1 Introduction

Polymetallic nodules continue to fascinate researchers due to their shear abundance in deep-sea basins of all major oceans in the world (Cronan 2000), and also because of 3% growth rate of consumption of metals they contain (Mn, Cu, Ni, Co) in the last four decades (Sudhakar and Das 2009). Although the actual exploitation and extraction have been on hold owing to fluctuating metal prices in the world market , projections suggest that these mineral deposits are expected to be the alternative source of metals in the twenty-first century (Lenoble 2000; Kotlinski 2001). Whereas, in view of relatively shallow depths and proximity to the shore, some entrepreneurs have plans to initially start mining massive sulfides from within the EEZ of island countries (Gleason 2008); given the ongoing development in technology, deep-sea nodule mining could well become a reality in future, as they are found loosely strewn on the seafloor (Fig. 8.1) and just have to be scooped up, which makes them relatively easy to mine as compared to massive sulfides and ferromanganese crusts that are “stuck” to the seafloor.

Fig. 8.1
figure 1

Nodules and crusts on the seafloor

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This chapter looks at the distribution characteristics of nodules and associated seafloor features and their influence on different mining scenarios based on mining rates (ranging from 1 to 3 Mt year−1), so as to provide useful information to the industry for designing a suitable mining system and optimizing the mining rate, while addressing the environmental concerns. This chapter also proposes certain equations for evaluating the relationships of polymetallic nodules with associated seafloor features as well as computing different mining estimates that can be applied to other deep-sea minerals.

2 Estimation of Nodule Characteristics and Associated Features

Data relating to distribution, quantity, and association of nodules with different substrates and topography on the seafloor is collected using freefall grabs, vanveen grabs, TV grabs, corers as well as sounding and sub-bottom profiling techniques (Kunzendorf 1986). Seafloor photography with single-shot underwater cameras and deep-towed seafloor photography systems have also proved to be effective tools for supplementing the data on distribution of nodules (Bastien-Thiry 1979; Fewkes et al. 1979; Sharma 1993). Estimation of nodule characteristics can be approached as follows.

2.1 Measurement of Area Covered on the Seafloor

Measurement of area and volume of a deposit recovered during sampling can be calculated based on the dimensions of the sampling device. While making any estimates from seafloor photographs, the area covered by the photograph on the seafloor is calculated by using the altitude (distance X) of camera above seafloor and camera lens angle (θ) (Fig. 8.2) as:

$$ D= X \tan \theta $$
(8.1)
Fig. 8.2
figure 2

Calculation of area photographed on the seafloor

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where D = ½ of length (L in meters)

so, 2D = L (length of area covered on seafloor in meters) and 2/3 L = B (breadth of area covered on seafloor in meters)

Hence, area covered on seafloor

$$ (A)= L\times B\left(\mathrm{in}\ {\mathrm{m}}^2\right) $$
(8.2)

Further, the dimensions of the features of interest (e.g., size or diameter of nodules or crusts) are measured from the vertical photographs, by using the scale factor (S f) which is derived as:

$$ {S}_{\mathrm{f}}={\left( A/{A}_{\mathrm{p}}\right)}^{1/2} $$
(8.3)

where A p is the area of the photograph.

Coverage (C) which refers to the area covered by a feature (nodules or crusts in this case) with respect to the area photographed on the seafloor is expressed in % and is calculated as:

$$ C=\left({A}_{\mathrm{c}}/{A}_{\mathrm{p}}\right)\times 100 $$
(8.4)

where A c is the total area covered by nodules (or crusts) in the photograph.

A c can be estimated using different methods such as point counting manually (Fewkes et al. 1979) and also image analyses electronically (Park et al. 1997; Sharma et al. 2013).

2.2 Calculation of Nodule Abundance

Nodule abundance from grab (N g) or core samples is estimated by dividing the weight of recovered nodules with the area of the grab or corer and expressed in kgm−2 (Frazer et al. 1978). Researchers have also attempted abundance estimation of the nodules based on photographs (Felix 1980; Handa and Tsurusaki 1981; Lenoble 1982; Sharma 1993). Of these the simplest method of estimating nodule abundance from photographs as described by Handa and Tsurusaki (1981) is as follows:

$$ {N}_{\mathrm{p}}=7.7\times C\times D/100 $$
(8.5)

where N p is the abundance in the photograph (kg m−2), C is the nodule coverage (%), and D is average nodule diameter (cm). Here, coverage (C) is calculated as in Eq. (8.4) and the average nodule diameter (D) is computed using the scale factor (S f) as in Eq. (8.3). Here, it is suggested that for better accuracy of the estimation the constant factor (7.7 in this case) may have to be derived independently for each deposit based on local conditions.

