\r\n\tsandwiches, etc.
\r\n\r\n\tListeria monocytogenes causes one of the most serious and life-threatening diseases (listeriosis), usually caused by eating food contaminated with Listeria monocytogenes. An estimate of 1,600 people get sick (especially at risk-groups including pregnant women, newborns, old people (65 years old and above), people with weakened immune systems, etc.) and about 260 die (Listeria is the third leading cause of death from foodborne illness in the U.S.) each year, in the U.S. from Listeriosis.
\r\n\t
\r\n\tThe main goal of the book is to provide accurate and updated information on Listeria monocytogenes so governments (decision-makers), food industry, consumers, and other stakeholders can implement appropriate preventative measures to control Listeria monocytogenes. This book will cover several topics including the prevalence of Listeria monocytogenes in developed countries, the prevalence of Listeria monocytogenes in developing countries, the prevalence of Listeria monocytogenes in ready-to-eat food, detection of Listeria monocytogenes in Food, control of Listeria monocytogenes in food-processing facilities, etc.
In the mobile cellular network, the rapid development has been observed. Modern telecommunication systems are being constructed as complex networks that involve various types of devices united into a single complex, operating in conditions of large load flows and large number of connections [1]. They can offer higher data transfer rates, with the integration of more services and guarantee of high quality of experience. Nevertheless, this development also means that the amount of data that is transferred in the mobile network is increasing and the volume of signaling traffic is increasing, respectively. According to [2], it is expected that total mobile data traffic will have increased to 77 exabytes per month by 2022, almost seven times more compared to 2017. Mobile data traffic will grow at an average annual growth rate (CAGR) equal to 46% from 2017 to 2022.
According to Shimojo et al. [3], vehicles, houses, personal devices, robots, sensors, etc. will be connected wirelessly. It means that an automatic and intelligent control system will be achieved. An increase in the number of devices will affect the IoT market, which is estimated to be $19 trillion [4], and is expected to reach 50 billion [5]. In addition, rich content services, such as real-time streaming movies that require high resolution and tele-surgery requiring small delay must be provided (Figure 1).
Services in the era of future generations’ networks.
In addition, the average signaling requirement per subscriber is up to 42% higher in LTE compared to the standard of the past generation communication [6].
Furthermore, market competition requires faster deployment of services and elasticity of changing service criteria as well as the ability to cope with higher service requirements. Therefore, there is a need to manage the signaling traffic in order to provide the necessary quality of service to end users and the proper use of resources of the network operator.
In such circumstances, operators are forced to build up the network infrastructure to ensure the process of service of telecommunication services at a given level of quality. During the day, the load differs, and according to [7], up to 80% of the computing capacity of the base stations and up to half of the capacity of the core network are unused. This leads to a low usage of resources as well as a high level of energy consumption, which reduce the cost-effectiveness of the network for mobile operators.
The emergence of the concept of network functions virtualization opens up new opportunities for the world of telecommunication systems. At the same time, there is a need for new approaches, models, and methods for organizing service handling. The use of virtual servers to solve the tasks of the mobile core network can greatly simplify the process of organizing resources on the service server and ensure its scalability and fault tolerance.
The principle of network function virtualization (NFV) [8] is aimed at transforming network architectures by deploying network functions into software that can run on a standard hardware platform. According to the ETSI [9], the network function is a functional block within a network infrastructure that has defined external interfaces and a defined functional behavior. Network functions are components of the LTE evolved packet core (EPC) network, such as MME, HSS, PGW, and SGW, which for the NFV case will be deployed on the basis of data center system, with the use of leased computing resources (CPU core, memory, disk space, and network interface card), which can be allocated and reallocated in the process of operation depending on actual load requirements.
Thus, the features of NFV can be characterized as follows [10]:
Separation of software from hardware. Since the network element is no longer an aggregate of integrated hardware and software entities, the evolution of both is independent of each other. This allows having separate terms of development and maintenance of software and hardware.
Flexible deployment of network functions. The separation of software from hardware helps to reallocate and share infrastructure resources; thus, together hardware and software can perform various functions at different times. It helps network operators to deploy new network services faster on the same physical platform. Consequently, the components can be created in any NFV-compliant device on the network and their connections can be installed on a flexible basis.
Dynamic scaling. Dividing the functionality of the network function into created software components provides greater flexibility in scaling the actual performance of the virtual network function (VNF) more dynamically and with greater details, for example, according to the actual traffic for which the network operator should provide capacity.
At present, numbers of problems remain unresolved. You need to consider hybridity of the service environment, where flexible, well-scalable, virtual servicing entities located in rented cloud-based databases operate along with specialized hardware with limited features. Therefore, the task to organize the computing resources of service nodes and flows between them in a hybrid environment, which consists of hardware telecommunications and virtual computing entities, is important.
Unlike the existing static architecture of the LTE EPC network, a system (Figure 2) in which service flows are processed by hardware, and in the case of expected overload, the redistribution of flows happens and takes into account the expansion of the service network by adding virtual service facilities located in the leased clouds of the data centers is proposed (Figure 3). After organizing a hybrid service environment, there is a need to adapt the computing resources of the system in the process of operation to ensure a high-quality service, and also it is necessary to consider the features of the reconfiguration process and the costs associated with it. So far, there has not been any comprehensive solution to the task of controlling the computing resources of the hybrid telecommunication environment. The peculiarities of the load distribution of resources of network elements, hardware or virtual ones, have it been considered yet either.
