*Atmosphere* **2021**, *12*, 1209

**Table A4.** Laslea River Habitat Characteristics.

*Atmosphere* **2021** , *12*, 1209

#### *Atmosphere* **2021** , *12*, 1209

#### *Atmosphere* **2021** , *12*, 1209

 **A4.** *Cont.*

**Table**

**Table A5.**Mălâncrav River Habitat Characteristics.

*Atmosphere* **2021** , *12*, 1209

**Table A6.** Flore¸sti/Fel¸ta River Habitat Characteristics.

*Atmosphere* **2021** , *12*, 1209

#### *Atmosphere* **2021** , *12*, 1209

**B.**

**Scheme 1.** Dupu¸s River—present refuge habitats and proposals for new refuge to be created.

**Scheme 2.** Biertan River—refuge habitats and proposals for new refuge to be created.

**Scheme 3.** Valchid River—refuge habitats and proposals for new refuge to be created.

**Scheme 4.** Laslea River—refuge habitats and proposals for new refuge to be created.

**Scheme 5.** Mălâncrav River—present refuge habitats and proposals for new refuge to be created.

**Scheme 6.** Fel¸ta River—refuge habitats and proposals for new refuge to be created.

## **References**


**a.** Feedstock composition prediction

applied to the operating condition optimization problem to obtain a more reliable optimization result. Based on this, a surrogate coke thickness model is proposed to make multi-period

The general idea of this chapter is outlined in Figure 1. The first step in the conceptual development of a detailed molecule-based model for a complex feedstock is to determine an accurate molecular representation of the feedstock. Then, a multi-scale steam cracking model is established following the feedstock prediction. Finally, operating condition optimization of

multi-period steam cracking is carried out using the established multi-scale model.

**Figure 1.** Diagram of simulation and optimization of the steam cracking process methodology

**2. Steam cracking model**

**2.1. Multi-scale model for steam cracking**

are presented and discussed. Finally, section 4 offers conclusions from this study.

This chapter is structured as follows. Section 2 offers a detailed discussion of the establishment of the multi-scale steam cracking model; a case study for naphtha steam cracking simulation is presented. Section 3 provides the operating condition optimization model; surrogate coke thickness model and parallel simulation are used to accelerate the computing of the optimi‐ zation model. A case study of operating condition optimization is presented and the results

A multi-scale model is proposed in this chapter to reveal the nature of the steam cracking process and to generate a reliable and robust model for accurately predicting the yields of

optimization with parallel simulation possible.

120 Advances in Petrochemicals

Conventional analytical techniques are generally incapable of directly measuring the identities of all the molecules in complex feedstock, especially for the high carbon number range; however, this applies only to indirect characteristics [37]. Here, the Shannon entropy method [38] and probability density function were used to predict detailed feedstock composition, based on the analytical data. A detailed model for feedstock composition prediction can be found in [39]. The objective function of this NLP (non-linear programming) problem is shown in eq. (2), where *S(x)* is Shannon entropy and *xi* is the mole fraction of component *i*.

$$\max S(\mathbf{x}) = -\sum\_{i=1}^{n} \mathbf{x}\_i \ln \mathbf{x}\_i \tag{2}$$

Figure 2 shows the predicted feedstock composition using Shannon entropy, compared with the experimental data. The predicted results show the effectiveness of the Shannon entropy theory in obtaining the missing information for models of the steam cracking process.

