Flocculated unclassified tailings settling efficiency improvement by particle collision optimization in the feedwell

Huazhe Jiao, Weilin Chen, Aixiang Wu, Yang Yu, Zhuen Ruan, Rick Honaker, Xinming Chen, and Jianxin Yu, Flocculated unclassified tailings settling efficiency improvement by particle collision optimization in the feedwell, Int. J. Miner. Metall. Mater., 29(2022), No. 12, pp.2126-2135. https://doi.org/10.1007/s12613-021-2402-3

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Flocculated unclassified tailings settling efficiency improvement by particle collision optimization in the feedwell

Huazhe Jiao^{1, 2, 5, 6)},
Weilin Chen^{1, 5)},
Aixiang Wu²⁾,
Yang Yu^{3, 4), ,},
Zhuen Ruan^{2, 4), ,},
Rick Honaker⁶⁾,
Xinming Chen^{1, 5)} and
Jianxin Yu^{1, 5)}

Author Affilications

1)
School of Civil Engineering, Henan Polytechnic University, Jiaozuo 454003, China
2)
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
3)
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
4)
China Geological Environmental Monitoring Institute, Beijing 100081, China
5)
State Collaborative Innovation Center of Coal Work Safety and Clean-efficiency Utilization, Jiaozuo 454150, China
6)
Mining Engineering Faculty, University of Kentucky, Lexington 40506, America

Corresponding author:
Yang Yu E-mail: yuyang2005@139.com

Zhuen Ruan E-mail: ustb_ruanzhuen@hotmail.com
Received: 17 June 2021; Revised: 08 November 2021; Accepted: 20 December 2021; Available Online: 24 December 2021

Graphical Abstract

Abstract

Abstract

Efficient thickening of tailings is a prerequisite for the metal mine tailings backfill and surface disposal operation. The effective collision of ultrafine tailings particles in suspension with flocculant molecules is essential for flocs aggregates formation and settling. Unreasonable feeding speed and flocculant adding method will lead to the failure of effective dispersion of flocculant and high particle content in thickener overflow. In this work, the effect of turbulence intensity and flocculant adding method on floc size, strength, and movement characteristics are analysed. Aiming to solve the turbidity increased, a pilot-scale continuous thickening test was carried out. Taking a single particle and multiple flocs of full tailings as the research object, the particle iterative settlement model of flocs was established. The influence of turbulence intensity on collision effect is studied by tracking and simulating particle trajectory. The results show that in the process of single particle settlement, chaos appears in the iterative process owing to particle adhesion which caused by micro action. When the turbulence intensity is 25.99%, the maximum particle size of tailings floc is 6.21 mm and the maximum sedimentation rate is 5.284 cm·s⁻¹. The tailings floc presents a multi-scale structure of particle-force chain system when hindered settling, and the interweaving of strong and weak force chains constitutes the topological structure of particles. The results are applied to a thicker in plant, the flocculant addition mode and feed rate are optimized, and the flocs settling speed and overflow clarity are improved.
- unclassified tailings,
- flocculation settling rate,
- thickener feedwell,
- turbulence intensity,
- flocs micro-structure

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1. Introduction

Mineral resource extraction is extremely important to the world wide economy development and is a foundation industry for global modernization [1–4]. Tailings are the main solid waste of metal mining. Storage of tailings in surface ponds poses safety and environmental risks, and the high-water content of tailings is the challenge for the disposal stability [5–6]. Cemented Paste Backfill (CPB) operation will pump the surface tailings slurry to underground, for supporting the excavated goaf. The CPB technology will not only decrease the surface solid waste quantity but also avoid ground subsidence of the mined area [7–9]. The ultra-fine tailings slurry is a kind of suspension that is difficult to dewater. It could reduce the collapse risk of tailings dams [10–11]. Thus, the tailings thickening for CPB materials preparation have lately received great attention [12–13].