As the photographs record the exposed nodules only and the grabs recover the buried nodules as well, it is observed that nodule abundance estimated from grabs and photographs at the same location may not agree. Here, relative abundance (R a) gives the percentage of agreement between the photo abundance (N p) and the grab abundance (N g) and is calculated as (Sharma 1993):

$$ {R}_{\mathrm{a}}=\left({N}_{\mathrm{p}}/{N}_{\mathrm{g}}\right)\times 100 $$
(8.6)

The complementary value of the relative abundance could also be used as an indicator of buried nodules for planning the depth of excavation in an area during mining.

3 Distribution of Nodule Characteristics and Associated Features

Frequency distributions of nodule characteristics that can influence mining were evaluated from different basins in the Pacific and Indian Oceans (Tables 8.1 and 8.2) based on sample data from research publications. Here, it must be noted that these studies were conducted over large areas in different ocean basins for the purpose of understanding the distribution characteristics of nodules with respect to geological setting and not for resource estimation in potential mine-sites that would normally have higher concentration of nodules in relatively small areas.

Table 8.1 Frequency (%) distribution of nodule characteristics in Pacific Ocean
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Table 8.2 Frequency (%) distribution of nodule characteristics in Indian Ocean
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3.1 Frequency Distribution of Nodule: Size, Coverage, Abundance

3.1.1 Nodule Size

Classification of nodules from different basins in the Pacific Ocean in size classes of 2 cm shows that a large number of samples (49–58%) belong to 2–4 cm size class, followed by <2 cm size class (11–40%) as well as 4–6 cm size class (up to 31%) and few nodules (0.5–7%) in higher (6–8 and >8 cm) size classes. However, when nodules from other basins were classified in 3 cm size classes, a majority of them (72–89%) fell in <3 cm size class. Similarly in the Indian Ocean, a large number of the nodules (23–67%) belong to 2–4 cm size class in Central Indian Basin, followed by 4–6 cm (20–53%); whereas the nodules from the Exclusive Economic Zone of Mauritius had many nodules (35%) in <2 cm size class. By weight (%), a majority of nodules (40–65%) fall in 2–4 cm size class, followed by 4–6 cm size (16–35%). Hence, by number as well as by weight, nodules between 2–4 cm size contribute the most to the total population of nodules, and many of the remaining nodules belong to <2 and 4–6 cm. Combining the data from both size classifications indicates that 2–3 cm could be the dominant size range for nodules in different ocean basins.

3.1.2 Nodule Coverage

In the Central Pacific Ocean as well as Central Indian Ocean, a majority of the locations (68–76%) had nil nodule coverage as observed from the seafloor photographs, followed by <10% coverage (in 10–18% locations) and higher (>10%) nodule coverage in very few locations (1–8%). However, Magellan Trough and Penrhyn Basin in the Pacific Ocean had relatively more locations (15–26%) with 10–20% nodule coverage indicating that different basins within the same ocean could have higher nodule coverage based on local geological conditions. It was also observed that many of seafloor photographs in the Pacific as well as Indian Oceans do not show higher coverages due to partial or complete burial of nodules under the sediment cover (Fewkes et al. 1979; Sharma 1989a).

3.1.3 Nodule Abundance

In the Pacific Ocean, a majority of the grab sampling locations (32–67%) had submarginal nodule abundance (<5 kgm−2), whereas the frequency ranged from 7 to 21% for different classes of paramarginal deposits (5–10, 10–15, 15–20, 20–25, >25 kg m−2) (Table 8.1). Similarly in the Indian Ocean, many locations (38–77%) from different basins had submarginal nodule abundance, with the remaining accounting for different classes of paramarginal deposits (Table 8.2). However these studies are based on basin scale studies, whereas it is reasonable to expect that the first-generation mine-sites identified after detailed resource estimation, would contain higher average nodule abundances so as to maintain the techno-economic feasibility of the mining venture.

Comparison of 964 photographs along with grab sample data from the same locations in Central Indian Ocean Basin showed that whereas nodules were recorded in both at 426 (44%) locations, no nodules were recorded in either at 206 (21%) locations indicating that these locations are totally devoid of nodules. Of the remaining 332 (35%) locations, nodules were recorded in photographs alone at 56 (6%) locations and only in grabs at 276 (29%) locations (Fig. 8.3). Of the locations where nodules were recorded in both, 71% had very high agreement (R a ~ 100%) and at the remaining locations, the agreement between photos and grabs varied from 30 to 90%. This shows that whereas at many locations, a large number of nodules are exposed which have been captured by photographs as well as collected by grabs and at the other locations, a few nodules are buried and their degree of burial ranges from 10 to 70% (R a = 30–90%). Also, occurrence of low abundance (<5 kgm−2) in majority of the photographs (76.5%) as compared to about half of the grab samples (48.4%) in contrast to high abundance (>5 kgm−2) in few photographs and many grabs (51.6%) indicates the occurrence of buried nodules (Fig. 8.4).

