**4. Acknowledgment**

The author gratefully acknowledge funding from the Alexander von Humboldt Foundation and the Federal Ministry for Education and Research (BMBF) in the form of a Sofja Kovalevskaja Award (M. C. L.) and from the DFG Graduate School ''Biointerface'' (GRK 1035).


**3. Materials and Methods**

The proposed PHM strategy is built around the MM, whose simulations can be run almost in real time [36,37]. The other inputs for the prognostic strategy are, of course, the signals coming from the sensors mounted on the physical system (as shown in Figure 1).

However, real-life actuator data is difficult to obtain, and the failures are very rare indeed. Therefore, in this study, data have been generated through the aforementioned RM, which has been used as a NTB. The authors set up the RM and generated the failure data; the prognostic algorithm was then employed to detect and identify the failures and, after its run, the results were compared and the errors were calculated. Figure 1 shows the overview of the overall methodology. On the right, the reader can notice data coming from sensors or from the database created by the NTB. y

**Figure 1.** FDI methodology overview [10].

The RM and MM are two physical-based models, developed in Simulink starting from equations implementing the real physical behaviours: actual equations are implemented in Simulink blocks (e.g., EM motor equations, dynamic equations, etc.). A more in depth description will be provided in Section 3.2. The models are defined by a set of top level parameters (TLPs), grouped inside a −→*k* vector, as better explained later on. Each component of the vector is linked and can be traced back to a failure in the EMA. By altering these parameters, it is possible to inject failures inside the models. For all intents and purposes, the TLPs change the simulation boundary conditions; hence, it is possible to simulate the actuator behaviours in different conditions. Both models have been verified and experimentally validated under nominal and non nominal settings, proving the models' capacity to estimate actual trends with high precision.

During a prognostic routine, the MM is then iteratively run with different sets of TLPs. In fact, in each simulation run, the TLPs are updated following the results obtained by the optimization using the MSA. The algorithm tries to find the monitoring TLPs that alter the MM outputs in order to reduce the error between the predicted and actual trends (real or simulated). A real-time solution is not feasible because of the total running time of the high number of simulation runs, as stated better later on. The error is defined using a suitable fitness function (Equation (4)). As a result, after the optimization process is complete, the output of the MM is extremely similar to the actual one. After finding a satisfactory match, the system's health is evaluated by comparing the related TLPs with the related failures. Therefore, it is possible to execute a basic but effective FDI process by examining TLPs to determine whether a failure is occurring and establish what kind of failure it is.

As stated before, and as [38] explained, the TLPs are grouped inside a normalised vector −→*k* (Equation (1)): each component *ki* is strictly linked to a physical system failure and it can assume values between 0 and 1 to characterize different failure magnitudes.

$$
\overrightarrow{\dot{k'}} = [k\_1 \ k\_2 \ k\_3 \ k\_4 \ k\_5 \ k\_6 \ k\_7 \ k\_8]\_\prime \tag{1}
$$

Table 1 shows in a schematic way each TLP along with the effects on the system at the relative maximum and minimum values. Note that maximum eccentricity refers to a situation where the eccentricity modulus is equal to the rotor air gap width.

As far as the failure implementation is concerned, each *k* coefficient is used in the MM and in the RM as a parameter that affects the model behaviour in different ways. For instance, *k*1 is multiplied with the static or dynamic friction coefficients within the coulombian friction implementation. *k*2 coefficient multiplies the global backlash value inside the model, appropriately modelled by a Simulink block. On the other hand, *k*3, *k*4, and *k*5 represent the percentages of each phase affected by short circuit. These coefficients modify the wirings resistance (in a linear way) and inductance (in a quadratic way), hence affecting the electrical modelling of the system, as reported in [39]. The outcome of rotor eccentricity is modelled according to the following formulas [40], shown in Equation (2):

$$\begin{array}{l} \mathcal{c}\_1 = \mathcal{c}\_{1,0} \cdot (1 + k\_6 \cdot \cos(\theta\_m + k\_7)) \\ \mathcal{c}\_2 = \mathcal{c}\_{2,0} \cdot (1 + k\_6 \cdot \cos(\theta\_m + k\_7 + \frac{2\pi}{3})) \\ \mathcal{c}\_3 = \mathcal{c}\_{3,0} \cdot (1 + k\_6 \cdot \cos(\theta\_m + k\_7 - \frac{2\pi}{3})) \end{array} \tag{2}$$

where *c*1, *c*2, and *c*3 are the resulting back-EMF coefficient for the three phases, starting from the nominal coefficients *c*1,0, *c*2,0, and *c*3,0. On the other hand, *θm* is the mechanical rotor angle. Finally, *k*8 simply multiplies the proportional gain in the controller block.

The nominal vector (i.e., the vector whose components would lead to a nominal behaviour of the EMA) is, hence, the one reported (Equation (3)):

$$
\overrightarrow{k'} = [0\,0\,0\,0\,0\,0\,0.5\,0.5],
\tag{3}
$$

The fitness function is defined as:

$$e\_{lls} = \sum\_{i} \frac{(I\_{MM,i} - I\_{RM,i})^2}{\left(\frac{\Delta I\_{RM,i}}{\Delta T}\right)^2 + 1} \cdot \Delta T\_{\prime} \tag{4}$$

where *IMM*,*i* and *IRM*,*i* are the MM and RM current outputs respectively at the instant time *i* [41]. Furthermore, the quadratic error between these two currents is evaluated to consider both the error in time and in the signal. Finally, the first order derivative is calculated numerically using Matlab's gradient command and divided by the integration step Δ*T*. Finally, to avoid the time dependence of the error, it is multiplied by the simulation integration step itself Δ*T*. The current values referred to the two models are considered at each instant of time *i*.

**Table 1.** Top level parameters meaning and effects.


**3. Lithostratigraphy of the formations of the Irkutsk Basin**

**The Cheremkhovskaya Formation** was first identified by Korovin [23] in mines within the Cheremkhovo settlement vicinity. It represents a complete megarhythm and is divided into three subformations: lower, middle, and upper ones with total thickness up to 380 m.

**Lower Cheremkhovskaya (Zalarinskaya) Subformation** occurs with discordance on Cambrian limestones of Angarskaya Group. The subformation base is composed of clastic deposits—conglomerates, gravelites, and coarse‐grained sandstones which are overlapped by siltstones, mudstones with thin coal bands.

On the right bank of the Zalari river, on the opposite side of the Zalari settlement is situated the stratotype of Lower Cheremkhovskaya Subformation (bottom‐up):

Clastic deposits of the lower subformation are traced by us in some natural outcrops along the ranks of the Bol'shaya Belaya river.