The dewatering and concentrating of fine unclassified tailings is primary procedure to CPB technology [14−15]. The high concentration tailings thickening in thickener concluded double sub-process of the flocs hindered settlings and the tailings bed consolidation. The reason for the low density underflow is that traditional research mostly focused on the flocs hindered settlings process, while ignoring the tailings bed consolidation process and the deep dewatering process of the compressive tailings bed [16].

The consolidation process aims to obtain a high concentration underflow, which is essential to CPB technology [17–18]. Presently, the rake drive shutdown and underflow concentration fluctuation are double challenges to the high density tailings thickening process. Ruan et al. [19] studied the influencing factors of rake blockage in Deep Cone Thickener (DCT) and established a mathematical model of rake power in DCT. Gheshlaghi et al. [20] simulated a semi-industrial pilot thickener based on the Computational Fluid Dynamics (CFD) method and studied the influence of operating parameters on the thickener performance. The rheological parameters of paste have an obvious impact on the operation of thickeners. The paste rheological parameters increasing will cause the thickener rake drive overload.

Various polymer-assisted flocculation dewatering technique have been widely introduced in tailings slurry separation. Arjmand et al. [21] improved the tailings dewatering performance by adjusting the solution pH and flocculant dosage. Nguyen et al. [22] proposed an advanced solid-liquid separation technology that combines chemical reagents and centrifuges. Mamghaderi et al. [23] studied the influence of chemical pretreatment on the dewatering effect of iron ore tailings.

The sedimentation of unclassified tailings can be improved by adding various types of polymer flocculants [24–26]. On the one hand, flocculant molecular chains can attract suspended particles simultaneously via ionic bond, hydrogen bond, and van der Waals force, forming a bridge between particles [27]; on the other hand, the fine particles of the unclassified tailings are attracted to form flocs and settle to the bottom of the thickener, thus achieving solid-liquid separation [28]. The effects of anionic polyacrylamide on the rheology, mechanics and heavy metal leaching properties of CPB are the focus in future.

The dynamics flocculation and concentration process could be explained by mathematical computational methods [29]. Fractal mathematical theory [30–31] has been applied in the floc micro structure analysis. Based on the continuous gravity thickening model, the compressive yield stress is an important index in the tailings dewatering process [32]. The three parameters, i.e., the compressive yield stress, hindered settling function, and gel point [33], has been widely used to predict gravity thickening performance, the nonlinear kinetic equations by controlling the strain rate increment, the selection of optimal flocculation and settling parameters [34], and also, the use of various mathematical algorithms [35] to establish a multi-physics field-coupled 3D model [36–37].

The process of floc formation in unclassified tailings involves a series of physicochemical interactions, as well as the neutralization of polymers and tailings. The resulting flocs could settle rapidly to achieve dewatering. Nasser et al. [38–40] investigated the effects of cationic and anionic flocculants on the dewatering performance of tailings suspensions. Yin et al. [41] applied the uniform design method to study the static sedimentation law. They analysed the influencing factors of tailings flocculation and sedimentation, namely, the slurry concentration, flocculant dose, and flocculant solution concentration, to evaluate the sedimentation of unclassified tailings flocs under various combinations of factors. Chen et al. [42] studied the compacting effect of tailings under dynamic and static conditions by using a continuous deep cone thickening model, proposing a compacting theory under dynamic perturbations. Yang et al. [43] used a back-propagation neural network and genetic algorithm to establish a tailings flocculation and settlement model, using slurry concentration and flocculant unit consumption as input factors to obtain the optimal flocculation and settlement parameters for unclassified tailings. However, the research attentions focused on the dispersed transport of the total aggregates of tailings in the flow by applying existing physical and chemical models based on experiments [44]. The development of tailings dewatering still experiences bottlenecks due to low dewatering concentrations of unclassified tailings and a single mathematical calculation method.

This work studies the influence of turbulence intensity on floc structure and settlement behaviour. The floc strength mechanism is explained by the collision probability and force chain network. Furthermore, the overdose turbulence would destroy the flocs to yield a stable diameter. The movement of tailings flocs under the influence of different turbulence intensities is tracked in a pilot scale thickener. A flocculation settlement model of unclassified tailings is established by a numerical simulation algorithm method. The flocs settlement rate in suspension is improved according to the evolution law of flocs collision settlement. A three-dimensional finite element model is constructed by digital image processing approach to simulate the force chain topology of flocs under turbulence. Finally, the test results apply on a field thickener to reform the feedwell and flocculant dilution feed rate could improve the overflow clarity and the particles settling velocity.