LTE EPC network architecture with the use of NFV (variable dependent on load volumes).
Distribution of load in hybrid network.
Thus, the chapter proposes a structured approach to the management of resources of network functions through sequential control of the following stages: monitoring, forecasting, controlling the sufficiency of resources, and controlling the given level of quality of telecommunication services.
Having analyzed the research and development processes of telecommunication networks of the next generation, we may argue that the existence of powerful data centers greatly expands the possibilities of organizing the process of providing services. One of the key aspects of network virtualization is the allocation of physical resources to virtual network functions. This involves mapping virtual networks on physical networks, as well as managing dedicated resources throughout the life cycle of a virtual network. The optimality and flexibility of resource allocation are key factors for successful network virtualization.
Most of the existing methods for solving the tasks of organizing hardware and virtual resources offer a static distribution of resources, in which, when computing and telecommunication environment is organized, the reallocation of resources does not occur throughout its life cycle. As the network traffic is not static, this may result in improper use of shared computing resources. It is important to organize monitoring of virtual nodes and provide the resources on the basis of their real needs.
The method is based on the shared embedding concept [11] of the individual virtualized services of the core network on the physical network. We suppose that the virtual network functions of the mobile core network have the same functionality and interfaces as the network components of the 3GPP LTE EPC architecture.
The number of service chains must be determined in advance. The extreme case would be consideration of one service chain for the mobile phone/eNodeB. Since realistic scenarios for mobile networks are up to 10,000 eNodeBs, the resulting optimization model will be enormous and quite long computation time is required to solve it. Therefore, we accept reasonably large clusters of eNodeBs and assume that each of these eNodeB clusters refers to a single service chain of the core network.
Consider the situation when the provider of telecommunication services already has an existing topology of base stations. You need to determine a subset of the network nodes where the load aggregation blocks will be placed which will generate the requests to the same virtualized EPC service. After that, for each base station site, we assign a node of aggregation (traffic aggregation point – TAP).
Let xi be a binary variable that is equal to 1 if we need to place TAP at point i and equals 0 in the other case. In addition, we define yji as a binary variable that is equal to 1 if the base station j sends the load to the i TAP and equals 0 in the other case. We need to define the values of xi and yji in order to find the optimal value of the objective function.
Objective function (1) aims to minimize network latency. Objective function (2) represents the total cost of placing aggregation nodes and the cost of establishing channels between base stations and the respective TAPs. The objective function (3) aims to leave more free bandwidth on each physical channel. The residual bandwidth of all channels is maximized, since high-downloaded channels can lead to network overload; so, it is advisable to get a solution where more free channels are left.
These optimization goals can be useful for network operators to plan the best deployment strategy.
where Lji is the delay of the communication channel between the site j and TAP i;
costi is the cost that consists of two parts: the fixed initial cost fi, which is responsible for fixed investments such as space and installation of equipment, and the additional costs – costNi – per unit of processing power on the computing node, where di is the amount of computational resources of processing:
costlji is the cost of establishing a connection between the site j and TAP i, and it is determined as a linear combination of the initial fixed cost flji and the variable part dependent on the bandwidth Bji, which is necessary for the channel, and the cost of the unit of capacity costLj:
cji – available bandwidth throughput.
It is possible to use the linear combination (4) of Eqs. (1)–(3) with weights a, b, and c, which can be applied not only to give importance the component but also in order to scale the values of these equations for the purpose of converting to comparable values and have meaningful summation:
Subject to:
Restriction (5) ensures that each base station will be connected only one TAP. Restriction (6) ensures that a channel is created between the base station site j and the TAP i only if i was placed.
Restriction (7) ensures that the maximum TAP does not exceed budget p, while (8) is a capacity limit that ensures that the general requirements for processing of all base stations assigned to a specific TAP do not exceed the actual physical resources installed. Restriction (9) makes sure the sufficiency of channel resources for the establishment of channels, and (10) ensures admissibility of delay value, i.e., not exceeding the threshold Tj.
Below we describe the method for solving the problem of placement and the capacity of reserved computing resources of virtual network functions.
Physical network is given in the form of graph SN = (N, NE), where N is a set of physical nodes and L is a set of channels. Each channel l = (n1, n2) ∈ NE, n1,n2 ∈ N has a maximum capacity of c(n1, n2) and each node n ∈ N is associated with certain resources cni, i ∈ R, where R is the set of resource types (CPU cores, memory, disk space, and network interface card). The set of all traffic aggregation points (TAPs), i.e., eNodeB clusters, is denoted as K ⊆ N. For each node n ∈ N, suitnk,j is a binary parameter that indicates whether it is administratively possible to deploy a function j ∈ V on the node n, where V is the set of types of network functions, k service, where k ∈ K.
A virtual mobile core network is represented by a set of services (one service per TAP) which are embedded in the physical network.