Fig. 8.3
figure 3

Venn diagram of nodule occurrence in grabs and photographs

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Fig. 8.4
figure 4

Distribution of nodule abundances in grabs (Gr) and photographs (Ph)

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Whereas, grab samplers are capable of collecting nodules in top 20–30 cm, core samplers that can penetrate deeper down to several meters have shown that the occurrence of buried nodules is generally within the top 1–2 m (Stoffers et al. 1982), but occasionally these also occur at deeper depths of more than 5 m in the Pacific (Usui 1986; Cronan 2000) as well as Indian Oceans (Pattan and Parthiban 2007).

3.2 Association of Nodules with Different Substrates

Nodule deposits are associated with different substrates, such as sediments and rocks that could have a bearing on mining of nodules. It is observed that generally R a is high (>50%) in the areas of thin sediment, that gradually becomes less in the sediments of intermediate thickness, and is even less in thick sediments because of the increase in the number of buried nodules. On the other hand, in the area of rock exposures, there is very good agreement (R a = 90–100%) between photography data and grab data at the locations with no sediment patches, and low agreement (R a < 60%) where sediment patches are seen in the micro relief of the rock outcrops (Sharma 1993).

3.2.1 Effect of Sediment Cover

An evaluation of ~20,000 photographs in the Indian Ocean has shown that almost two-thirds (65%) of the photographs have nil nodule coverage (i.e., no exposed nodules), whereas many (17–21%) of the remaining photos have low nodule coverage (1–20% coverage), some (~11%) have moderate coverage (20–50% coverage) and very few have higher nodule coverage (50–80%) (Sharma et al. 2010). The fact that at many of the locations where no nodules are exposed in the photographs, nodules have been collected in grab samples as observed earlier, indicates at the occurrence of buried nodules under sediment–water interface boundary layer (Sharma 1989a). Analyses of sounding data on thickness of the acoustically transparent layer in the nodule areas have shown that the thickness of this uppermost sediment layer is highly variable from a few meters to several tens of meters (Sharma et al. 2013).

Studies in the Pacific Ocean also have shown that the “Sediment–Water Interface Boundary (SWIB)” layer is known to bury the nodules and obscure them from camera view at some locations (Fewkes et al. 1979) and whereas these buried nodules cannot be accounted for in the photographs, they are collected in the grab samplers. The extent of nodule burial depends on the size and shape of nodules and thickness of SWIB layer (Cronan and Tooms 1967; Felix 1980). Hence, photography data and grab sample data can be used to complement each other for evaluating the distribution of nodules in terms of their occurrence and also exposure or burial.

Due to “fractionation” effect, nodules look smaller and the nodule abundance estimates are lower in the photographs, as the average diameter of the nodules is less that results in an underestimation of nodule abundance at these locations (Sharma 1989a). Nodule coverage and abundance in the photographs and grabs have an inverse relation with SWIB thickness because thicker the SWIB layer, the more is the burial resulting in a lower number of nodules being seen in the photographs and collected in the grabs (Sharma 1989b). Horn et al. (1973) also observed that in the Pacific Ocean, barren areas have resulted either through burial by sediments or prevention of nodule development by covering the potential nuclei.

3.2.2 Distribution of Rock Exposures

Rock exposures are observed as hard, dark colored substrates in the seafloor photographs (Fig. 8.1) that generally have a coating of ferromanganese oxides, also called as encrustations or “crusts.” Distribution of rock outcrops/crusts in a part of the Pacific Ocean showed that their coverage ranged from 1 to 100% and a majority of them (63.5%) had low (<20%) coverage, few (14%) had medium (20–50%) coverage, and the remaining (22.5%) had high (>50%) coverage (Yamazaki and Sharma 1998). Similar observations from seafloor photographs in the Central Indian Ocean showed that these had a wide range of coverage (from 1 to 100%) with about half (48%) having low coverage (<20%); one-third (34%) having medium coverage (20–50%) and the remaining (17%) having higher coverage (>50%) on the seafloor (Sharma et al. 2010).

These outcrops when broken up due to submarine erosion act as sources of nucleating material that originates from weathered basalts that are the products of volcanic eruptions represented by the present day seamounts (Iyer and Karisiddaiah 1988). The broken fragments from the rocky outcrops, which are eroded and transported along the slopes and flanks of the seamounts and abyssal hills, act as nuclei for accretion of the oxides from the hydrogenous as well as digenetic sources of metals on the seafloor, thereby resulting in large concentrations of nodules in these areas (Iyer and Sharma 1990). This phenomenon has been explained as “seed” hypothesis wherein the nodule population is controlled by the distribution of the “seeds” or rock fragments that act as nuclei for nodules (Horn et al. 1973).