2. Materials and methods

2.1 Unclassified tailings

The tailings were from the China Minmetals Group's Xianglushan tungsten mine. The specific gravity was 2.992 t·m⁻³, the bulk density was 1.955 t·m⁻³, the porosity was 34.66%, and the specific surface area was 300–500 m²·kg⁻¹. The particle size distribution and the mineralogy composition is shown in Fig. 1 and Table 1, respectively.

Fig. 1. Particle size distribution of the unclassified tailings.

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Table 1. Mineralogy composition wt%

SiO₂	CaO	Fe₂O₃	Al₂O₃	SO₃	F	MgO	Bi	NaO	Mn	K₂O	W
49	17	14	7.6	4.7	3.6	1.5	0.05	0.7	0.7	0.6	0.2

Ti	P₂O₅	Zn	Cu	Pb	Sr	Cl	Hg	Zr	Rb	Y
0.1	0.1	0.07	0.05	0.01	0.01	0.01	0.01	0.01	0.01	<0.01

| Show Table

DownLoad: CSV

2.2 Experimental protocol

The pilot-scale continuous thickening test platform was adopted, the structure and apparatus are shown in Fig. 2. The platform features are continuous feeding, continuous discharging, an adjustable rake speed, and torque detection, which can simulate the actual operation of thickeners.

Fig. 2. Pilot-scale thickener experimental platform: (a) layout; (b) laboratory device.

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The flocculant dosage was 20 g·t⁻¹; the flocculant solution was selected at 0.2wt%; the feed solid flux was 0.1–0.3 t·m²·h⁻¹; the feed slurry concentration was 10wt%. The experiments were conducted in three different feed wells (height of 10 cm and cross-sectional diameter of 40, 50, and 60 mm), as shown in Fig. 3. The experimental scheme is shown in Table 2. In Table 2, Nos. 1, 3 and 5 are group A, and Nos. 2, 4 and 6 are group B. The turbulence intensity error of the different feeding wells is shown in Fig. 4.

Fig. 3. Scheme diagram of feedwell.

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Table 2. Calculation of the turbulence intensity in the feedwell

Factor	Kinematic viscosity / (Pa·s)	Pipe diameter / mm	Flow rate / (m³·h⁻¹)	Feed pipe flow rate / (m·s⁻¹)	Feedwell diameter / (mm)	Re × 10⁶	Turbulence intensity / %
1	1.02	4	0.01	0.24	40	0.0094	28.66
2	1.02	4	0.02	0.42	40	0.0243	25.46
3	1.02	4	0.02	0.49	50	0.0206	25.99
4	1.02	4	0.03	0.73	50	0.0304	24.76
5	1.02	4	0.05	1.06	60	0.0613	22.68
6	1.02	4	0.05	1.06	60	0.0518	23.16

| Show Table

DownLoad: CSV

Fig. 4. Turbulence intensity error of the different feedwells.

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A slurry preparation material with a target flow rate was selected to test the material transport in the feedwell, the pumping speed was adjusted to calculate the turbulence for unclassified tailings. The turbulence intensity in feedwell were 22.68% (control), 23.16%, 24.76%, 25.5%, 25.99%, and 28.66%. The turbulence intensity was determined by the feed flow rate and feedwell diameter as the following equations [45]:

$Re = \rho ud/v$

(1)

$I = 0.16 \times R{e^{ - \tfrac{1}{8}}}$

(2)

where $\rho$ is the slurry density, kg·m⁻³; $u$ is the slurry flow rate, 0.24 m·s⁻¹; $v$ is the kinematic viscosity, 1.02 Pa·s; $d$ is the characteristic length, m; $I$ is the turbulence intensity, and $Re$ is the Reynolds number.