Industrially coal‐bearing **Middle Cheremkhovskaya Subformation** is a natural sedimentary continuation of the lower subformation, and it is related to the latter by gradual transitions. This subformation is characterized by abundance in section of siltstones, mudstones, and coal seams with thickness up to 10–15 m. The thickness was cumulated during the epoch of maxi‐ mum regional coal storage within the Basin and is abundant all over. Lower boundary line is drawn in bottom of siltstones and sandstones underlying the first coal‐bearing horizon.

deposits—conglomerates, gravelites, and coarse‐grained sandstones which are overlapped by

On the right bank of the Zalari river, on the opposite side of the Zalari settlement is situated

horizontal bedding and abundant plant detrital matter

**Thickness, m**

0.65

0.70

0.60

1.10

3.54

0.75

Clastic deposits of the lower subformation are traced by us in some natural outcrops along the

and siltstones with horizontal bedding and impressions of *Cladophlebis haiburnensis* (L. et H.) Sew., *Sphenobaiera ex gr. czekanowskiana* (Heer) Flor., *Czekanowskia ex gr. rigida* Heer, *Podozamites eichwaldii* Pryn. var. *minor* Pryn. *Ixostrobus grandis*

Industrially coal‐bearing **Middle Cheremkhovskaya Subformation** is a natural sedimentary continuation of the lower subformation, and it is related to the latter by gradual transitions. This subformation is characterized by abundance in section of siltstones, mudstones, and coal seams with thickness up to 10–15 m. The thickness was cumulated during the epoch of maxi‐ mum regional coal storage within the Basin and is abundant all over. Lower boundary line is drawn in bottom of siltstones and sandstones underlying the first coal‐bearing horizon.

the stratotype of Lower Cheremkhovskaya Subformation (bottom‐up):

84 Seismic and Sequence Stratigraphy and Integrated Stratigraphy - New Insights and Contributions

2. Sandstones, coarse‐grained, massive 1.34 3. Conglomerates, fine‐pebbled 2.00 4. Sandstones, white, coarse‐grained, massive 0.30 5. Conglomerates, fine‐pebbled 0.80 Disappearance of outcrop 3.00

Disappearance of outcrop 3.50 7. Sandstones, pale gray, coarse‐grained, massive 0.60

Disappearance of outcrop 1.39

Developed thickness 13.12

1. Conglomerates interstratifying with coarse‐grained sandstones

6. Sandstones, gray, medium‐fine‐grained with

8. Sandstones, gray, medium‐grained with horizontal

9. Sandstones, red, medium‐fine‐grained with horizontal

10. Alternation of red, burnt rocks presented by sandstones

11. Speckled sandstones, coarse‐medium‐grained with horizontal bedding

bedding

bedding

Tesl.

siltstones, mudstones with thin coal bands.

ranks of the Bol'shaya Belaya river.

Section uncovering within the Cheremkhovo coal deposit situated in the central part of the Irkutsk Basin is a stratotype of Middle Cheremkhovskaya Subformation (bottom‐up) (**Figure 2**):

Middle Cheremkhovskaya Subformation is opened in coal opencasts of the Glinki and Mugun deposits. The Glinki coal‐bearing deposits occur on erosional contacts with a weathered surface of the Upper Cambrian clays (**Figure 2**). Opened thickness of Jurassic deposits is 18.4 m. Middle Cheremkhovskaya Subformation composed of gray, flat bedded, fine‐grained sandstones, silt‐ stones, and mudstones with two coal seams of industrial thickness (2–5.8 m) is exposed in the lower part of the opencast. We revealed the following plant remains: *Equisetites asiaticus* Pryn., *Equisetites* cf. *lateralis* (Phill.) Phill., *Hausmannia crenata* (Nath.) Maell., *Cladophlebis williamsonii* Brongn., *Cl. haiburnensis* (L et H) Sew., *Ginkgo concinna* Heer, *Sphenobaiera czekanowskiana* (Heer) Florin, *Pseudotorellia* cf. *paradoxa* Dolud., *Czekanowskia baikalica* Kiritch. et Samyl., *Cz. rigida* Heer, *Phoenicopsis* ex gr. *angustifolia* Heer, *Leptostrobus laxiflora* Heer, *Podozamites* cf*. lanceolatus* (L. et H.) Schimp., *P.* cf. *eichwaldii* Pryn. var. *major* Pryn., *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath., *Schizolepis* cf. *moelleri* Sew., *Carpolithes minor* Pryn., and *Ixostrobus heeri* Pryn. in siltstones and mudstones of industrially coal‐bearing part of the opencast.

The Mugun lignite deposit is situated in the northwestern part of the Irkutsk Basin, at 40 km to the south of the Tulun city. The deposit has an irregular shape and a simple geological structure. Lower band of the deposit belongs to Middle Cheremkhovskaya Subformation. It is composed of siltstones, mudstones, and fine‐grained sandstones alternating among them‐ selves. Productive coal seams (thickness from 3.5 up to 10 m) are confined to this part of

Section uncovering within the Cheremkhovo coal deposit situated in the central part of the Irkutsk Basin is a stratotype of Middle Cheremkhovskaya Subformation (bottom‐up)

26. Siltstones, gray, with burst plant detritus 0.30

28. Coal 0.20 29. Sandstones, yellowish‐gray, coarse‐grained 1.65

31. Coal 0.20

cemented, flat‐bedded with abundant plant detritus

33. Coal 0.20

34. Sandstones, yellow, medium‐grained 4.50 35. Conglomerates, fine‐pebbly 0.30 36. Sandstones, yellow, medium‐grained polymictic 4.60 Thickness of the opencast developed part 29.47

*Czekanowskia* ex gr. *rigida* Heer and coalified plant detritus

of *Czekanowskia* ex gr. *rigida* Heer and *Pityophyllum* ex gr. *nordenskioldii*

27. Sandstones, yellow, medium‐fine‐grained with impressions

86 Seismic and Sequence Stratigraphy and Integrated Stratigraphy - New Insights and Contributions

30. Sandstones, gray, medium‐fine‐grained with leaf impressions of

32. Sandstones, yellowish‐gray, medium‐fine‐grained, poorly

(Heer) Nath.

Upper Cheremkhovskaya Subformation

**Thickness, m**

2.00

0.80

0.80

Middle Cheremkhovskaya Subformation is opened in coal opencasts of the Glinki and Mugun deposits. The Glinki coal‐bearing deposits occur on erosional contacts with a weathered surface of the Upper Cambrian clays (**Figure 2**). Opened thickness of Jurassic deposits is 18.4 m. Middle Cheremkhovskaya Subformation composed of gray, flat bedded, fine‐grained sandstones, silt‐ stones, and mudstones with two coal seams of industrial thickness (2–5.8 m) is exposed in the lower part of the opencast. We revealed the following plant remains: *Equisetites asiaticus* Pryn., *Equisetites* cf. *lateralis* (Phill.) Phill., *Hausmannia crenata* (Nath.) Maell., *Cladophlebis williamsonii* Brongn., *Cl. haiburnensis* (L et H) Sew., *Ginkgo concinna* Heer, *Sphenobaiera czekanowskiana* (Heer) Florin, *Pseudotorellia* cf. *paradoxa* Dolud., *Czekanowskia baikalica* Kiritch. et Samyl., *Cz. rigida* Heer, *Phoenicopsis* ex gr. *angustifolia* Heer, *Leptostrobus laxiflora* Heer, *Podozamites* cf*. lanceolatus* (L. et H.) Schimp., *P.* cf. *eichwaldii* Pryn. var. *major* Pryn., *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath., *Schizolepis* cf. *moelleri* Sew., *Carpolithes minor* Pryn., and *Ixostrobus heeri* Pryn. in siltstones

The Mugun lignite deposit is situated in the northwestern part of the Irkutsk Basin, at 40 km to the south of the Tulun city. The deposit has an irregular shape and a simple geological structure. Lower band of the deposit belongs to Middle Cheremkhovskaya Subformation. It is composed of siltstones, mudstones, and fine‐grained sandstones alternating among them‐ selves. Productive coal seams (thickness from 3.5 up to 10 m) are confined to this part of

and mudstones of industrially coal‐bearing part of the opencast.

(**Figure 2**):

Stratigraphy of Jurassic Sediments of the Southern Siberian Platform (Russia) Studied through Lithologic... http://dx.doi.org/10.5772/intechopen.69572 87

**Figure 2.** Correlation scheme of the key sections of Jurassic sediments of the Irkusk Basin. Numbering of sections is given in **Figure 1**. 1—conglomerates, 2—coarse‐grained sandstones, 3—medium‐ and fine‐grained sandstones, 4—siltstones, 5—mudstones, 6—coals, 7—lenses; plant remains: 8—with no stratigraphic importance, 9—typical of Cheremkhovo plant assemblage, 10—typical of Prisayan plant assemblage.

opencast. We distinguished the following plant remains: *Coniopteris* sp., *Lobifolia nana* A. Frol., *Czekanowskia rigida* Heer, *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath., and *Carpolithes heeri* Tur.‐Ket. [10] in siltstones and mudstones of this band.