3.3 Nodule Distribution in Different Topographic Settings

When nodule and crust coverage data were plotted with respect to depth and distance in the Central Indian Ocean, it showed that whereas the crust/rock outcrops were predominantly exposed on the top of a seamount/abyssal hill, more nodules were exposed on the lower slopes where sediment accumulation is less as compared to flat abyssal plains and valleys (Iyer and Sharma 1990; Sharma and Kodagali 1993; Sharma et al. 2010) indicating that the topographic settings appeared to control the distribution of nodules, rocks, and sediment as follows:

  • Rock exposures (and Fe-Mn encrustations) at the summits with no sediment cover or nodules

  • Transition zone between rock outcrops with encrustations and thin sediment cover with nodules on upper slopes

  • Nodule fields with thin sediment cover on lower slopes

  • Partial to complete burial (or absence) of nodules due to variable thickness of the sediment cover in the plains and valleys

Similar study on distribution of nodules, rock outcrops/encrustations and sediment with respect to slope angles in the Pacific Ocean (Yamazaki and Sharma 2000) revealed the following topographic control:

  1. 1.

    15–40° slope angle: crust-dominant zone

  2. 2.

    7–15° slope angle: transition zone between nodule fields and crusts

  3. 3.

    3–7° slope angle: sediment-dominant zone

  4. 4.

    0–3° slope angle: nodule-dominant zone

These indicate that whereas the overall distribution patterns are similar, there could be minor variations depending on local geological conditions between different ocean basins.

4 Estimation of Mining-Related Variables

On the basis of characteristics of the mineral deposits in an area, several variables related to mining can be estimated which are demonstrated in this section, based on the cut-off abundance of 5 kg m−2 and life of a mine-site as 20 years (UNOET 1987).

4.1 Estimation of Mining Rates

Mining rate is generally expressed in terms of rate of mining of dry nodules that will eventually be available for further processing. However, higher quantities of wet nodules will have to be mined to account for loss of water (moisture) content before processing. These can be estimated as follows:

  1. 1.

    If mining rate is given for dry nodules, i.e., MR(dry), then

$$ M{R}_{(wet)}=100\times M{R}_{(dry)}/\left(100-{W}_{\mathrm{C}(nod)}\right) $$
(8.7)
  1. 2.

    If mining rate is given for wet nodules, i.e., MR(wet), then

$$ M{R}_{(dry)}= M{R}_{(wet)}\hbox{--} \left({W}_{\mathrm{C}(nod)}\times M{R}_{(wet)}/100\right) $$
(8.8)

where MR(wet), MR(dry) are in Mt year−1

W C(nod) = Water (moisture) content of nodule is in %

4.2 Estimation of Metal Production (M P)

$$ {M}_{\mathrm{P}}={T}_{M C}\times M{R}_{(dry)} $$
(8.9)

where T MC = total concentration of extractable metals (%) and MR(dry) and M P are in Mt year−1

4.3 Estimation of Metal Value (M V)

$$ {M}_{\mathrm{V}}={M}_{\mathrm{P}}\times mp\times 1000 $$
(8.10)

where M P = metal production (Mt) and mp = metal price (in $ kg−1)

Note: Here, mp is multiplied by 1000 to convert from tonnes to kg (an mp is per kg).

4.4 Estimating Total Mineable Area (M) According to UNOET (1987)

$$ M={A}_{\mathrm{t}}\hbox{--} \left({A}_{\mathrm{u}}+{A}_{\mathrm{g}}+{A}_{\mathrm{a}}\right) $$
(8.11)

where

  • A t = the total area

  • A u = area un-mineable due to the topography

  • A g = area below cut-off grade

  • A a = area below cut-off abundance

4.5 Size (or Area) of Mine-Site (A s) According to UNOET (1987) Is

$$ {A}_{\mathrm{s}}={A}_{\mathrm{r}}\times D/\left({A}_{\mathrm{n}} \times E\times M\right) $$
(8.12)

where

  • A s = size of mine-site (km2)

  • A r = annual nodule recovery rate or mining rate (dry tonnes per year)

  • D = duration of mining operation (years)

  • A n = average nodule abundance in the mineable areas (Kg m−2)

  • E = efficiency of the mining device (%)

  • M = proportion of mineable area (as fraction of total area)

4.6 Area of Contact/Year (A c)

$$ {A}_{\mathrm{c}}= M{R}_{(dry)}/{A}_{\mathrm{n}} $$
(8.13)

where A n = average nodule abundance (Kg m−2).

4.7 Ore Production/Day (O p)

$$ {O}_{\mathrm{p}}= M{R}_{(dry)}/ D $$
(8.14)

where D = no. of days of operation.