The requirements to the bandwidth between two functions, j1 and j2, (j1,j2)∈E, referring to the TAP k∈K service are denoted as dk(j1,j2). dkj,i is the amount of computing resource type i allocated to the network function j in the service k. sn,ik,j specifies the processing time for the type resource i of the virtual network function j for the service k with one resource unit on node n. The requirements to the admissible processing time of the network function j related to the service k are designated as Pkj. Tk – the maximum delay for k∈K, L(n1,n2) is the network latency for the channel (n1,n2)∈NE.
The goal of optimization is to find the location of the virtualized services of the core network (i.e., the allocation of network functions and the allocation of resources, as well as definition of the ways to transfer traffic between them), so as to minimize the cost of the occupied resources of channels and nodes in the physical network, while satisfying the load requirements λk,j. Let us formulate an objective function (Eq. (11)) in the form of a linear combination of two value expressions: the occupied capacity of computing node resources, where the value of resource unit i on node n is denoted by costN(i,n), and the occupied bandwidth of the channels, where costL(n1,n2) is the cost of the unit of bandwidth of the physical channel (n1,n2)∈NE.
The following Eqs. (11)–(20) represent the formulation of the optimization problem of mixed integer nonlinear programming. The Boolean variables xnk,j indicate whether the network function j associated with the service k is located on the physical node n. For j = TAP, xnk,TAP are not variables but input parameters that indicate where TAP k is, i.e.,
Similarly, Boolean variables
Eq. (12) ensures that for each TAP/service, only one network function of each type is placed. Eq. (13) ensures that the allocation of resources is carried out on physical nodes, which have an administrative opportunity to locate the corresponding network functions. Eqs. (14)–(16) represent restriction for the available resources of physical nodes and channels. Eq. (17) represents a restriction for flow conservation of all paths in the physical network. Eq. (18) ensures that the variables in the task of locating network functions and displaying a path are Boolean.
In order to limit the delays on channels, the delay limit shown in Eq. (19) is also added. And to take into account the necessary performance of the virtual network function, the restrictions for the value of the processing time of the request determined in Eq. (20) are necessary.
It is supposed to solve the problem (11)–(20) in the offline mode at the initial stage. According to the solution, each network function reserves a certain number of resources of the virtual network function based on the assessment of its greatest resource requirements. The instantaneous needs of different network functions are dynamically satisfied by activating the necessary configuration of virtual machines during execution in such a way as to satisfy the guarantees provided for each network function.
The decision when to provide resources depends on the dynamics of traffic loads. Telecommunication loads undergo long-term changes, such as hourly effects or seasonal effects, as well as short-term fluctuations such as unexpected crowds. While long-term fluctuations can be predicted in advance, observing changes in the past, short-term fluctuations are less predictable, and in some cases, unpredictable. The proposed method uses two different approaches for working in conditions of changes that are observed at different time scales. Proactive resource management is used to assess the load and corresponding management, as well as reactive resource management is used to correct long-term errors or to respond to unforeseen overload.
We propose to apply a mechanism which implies dynamic change in the duration of the constant configuration of the resources of the virtual network function, depending on the difference between the maximum load value at a certain base interval and the minimum one. Eq. (21) describes the principle:
Int is the interval during which the appropriate specified resources will be allocated; Intbase is the base value of the interval calculated according to the load discretization approach described below; K is the coefficient of the duration change of constant configuration determined by the network operator according to the experiment; λbasepred(t) is the average predicted arrival rate in the period t, and Intminbase is the minimum acceptable value of the base interval.
To do this, you need to define the base interval. The goal is to present a daily load pattern, sampling its requests into successive, non-overlapping time intervals with a single representative value in each interval. Load discretization: having a time series X in the interval [v, τ], time series Y on the same interval is the discretization of the load X, if [v, τ] can be divided into m consecutive non-overlapping time intervals, {[v, τ1],[τ1, τ2],…,[τm-1, τ]}, so that X(j) = ri, for all j in i-th interval, [τi-1, τi].
The solution for time series discretization (Eq. 22) is given as follows:
Eq. (22) is an objective function which has to be minimized, where X is the time series and f(m) is a function of the value of the number of changes or intervals, m. The purpose of Eq. (22) is to simultaneously minimize the load representation error and number of changes. Basic interval is calculated as
At the same time, it is proposed to continuously monitor the values of the request arrival rate and use the predicted values if the load does not exceed the threshold; otherwise, current trends are evaluated and resources are scaled on the basis of the new forecast.
Load forecasting for the next time interval is carried out by taking into account long-term statistics and adjusting it according to the model of exponential smoothing, where errors of more recent past periods have a greater importance factor:
α (smoothing constant) is the coefficient that characterizes the weight rate reduction and takes values from 0 to 1; the closer the value of this parameter is to 1, the better is the consideration of the influence of the last levels of the series during the forecast. The model parameters are set by the network administrator according to the experiment. λobs(t) is the request arrival rate on interval t, h is the interval of previous observations, which is considered by the algorithm, and x+ denotes max(0,x).
There might be situations when the resources available on the nodes will be insufficient or if the node fails. Potential failures can be physical nodes failures, failures of servers that have higher failure rates than telecommunication hardware or the infrastructure provider will perform node maintenance tasks and this will require the migration of nodes.