The sedimentation tank was filled with water; the thickening test was carried out according to industrial operation conditions by adjusting the pumping speed.

The samples were taken at specified time to test the slurry concentration. During the experiment, high-definition cameras were used to evaluate the tailings floc settlement process. The images obtained from the experiment are shown in Fig. 5. Through the use of high-definition camera technology, the sedimentation process of tailings floc under different initial turbulence intensity is photographed, combined with the Canny edge detection method for processing, the floc size is obtained, the characteristics of tailings floc and the effect of sedimentation are analysed, and the fractal of tailings floc and the flocculation sedimentation process are explored.

Fig. 5. Feedwell with different turbulence intensities: (a) 23.16%; (b) 24.76%; (c) 25.5%; (d) 25.99%; (e) 28.66%.

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3. Theory

3.1 Feedwell flow calculation

The thickener feeding slurry forms a turbulent flow in feedwell. A molecular dynamics model focused on the movement of microscopic particles in the fluid was used to analyse the continuous motion images in feedwell. The floc velocity field in feedwell is obtained as shown in Fig. 6.

Fig. 6. Flow field in feedwell: (a) flocs locations at n s; (b) flocs locations at n+1 s; (c) location differences in 1 s; (d) velocity field result.

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3.2 Flocs distinguishing and settlement tracking

The edge detection method was used to process the binary images to distinguish flocs. The discrete boundary points were connected into a smooth curve, which could represent the boundary information, to obtain the floc size and flocs particle size distribution, as shown in Fig. 7.

Fig. 7. Flocs distinguishing.

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Unclassified tailings flocs settled from the feedwell into the settling tank. When the specific sizes of flocs are selected, the flocculation and settling effects under the combined effects of turbulence kinetic energy, shear force, and gravity are different. To investigate the influence of the settling effect of tailings flocs, the path of the selected flocs were tracked, as shown in Fig. 8.

Fig. 8. Flocs tracking.

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3.3 Floc growth kinetics

Individual flocs growth kinetics can be described by a logistic Eq. (3) [46].

${x_{n + 1}} = w{x_n}(1 - {x_n})$

(3)

where $w$ is the scale factor and ${x_n}$ is the ratio of the new flocs quantity produced in the n-th flocculation to the retained maximum flocs quantity.

The independent variable refers to the ratio of the new flocs quantity produced in the n-th flocculation process to the maximum retention, the dependent variable refers to the quantity of flocs produced in the n+1-th time. The initial value and proportion coefficient are constants (the proportional coefficient $w$ can also be understood as the growth potential index). When the growth potential index changes, $w$ and the independent variable will simultaneously interact with the dependent variable, and this interaction can also be considered effect modification.

After selecting the initial value ${x_0}$ and the scale factor value $w$ , a series was obtained from Eq. (3), as follows:

${x_0}, {x_1}, {x_2}, \cdots,{x_n},\cdots$

(4)

The equation iterative orbit varies with the value of the scale factor $w$ . The logistic equation variation is investigated under the increasing $w$ . A single tailings floc growth tendency is simulated by the iterative sequence. The Lyapunov index $\lambda$ is an important indicator for describing chaotic phenomena, the $\lambda$ value reflects the floc transport characteristics, where $x = f(x,a)$ and the mapped Lyapunov index $\lambda$ are calculated by [47].

$\lambda = \mathop {\rm lim}\limits_{n \to \infty } \frac{1}{n}\sum\limits_{k = 0}^{n - 1} {\rm ln} \left| {\frac{{{\rm{d}}f{\text{(}}xk,a{\text{)}}}}{{{\rm{d}}x}}} \right|$

(5)

From Eq. (5), when a single particle size is taken as the object, the movement track of the unclassified tailings floc bifurcates from one branch into two branches, which continues to collision, and the two branches are divided into four branches. This phenomenon is called the doubly periodic (discrete chaos) phenomenon, and the sequence of n floc collision iterations ${x_n}$ can be derived. In the logistic equation, the interaction between variables refers to the effect of a certain factor varying with the level of other factors. The interaction can be understood as the relationship between the quantities of new particles caused by multiple flocculation processes.