4.8 Volume of Sediment Disturbed at the Seafloor (V s in m3)

$$ {V}_{\mathrm{s}}={A}_{\mathrm{c}}\times {D}_{\mathrm{p}}\times {C}_{\mathrm{s}}/100 $$
(8.15)

where

  • A c = area of contact

  • D p = depth of penetration (m)

  • C s = coverage of sediment (%)

4.9 Wt. of Disturbed Sediment (Wet) or Water Laden Sediment (W s(wet) in t)

$$ {W}_{\mathrm{s}(wet)}={V}_{\mathrm{s}}\times {D}_s $$
(8.16)

where Ds = density of sediment (g cm−3).

Note: Density in g cm−3 to be multiplied by 1000 to convert into Kg m−3.

4.10 Wt. of Disturbed Sediment (Dry) or without Water (W s(dry) in t)

$$ {W}_{\mathrm{s}(dry)}={W}_{\mathrm{s}(wet)}\times \left(100\hbox{--} {W}_{\mathrm{C}(sed)}\right)/100 $$
(8.17)

where W C(sed) = Water content of sediment (%).

4.11 Wt. of Unwanted Material (M u) to be Disposed Off (in Mt)

$$ {M}_{\mathrm{u}}= M{R}_{(dry)}\times \left(100\hbox{--} {T}_{M C}\right)/100 $$
(8.18)

where T MC = total concentration of extractable metals (%).

5 Mining Estimates Based on Geological Factors

5.1 Estimation of Mining Rates for Dry and Wet Nodules

Literature on nodule mining shows that over the years different mining rates ranging from 1 to 25 Mt year−1 have been suggested by various workers (Table 8.3). Considering water (moisture) content of nodules as 25% (Mero 1977), the eventual mining rates for 1.5 Mt year−1 (as suggested in ISA 2008), and 3 Mt year−1 (as per UNOET 1987) have been estimated (Table 8.4). Accordingly, at least 2 Mt of wet nodules will have to be mined annually to achieve the target of 1.5 Mt of dry nodules. On the other hand, only 1.125 Mt of nodules will be mined annually if the mining rate is considered for 1.5 Mt of wet nodules. These quantities will be doubled in case of mining rate of 3 Mt year−1. However, in order to avoid the confusion between mining rates for dry and wet nodules, it is proposed that henceforth the mining rates should be expressed clearly either in terms of dry nodules or as wet nodules. Mining rate for dry nodules expressed as MR(dry) is the quantity that will eventually be available for further processing; whereas, mining rate for wet nodules expressed as MR(wet) should be used to represent the mining rate at which wet nodules will have to be mined so as to achieve the MR(dry).

Table 8.3 Proposed mining rates for polymetallic nodules
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Table 8.4 Actual and eventual mining rates based on water content in nodules
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For a given MR(wet), MR(dry) may vary from place to place depending upon the average moisture/water content in nodules which will have to be estimated for each mine-site during exploration in order to fix the target quantity of wet nodules to be mined to achieve the required rate of mining of dry nodules.

5.2 Metal Production for Different Mining Rates

Estimation of metal production at different mining rates ranging from 1 to 3 Mt year−1 of dry nodules reveals that Mn production would range between 0.24 and 0.72 Mt, Ni between 0.011 and 0.033 Mt, Cu between 0.0104 and 0.312 Mt and Co between 0.001 and 0.003 Mt annually, the total metal yield ranging from 0.2624 Mt (for 1 Mt year−1 mining rate) to 0.7872 Mt (for 3 Mt year−1 mining rate) for a certain concentration of these metals (Table 8.5).

Table 8.5 Estimated metal production (Mt) for different mining rates
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5.3 Mining Estimates for Different Mining Rates

Estimates for various parameters related to mining of polymetallic nodules have been worked out for mining rates ranging from 1 to 3 Mt year−1 of dry nodules (Table 8.6), and their implications have been discussed in this section.

Table 8.6 Estimates for mining of polymetallic nodules at different mining rates
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5.3.1 Estimation of Mineable Area

According to Eq. (8.5), estimation of mineable area can be made by subtracting the un-mineable areas due to factors such as unfavorable topography (A u), grade (A g), and abundance (A a) from the total area (A t) available to a contractor. Considering A u as 20% (UNOET 1987), and assuming A g as 15% and A a as 15% and subtracting them from A t = 75,000 km2 (maximum area allotted to each Contractor); the total mineable area (M) will be 37,500 km2 (Sharma 2011). However, this would vary for each “Contractor” depending upon the actual area allotted and also other ground conditions in different mining areas with respect to topography, grade, and abundance.

5.3.2 Area (Size) of Mine-Site

As per Eq. (8.6), the area (size) of the mine-site could vary from 4267 to 12,800 km2 for different mining rates (Table 8.6) for the nodule abundance (A n) of 5 kg m−2 and efficiency (E) of the mining system as 25% (as suggested by UNOET 1987). The other components being constant, variation in A n and E could alter the size of the mine-site (A s). Also, it is reasonable to expect a higher average nodule abundance (A n = 8–10 kg m−2) in the potential first-generation mine-sites and a higher efficiency (E) of the mining system with advancement in technology. This would reduce the size of the mine-site (A s) considerably restricting the mining activities to a smaller area, hence reducing the environmental impacts, especially as first-generation mining would start in areas of high nodule abundances.