For this case, the methods of reconfiguration are used which seek to find the places for migration of network functions from the affected nodes, minimizing the cost of recovering the node after failure and maintaining a high level of physical performance of the network. The proposed improved recovery methods differ from existing ones by taking into account the cost of resources on the nodes and the final quality of service, as well as the case of node overload. In addition, in previous research, the problem of locating management nodes, which are coordinators of the movement of virtual network functions, remained unsolved.
MN represents a set of control nodes (hereinafter–managers), where managers MN⊆N are responsible for the operation of the proposed recovery mechanism after the failure. Each control node is connected to one or more nodes in the physical network and performs the steps required to recover from the failure. Let us assume that managers can be located in nodes N. For a given number of managers A, there is a finite set of possible
The main purpose of the optimal placement of managers is to minimize delays between nodes and managers in the network. However, considering only delays is not enough. The placement of managers should also take into account certain restrictions of stability. Figure 4 shows different issues that need to be considered when evaluating the stability of the placement. Below we will briefly explain these issues and what is needed to be sustainable in relation to them. Figure 4 shows normalized delays between nodes and arrival rate at nodes.
Assignment according to different criteria: (a) minimal delay to the manager; (b) a minimum load imbalance of the managers; and (c) the minimum delay between managers.
Let us presume that the nodes are assigned to their closest manager, using as the metric of delay, i.e., the shortest path dlg1,g2 between the node g1 and the manager g2. The number of nodes per manager may be unbalanced – the more nodes the manager has to control, the greater is the load per this manager. If the number of site requests to the manager in the network increases, additional delays probability due to the queues in the control system increases too. In order to be resilient to manager overload, the assignment of nodes to different managers should be balanced properly.
It is obvious that one manager is not enough to achieve network resilience. On the other hand, when multiple managers are hosted in the network, the logic of network management is distributed across multiple managers, and these managers must be synchronized to maintain a consistent global state. Depending on the frequency of synchronization between managers, the delay between individual managers plays an important role.
Based on the dl matrix, which contains the distance of the shortest paths between all nodes, the maximum transmission latency between the node and the manager for a certain placement of managers can be calculated as follows:
UAlatency(p) = max(ddcn),
ddcn is the maximum transmission delay from the network node to the manager at the point n;
ddcn is calculated as follows:
where latencyg is the delay between manager and node g,
πg,n is a Boolean variable equal to 1 if node g is served by a manager located at the point n.
We consider not the average, but the maximum delay value, since the average hides the values of the worst case which are important when resiliency needs to be improved.
Depending on the situation, it may be desirable to have an approximately equal load for all managers, so that no manager is overloaded, while others have little work. Next, we consider the balanced distribution of nodes between managers. As a formal metric, we introduce the balance of placement, or rather, the imbalance, UAimbalance, i.e., the deviation from the fully balanced distribution, as the difference between the load for the most downloaded manager and the least downloaded manager.
UAimbalance is calculated as follows:
UAimbalance(p) = max(ldcn)– min(ldcn), де ldcn > 0,
ldcn – manager load at n;
ldcn is given as follows:
where loadg is the load factor for the node g.
As the last aspect of a resilient placement of managers, let us consider how the delay between managers can be taken into account when choosing managers. Formally, the delay between managers UAinterlatency is defined as the greatest delay between any two managers at a given placement:
In general, placement with a delay between managers’ considerations tends to place all managers closer to each other. This increases the maximum delay from nodes to managers.
Thus, the target optimization function is given by:
where wu is the set of importance coefficients.
The recovery algorithm is based on prototype described in [12] but considers modified problem formulation and expands the solution on node overload case.
The physical network is given in the form of a graph SN = (N,NE), where N is a set of physical nodes and NE is a set of channels. Each channel (n1,n2)∈NE, n1,n2∈N has a maximum throughput of c(n1,n2) and a network delay L(n1,n2), and each node n∈N is associated with certain resources cni, i∈R, where R is the set of types of resources. The communication network is represented by a set of services (or virtual network requests) K that are embedded into the physical network. The virtual network request k, k∈K, can be given as a graph Gk = (Vk,Ek), where Vk is the set of virtual nodes containing hk elements and denoted as Vk = (vk,1,vk,2,…,vk,hk), where vk,j indicates the j-th network function in the service chain of k. Ek is the set of virtual channels ek(vk,j,vk,g)∈Ek. The channel throughput requirements between the two functions, j1 and j2, referring to the k∈K service are marked as dk(j1,j2), dkj,i is the number of resource type i allocated to the network function j in the k-th service. The Boolean variables xnk,j indicate whether the network function j associated with k∈K is located on the physical node n, and the variables f(n1,n2)k,(j1,j2) determine whether the physical channel (n1, n2) is used in the path between j1 and j2 for request k. Lk is the maximum delay for request k. costN(i,n) is the cost of the occupied resource unit on the physical node n, and costL(n1,n2) is the cost per unit occupied bandwidth on the physical channel (n1,n2)∈NE. suitnk,j means that the j function k can be placed on node n.