**Upper Cheremkhovskaya (Ust'‐Baleiskaya) Subformation** overlaps in concordance with the Middle Cheremkhovskaya Subformation, and more than a half of the former is composed of fine‐ and medium‐grained sandstones with lenses of siltstones and mudstones. Lower bound‐ ary line is drawn in the bottom of sandstones of channel facies [1] and it is observed within opencasts of developed coal deposits (Cheremkhovo, Glinki, and Mugun; **Figure 2**). Outcrop situated at 2.1 km below the Ust'‐Baley settlement is a stratotype of the upper subformation. Gray, differently grained polymictic sandstones with horizontal cross, uni‐, and diversidirec‐ tional lamination dominate in its lower part. Sandstones contain two lenses composed of silt‐ stones and mudstones with horizontal and sometimes banded lamination. Lower lens is long about 70 m with thickness up to 2 m. Impressions of the following plants: *Lycopodites tenerrimus* Heer, *Czekanowskia rigida* Heer, *Elatides ovalis* Heer, *Ixostrobus* sp., *Carpolithes deplanatus* Pryn., *C. cinctus* Nath., and *Samaropsis rotundata* Heer are found on the surface of mudstone bedding.

Thickness of the upper lens reaches 5.8 m, and its length is about 120 m. Siltstones and mud‐ stones of the lens contain numerous impressions of insects and fewer those of fishes and shells of limnetic myarians. Plant remains of *Lycopodites tenerrimus* Heer, *L. trichiatus* Pryn. emend. A. Frol. et Mash., *Phyllotheca sibirica* Heer, *Coniopteris murrayana* (Brongn.) Brongn., *Sphenobaiera czekanowskiana* (Heer) Florin, *Czekanowskia rigida* Heer, *Leptostrobus laxiflora* Heer, *Elatides ovalis* Heer, *Ixostrobus heeri* Pryn., and *Samaropsis rotundata* Heer are found on several plates of beddings.

Outcrops situated on the opposite side of the Tel'ma settlement (**Figure 2**) and the Tolstyi Cape have similar lithologic structure. The following plant remains: *Cladophlebis* cf. *haiburnensis* (L. et H.) Sew., *Cl. williamsonii* Brongn., *Sphenobaiera czekanowskiana* (Heer) Flor., *S. vigentis* Kiritch. et Bat., *Pseudotorellia longifolia* Dolud., *Czekanowskia rigida* Heer, *Cz. baikalica* Kiritch. et Samyl., *Leptostrobus laxiflora* Heer, *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath., *Schizolepis* sp., *Samaropsis rotundata* Heer, and *Carpolithes* cf. *minor* Pryn. are revealed in siltstone lens opened by these outcrops.

**Prisayanskaya Formation** is less abundant relative to the Cheremkhovskaya one which is overlapped with conformability by it.

**Lower Prisayanskaya (Idanskaya) Subformation** is predominantly confined to the south‐ eastern part of the Basin, its thickness is of 50–70 m. Characteristic feature of its sections is the prevalence of coarse‐grained and gritty sandstones as well as siltstones containing indeter‐ minable plant remains of poor preservation. Conglomerates and gritstones occur on several areas in the base of subformation. Lower boundary line of subformation is drawn relative to band bottom of rhythmically alternating sandstones, gritstones, conglomerates, and silt‐ stones. It is sufficiently conventional.

**Upper Prisayanskaya (Sukhovskaya) Subformation** is preserved from erosion only in Priirkut depression. This subformation is composed of small and fine‐grained sandstones and siltstones, and coarse‐grained varieties of sandstones are rare. The characteristic feature of this subformation is the significant enrichment of rocks in coaly matter and the presence of coal bands. Thickness of this subformation is about 50 m. Its lower boundary with the lower subformation is not always distinct. Within Priirkut depression we studied outcrops of this Upper Prisayanskaya Subformation situated along the banks of the Angara, Irkur, Kaya, and Topka rivers.

opencast. We distinguished the following plant remains: *Coniopteris* sp., *Lobifolia nana* A. Frol., *Czekanowskia rigida* Heer, *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath., and *Carpolithes heeri*

**Upper Cheremkhovskaya (Ust'‐Baleiskaya) Subformation** overlaps in concordance with the Middle Cheremkhovskaya Subformation, and more than a half of the former is composed of fine‐ and medium‐grained sandstones with lenses of siltstones and mudstones. Lower bound‐ ary line is drawn in the bottom of sandstones of channel facies [1] and it is observed within opencasts of developed coal deposits (Cheremkhovo, Glinki, and Mugun; **Figure 2**). Outcrop situated at 2.1 km below the Ust'‐Baley settlement is a stratotype of the upper subformation. Gray, differently grained polymictic sandstones with horizontal cross, uni‐, and diversidirec‐ tional lamination dominate in its lower part. Sandstones contain two lenses composed of silt‐ stones and mudstones with horizontal and sometimes banded lamination. Lower lens is long about 70 m with thickness up to 2 m. Impressions of the following plants: *Lycopodites tenerrimus* Heer, *Czekanowskia rigida* Heer, *Elatides ovalis* Heer, *Ixostrobus* sp., *Carpolithes deplanatus* Pryn., *C. cinctus* Nath., and *Samaropsis rotundata* Heer are found on the surface of mudstone bedding. Thickness of the upper lens reaches 5.8 m, and its length is about 120 m. Siltstones and mud‐ stones of the lens contain numerous impressions of insects and fewer those of fishes and shells of limnetic myarians. Plant remains of *Lycopodites tenerrimus* Heer, *L. trichiatus* Pryn. emend. A. Frol. et Mash., *Phyllotheca sibirica* Heer, *Coniopteris murrayana* (Brongn.) Brongn., *Sphenobaiera czekanowskiana* (Heer) Florin, *Czekanowskia rigida* Heer, *Leptostrobus laxiflora* Heer, *Elatides ovalis* Heer, *Ixostrobus heeri* Pryn., and *Samaropsis rotundata* Heer are found on several

Outcrops situated on the opposite side of the Tel'ma settlement (**Figure 2**) and the Tolstyi Cape have similar lithologic structure. The following plant remains: *Cladophlebis* cf. *haiburnensis* (L. et H.) Sew., *Cl. williamsonii* Brongn., *Sphenobaiera czekanowskiana* (Heer) Flor., *S. vigentis* Kiritch. et Bat., *Pseudotorellia longifolia* Dolud., *Czekanowskia rigida* Heer, *Cz. baikalica* Kiritch. et Samyl., *Leptostrobus laxiflora* Heer, *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath., *Schizolepis* sp., *Samaropsis rotundata* Heer, and *Carpolithes* cf. *minor* Pryn. are revealed in siltstone lens

**Prisayanskaya Formation** is less abundant relative to the Cheremkhovskaya one which is

**Lower Prisayanskaya (Idanskaya) Subformation** is predominantly confined to the south‐ eastern part of the Basin, its thickness is of 50–70 m. Characteristic feature of its sections is the prevalence of coarse‐grained and gritty sandstones as well as siltstones containing indeter‐ minable plant remains of poor preservation. Conglomerates and gritstones occur on several areas in the base of subformation. Lower boundary line of subformation is drawn relative to band bottom of rhythmically alternating sandstones, gritstones, conglomerates, and silt‐

**Upper Prisayanskaya (Sukhovskaya) Subformation** is preserved from erosion only in Priirkut depression. This subformation is composed of small and fine‐grained sandstones and siltstones, and coarse‐grained varieties of sandstones are rare. The characteristic feature of this subformation

Tur.‐Ket. [10] in siltstones and mudstones of this band.

