mol/L thiourea and 0.05 mol/L sulfuric acid followed by adsorption using bioadsorbent of cotton. Figure 15 shows the effect of liquid/solid ratio (ratio of volume of the leach liquor to unit dry weight of the sample of ore powder) on the leached amount of gold and platinum from the ore sample. From this result, 30 mL/g appears to be the most suitable liquid/solid ratio for extracting gold and platinum from the ore sample; i.e., addition of about 0.23 g of thiourea and 0.15 g of sulfuric

#### Table 2.

Comparison of dissolution rates of gold using some lixiviants [32].

5. Recovery of gold from Mongolian gold ore

The extracted gold(I) as anionic species, AuCN2

the following reaction:

Figure 13.

Elements of Bioeconomy

Figure 14.

114

At present, majority of gold has been recovered from gold and silver ores by means of cyanide process developed at the beginning of twentieth century as schematically shown in Figure 14. In this process, pulverized ores are leached using alkaline cyanide solution to extract gold as gold(I)-cyanide complexes according to

Effect of solid/liquid ratio on the % adsorption of various metals from leach liquor of chlorine-containing HCl

using the bioadsorbent of cotton prepared by treating in boiling concentrated sulfuric acid.

� þ 4OH� (3)

�, is adsorbed onto activated

4Au þ 8CN� þ O2 þ 2H2O ¼ 4AuCN2

Flow sheet of conventional gold recovery process from gold ores using alkaline cyanide solutions.

carbon or strongly basic anion exchange resins, which are termed as CIP and RIP

#### Figure 15.

Effect of liquid/solid ratio on the leaching amount of gold and platinum from the Mongolian gold ore sample using acidothiourea consisting of 0.1 mol/L thiourea and 0.05 mol/L sulfuric acid.

elemental gold, suggesting that gold was recovered as elemental gold particles also

As mentioned earlier, cyanide solution has been extensively employed for a long

ered by means of adsorption on activated carbon or strongly basic anion-exchange resin. However, such adsorptive recovery of gold is not always quantitative and trace concentrations of gold still remain in the cyanide solution. Spent cyanide solutions generated after the recovery of gold are treated for cyanide decomposition before discharging in environments according to the following processes [34]:

In this process, cyanide ion is decomposed by the aid of sulfur dioxide and oxygen gasses blown into the cyanide solution catalyzed by cupric sulfate according

where OCN� ion is unstable and easily hydrolyzed into ammonium bicarbonate.

Cyanide ion is decomposed by the aid of hydrogen peroxide also catalyzed by

CN� þ SO2 þ O2 þ H2O ¼ OCN� þ SO2�

The sulfur dioxide gas can be replaced by sulfurous acid or sodium pyrosulfite

2. Oxidative decomposition using hydrogen peroxide

cupric sulfate according to the following reaction:

� com-

� complex is recov-

4 þ 2Hþ (4)

6. Recovery of gold from simulated spent cyanide solutions using

Gold Recovery Process from Primary and Secondary Resources Using Bioadsorbents

DOI: http://dx.doi.org/10.5772/intechopen.84770

time in gold mining and also in plating applications because of its special complexing capabilities in aqueous solutions, creating the soluble Au(CN)2

1. Oxidative decomposition using sulfur dioxide (INCO process)

plex. Also as mentioned in the preceding section, such Au(CN)2

XRD pattern of the cotton adsorbent after adsorption of gold(III).

in this system.

Figure 17.

bioadsorbents

to the following reaction:

(Na2S2O5).

117

Figure 16.

Effect of solid/liquid ratio on the adsorption of gold using cotton adsorbent from the leach liquor of the Mongolian gold ore.

acid is necessary for complete extraction of gold and platinum from unit gram of the ore sample.

Figure 16 shows effect of solid/liquid ratio (ratio of dry weight of the added adsorbent to unit volume of the leach liquor containing gold(III)) on the adsorption of gold using bioadsorbent of cotton prepared by treating in boiling concentrated sulfuric acid from the leach liquor of Mongolian gold ore. This figure indicates that addition of at least 3 g of bioadsorbent of cotton is necessary for quantitative adsorption of gold(III) from this leach liquor.