The process of moving the nodes of the virtual network hosted on the failed node, vk,jfail, starts when the system sends a recovery request to the corresponding host manager. The recovery process for each failed virtual node proceeds as follows: the manager sends the recovery request to all nodes of the physical network, which hosts the virtual nodes adjacent to the affected virtual nodes. Each of these nodes builds the shortest path tree (SPT) to all nodes of the physical network at a distance of not more than l (the threshold is set by the service provider) from the node, where the SPT root is the node. The manager uses these paths to select the node with the optimal distance to all nodes in the physical network, where the nodes of the virtual network are located adjacent to the failed node. This node eventually becomes the best candidate for hosting the affected virtual host. In addition, the capacity of the end nodes of the paths with the SPT should be at least the capacity of the virtual node located on the failed node. We select a node with a minimum cost of the path to all root nodes in the SPT trees and the minimum processing cost. Figure 5 contains a description of the pseudo-code of the recovery algorithm (Figure 6) after failure and is applied for all {vk,j: xnk,j = 1 & n = failed}.
Algorithm of recovering the node with a failure.
Recovering the node with a failure.
There is also a probability of the node failure due to overload. To perform a recovery in an overloaded network, the reconfiguration procedure is performed to migrate the virtual nodes hosted on the overloaded physical node.
The recovery process begins with sorting all the virtual nodes located on the overloaded physical node. The criterion (CRT in Figure 7) used to sort these nodes in a virtual network is the capacity of the virtual nodes. Then, the recovery procedure is performed on the first sorted virtual network node, which has a capacity equal to the overloaded, to migrate to the new node of the physical network.
Recovering the node with overload.
When the load or resources change, some virtual network functions (VNFs) may have to be moved. There is a probability that finding a new node candidate for a node of a virtual network hosted on a failed site will not be possible. In this case, the reconfiguration procedure is performed to migrate one or more virtual nodes. Let us consider the problem of migration as an optimization problem, which is aimed at minimizing the general migration costs with the limits of permissible delay and computational resources.
The goal of optimization is to find the location of virtual network functions (i.e., the location of network functions and resource allocation as well as channels to transfer traffic between them), so as to minimize the cost of the occupied resources of channels and nodes in the physical network, while satisfying the requirements of traffic. Let us give the objective function (26) in the form of a linear combination (with weighted coefficients a, b, c, and e) of the cost expressions.
Let us determine the binary variable xnk,j∈{0,1} to indicate that VNF j is associated with the service chain k placed on the node n after migration. The indicator xnk,j = 0 means that VNF j is not placed on node n after migration; otherwise, j is placed on node n after migration.
Then, we enter the binary variable ynk,j to display the network status before migration. It is similar to variable xnk,j, ynk,j = 0 means that VNF is not located on node n before migration; otherwise, j is located on node n before migration.
Thus, we can use the Ik,j indicator to indicate whether the VNFj by k service was moved in the current migration process.
Ik,j = 0 indicates that the VNF has been moved in the current migration process, and Ik,j = 1 indicates that the VNF has not been moved.
xn denotes whether the n physical server works or not after migration.
yn indicates whether the n physical server works or not before migrating.
In order to consider the resources that are consumed while migrating, we introduce the following equations:
Bn indicates the required bn costs to launch the n-th server:
where li(x) is the function of using resource i for migration from the server and l’i(x) is the use of resource i for migration to the server.
The objective function will be calculated as follows:
Taking everything into account, we formulate the problem as follows.
Objective function:
Min MCost.
With constraints:
Hence, the objective function (26) is a linear combination of four equations which aims to minimize: the cost of starting and using a server, using server resources, communication channels, and resources for migration. Eq. (27) ensures the one-time allocation of network functions, and Eq. (28) is the administrative possibility of placement on the node. Eqs. (29) and (30) represent a limit for the resources of physical nodes and channels, i.e., they ensure that the amount of resources involved in a node does not exceed the amount of available resources. Eq. (31) represents a flow conservation limit, i.e., the input stream at the node is equal to the output stream. Eq. (32) ensures that the variables in the problem are Boolean. Eqs. (33) and (34) represent a limit for the time of transmission by telecommunication channels and time of processing by service nodes, respectively, and ensure compliance with the specified time requirements for the service.
Thus, before operation starting, it is necessary to have statistics on the requests arrival rate for the network function and the probability characteristics of the request servicing. According to the allocation method, the binding of each network function of the traditional network to the data center and the amount of resources that should be reserved for the corresponding virtualized network function is determined. Next, it is necessary to divide the lifecycle of the network function into intervals during which its configuration will remain unchanged and a certain amount of resources will be activated in accordance with the method of determining the size of the resources constant configuration time interval, while taking into account the expected load. When a mobile network operates, a physical node may not be able to continue to handle an incoming load due to lack of resources or due to its failure, and in this case, a distributed local reconfiguration of resources that re-distributes virtual nodes is triggered.
The general resource management system is shown in Figure 8.
Modified resource management system.
The monitoring system tracks traffic and counts the number of requests. The monitoring system sets the threshold for the number of requests and sends a message to the coordinator if detecting an overload. When the coordinator accepts an overload message from the monitoring system, the resource allocation unit calculates the required amount of resources to process the applications properly and dynamically distributes the estimated volume. Then, the coordinator redirects the requests and the overload is eliminated.
The coordinator is launched periodically. To predict the base load, one can take the average value of historic daily load. The coordinator sends an incoming load to a data center, which maintains excessive workload, and also exchanges data with a resource allocation unit to provide information about the predicted input load.