88 Seismic and Sequence Stratigraphy and Integrated Stratigraphy - New Insights and Contributions

plates of beddings.

opened by these outcrops.

overlapped with conformability by it.

stones. It is sufficiently conventional.

In the quarry situated on Kaiskaya Gora at 400 m above the Kaya river mouth in the Irkutsk city the Jurassic deposits of Upper Prisayanskaya Subformation are opened (bottom‐up).

The section of Upper Prisayanskaya Subformation is opened within Sinyushina Gora in the Irkutsk city. Gray, fine‐ and medium‐grained sandstones dominate in the lower part of the section. The section's upper part is composed of alternating medium‐grained sandstones and siltstones, only one thin (0.07 m) coal band is noted. *Coniopteris murrayana* (Brongn.) Brongn., *Cladophlebis nebbensis* (Brongn.) Nath., *Cl*. *williamsonii* Brongn., *Czekanowskia* ex gr. *rigida* Heer, *Phoenicopsis angustifolia* Heer, *Ph. samylinae* Kiritch. et Moskv., *Ph. irkutensis* Dolud. et Rasskaz., and *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath are revealed among plant remains.

The upper part of Upper Prisayanskaya Subformation of 5.4 m thickness and 400 m length is opened in roadside quarry near the Smolenshchina settlement. The section is of rhythmic structure. In this section, quartz‐feldspathic coarse‐ and medium‐grained sandstones alternate upward with fine‐grained sandstones, siltstones, and mudstones with thin (0.1–0.3 m) coal bands. *Hepaticites arcuatus* (L. et H.) Harris, *Coniopteris murrayana* (Brongn.) Brongn., *C. maakiana* (Heer) Pryn. emend. Kiritch. et Trav., *C. spectabilis* Brick, *Cladophlebis williamsonii* Brongn., *Cl. haiburnensis* (L. et H.) Brongn., *Cl.* cf. *nebbensis* (Brongn.) Nath., *Raphaelia diamensis* Sew., *Sphenobaiera* ex gr. *czekanowskiana* (Heer) Flor., *Ginkgo* ex gr. *sibirica* Heer, *Czekanowskia* ex gr. *rigida* Heer, *Phoenicopsis angustifolia* Heer, *Ph.* cf. *mogutchevae* Kiritch. et Trav., *Ph. cognata* Kiritch., *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath., and *Stenorachis* (?) *clavata* Nath. are identified among plant remains.

One of the key sections of the Upper Prisayanskaya Subformation is outcrops situated on the left bank of the Irkutsk water reservoir on the opposite side of the Stroitel' settlement (**Figure 2**). The section has a rhythmic structure. Bases of rhythms are presented by quartz fieldspathic coarse‐ and medium‐grained sandstones alternating upward the section with fine‐grained sandstones, siltstones, and mudstones with thin (0.1–0.3 m) coal bands. Near water's edge are exposed the following rocks:

One of the key sections of the Upper Prisayanskaya Subformation is outcrops situated on the left bank of the Irkutsk water reservoir on the opposite side of the Stroitel' settlement (**Figure 2**). The section has a rhythmic structure. Bases of rhythms are presented by quartz fieldspathic coarse‐ and medium‐grained sandstones alternating upward the section with fine‐grained sandstones, siltstones, and mudstones with thin (0.1–0.3 m) coal bands. Near

1. Sandstones, gray medium‐grained, horizontally bedded micaceous 0.14

3. Hard coal 0.12 4. Mudstones, gray 0.04 5. Hard coal 0.16 6. Mudstones, coaly, horizontally bedded with abundant plant detritus 0.13 7. Hard coal 0.07

> of *Coniopteris maakiana* (Heer) Pryn. emend. Kirich. et Trav., *Cladophlebis* cf. *williamsonii* Brongn., *Raphaelia diamensis* Sew., *R. tapkensis* (Heer) Pryn. emend. Kost., *Czekanowskia* ex gr. *rigida* Heer, and *Phoenicopsis* ex gr.

9. Sandstones, gray, medium‐grained with plant detritus 0.07

impressions of trunks. Leaf mats of *Phoenicopsis* ex gr. *angustifolia* Heer are

11. Sandstones, gray, horizontally bedded with plant detritus 0.10

13. Sandstones, gray, medium‐grained with cross bedding and plant detritus 0.90 14. Hard coal 0.06 15. Mudstones with plant remains, poorly preserved 0.03 16. Coal 0.04

18. Sandstones, medium‐grained, massive 0.60

(Heer) Pryn. emend. Kiritch. et Trav., *C. murrayana* (Brongn.) Brongn., *Cladophlebis haiburnensis* (L. et H.) Sew., *Cl. williamsonii* Brongn., *Phoenicopsis* ex gr. *angustifolia*

17. Mudstones, gray, with impressions of fossil plant remains: *Coniopteris maakiana*

Heer, and *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath.

2. Sandstones, silty, gray, plant detritus is exposed, plant detritus is found in

90 Seismic and Sequence Stratigraphy and Integrated Stratigraphy - New Insights and Contributions

8. Mudstones, coaly, horizontally bedded, contain abundant plant detritus

10. Sandstones, gray medium‐grained with distinct cross bedding and

12. Mudstones, gray, horizontally bedded. Impressions of *Coniopteris maakiana* (Heer) Pryn. emend. Kirich. et Trav., *Cladophlebis nebbensis* (Brongn.) Nath., *Cl. williamsonii* Brongn., *Cl. haiburnensisi* (L. et H.) Sew., *Raphaelia diamensis* Sew., *R. tapkensis* (Heer) Pryn. emend. Kost., *Sphenobaiera* ex gr. *czekanowskiana* (Heer) Florin, *Pseudotorellia* cf. *ensiformis* (Heer) Dolud., *P.* cf. *paradoxa* Dolud., *Czekanowskia* ex gr. *rigida* Heer, *Phoenicopsis angustifolia* Heer, *Elatocladus manchuricus* (Yok.) Yabe, and *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath. are found in the lower part of the layer

found at 0.4 m from the bottom layer

**Thickness, m**

0.46

0.11

1.10

0.10

0.55

water's edge are exposed the following rocks:

the upper part of the layer

*angustifolia* Heer

The Upper Prisayanskaya Subformation outcrop within the Topka creek valley is presented by 18‐meter‐thick section having a rhythmic structure (**Figure 2**). The bases of rhythms are composed of quartz fieldspathic coarse‐ and medium‐grained sandstones, siltstones, and mud‐ stones with thin (0.1–0.3 m) coal bands. *Coniopteris murrayana* (Brongn.) Brongn., *C. maakiana* (Heer) Pryn. emend. Kiritch. et Trav., *Cladophlebis williamsonii* Brongn., *Cl. haiburnensis* (L. et H.) Sew., *Czekanowskia* ex gr. *rigida* Heer, *Phoenicopsis* ex gr. *angustifolia* Heer, *Phoenicopsis* ex gr. *speciosa* Heer, and *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath. are revealed among plant remains.