4. Results and discussion

4.1 Relationship between influence factors

With the gradual increase of the initial turbulence intensity of the slurry in the feeding well, the tailings could flocculate with the flocculant more quickly and evenly, promoting the destabilization and agglomeration of the colloidal system and forming large-size flocs. When the initial turbulence intensity is too large, the collision frequency of total tailings increases, resulting in the rupture of large flocs and the formation of small flocs. Under the same turbulence intensity, the tailings flocs formed by flocculation form a physical bridge between multiple particles through the long chain structure of water-soluble polymer.

The interactions between the floc size, turbulence intensity, and floc settlement velocity in the feedwell are obtained, as shown in Fig. 9, where the turbulence intensity is 25.99%.

Fig. 9. (a) Relationship between turbulence intensity, settling velocity, and floc size; (b) simulation diagram.

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From Fig. 9(a), the floc size increases and then decreases with turbulence intensity increasing. The average flocs size was minimum 0.56 mm under the turbulence intensity 22.68%. The average flocs size increase to a peak value 6.21 mm in the turbulence intensity flow environment 25.99%.

e unclassified tailings settling rate is positively with floc size. As the floc size increases from 0.56 mm to 6.21 mm, the settling velocity increases from 1.637 to 5.284 cm·s⁻¹. Hence, the floc settling efficiency could be improved through controlling the turbulence intensity to increase the floc size.

4.2 Simulation of the collision growth of unclassified tailings flocs

Tailings floc collision simulation is based on the floc binarization image analysis process to obtain the size of tailings flocs under different initial turbulence intensity, so as to calculate the settlement of selected whole tailings flocs of different sizes with the help of MATLAB software. In the 3D simulation of tailings flocs, the X-axis represents the horizontal displacement, the Y-axis represents the vertical displacement, and the Z-axis is the collision time. The collision at different turbulence intensities are shown in Fig. 10.

Fig. 10. Flocs growth at different turbulence intensities: (a) 23.16%; (b) 24.76%; (c) 25.5%; (d) 25.99%; (e) 28.66%.

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As shown in Fig. 10, the average diameter of unclassified tailings flocs increases first and then decreases under a specific turbulence intensity, where each turbulence intensity corresponds to a maximum average diameter. When the turbulence intensity is 25.99%, the average diameter of the flocs reaches a maximum value of 6.21 mm.

Slurry turbulence increases the growth of flocs. Under slurry turbulence, the flocs are physically bridged by long chains of water-soluble polymers between multiple particles. Force chains with different strengths intertwine to form a complex network to which the unclassified tailings adhere and form flocs.

4.3 Mechanisms of force chain formation and destruction

In the process of flocculation and sedimentation, the water-soluble polymeric force chains of the unclassified tailings particles are adsorbed and bridged to form flocs. Since the force chains in the floc have different strengths, there is interweaving and fracture of the force chains. A nonuniform force chain network distribution pattern is calculated using the floc fractal equation, and the force chain-extrinsic ball algorithm is applied to calculate the transport of unclassified tailings flocs [48], as shown in Fig. 11.

Fig. 11. Ball-link force chain model of flocculated tailings aggregates: (a) original force chain; (b) force chain breaking; (c) force chain transfer.

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The three exoteric sphere algorithms are adopted in this work. The midpoints of the circles ( ${x_1}$ , ${y_1}$ , ${z_1}$ ) and ( ${x_2}$ , ${y_2}$ , ${z_2}$ ) are used as the origin of the new coordinate system, the connection between the two points is used as the X-axis, and the normal direction is taken as the Y-axis. The circumscribed ball centre is shown in Fig. 11, with three balls surrounding the region. Setting the coordinates of the ball centre as the circumscribed ball ( ${x_0}$ , ${y_0}$ , ${z_0}$ ), with a radius of $r$ , yields Eq. (6).