5.3.3 Area of Contact

Whereas the estimation of area of mine-site takes into consideration the duration and efficiency of mining, the area of contact is the actual area that will be scraped during mining and has a bearing on the volume of associated sediment that will be disturbed leading to environmental impact on the benthic ecosystem. For different mining rates and average nodule abundance of 5 kg m−2, with an annual operation time of ~300 days, the actual area of contact (scraped) will vary between 200 and 600 km2 year−1 that is 0.66–2 km2 day−1 (Table 8.6) which are in effect miniscule with respect to the area of the ocean basins where these nodules are found. Also, as the average nodule abundance is expected to be higher in the actual mine-site than the cut-off value considered here, the actual area scraped (or the “area of contact” on the seafloor) will be much smaller than those estimated here.

5.3.4 Ore Production

Irrespective of nodule abundance, mining of 1–3 Mt of nodules for ~300 working days in a year, would lead to production of 3333–10,000 t of ore per day (Table 8.6) that will not only require lift mechanism to bring them to the surface through >5 km of water column, as well as other infrastructure on the mining platform. Concerns have been raised over the effect of large-scale mining on the prices of the metals extracted because of the disparity between the ratios of constituent metals in the nodules and the ratio of their world demand (Pearson 1975). Hence, optimum production of ore, containing the desired composition of metals would be necessary to maintain a balance in the metal prices because over-production (more than the demand) could lead to lowering of metal prices, eventually making deep-sea mining uneconomical.

5.3.5 Volume and Weight of Disturbed Sediment

According to a study, the ratio of nodule to sediment on the seafloor is 1:9 (Sharma 2011), and so during mining, a large volume of sediments will be disturbed. Considering a minimum penetration of nodule collector in the sediment as 10 cm, the total volume of sediment disturbed on the seafloor will range from 60,000 to 180,000 m3 day−1 depending on the mining rate (Table 8.6) which will be a major source of environmental impact that will require certain measures to restrict it to the seafloor, instead of being transported to the surface or even discharged midway through the water column. The key factor in plume dispersion is the high proportion (>50%) of clay sized particles (<4 u) that can remain in suspension over long period of time, whereas nodule debris will settle faster. Even if it is expected that the areas being mined would have relatively higher nodule abundances and hence lower nodule to sediment ratio, it would be desirable to screen out as much sediment as possible close to the seafloor before lifting the nodules so as to contain the area from environmental impacts.

Since wet density of sediments is 1.15 g cm−3 (Khadge and Valsangkar 2008), i.e., 1150 kg m−3, the total weight of the water laden sediment would range between 69,000 and 207,000 t day−1 depending upon the mining rate (Table 8.6). Also, as ~80% of the total weight of wet sediment is water (Khadge and Valsangkar 2008), the weight of solid particles would vary between 13,800 and 41,400 t (Table 8.6) that will be disturbed for each day of mining. Once again, the nodule to sediment ratio considered here is for a large area that includes locations without any nodule coverage and low nodule coverage as well, and whereas the first-generation mine-sites are expected to be in areas of dense nodule populations, where the associated sediments would be proportionately less, leading to lesser disturbed sediment particles.

5.3.6 Unwanted Material After Metallurgical Processing

If four metals (Mn, Cu, Ni, Co) are extracted with a total metal content of ~26% (Jauhari and Pattan 2000), the remaining 74% of unwanted material in the range of 0.74–2.22 Mt year−1 (Table 8.6) will have to be disposed off, which could pose a major environmental challenge, specially because the processing is expected to be in land-based plants and generation of such large quantities of material will require proper disposal or alternative use will have to be thought of.

6 Influence of Geological Factors on Mining Design

6.1 Nodule Characteristics

Since nodule sizes vary from <1 to 10 cm and the size distribution is variable between different locations, the mining system will have to be designed for an optimum nodule size for efficient recovery and for pumping them to the surface. Use of a crusher on the nodule collector may help in maintaining the consistency in size of the nodules to be lifted to the surface, and use of buffer may help in storing them at an intermediate level before pumping them in fixed quantities to the mining platform for energy conservation. Nodule concentration on the seafloor would have a direct bearing on the amount of nodules recovered by the mining system, as for higher abundance, larger quantities can be recovered in a shorter duration, and vice versa. Higher concentration of nodules along the rugged topography and lower slopes at the base of seamounts and abyssal hills might enhance the recovery of nodules, as compared to the low nodule concentrations in the valleys and plains. Moreover, the local topographic variations and sediment thickness as well as patchy distribution of nodules may also affect the performance of the collector. Hence, it is proposed that the collector system should be capable of detecting the zones of higher nodule concentrations, acoustically or photographically, before sweeping the seafloor (Sharma 1993).