The Upper Prisayanskaya Subformation is exposed on the left bank of the Irkut river, 1.5 km below the Pionersk settlement (**Figure 2**). Outcrop of 800 m length has a rhythmic structure. The rhythm bases are composed of quartz fieldspathic coarse‐ and medium‐ grained sandstones (3–4 m) alternating upward with siltstones and mudstones with thin (0.1–0.2 m) coal bands. The following plant remains of *Coniopteris maakiana* (Heer) Pryn. emend. Kiritch. et Trav., *C.* cf*. spectabilis* Brick, *Coniopteris* sp., *Cladophlebis williamsonii* Brongn., *Cl. williamsonii* Brongn. var. *punctata* Brick, *Cl. nebbensis* (Brongn.) Nath., *Raphaelia diamensis* Sew., *Raphaelia tapkensis* (Heer) Pryn. emend. Kost., *Ginkgo* ex gr. *sibirica* Heer, *Sphenobaiera* ex gr. *czekanowskiana* (Heer) Flor., *Czekanowskia* ex gr. *rigida* Heer, *Phoenicopsis angustifolia* Heer, *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath., *Ixostrobus heeri* Pryn., *Carpolithes deplanatus* Pryn., and *Stenorhachis* sp. are identified in siltstones and fine‐ grained sandstones.

**The Kudinskaya Formation** is located in the northeast of the Irkutsk city within the Kuda trough, overlaps discordantly with the Prisayanskaya Formation. The formation is charac‐

terized by rubbly pebbled conglomerates and coarse‐grained sandstones, thin interlayers of siltstones and mudstones are rare. Thickness of the formation is 50–80 m. Identifiable remains of plants from the Kudinskaya Formation have been long unknown. In 2014 we studied the stratotype of Kudinskaya Formation which is located at 2 km to the north of the Zherdovka village. Clastic rocks opened within these outcrops contain thin interlayers of siltstones and mudstones. The following fossil plants: *Equisetites* sp., *Coniopteris maakiana* (Heer) Pryn. emend. Kiritch. et Trav., *Czekanowskia curta* Kiritch. et Samyl., *Czekanowskia rigida* Heer, *Leptostrobus laxiflora* Heer, *Pityophyllum* ex gr. *nordenskioldii* (Heer) Nath. *Carpolithes cinctus* Nath., and *C. minor* Pryn. are found in mudstones [13, 26].


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### **Conclusion: Recasting the Future of the European Steel Industry**

**Tugce Akyazi, Aitor Goti, Elisabete Alberdi, Clara Behrend, Antonius Johannes Schröder, Valentina Colla, Dean Stroud, Luca Antonazzo, and Martin Weinel**

When the European Coal and Steel Community (ECSC) was established in 1952 to unite European countries economically and politically to secure long-lasting peace, steel production was one of the two main strategic industries in which a common European project was first launched. Today, the steel industry still represents a critical asset for the European economy and contributes to the development of a huge number of transforming industries, in particular the automotive, construction and infrastructure, robotics, advanced machinery and tools, and household appliances sectors. During the period till the end of the ECSC in 2002 and then beyond into the new millennium, the steel industry in Europe more broadly and within the EU has gone through significant waves of expansion, consolidation, modernisation, rationalisation and shrinkage, and it is currently facing tremendous pressures and challenges (see Chap. 2).

The EU steel industry has been dealing with urgent issues, such as overproduction, dumping from non-EU competitors, protectionist measures, high energy prices and serious environmental concerns:

C. Behrend · A. J. Schröder Technische Universität Dortmund, Dortmund, Germany

V. Colla Scuola Superiore Sant'Anna, TeCIP Institute, Pisa, Italy

#### D. Stroud · M. Weinel School of Social Sciences, Cardiff University, Cardiff, United Kingdom

#### L. Antonazzo Center for Workplace Research, Prague University of Economics and Business, Prague, Czechia

T. Akyazi (B) · A. Goti · E. Alberdi Department of Mechanics, Design and Organization, Deusto University, Bilbao, Spain e-mail<EMAIL_ADDRESS>

This volume (and conclusion section) explores some of these challenges aiming to offer reflections and recommendations to the various stakeholders involved in the industry, as well as insights to researchers interested in the transformation of the sector.

Despite all the challenges, the ambition of the European steel sector is to become the most advanced steel industry in the world. The European Steel Technology Platform (ESTEP) describes the vision for the EU steel industry up to 2030 anticipating major changes, many of which will be driven by new scientific and technological developments, evolving customer and stakeholder demands, and the ambitious European climate goals. Resilience has progressively (and rightfully) become a major concern in national and international debates and policies. To achieve industry resilience for the EU steel sector, current and future research needs to be focused on innovative techniques, such as more modular production lines, remotely operated factories, use of new materials, and real-time risk monitoring and management. In this context, digital technologies will enable resilience-enhancing approaches, such as data gathering, automated risk analysis and automated mitigation measures.

To date, the sector has taken significant steps towards Industry 4.0 and evolving into a smart industry, even though the steel industry has been recognised as a mature sector with relatively minor technological updates, compared to, say, automotive for example. Steel companies are digitising their manufacturing processes by integrating 4.0 technologies into the melting, casting, rolling and finishing sub-processes. The manufacturing models are changing with the adoption of smart technologies such as Internet of things (IoT), Artificial Intelligence (AI), robotics, predictive maintenance and so on. The use of digital technologies is resulting in a new stage of automation, which enables more efficient and creative processes, products and services. In addition, the adoption of new technologies aims at improving energy efficiency and at monitoring and controlling environmental impacts (see Naujok and Stamm 2017).

The digital transformation of the steel industry together with strong demands to put the sector on a more environmentally sustainable footing and to align with European climate objectives represent the main drivers for increasing energy and resource efficiency and contributing to keeping materials in use for a longer time. The European steel sector has been undergoing continuous and considerable changes due to these key factors in the last years. The sector's digital transformation and its move towards a circular economy can further be supported by exploiting synergies between the different EU initiatives. Moreover, as the European steel industry continues to face substantial challenges, it is imperative that a coordinated effort of unions and employers is directed towards achieving net zero emissions (Antonazzo et al. 2021a). It is more essential than ever that unions work together with employers as the industry takes on, perhaps, the greatest challenge it has ever faced.

A new relationship between environmental sustainability and competitiveness is emerging. The simple externalisation of costs associated with pollution and nature depletion is no longer an option. Changing consumer demands and societal values, together with a regulatory environment that makes unsustainable steel production more and more costly, offer an opportunity for the European steel industry to increase its competitiveness by embracing sustainable production regimes. Costs related to energy transition and the EU environmental legislation represent, on the one hand, two pressure factors for the EU steel industry as they constitute a significant share of fixed costs compared to global competitors. On the other hand, in the last few decades, these policies and legislation have represented drivers for companies to adopt measures and solutions for facilitating innovation to stay competitive. New relationships between environment and industrial competitiveness have recently been mainly based on innovative solutions to achieve both environmental protection and industrial competitiveness (Eurofer 2023). And these new relationships are backed up with policies and legislation.

Of course, not all regulations are beneficial to the industry. Some specific regulations and policies, although encouraging the implementation of innovative measures in the steel sector, can limit the industry. The key to overcome these potential disadvantages for EU steel companies is to have clear, consistent, and less bureaucratic legislation and policies as well as economic incentives. Increasingly stringent environmental legislation represents a push factor for the steel sector to implement digital technologies for coping with energy demands, improving energy efficiency and adopting low-carbon energy systems (Antonazzo et al 2021a; Eurofer 2023). The transformation of processes through digital technologies (e.g. through the adoption of high-performance components, machines and robots to optimise the materials and energy consumptions) can help to significantly reduce emissions and improve resource efficiency, by optimising materials and energy consumption.