$\left\{\begin{aligned} & r+{r}_{1}=\sqrt{({x}_{0}-{x}_{1}{)}^{2}+{({y}_{0}-{y}_{1})}^{2}+{({z}_{0}-{z}_{1})}^{2}}\\ & r+{r}_{2}=\sqrt{({x}_{0}-{x}_{2}{)}^{2}+{({y}_{0}-{y}_{2})}^{2}+{({z}_{0}-{z}_{2})}^{2}} \\ & r+{r}_{3}=\sqrt{({x}_{0}-{x}_{3}{)}^{2}+{({y}_{0}-{y}_{3})}^{2}+{({z}_{0}-{z}_{3})}^{2}} \end{aligned}\right.$

(6)

where $r$ is the circumscribed ball radius, mm; ${r_1}$ , ${r_2}$ , ${r_3}$ are the No.1, No.2, No.3 balls radius, respectively, mm.

From Eq. (6), the difference in distance from ( ${x_0}$ , ${y_0}$ , ${z_0}$ ) to ( ${x_1}$ , ${y_1}$ , ${z_1}$ ) and ( ${x_2}$ , ${y_2}$ , ${z_2}$ ) is equal to ( ${r_1}$ − ${r_2}$ ). From mathematical geometry, ( ${x_0}$ , ${y_0}$ , ${z_0}$ ) falls on one of the branches of a hyperbola in space, that is, the hyperbolae shown in Fig. 11(b) (the dashed lines). Similarly, the difference in the distance from ( ${x_0}$ , ${y_0}$ , ${z_0}$ ) to ( ${x_1}$ , ${y_1}$ , ${z_1}$ ) and ( ${x_3}$ , ${y_3}$ , ${z_3}$ ) is equal to ( ${r_1}$ − ${r_3}$ ), and the difference in distance from ( ${x_0}$ , ${y_0}$ , ${z_0}$ ) to ( ${x_2}$ , ${y_2}$ , ${z_2}$ ) and ( ${x_3}$ , ${y_3}$ , ${z_3}$ ) is equal to ( ${r_2}$ − ${r_3}$ ). The track of ( ${x_0}$ , ${y_0}$ , ${z_0}$ ) can be determined by solving the equation. Hence, the flocs diffusion track is the multiple circumscribed balls of ( ${x_0}$ , ${y_0}$ , ${z_0}$ ), which can be obtained by the dichotomy ideology and recursive procedure.

The equation shows that the unclassified tailings cannot be flocculated effectively at every collision for the limited tailings surface space. The force chains with different strengths are linked between neighbouring flocs to form a complex force chain network in the turbulent environment [49]. The force chain extrusion algorithm and fractal dimension of the flocs are simulated by the ball-beam model, as shown in Fig. 12.

Fig. 12. Floc evolution model under shear: (a) 0; (b) 0.4 r·min⁻¹; (c) 0.8 r·min⁻¹.

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From Fig. 12, the unclassified tailings flocs have a multiscale particle-force chain system. During flocculation and settlement, the particles network forms a topological structure flocs. The force chains within a floc also constantly break and reform in the turbulent environment. The branches of the force-chain structure can also be break by turbulence to dispersing flocs fragment or small-sized flocs.

As shown in Fig. 13, the flocs in the network are linked by bridging between tailings particles, the morphological similarities between the flocs and network structure are evident with clear fractal features.

Fig. 13. Flocs morphology analysis: (a) settlings flocs; (b) floc edge detection; (c) floc skeleton; (d) flocs segmentation.

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5. Case study

5.1 Background

The mine adopted the CPB plant to disposal tailings waste, recover residual ore resources, reduce the goaf volume, and improve the ground pressure control, a deep cone thickener is used for paste preparation.

The capacity of the backfill system is limited by the high content of fine tailings particles (particle size ≤20 μm content of 30.397%) and slow settling speed, which also affects the CPB materials quality and increases the cement dosage. The feeding rate was 1.63 m·s⁻¹, with ϕ150 mm single feed pipe. The thickener underflow concentration is only 60%, which could not meet the requirement of CPB operation.