6.2 Association with Different Substrates

The mining head will encounter substrates such as sediments and rocks associated with nodules on the seafloor. The effect of sediment cover in burying the nodules (Felix 1980) can pose problems in nodule recovery and as the extent of nodule burial varies from 0 to 100% (Sharma 1989b), the nodule collector will have to be designed to penetrate within the sediments to collect at least a part of the buried nodules in order to be efficient. The binding strength of nodules to the sediment will also be a critical factor in the design of the nodule collector for which use of water jets may help dislodge the nodules from the seabed. Pumping of large quantities of sediments along with nodules will not only increase the energy consumption, but will also cause a major environmental problem if disposal of debris is at or close the surface. Hence, the sediments may have to be discharged in deeper areas below the photic zone to reduce the impact on marine life in the water column. Alternatively, there should be a mechanism to wash them out near the seafloor to enable pumping of nodules only.

Rock outcrops, which appear to extend up to a few tens of meters on the seafloor, and at times occur in the nodule fields along the slopes of seamounts as well as abyssal plains, may act as obstruction to the nodule collector. These areas need to be mapped carefully in order to identify locations where the mining system may not be able to operate or is likely to get damaged due to the occurrence of hard substrates. Also the photographs, in which nodules and crusts co-occur, indicate transition zones between the outcrops and nodule fields (Fig. 8.1), mapping of which is required for deciphering the margins of the nodule mining areas. Hence, the nodule collector should be capable of sensing such zones, as well as being “driven” around or “flown” over these outcrops in order to avoid any damage to the mining equipment.

6.3 Relation with Topography

As the seafloor is made up of abyssal hills (>200 m relief), seamounts (>1000 m), and abyssal plains and valleys, the collector should be designed to negotiate certain gradients, but steeper areas and those with more frequent undulation may pose problems in operation of the nodule collector. Morphometric analysis in a part of the CIOB has shown that a majority (92%) of the area has 0°–3° slope, the remaining areas have higher slopes (up to 15°) (Kodagali 1989). Slope angle studies in a nodule field in the Pacific Ocean had identified <3° slope angles as being nodule dominant, 3°–7° slope angles as sediment dominant, 7°–15° slope angles as a transition zone and >15° slope angles as rock/crust-dominant zones (Yamazaki and Sharma 2000). Hence, the collector system would have to negotiate the changes on the seabed characteristics in terms of reliefs, substrates, and nodule concentrations for better efficiency as well as safety.

6.4 Optimization of Mining Rates

The annual recovery rate would be determined by the requirement of providing adequate feed to the large-scale processing facility (UNOET 1987). Considering the metal production (Table 8.5) for different mining rates, it appears that an optimum mining rate of nodules would be 1.5 Mt year−1 of dry nodules at the least (preferably 2.0 Mt year−1), so as to yield reasonable quantities of metals as well as net returns for the investment. This is especially for those metals such as cobalt which are available in very small concentrations as only 1500 t of cobalt will be produced at mining rate of 1.5 Mt year−1 (dry).

6.5 Ore Production and Area of Mine-Site

Estimates show that between 3333 and 10,000 t of nodules (ore) will be recovered every day with the area of the mine-site varying between 4267 and 12,800 km2 for different mining rates (Table 8.6). The area of the mine-site would be determined on the basis of mean nodule abundance and efficiency of the collector mechanism. This mine-site will be a small portion within the mineable area that would be delineated based on the best combination of nodule grade, abundance and topographic conditions, and the first such site where mining would commence is termed as First-Generation Mine-site (FGM). The area of the mine-site (~4200–12,800 km2) as well as the area of contact (that will be actually scraped) on the seafloor (<1–2 km2 day−1) for a cut-off abundance of 5 kg m−2, would be extremely small in proportion to the mineable area.

6.6 Environmental Impact and Waste Disposal

The main source of environmental impact on the seafloor would be the movement of nodule collector while picking up the nodules (Thiel et al. 1998). Earlier studies have indicated that the overall impact of deep-sea mining will be small compared to natural, large-scale processes of ocean circulation and sediment redistribution (Amos et al. 1977). Estimates have also been worked out for benthic and surface discharges during mining operation (Morgan et al. 1999). Current estimates show that the actual area of the mine-site as well as the area scraped by the mining system (i.e., the area of contact) would be extremely small (Table 8.6) even for the lowest nodule abundance (5 kg m−2). Moreover, expecting a higher nodule abundance in FGM, these could still be smaller (Figs. 8.5 and 8.6), restricting actual mining to a tiny speck in the area allotted to the contractor. However, the total volume of sediment disturbed (60,000–180,000 m3 day−1) as well as the weight of the wet sediment, i.e., in slurry form (69,000–207,000 t day−1) and also the weight of dry sediment, i.e., as solid particles (13,800–41,400 t day−1) appears to be significant but not enormous considering the area and volume of the water column.