As argued in various chapters of this volume, the ongoing transformation of the steel industry does not involve only a technological dimension but needs to be accompanied by a complementary process of social innovation that recognises that technical fixes need to be run within an adequate and supportive social framework. As it has been noted in this volume, the technological transformation will entail a social one too and the need, for example, to recruit, retain and develop a highly skilled workforce (by means of, for instance, addressing the industry's poor image to attract new talent, develop iterative upskilling and shift the focus of training from solely technical to include more in the way of transversal skills at all levels, etc.). It is noted, moreover, that the industry's social transformation is not only determined by technological innovations and questions of sustainability alone, but is part of interrelated processes of globalisation, internationalisation, privatisation, rationalisation and restructuring, among other pressures (e.g. Fairbrother et al 2004; Eurofer 2022. See Chaps. 2 and 3 of this volume, for example).

The recently proposed EU Industry 5.0 approach envisions a way to overcome some of the challenges addressed in this volume, along with the social innovation that the EU steel industry is confronted with. While in Industry 4.0 technologies are deemed to have an inherently transformative potential, the Industry 5.0 concept flips the perspective and provides a holistic framework that emphasises the importance of environmental and social elements when it comes to implementing Industry 4.0 technologies. It highlights not only a sustainable and resilient industry but also the human-centric orientation, developing technology for people and at the same time addressing societal challenges. As Chap. 3 notes, what is needed is an overview of the state of the art and the preconditions for integrating the Industry 5.0 perspective (human-centric, resilient, sustainable) and a framework for engaging stakeholders, raising awareness, increasing acceptance, gathering and exchanging good practices, enabling policy and regulations, developing indicators and so on.

What emerges is the need for an evidence-based and long-term management of the European industry workforce and skill needs, which accounts for an inclusive working environment and empowered workforce strategy to build a human-centric European industry. Multi-level and multi-stakeholder governance that engages the relevant societal domains (industry, policy, research and education, civil society) is the necessary ground for setting the foundations for a comprehensive social innovation process and an Industry 5.0 roadmap. As such, the transformation of the European steel industry might well be led in many ways by 'science' and technological innovation, but its people must be part of the journey too–it requires a social consensus on the direction it takes.

Furthermore, what many of the chapters in this volume have focused on is that to realise a healthy, digital, green and social transformation, the industry requires the right people and the right skills. To meet the industry's future challenges it must be cognisant of, and active on, demographic changes occurring in the sector, and focus on recruiting and developing a multi-skilled and competent workforce (White Research et al. 2020). The workforce needs to be skilled and qualified to handle the implementation of smart technologies, contemporary business models and organisational structures. This is only possible through: (1) upgrading the skills, knowledge and credentials of the current workforce and (2) recruiting new talents.

A workforce with updated skills is key for the steel sector to keep up with growing digitalisation, and to adapt to green transformation and novel working systems. The initial step of this continuous re-skilling and up-skilling of the workforce is addressing the current skills needs and trends, as well as foreseeing future ones. Once the anticipated evolution of skills needs is identified, a long-term methodology may be established for reducing the skills gap between the industry expectations and the current workforce. Such a methodical approach would ideally provide tools for the recruitment of new talents, as well as introduce well-designed education and training infrastructure developed by society-wide effort, and redesign work processes (see Chap. 12).

The steel industry has already invested considerable sums in innovation and in research and development. At the same time, it is (and will continue to be) necessary to invest in the current and future workforce. Considering that it will be a relevant investment, it will be necessary to involve in future educational activities not only steel companies, but also national public education authorities, training and education providers, so that these correspond to the current requirements for an educated workforce of steel and metallurgical companies. At the same time, it will be critical to introduce concepts of lifelong learning on the part of employees, who will have to continuously respond to new concepts and production changes that are constantly coming.

Research from ESSA and other projects presented within this volume, as well as the reflections offered by industry professionals, has pointed out how many workers in the industry still lack adequate digital skills despite the increasing digitalisation of workplaces. One of the perhaps more interesting arguments presented in this volume is the importance of (non-digital) transversal skills in connection with digital (Anotonazzo et al., 2021c). Digital skills, at least in their basic form, are playing an increasingly important role at all qualification and skill levels. When it comes to higher digital skills (e.g., programming), however, the degree to which these are needed varies greatly depending on the skill level of the employee. For example, stronger digital skills are needed for higher skilled employees, while the challenges are less demanding at lower skill levels.

The research presented in this collection demonstrates, however, that the acquisition of digital skills depends on the prior acquisition of transversal or soft skills such as the willingness to change, autonomous learning and adaptability, as well as intrinsic motivation to continuously improve and adapt one's competence and skill levels are particularly important in this respect. As many different types and forms of skills interact with each other, many of the non-digital soft skills can be seen as a prerequisite for the learning of digital skills. This becomes particularly clear with the methodological skills numeracy and literacy, without which it is difficult to acquire digital skills. Another central finding with regard to the skill demands in the steel ecosystem are the challenges that arise from the fast pace of digitisation. This fast-moving nature means that it is difficult to impart specific knowledge, especially in dealing with digital technologies, because the software and tools used change at regular intervals. This, once again, puts emphasis on the importance of soft skills (Antonazzo et al. 2021c).

Continuous learning and the reskilling and upskilling of the workforce rely in part on robust company programmes (e.g. on-the-job training). But the foundations rest on the development of well-designed Vocational Education and Training (VET) programmes devised to minimise the skill gap between the workforce and job profiles. VET programmes are required to provide theoretical knowledge, specialised technical skills, basic and advanced digital skills, and a wide range of soft skills (social, methodological, personal, etc.), along with on-the-job training to consolidate learnings. The 'human capital 4.0' approach proposed by Flores and colleagues is intended accordingly as a 'holistic shift in terms of competence, well-being, education and innovation' (Flores et al. 2020). In line with this, Chap. 11 calls for a holistic shift in vocational education and training to enhance workers' adaptivity, as well as businesses innovation and resilience (see Antonazzo et al. 2021b).

The Covid-19 pandemic has also permanently changed the training approach of the steel companies. Fortunately, accelerated digitalisation in recent years has provided companies with the opportunity to improve their asynchronous training programs and provide new training opportunities for greater numbers of attendees through remotely delivered content by using software tools such as Teams or Zoom.1

Recruiting new talents is another key to build a highly capable steel workforce, in addition to upgrading the skills of the current employees. The age structure in most European steel-producing companies is such that more than 25% of the workforce will leave the industry in the period 2020–2030. Therefore, to ensure competitiveness, attracting top talents to the EU steel industry is vital. However, it is first of all essential to pro-actively respond to talents' needs and expectations (on both professional and personal aspects) by developing suitable work-life balance models. In addition, with a negative public image and an uncertain economic future, the steel industry in the EU does not look like a prospering and attractive place to work to potential future employees even if they have a personal affinity to steelmaking as a secure job and a good economic outlook are important criteria when choosing an employer (White Research et al. 2020).

With these obstacles in mind, it is important to highlight the positive opportunities and chances for a career in the European steel industry, to counter-balance negative public perceptions. Therefore, the industry needs to step up its efforts in communication with the public and with potential candidates and to send a clear message: the European steel industry is a high-tech employer with state-of-the-art production facilities, strong research and development departments and develops sustainable solutions for its customers. It has a great opportunity to create exciting and innovative jobs and to communicate the upcoming innovative technological developments.

For better and more sustainable products in our future world, steel, due to complete recyclability and versatile properties, is and will remain indispensable. However,

1 One example of this comes directly from the ESSA project and the *steelHub* developed in cooperation with Worldsteel (Steel University), which provides a repository of technical and transversal training programmes for many different roles within the industry.

the European steel industry will still have to deal with unequal conditions on the global market, unfair competition, overcapacity, protectionism, problems with the supply of raw materials. But an educated workforce and investments in research and development will support the transition to a greener and more sustainable steel production, and increase the potential for a competitive position on world markets. It becomes clear that the transformation of the steel industry needs to go in the direction of environmental and social sustainability, jobs and production stability, enhanced resilience and a skilled workforce.