### **b.** Process-level model

A process-level model consists of mass balance equations, momentum balance equation and energy balance equation (eqs. (3-5)) [40, 41]. For the simulation of smooth tubular reactor types, the use of a one-dimensional reactor plug-flow model is generally recognized as providing a sufficient degree of accuracy, as all radical profiles are wiped out due to the high turbulence corresponding to Reynolds numbers of over 250 000. The plug-flow reactor model implicitly assumes that there is no mixing in the axial direction, but rather, perfect mixing in the transverse direction [2].

$$\frac{dN\_m}{dL} = \frac{S}{V} \sum\_i \nu\_{i,m} r\_i = f\_N\left(T, P, N\_m\right) \tag{3}$$

$$\frac{dP}{dL} = -\frac{f \cdot E(L) \cdot G^2}{5.07 \times \rho \cdot D\_i \times 10^4} = f\_P \left( T, P, N\_m \right) \tag{4}$$

$$\frac{dT}{dL} = \frac{k\pi D\_o(T\_w - T) - \sum\_m \Delta H\_{fm}^0 \cdot \frac{dN\_m}{dL}}{\sum\_m c\_{pw} N\_m} = f\_T \left( T, P, N\_m \right) \tag{5}$$

In eq. (3), *Nm* is the concentration of species *m* and *L* is the length of the reactor tube. *S* is the flow area, *V* is the volume flow rate, *μim* is the stoichiometric coefficient of reaction *i* and *ri* is the reaction rate of reaction *i*. In eq. (4), *P* is the pressure of the mass flow, *f* is the Fanning friction factor, *E(L)=Le/L*, *Le* is the equivalent length of the reactor tube, *G* is the mass flow rate, *Di* is the internal diameter of the tube and *ρ* is the density of the gas mixture. In eq. (5), *T* is the temperature of the mass flow, *Do* is the outer diameter of the tube, *k* is the overall heat transfer coefficient, *Tw* is the outer wall temperature of the tube, *ΔHfm0* is the standard heat of formation of species *m* and *cpm* is the specific heat of species *m*.

It should be noted that the measuring point of the COT (coil-outlet temperature) of an industrial furnace is usually on the outer wall of the tube. Thus, the measured COT has a temperature difference from the outlet temperature of the gas mixture. Eq. (6) has been derived from heat balance equations in order to adjust to the temperature difference; the results show that there exists a 15-20K temperature difference, which agrees well with what has previously been reported.

$$\Delta \text{COT} = \frac{\lambda\_{\text{Isolation}} \left( \text{COT} - T\_{\text{isolation}\_{\text{-}o}} \right)}{\ln \frac{r\_o + d\_{\text{isolation}}}{r\_o}} \left( \ln \frac{r\_i}{r\_i - d\_{\text{coke}}} + \frac{\ln \frac{r\_o}{r\_i}}{\lambda\_{\text{nale}}} + \frac{1}{\alpha\_{\text{uniform}} (r\_i - d\_{\text{coke}})} \right) \tag{6}$$

In eq. (6), *λisolation* is the heat transfer coefficient of the isolation layer. *Tisolation\_o* is the outer wall temperature of the isolation layer, *ro* is the external diameter of the tube, *ri* is the interior diameter of the tube, *disolation* is the thickness of isolation layer, *dcoke* is the thickness of coke and *αmixture* is the heat transfer coefficient of the gas mixture in the tube.


*3.5. Achievements*

A motive or need for achievement is a desire to do well to achieve a sense of personal accomplishment [73]. Achievement orientation has been taken as one of the EO factors as individuals with a high need for achievement perform better with non-routine tasks and take responsibility for their performance [42]. McClelland [73] stated that owners with strong achievement orientation lead to growth and success in the business. According to Koop et al. [74], Rauch and Frese [75], and Spencer and Spencer [76] success of a firm depend on owners' achievement orientation. According to Krauss et al. [42], it is observed that achievement orientation has a significant relationship with the firm's performance. Miner et al. [77], in a longitudinal study, found their measure of achievement motivation to significantly predict firm performance (i.e., growth in number of employees, sales growth, and entrepreneur annual income).

**H5:** *Achievement orientation is positively related to business performance*.

#### **4. Research Design**

In this research, Malay-based SMEs were chosen to test the empirical model. Data analysis techniques, respondents' profiles, and measurements are discussed below.