Figure 17 shows the XRD pattern of the bioadsorbent of cotton after adsorption of gold(III). Four sharp peaks in this figure obviously evidence the presence of solid Gold Recovery Process from Primary and Secondary Resources Using Bioadsorbents DOI: http://dx.doi.org/10.5772/intechopen.84770

Figure 17. XRD pattern of the cotton adsorbent after adsorption of gold(III).

elemental gold, suggesting that gold was recovered as elemental gold particles also in this system.


**6. Conclusion**

Persuing at the information provided above there are a number of discernable patterns. One is the preponderance of pre-Linnean researchers interested in marine mammals who had a medical background of some sort. That is not surprising because medicine was the closest thing to science as a career existed until the eighteenth century. Also, being interested in medicine created more opportunities to dissect animals and, therefore, understanding of their internal anatomy that was particularly crucial in establishing the homology between cetaceans and the "viviparous quadrupeds." Yet, this positive influence was marred by the proliferation of encyclopedists who, for the most part, were uncritical compilers of other authors' information. However, the major impediment to any attempts to develop a natural classification for cetaceans was the insistence on classifying them by virtue of the environment in which they live, something that even diverted the thoughts of keen observers such as Ray and Artedi, despite of abundant evidence to the contrary having been collected since Aristotle.

Finally, we should not overlook the role played by intellectual inertia in the development of science. As Horder (1998) clearly demonstrated, scientists need to know the history of their field to avoid errors of the past, something that has also been argued for specific fields of biology (see Romero 2009, Chapter 1).


*4.4. Carbonatation Process*

CAC paste is known to react with atmospheric carbon dioxide. Carbonation causes numerous chemical-mechanical changes in the cement paste, but can also improve properties of the cement material [42–45]. For these reasons, the active use of carbonation as a tool to manipulate the properties of CAC-based materials is a very promising research area that could potentially improve fiber-reinforced cementitious compounds and promote concrete recycling and immobilization of waste.

The carbonation of CACs has been investigated in order to find adequate conditions to improve the mechanical properties of CACs. However, the very specific manufacturing conditions of CAC paste, such as water/cement ratios greater than 0.4, hydration in the presence of alkalis and hydration in a high-humidity atmosphere [44,46], produce a paste with very poor mechanical performance for construction and refractories. However, these pastes do have a high CO2 absorption capacity. Therefore, hydrating CAC in conditions that are unfavorable to its mechanical properties could drastically increase its CO2 absorption capacity, which could be used to reduce CO2 contamination elsewhere. After these pastes are saturated with CO2, they could be included in the circular economy as a construction material. While no research has been found in this area, it would be an excellent topic to review. The research presented here describes advances in sustainability and many other specific and important topics related to the circular economy of CAC. However, the lack of progress in these areas is quite evident considering the social demands on modern construction materials. Moreover, the required approaches demand a rethinking of the material and process in the circular economy. The process needs to be not only reused but also optimized to reduce energy, water and material waste. From an academic, innovation and production point of view, this technological lag opens up new opportunities to produce a CAC that fits in with the advances of the 21st century. Such research is important because it would pave the way for not only significant progress towards a more sustainable CAC, but also reveal the limitations and opportunities in the area for a real modern CAC material.


2. Modeling procedures

compositions, influencing the process performance. Therefore, the accuracy of the selected model is essential and greatly affects the final results of the simulation and

The first one is the selection of the model, built-in with the mathematical relationships that best represent the variables of the system. There is no a priori a procedure to make this choice, so heuristic or experience-based criteria are generally used [2]. The second question refers to get the best parameters that complete the definition of the model for a given data series. For this latter case, there are several numerical procedures [3, 4] that allow to address the problem to optimize

The thermodynamic properties having the greatest influence on the simulation of separation processes are those related to phase equilibria (vapor-liquid equilibrium, VLE, in the scope of this work), as well as other thermodynamic quantities that arise in the mixing process. These properties are associated with the excess