### **References**

**Tugce Akyazi** Tugce Akyazı graduated from Istanbul Technical University as a metallurgical and materials engineer. Subsequently, she obtained a double master's degree in Materials Science and Engineering at Politecnico di Milano and Istanbul Technical University, graduating with honors. After several work experiences in industrial companies, she worked as a researcher at the Quantum Devices and Nanophotonics Research Laboratory of Middle East Technical University (METU) in Ankara. After, she started working on her PhD at the University of Navarra in Spain, while working as a researcher in the microtechnology area at CIC MicroGUNE. Later, she joined the University of the Basque Country EHU/UPV and also conducted studies at the University of Central Florida (UCF) (USA) as an exchange researcher. In 2018, she defended her doctoral thesis in San Sebastian. She has been working as an associated researcher since 2019 at the University of Deusto/Bilbao and since 2023 at the Cardiff Univesity (Wales/UK), specializing in industrial management and researching in skills and workforce development.

**Aitor Goti** is Associate Professor at the University of Deusto. Prior to that, and among others, he was the Director of the Deusto Digital Industry Chair, Operations Deputy Director of an industrial multinational included at the Spanish trading market, and responsible of a research line at the University of Mondragon. Aitor has published over 100 articles, over 40 of them in journals with impact in the Journal Citation Reports and/or the Scimago Journal Rank. As a consequence of his activity, Aitor has made about 40 transfer projects financed by companies, resulting in the training and formation of 50 intern or hired personnel.

**Elisabete Alberdi** studied Mathematics and she did her doctoral thesis in the Superior School of Engineering of Bilbao, Department of Applied Mathematics, University of the Basque Country (UPV/EHU). The thesis was entitled "Numerical methods for stiff differential equations. Application to the discretization of the Finite Element Method". She has worked in Lea Artibai Vocational Training School in the period 1998-2011, where she has taught in Secondary School and University Levels. Lea Artibai is a cooperative of Mondragon Cooperatives Group and it belongs to the Basque Science Net. Since 2011 she works in the department of Applied Mathematics in the Engineering School of Bilbao (University of the Basque Country UPV/EHU), where she is Associate Professor.

**Clara Behrend** is a researcher based at Sozialforschungsstelle, Technical University of Dortmund. Her research interests are focused on the area of work and employment, in the context of Social Innovation, Digitalisation of Work, Social Inequalities, Social Policy, Political Economy, Industrial Relations and Sociology of Labour, Gender Studies and Education Systems, areas in which she has held several research posts.

**Antonius Johannes Schröder** is senior researcher and member of management board of Sozialforschungsstelle (sfs) at the Technical University of Dortmund), responsible for international research. He has worked in and managed several European projects, recently co-ordinating two EU Erasmus+ funded sectoral European Skills Alliances (ESSA and SPIRE-SAIS). He is Chairman of the Focus Group People within the European Steel Technology Platform ESTEP, the chair of the Task Force Non-technological and Social Innovation within Sustainable Process Industry through Resource and Energy Efficiency (SPIRE) and Chair of the senate of the German Professional Association of Social Scientists (BDS).

**Valentina Colla** holds a Master Degree in Engineering and a Ph.D. in Industrial and Information and Engineering. Presently, she is the Assistant Professor of Metallurgy and responsible for the research team on Information and Communication Technologies for Complex Industrial Systems and Processes (ICT-COISP) of the Telecommunications, Computer Engineering, and Photonics Institute (TeCIP) of Scuola Superiore Sant'Anna in Pisa (Italy). Her research activity concerns simulation, modelling, management and control of industrial processes via traditional and Artificial Intelligence (AI)-based techniques, including AI-supported metal science and technology, and solutions to improve energy and resource efficiency in the steel sector. She participated in many EU-funded projects also as project coordinator and She is an active member of the European Steel Technology Platform (ESTEP).

**Dean Stroud** is a Reader at Cardiff University School of Social Sciences teaching and researching on skills and workforce development, particularly in relation to the European steel industry, on which he has numerous publications. He has led and participated in numerous projects funded from a range of sources, mostly European, which have focused on skills needs, technological innovation, greening, equality and diversity, and, more generally, the 'human factors' of steel production. Currently, he is co-coordinator of the Erasmus+ European Steel Skills Agenda (ESSA) project and was a sector expert, with Antonius Schröder and Enrico Gibellieri, to the EASME-COSME funded Steel Sector Careers project.

**Luca Antonazzo** is a sociologist researching work and organizations. He is currently an external scholar at the Centre for Workplace Research (CWER) – Prague University of Economics and Business and a Research Fellow at the Department of Human and Social Sciences – University of Salento (Italy). He is interested in studying how macro-trends such as digitalization and technological innovation, the green transition, and global market competition impact the labour markets, reshaping businesses and jobs, and bringing about new skills needs and new perspectives on human capital. He is also interested in social resilience, the factors that enhance it, and how social systems adapt and respond to change.

**Martin Weinel** is a researcher based at Cardiff University. His area of research includes the Sociology of Scientific Knowledge, in particular scientific controversies; generation and validation of scientific knowledge, studies in expertise and experience: classification of expertise, technical decision making; relationship between technical and political decision-making. More recently his research has focused on steel and industrial contexts, and he has published extensively across all areas of his research interests.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

### **Index**

#### **A**

Automation, 3, 12, 24, 28, 29, 31, 35, 37, 41, 54, 55, 59, 61, 64, 65, 68, 113, 119–122, 124, 167, 168, 170, 183, 185–187, 190, 194, 221

#### **C**

Competences, 12, 40, 42–46, 59, 66, 135, 137, 145, 170, 175, 178, 184, 185, 187, 188, 190–194, 197, 223, 224

#### **D**

Decarbonisation, 2, 5, 6, 18–20, 22, 24, 25, 29–31, 53, 56, 58, 59, 62, 99, 108, 118, 139, 207 Digitalisation, 2, 6, 11, 12, 18, 19, 24, 28, 30, 31, 37, 43, 56, 59, 60, 119, 122,

139, 168, 175, 177, 203–205, 207, 208, 210–215, 220, 223, 224

#### **E**

European Coal and Steel Community (ECSC), 1, 2, 19, 20, 22, 219

#### **H**

Human centric, 38–40, 45, 47, 48, 56, 66, 69, 222

Human Resources, 9, 11, 26, 27, 29, 132–135, 139, 145, 152, 168, 170, 173, 197

#### **I**

Industry 4.0, 2–6, 8–11, 18–20, 22, 24, 27–31, 35, 37, 38, 40, 41, 44, 47, 48, 53, 55, 57, 62, 66, 77, 113, 118–120, 122, 123, 125, 143, 167, 170, 174, 176–178, 183, 185, 186, 191, 197, 211, 220, 222 Industry 5.0, 7–9, 18, 35–43, 45–48, 56, 66, 69, 211, 222 Institutions, 12, 41, 43, 55, 67, 105, 131, 132, 158, 168, 169, 176, 178, 204