5.2 Feed rate and feedwell optimization

The thickener is assembled and modified based on the experiment results. The single-point feed of dilution system would replace by multi-pipe feeding at different locations to increase the flocculation efficiency.

The optimal tailings flocculating process occurred at an initial turbulence intensity of 25.99%, with a feedwell diameter of 50 mm and an optimal feed rate of 0.49 m·s⁻¹. It is believed that the slurry flow in the experimental and industrial tailings feed pipes is similar, and that industrial production can be designed using similar principles.

With an industrial pipe diameter of 150 mm, the geometric scaling followed Eq. (7).

${C_{\text{l}}} = \frac{{{l_{\text{p}}}}}{{{l_{\text{m}}}}}$

(7)

where ${l_{\text{p}}}$ is the industrial feed pipe external diameter, 150 mm; ${l_{\text{m}}}$ is the experimental feed pipe external diameter, 5 mm; ${C_{\text{l}}}$ is the geometric scaling.

The density scaling is fixed at ${C_{\text{ρ }}}{\text{ = }}1$ . The speed scaling is expressed by Eq. (8).

${C_{\text{v}}} = \dfrac{{{C_{\text{l}}}}}{{\sqrt {\dfrac{{{C_{\text{l}}}}}{{{C_{\text{g}}}}}} }} = {C_{\text{l}}}^{\tfrac{1}{2}}$

(8)

where, ${C_{\text{g}}}$ is the gravity scaling of 1 and ${C_{\text{v}}}$ is the speed scaling, calculated to be 5.

The feedwell diameter in experiment was 50 mm, the calculated geometric scaling of 30 indicated that the industrial feedwell diameter should be set to 1500 mm. As optimum feed rate in test is 0.49 m·s⁻¹, the industrial feed rate should be 2.45 m·s⁻¹, and the feed flow rate should be 155 m³·h⁻¹. Before the transformation, the feeding rate is 1.63 m·s⁻¹, and the feed flow rate is 104 m³·h⁻¹. Therefore, the treatment capacity after the optimization transformation is significantly increased.

5.3 Flocculant addition position optimization

The flocculant addition point would be optimized to increase collide efficiency and the floc size. The flocculation system is modified from one dilution feed pipes to four diversion pipes, as shown in Fig. 14.

Fig. 14. Modified four addition pipes.

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After modification, the thickener overflow’s overflow turbidity reduced from 2000×10⁻⁶ to 50×10⁻⁶, the foaming on the top of the overflow are avoided, as shown in Fig. 15.

Fig. 15. Overflow of deep cone thickener: (a) before modification; (b) after optimization.

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6. Conclusions

(1) The turbulence intensity is essential to the flocs diameter in the feedwell. During the collision between tailings particles and long chain flocculant, the flocs aggregates will formed by the bridge link and then broken by the overdose turbulence, the steady flocs will form at a specific turbulence intensity.

(2) The floc settling efficiency could be improved by controlling the turbulence intensity to increase the floc size. The floc size increases first and then decreases with turbulence intensity. In range of 23.16%–28.66% turbulence intensity, the average flocs size increase from 1.06 mm to 6.21 mm, settling velocity increases from 2.21 to 5.28 cm·s⁻¹.

(3) The steady aggregates diameter is controlled by the force chains inside the flocs. The multiscale particle force chain system of the full tailings flocs was observed in the simulated internal growth of the flocs. Fluid turbulence could change the internal skeleton growth, leading to the redistribution of the strong and weak force chains of the flocs and the secondary coalescence of unclassified tailings flocs.

(4) The thickener in a tungsten mine is modified by applying the experimental results. With the optimization of the feedwell structure, feeding rate, and flocculant addition of the thickener, the turbid overflow is alleviated, and the sedimentation of fine tailings is accelerated. The thickener overflow’s overflow turbidity reduces from 2000×10⁻⁶ to 50×10⁻⁶.

Acknowledgement

This work was funded by the National Natural Science Foundation of China (No. 51834001).

Conflict of Interest

The authors declare that they do not have any competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Flocculated unclassified tailings settling efficiency improvement by particle collision optimization in the feedwell