The resource distribution module is responsible for distributing the appropriate amount of resources needed to handle the load with the specified quality indicators. During the direct operation of the system, this module is started when the actual load exceeds the base predicted value of the load in order to provide additional resources for excessive load. Since the resource distribution module and coordinator do not start when the actual load is lower than the predicted one, the resource reconfiguration procedure creates minimal additional costs associated with this process.
The general operating scheme of the resource management system is shown in Figure 9.
The method of system resource management.
Quantitative and qualitative analysis (Figure 10) of the proposed methods showed a reduction of the cost associated with reserved resources up to 15%, which contributes to increasing the efficiency of load processing, saving computing resources.
Consumption of system resources by using the method of allocating virtual network functions and without it.
The examples of representation of time series values, i.e., loads that illustrate the accuracy of representation, depending on the selected interval of the constant configuration, are presented in Figure 11, where the representation error for the case of intervals in 10 minutes is 7%, and for the case of 60 minutes—19%.
Representation of load values depending on different values of the constant configuration interval.
The results of simulation of the method of determining the size of the resources constant configuration time interval (Figure 12) showed that the difference between representational value and actual one can be 9%. If you do not apply a dynamic adjustment system to the value of the constant configuration interval, then the deviation will be 18%, i.e., 9% more, and the resources will be spent more.
Results of simulating the system with dynamic change in the value of constant configuration time interval and the system without it.
In order to assess the proposed approach, the average amount of free resources per day was determined as the difference between fixed allocations, i.e., when 100% of resources were always allocated during the day, and dynamically allocated resources by using NFV. According to the results of simulation, the volume of resources allocated dynamically on average is 42% less than in the case of using the traditional distribution approach. Figure 13 depicts the result of the dynamic distribution of resources in the virtualized EPC of the mobile network in a graphical form. A gray line illustrates the fixed allocations for the worst case scenario. The black curve shows the amount of resources distributed dynamically according to the proposed method.
Results of simulating the system with variable configuration of resources and the system with fixed resources.
According to the simulation results (Figure 14), the proposed local reconfiguration method showed up to 27% lower costs compared to a strategy aimed at minimizing delay, the delay being within the permissible limits but by 20% greater.
Results of simulating the system with variable configuration of resources and the system with fixed resources.
The main result of the study has become the development of the method for reconfiguring resources of the core network by means of virtualization technology. As a result of the research, the following basic scientific results have been obtained.
The analysis of the current situation in the wireless communication market shows an increase in the workload, which leads to an increase in the need in additional resources. However, the uneven loading of the infrastructure nodes leads to their loss of use; so, there is a need in introducing technologies that both do not lead to downtime of equipment and ensure the quality of load service during the day.
An overview of the NFV virtualization technology has shown that it is appropriate to build wireless networks, since it provides the necessary flexibility and scalability.
We have developed the method for determining the location and capacity of reserved computer resources of virtual network functions in the data centers of the mobile communication operator, which guarantees the quality of providing telecommunication services with the minimum necessary resources by determining their sufficient configuration in a heterogeneous environment of available resources. This allows reducing costs by 13% compared to the randomly selected monocloud and by 47% compared with the traditional approach to deploying the network.
In addition, we have developed the method for determining the size of computing resources constant configuration time interval, which involves its changing and the consideration of both the cost of reconfiguration and the use of resources, as well as provides a flexible use of resources in the virtualized environment, which reduces the percentage of free resources by 42% compared to the dedicated equipment and by 9% compared to existing analogs and reducing the workload on the network.
Furthermore, we have improved the distributed method of local reconfiguration of the virtual network computing resources in the case of a failure or overload, which uses decentralized management and considers migration costs, that redistributes virtual network functions in normal and emergency modes while providing rational resource usage and reducing costs on average by 21%.
Bioleaching is the extraction of metals from ores using the principal components water, air and microorganism [1]. It is the extraction or mobilization of valuable (target) metal from the ore, can also be defined as a process of recovering metals from low grade ore [2, 3], with regard to solubility, bioleaching can be defined as a process of recovering soluble one from insoluble impurities after dissolving sulfide metal as soluble salt in a solution [4] that results toxics and heavy metals removed. It is isolation of metals from their ores, concentrates and mineral wastes under the influence of microorganisms leading to dissolution of metal solutions of leach liquor containing metals [5], followed by solvent extraction, stripping, ion exchange, electro wining to get pure metal.
Both bioleaching and biooxidation leads to recovery target mineral; but there is technical difference between the two technologies. Bioleaching refers to the use of bacteria, the common Thiobacillus Ferrooxidans and other bacterial as a leachant to leach sulfide minerals where the target elements remains in the solution during oxidation process, after the metal recovery the solid left behind regarded as residue and in the contrary biooxodation discard the solution after having metal values in solid phase [6, 7] microorganism also engaged in removal of radionuclides and leaching of metal that are regarded as toxic in some cases and good for bioremediation of soil, the process stops radio nucleation that result the removal of stability of target elements [7].
Bioleaching has been used for a long period of time without regarded as microbial leaching process; it has been used as early as 1000 BC when a man from metal laden recovered copper from a water, passes through copper ore deposit [8]. It was in 1556 at the mine located in Spain at Rio Tinto (Rd River) mine, slurry containing very high concentration of ferric ions leached due to the action of microorganisms [4]. Copper was precipitated from the solution obtained from this river, the very first bio mineralization process was copper dissolution, then the process continued to be developed in countries like Norway, Germany and English at different era of time, in the year 1947 heap and dump leaching was practiced that leads to the development of bacterial bioleaching process [9].