#### *4.1. Survey Design and Sampling*

A cross-sectional approach was used to test the hypothetical relationships among Malay-based SMEs in Malaysia. To focus on SMEs, the respondents were found from MARA (Majlis Amanah Rakyat) and sent 1000 emails to obtain acceptance from SME owners. More than 50% of the Malaysian population are Malay. So, Malay people were chosen as the respondents for this study. The sample size was calculated using G-Power software with a 95% confidence level for the two-tailed test [78]. As per [79], with a power of 0.85 (power in behavioral and social science should be greater than 0.80) and an effect size of 0.20, a minimum sample size of 89 participants with five predictor variables was necessary for this study. Furthermore, Reinartz et al. [80] recommended that the sample size in the SEM study should be a minimum of 100 participants. Altogether 443 respondents agreed to participate in this research. Researchers personally visited the company and collected data from them. A total of 36 respondents were discarded due to at least one section missing response and finally, 407 cases were then accepted and analyzed. Data was collected within March to September 2021. Since the sample size issue is a potential source of problems, this study utilized a total of 407 samples. The targeted respondents were the owner of the SMEs as the owner makes all decisions in all aspects of business and this projecting EO should be clearer.

More than 50% of respondents were male compared to their female counterparts. Age groups between 41 and 50 are found to respond to our questionnaire and 66 of them are married. Out of 407 respondents, 237 of them involved in business for more than four years and they have less than 8 employees.

The equations of the research model are as follows:

$$\mathbf{Y} = \beta\_0 + \beta\_1 \mathbf{x}\_1 + \beta\_2 \mathbf{X}\_2 + \beta\_3 \mathbf{X}\_3 + \beta\_4 \mathbf{X}\_4 + \beta\_5 \mathbf{X}\_5 + \varepsilon\_i \tag{1}$$

where Y is the business performance (dependent variable); β0 is the regression coefficient (constant); β1, β2, β3 ... are the regression beta values; and X1 = autonomy orientation, X2 = risk taking orientation, X3 = proactiveness orientation, X4 = innovativeness orientation, X5 = achievement orientation, and i = error or residual (Equation (1)).


*2.6. SCMs Characterization*

#### 2.6.1. Mechanical Tests

The elastic moduli G' (filled symbols) and the viscous moduli G (open symbols) of the F1 and F3 SCMs and nc-SCMs are reported in Figure 5. G indicates the capability of the material to store energy and G refers to the capability of the material to dissipate energy. All of the samples that were examined showed relatively high moduli (in the range of 104–105 MPa for the elastic modulus).

**Figure 5.** The elastic moduli G (filled symbols) and the viscous moduli G (open symbols) of the SCMs (SCM-F1, triangles; SCM-F3, stars) and not crosslinked SCMs (nc-SCM-F1, circles; nc-SCM-F3, diamonds) reported in function of the frequency applied by the rheometer in the dynamic mechanical analysis (for methods see Section 4.5.3).

As expected, for a gel system, a solid-like behaviour was observable. Indeed, it was possible to note the predominance of G upon G at low frequencies in all of the membranes, while G increased on increasing the frequency till reaching a crossover between the moduli. These data indicate that all of the tested samples show an evident elastic behaviour in the frequency range that was studied.

Both SCM-F1 (triangles) and nc-SCM-F1 (circles) showed lower moduli than SCM-F3 (stars) and nc-SCM-F3 (diamonds), indicating, in general, a lower mechanical stiffness. Moreover, as is clearly observable, the crosslinking did not affect the mechanical performance of F1-derived SCMs, with the two samples (nc-SCM-F1 and SCM-F1) showing comparable values of both G and G. On the contrary, in F3-derived SCMs, a significant difference was observable between the nc-SCM-F3 and the SCM-F3, with an evident increase of the mechanical properties in the crosslinked sample, as evidenced by a higher G elastic modulus when compared to the nc-SCM-F3.