(xi,p,T). As such, the goal of the

. Thus, the VLE fitting process becomes a bi-objective

E values can only be

(x,p,T), are

) is

E with each one of the vari-

E = g E

obtained from VLE, which satisfies a dependency of the type, F(xi,p,T) = 0,

ables. On the other hand, defects in the experimental data could give place to incoherencies between experimental activity coefficients γi = γi(xi,yi,p,T) and the

optimization problem; hence, two error functions are included in the correlation

included in the modeling, since new error functions should also be minimized. However, one of the benefits of this approach is to use a unique thermodynamic model that avoids the issues caused by possible discontinuities or inconsistencies between partial models of some properties. Even more relevant is that this allows the model to describe better the physics of the system under study. This supposes a mean to verify the coherence of the mathematical formalism imposed by thermodynamics, validating the different properties. A drawback of this practice is the increase of the complexity of the procedure, but this is just a numerical issue of relative complexity. Therefore, addressing the global modeling of the thermodynamic behavior of solutions as a problem with multiple objectives is a notable

It is known that the resolution of multi-objective problems does not produce a single result; on the contrary, a set of non-dominated results that constitute the socalled Pareto front [5] is obtained. There is no precise mathematical criterion that allows the selection of a unique result. However, the process simulation requires a single result to define the intended design, having to resort to external criteria

This study evaluates the effect of choosing the different results from the Pareto front in the simulation task. To achieve this, some partial goals are proposed such as (a) to establish a rigorous methodology to carry out the optimization procedure with the suggested modeling and (b) to check the real impact of the chosen model on the simulation, with the purpose of proposing a selection criterion. Thus, the designed methodology should include different stages, like the data selection used in the procedure, obtaining the result front and the election of the final result. Two systems, considered as standard in many studies on thermodynamic behavior of solutions, are selected since the necessary experimental information (VLE and hE

available in literature. After checking the data sets [6, 7], making up the

modeling is to achieve a functional type of f = f(θ,xi,p,T) that minimizes the norm

E f |. The vector θ represents a set of parameters in the model, which must be optimized. However, due to the existing relation between phase and mixing prop-

design processes. Two milestones should be considered.

Distillation - Modelling, Simulation and Optimization

the parameter set considering the starting hypothesis.

, which is written as g

preventing us from obtaining individual relations of g

E

contribution in chemical engineering.

different from those used to obtain the front.

erties, former approach may not be enough. In the first place, g

problem. The complexity grows as other properties, such as hE = hE

Gibbs energy g

48

E

excess Gibbs function g


**Experimental Geomechanics**

**Chapter 50**

**Comparison of Hydraulic and Conventional Tensile**

Tensile strength is paramount for reliable simulation of hydraulic fracturing experiments on all scales. Tensile strength values depend strongly on the test method. Three different labora‐ tory tests for tensile strength of rocks are compared. Test methods employed are the Brazilian disc test (BDT), modified tension test (MTT) and hydraulic fracturing experiments with hollow cylinders (MF = Mini Frac).  Test results reveal a relationship between the area under tensile stress at failure and the measured tensile strength. This relationship becomes visible when the area under tensile strength ranges over one order of magnitude from 450 to

. This observation becomes relevant when selecting the tensile strength values of

**Keywords:** hydraulic fracturing, Brazilian Disc test, Modified Tension Test, Acoustic Emission,

Tensile strength tests are widely applied in rock mechanics to obtain input parameters for planning of hydraulic fracturing on all scales. In literature only few experimental data sets are published dealing with samples size effects on tensile strength tests [1,2] or the comparison of different tensile tests in general [1,3]. Usually, results of laboratory tensile

and reproduction in any medium, provided the original work is properly cited.

© 2013 Molenda et al.; licensee InTech. This is an open access article 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.

© 2013 The Author(s). Licensee InTech. 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,

**Strength Tests**

http://dx.doi.org/10.5772/56300

**Abstract**

4624 mm2

lithologies.

numerical simulation

**1. Introduction**

Michael Molenda, Ferdinand Stöckhert, Sebastian Brenne and Michael Alber

Additional information is available at the end of the chapter