The gram-negative chemolithotroph bacterial, Thiobacillus Ferooxidans was first cultured and isolated from mine water by Colmer and Hinkel [9]. Thiobacillus Ferrooxidans is rod shaped ranging in diameter from 0.3 to 0.8 micrometers (μm), in length from 0.9 to 2 μm, 0.5 μm in width in which its movement is due to a single polar flagellum [10]. Since now this bacteria is the most studied. These bacteria were able to oxidize sulfur to sulfuric acid and ferrous to ferric in acidic environment where pH value is less than 5 [7, 10, 11]. From this point onwards the technology of bioleaching has shown tremendous growth, especially industrial coppers production, which makes annualized world copper production reach up 10% from 0.2%. It was in Chile the first industrial scale copper bioleaching plant was established in 1980 using Thiobacillus bacteria [12] large-scale production begins and bioleaching taken as main manufacturing process as any convection techniques in Chile 1984 [13]. Among the many microorganism involved, bacteria (autotrophic and heterotrophic), fungi and yeasts can be mentioned. The bacterium has these calcification based on their species as Thiobacillus Ferrooxidans, Leptospirillum Ferrooxidans, Thiobacillus Thiooxidans, Sulfolobus, but there are many classifications based on different characteristics reveled by the organisms.
Acidophilic Thiobacillus species are used to leach refractory elements like gold, they generally characterized as aerobic, acidophilic, and autotrophic used to leach sulfide minerals (copper, nickel, zinc and soon). Most common bacteria involved in bioleaching are Acidithiobacillus Ferrooxidans (Thiobacillus Ferrooxidans), Acidithiobacillus Thiooxidans, Leptospirillum Ferrooxidans, Sulpholobus Spp, Sulpholobus Thermosulphidoxidans and Sulpholobus Brierleyi. Acidithiobacillus Ferrooxidans is most vital one, which was named and characterized in 1951. Most common fungi are Aspergillus Niger and Penicillium Simplicissimum. The efficiency of bioleaching depends up on physiological requirement and capability of Thiobacillus to oxidize ferrous ion (Fe2+) and sulfur (S). There are five main species of Thiobacillus, these are Thiobacillus Thioparus, Thiobacillus Dentrificans, Thiobacillus Thiooxidans, Thiobacillus Intermedius, and Thiobacillus Ferrooxidans. On the bases of pH values for growth genus Thiobacillus can be divided into two groups, those that can grow only in neutral pH values are T. Thioparus and T. Dentrificans. The second Thiobacillus species those grow at lower pH value are T. Thiooxidans, T. intermedius, and T. Ferrooxidans.
Study of different scholars at the inceptions shows the capability of bacteria (genus Thiobacillus) to oxidize sulfur compounds to sulfuric acid; it can oxidize also range of sulfur compounds (S2−, S0, S2O4, S2O42−, SO42−) [11], followed by separation process of the iron and the bacteria Acidithiobacillus Ferrooxidans (Thiobacillus Ferrooxidans) from the solution. A. Ferrooxidans is found in drainage waters and it is commonly identified as pyrite oxidizer [14]. The bacterial (acidophile) obtain energy from inorganic sources, it grows in acidic medium that fixes carbon to the bacteria itself. Most economically important metals like iron, copper, gold, and uranium can be easily extracted by using acidophilic and chemo-litho-autotrophic microorganism. Acidithiobacillus Ferrooxidans is chemoautotrophic microorganism or acidophilic. Let see the ecology, physiology, availability and genetics of microorganism involved in bioleaching. There are three basic principles for microorganism to leach and mobilize target metals from ore concentrate – redox reaction, formation of organic and inorganic acid and finally the excretion of complexing agent (Figure 1) [4].
Image of bioleaching bacterial [4].
Here is a generalized reaction used to express biological oxidation of sulfide mineral.
MS + 2O2 → MSO4, where M is bivalent metal and reaction below show a metal sulfide directly oxidize by Acidithiobacillus Ferrooxidans to soluble metal sulfate according to the reaction.
MS + 2O2 → M+2 + SO4+2 [15].
The two majorly known mechanism in bacterial leaching are direct mechanism (involves physical contact of the organism with the insoluble sulphide) or hypothesized enzymatic reaction taking place between an attached cell and the underlying mineral surface which is independent of indirect mechanisms and it is where reduced sulfur dissolution takes place [16], it is only the direct attack of the bacteria can lead to leaching. Check the following reactions.
Indirect (involves the ferric-ferrous cycle) or it is a mechanism of sulfide oxidation involves non-specific oxidation of surfaces by Fe3+ that is generated by microorganisms that oxidize iron or oxidation of mineral by ferric ions [16]. The attached cells of bacterial oxidize the surface using either of the two mechanisms [9, 11, 14]. The reaction below shows oxidation of iron.