Fig. 8.5
figure 5

Area of mine-site for different mining rates and nodule abundances

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Fig. 8.6
figure 6

Area of contact for different mining rates and nodule abundances

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Care is needed to ensure that these sediments are discharged as close to the seafloor as possible, so as to avoid being transported to adjacent areas by currents. Also, clays (<4 u) being the dominant component (>50%) of these sediments (Khadge and Valsangkar 2008) are likely to remain in suspension for considerable period of time. Hence, suitable mechanisms will have to be devised to ensure that most of the sediment associated with nodules is not discharged into the water column, but as close to the seafloor as possible, so as to reduce the impact on physico-chemical conditions that could in turn affect the biological communities in the marine ecosystem.

Besides lifting of nodules from the seafloor to the mining platform, another environmental concern is discharge or spillage of ore during pre-processing and at-sea transfer and also transport to shore that could increase turbidity in surface waters, affecting the biological productivity in these areas. Also in case of at-sea processing, discharge of waste chemical products could be more hazardous than the discharge of mining effluent (Amos et al. 1977). Finally, the disposal of unwanted material (0.74–2.22 Mt year−1) left over after metallurgical processing is the major issue that will have to be addressed. Finding an alternative use such as those for land reclamation or as agricultural material (Wiltshire 2000, Wiltshire, this publication) could be some of the options.

7 Conclusions

Several formulae suggested in this chapter can be adopted for evaluating different mineral characteristics that can be used for designing different components of the mining system. Equations have also been proposed for making estimates associated with mining such as metal production (and values), area (size) of mine-site, ore production, as well as volume and weight of sediments that will be disturbed for different mining rates. Considering the metal values and estimated expenditures, it is suggested that the mining rate for nodules should be at least 1.5 Mt year−1 or more, preferably 2 Mt year−1, as for lower mining rates, the metal production will be extremely low especially for metals of low concentrations such as cobalt.

It is proposed that mining rates should be mentioned in terms of dry nodules and termed as “Mining Rate (dry),” as it represents an absolute quantity of nodules that will be supplied to the processing plant for metallurgical extraction. The term “Mining Rate (wet)” may be used for describing the quantity of wet nodules that will have to be mined to achieve the Mining Rate (dry), after removal of average water (moisture) content in nodules that could vary between different mine-sites.

Wide range in nodule distribution characteristics in terms of their size (0–10 cm), coverage (1–90%), abundance (5–25 Kg m−2) and burial (few cm to few m); as well as their association with substrates such as sediments and rocks; and also the influence of topography on their distribution are critical geological factors for designing the nodule mining system. The nodule collector will have to be an intelligent device to negotiate the variable slopes, patchy distribution as well as partial to complete nodule burial under the sediment–water interface and occurrence of rocks/crust outcrops in the nodule fields . As estimations from photography data consider only the exposed nodules and the abundance data from grabs considers only a part of the buried nodules (down to the “biting” depth of the grab), their calculation may be an underestimate at times, implying that in effect there may be more nodules at a given location.

An area claimed by a Contractor will comprise the following:

  1. 1.

    Exploration area—The area eventually retained by a Contractor or allotted by ISA to a Contractor with exclusive rights for exploration.

  2. 2.

    Mineable area—The portion of the allotted area that is mineable after subtracting the un-mineable areas due to unfavorable topography, grade and abundance.

  3. 3.

    Mine-site—The final site within the allotted area in which mining operation will be conducted.

Although, large areas may be allotted to different contractors, the final area that will be scraped (or area of contact) on the seafloor, would be much smaller in size with respect to the area of the ocean basins as well as the allotted areas; even if several mine-sites are located in it.

In view of the large quantities of associated sediments expected to be disturbed while mining the nodules, necessary measures need to be taken, so as to restrict them within the mine-site on the seafloor and minimize the chances of being transported to adjacent areas. Another area that requires attention is the disposal or “constructive” use (at least partially) of large quantities of unwanted material that will be left over after extraction of metals.

Whereas, research has been mainly concentrated in core areas of designing the technology for mining as well as processing of nodules, some attention also needs to be paid towards logistic support in terms of ore handling, storage and power generation facilities on the mining platform, as well as supply vessels for transportation of ore, supplies and personnel, especially considering the large distances and uncertain weather conditions of the mining areas.

With the depletion of land resources and lack of other alternative sources of metals for industrial use, nodule mining could become a reality in future, the timing of which will be determined by the cost of bringing them to market under prevailing conditions (Rona 2003). As Morgan (2000) has optimistically concluded: “If the international regulation of seabed mining can be accomplished without imposing untenable restrictions on the development of the deep seabed, commercial development of these deposits will surely occur.”