#### **R**

Robots, 3, 9, 27–29, 64, 69, 77–87, 93, 94, 186, 192, 195, 221

### **S**

Skill digital, 13, 56, 64, 67, 68, 138, 160, 172, 173, 187, 188, 203, 205, 206, 208, 209, 211, 213–215, 223, 224 green, 19, 27, 187, 188 high, 43, 168 low, 56, 168, 208, 209, 211, 214, 215, 223 methodological, 43, 44, 203, 205–207, 209, 213–215, 223 needs, 7, 29, 35, 40, 42, 43, 48, 63, 130, 158, 168, 184, 185, 189, 190, 223 personal, 13, 44, 172, 205, 206, 210, 211, 214, 215 social, 172, 188, 205, 206, 211, 212 technical, 44, 45, 173, 175, 178, 224

© The Editor(s) (if applicable) and The Author(s) 2024

D. Stroud et al. (eds.), *Industry 4.0 and the Road to Sustainable Steelmaking in Europe*, Topics in Mining, Metallurgy and Materials Engineering, https://doi.org/10.1007/978-3-031-35479-3

229

transversal, 12, 13, 43–45, 67, 68, 170, 174, 203, 206, 207, 222, 223 Steel industry cold rolling, 142, 144 employee value proposition, 130, 159 hydrogen-based steelmaking, 26 image, 158, 159, 161 recruitment, 129, 130, 139, 158 small and medium enterprises, 63, 190

#### **T**

Talent, 4, 9, 11, 66, 68, 130, 132, 135–137, 139, 155–157, 159–162, 184, 222–224

Trade unions, 7, 8, 10, 19, 25, 31, 99–101, 103, 104, 106, 109, 122, 168, 172, 173, 177 Training, 7–12, 18, 24, 29–31, 40–44, 47, 64, 67, 68, 92, 93, 106, 124, 126, 130–133, 137–139, 144, 149, 151, 152, 168, 170, 172–179, 184, 185, 189, 190, 193, 196, 197, 209–215, 222–224 Twin transition, 12, 18, 39, 57

#### **V**

Vocational education, 159


It was clear from the analysis that the hybridization modification technique was applied in 75 references to solve FS. This counting result shows that hybridization is the most widely applied modification technique to enhance NIAs wrappers in the FS domain. This high number of work comes from GA, which is the NIA that had the most number of works regarding wrapper hybridization. GA wasapplied hybridization in 38 different works, which is much higher than 6, the number of works that adopted new operators to GA. We can infer from this works count and from the contribution of studies that hybridization is the best suitable modification technique to be applied with GA. ACO also were hybridized in 7 references, while no work adopted a new operator to modify ACO. Conversely, PSO hybridization works were 11, which is less than 21, the number of works with new operators, thus we can again infer from these counts and the contribution of studies that adopting a new operator to PSO is more suitable than hybridization. It was also noticed that hybridization using different kinds of classifiers was the most prominent hybridization technique.

There are 49 studies that tried to investigate the influence of the classification technique on the performance of wrappers for optimizing FS, some of these studies applied simultaneous optimization for FS and a classification/prediction task by tuning the parameters of the classifier with applying FS. The next widespread hybridization technique is a filter-wrapper, which was applied in 14 studies and was very effective in dealing with large dimensionality feature space. Hybridization techniques that tried to balance the exploration/exploitation of the search space also were adopted by a considerable number of works. In summary, PSO and GA are the most widely modified NIAs-FS approaches. They were equally modified and used. Each one of them was adopted and modified for FS in 56 references of the gathered studies.

On the other side, regarding applications of NIAs-FS, it was evident that microarray gene expression classification is the most dominant application where NIAs-FS approaches were applied in 18 studies with a ratio 24% concerning other applications. The medical application was the second prominent application for applying NIAs-FS approaches with a ratio of 21%. The medical application includes different medical branches SONAR, tumor, mass, and various disease detection, medical diagnosis, medical data, and bio-signal analysis. Then, follows hyper-spectral image with a ratio of 17%, Arabic handwritten recognition with a ratio of 13%, facial expression recognition with a ratio of 9%, EEG application with a ratio of 7%, financial diagnosis with a ratio of 5%, and spam detection with a ratio of 4%. Furthermore, it is noticeable that GA is the most dominant NIA optimizer for optimizing FS in applications with a ratio of 45%. PSO is the second most widespread optimizer with a ratio of 26%, then ACO with a ratio of 11%, MFO with a ratio of 7%, GWO with a ratio of 6%, CS with a ratio of 2%, WOA with a ratio of 2%, and GOA with a ratio of 1%.

No Free Lunch (NFL) theorem [201] states no optimization algorithm can solve all the optimization problems equally. The success in solving a specific problem does not guarantee that the algorithm will perform similarly for other problems. On average, all the optimization algorithms perform equally. This theorem has motivated researchers to develop new algorithms or improve the existing ones to solve another wide area of optimization problems, such as feature selection. Researchers are advised to read the following references as they are the most cited papers after 2010 in the field of NIAs-FS: [17,51,80,90,94,124,202].

**Table 1.** Summary of main ro pub the tables, and please check if it is the background color can be deleted, same as follows. studies in the literature that adopted new operators as modification techniques for Nature Inspired Algorithms Feature Selection NIAs-FS.

**Table 2.** Summary of main studies in the literature that adopted hybridization modification technique for NIAs-FS.

**Table 2.** *Cont.* **Table 3.** Summary of main studies in the literature that adopted the remaining modifications techniques for NIAs-FS.

**Table 4.** Summary of main modifications applied in the literature on main NIAs-FS (applied/not applied).

**Table5.**Summaryofmainmodificationsapplied in the literatureonmainNIAs-FS(bynumberofstudies).

**Table 6.** Summary of main studies in the literature that applied modified NIAs-FS in applications.

#### **Table 6.** *Cont.*

**Table 7.**Summary of modifications applied on NIA-FS in the main applications.

*Mathematics* **2022** , *10*, 464

> **Table 7.** *Cont.*

#### **8. Conclusions and Future Research Directions**

In this study, a survey about modifications of NIAs for tackling the FS optimization problem is presented. The review is based on a solid theoretical, applied, and technical foundation. Three main research streams are identified in this review: Meta-heuristic optimization, feature selection, and modification on NIAs for tackling FS. This review aims to draw the map for researchers and guide them when creating new research in this area. This survey is based on 156 articles collected and studied on modifications of NIAs for solving the FS problem. The sources of the information search came mainly from six well-regarded scientific databases: Elsevier, Springer, Hindawi, ACM, World scientific, and IEEE. From the review, it can be seen that the NIAs algorithms have been extensively investigated over the past years to improve the FS problem. About 34 different operators were investigated. The most popular operator is chaotic maps. Hybridization is the most widely used modification technique. There are three types of hybridization: Integrating NIA with another NIA, integrating NIA with a classifier, and integrating NIA with a classifier. The most widely used hybridization is the one that integrates a classifier with the NIA. Microarray and medical applications are the dominated applications where most of the NIA-FS are modified and used. Despite the popularity of the NIAs-FS, there are still many areas that need further investigation:

Based on the above trends, the size of the NIAs-FS research area can be recognized. Besides, it can be imagined that a thorough investigation and improvement of NIAs will improve the FS process in various high-dimensional areas. This review paper will be used to help researchers take an excellent view of the modification strategies in nature-inspired algorithms for tackling the feature selection problem.

**Author Contributions:** Conceptualization, R.A.K., I.A. and A.S.; methodology, R.A.K., I.A. and A.S.; formal analysis, R.A.K., I.A. and A.S.; resources, R.A.K.; validation, M.A.E., R.D. and T.K.; writing—original draft preparation, R.A.K., I.A. and A.S.; writing—review and editing, M.A.E., R.D. and T.K.; supervision, M.A.E.; funding acquisition, T.K. All authors read and agreed to the published version of the manuscript.

**Funding:** No funding received for this research.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Not applicable.

**Conflicts of Interest:** All authors declare that they have no conflict of interest.

#### **References**

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