Minerals are broken due to the attack to their constituents, that results energy production for the microorganism. This energy production or oxidation passes through intermediates reaction processes. Two mechanisms have been proposed for the oxidation, viz. thiosulphate mechanism and polysulfide mechanism. Thiosulfate mechanism includes acid-insoluble metal sulfides like pyrite (FeS2) and molybdenite (MoS2) and polysulfide mechanism includes acid-soluble metal sulfides like chalcopyrite (CuFeS2) or galena (PbS) [15]. In thiosulfate mechanism, the attack of ferric ion on acid insoluble metal sulfides brings about solubilization via thiosulfate as an intermediate and sulfates as end product. The breaking reaction shown below.
In polysulfide mechanism, a combined attack of ferric ion and protons on acid-soluble metal sulfides causes the solubilization with sulfur as an intermediate in its elemental form which can be oxidized to sulfate by sulfur-oxidizing microbes that the reaction is shown below [17].
0.125 S8 + 1.5O2 + H2O → SO42− + 2H+ the reaction show the production of sulfuric acid results hydrogen (proton) for attacking mineral.
Fe (II) is re-oxidized to Fe (III) by iron oxidizing organisms (chemotrophic bacteria), the role of microorganisms in solubilization.
2Fe2+ + 0.5O2 + 2H+ → 2Fe3+ + H2O this reaction keep iron in ferric state that oxidize mineral.
The process of chemical attack takes place on a substrate or the mineral surface where the bacteria forms a composite and attach itself as firm as possible in order to increases maturity that finally detached and dispersed into the solution.
An important reaction mediated by Acidithiobacillus Ferrooxidans is:
Strong oxidizing agent, ferric sulfate that basically used to dissolve metal sulfide minerals, and leaching brought about by ferric sulphate is termed indirect leaching due to the absence of both oxygen and viable bacterial. Check the following leaching mechanism of reaction on several minerals.
Acidithiobacillus Ferrooxidans can convert elemental sulfur generated by indirect leaching to sulfuric acid –.
This sulfuric acid maintains the pH value at levels, which is favorable to the growth of bacteria and also helps for effective leaching of oxide minerals:
Chemolithotrophic (uses carbon for the synthesis of new cell material) bacteria can be categorized based on response to temperature as mesophiles, moderate thermoacidophiles and extreme thermoacidophiles.
Mesophiles-grows at a temperature values ranges (28°C -37°C) where Thiobacillus Ferrooxidans is able use the inorganic substrate to draw energy by oxidizing Fe (II) to Fe (III) and sulfur to sulfide and sulfate. The other mesophiles is Leptospirilium Ferrooxidans that use Thiobacillus Ferrooxidans to effect the oxidization of sulfur to sulfate. Moderate thermoacidophie-temperature values ranges (40–50°C), Sulfobacillus Thermosulfidooxidans is common one, which oxidize both sulfur and iron from energy production. This category includes Archaea and Eubacteria, and most of gram-positive microorganisms are included here. Extreme thermoacidophiles-temperature ranges 60–80°C, genera Sulfolobus, Acidanus, Metallosphaera and Sulfurococcus are in this category, [11, 18, 19]. Thermal value some time extends above the limitation values, it is due to exothermal reaction which is above the maximum growth temperature of microorganism, some microorganism genus like Archaea withstand thermal values up to 90° [19, 20].
This category is formed by closely related species that can act together with a common name given Sulfolobus. Sulfolobusa Acidocaldarius, Sulfolobus Sofataricus, Sulfolobus Brierley, and Sulfolobus Ambioalous that can oxidize Fe (II) to Fe (III) and sulfur to sulfate. Aspergillus Niger and Penicillium Simplicissimum are both used to leach sulfide minerals like copper with mobilization rate of 65% and aluminum, nickel, lead and zinc by more than 95% mobilization rate. Thiobacillus and Leptospirillum are characterized by the oxidation of sulphide minerals in acidic environment and temperature values less than 35°C, with regard to area of application these two are mostly used in dump and tank leaching of metal from sulphide based mixed ores [20, 21]. The other group of genus Sulphobacillus used under the same areas of application but relatively higher temperature up to 60°C, the temperature reaches up to 90°C in case of genera Sulpholobus and Acidianus, Organotrophic microorganisms like yeast, fungi and algae which destruct sulphide mineral and aluminum silicate, facilitate bio sorption of metals that solubilize gold, these microorganism uses carbonate and silicate ore for the extraction of metals and selective gold extraction from ore floatation and metal solution.
The two bacterial leaching namely autotrophic and heterotrophic leaching has their distinct characteristics while bioleaching process takes place, in case of autotrophic bioleaching (effective on sulfide minerals) there are two proposed mechanism of Acidithiobacillus Ferrooxidans action on sulfide minerals, first the mechanism, that the bacterial oxidize ferrous ion to ferric ion in which the bulk solution where the mineral is leached counted as indirect, this mechanism which is indirect oxidation of ferrous ions to ferric ions is exopolymeric process, both takes place on the layer where the mineral is leached. The second proposed mechanism, does not require ferrous or ferric ions, the bacteria directly oxidize the minerals by biological means having direct contact mechanism of reaction. Autotrophic leaching uses both Thiobacillus Ferooxidans and Thiobacillus Thiooxidans to leach sulfide mineral and studies shows combining the two bacterial results an increase in selectivity and rate of leaching efficiency while leaching of nickel sulfide. From the heterotrophic genus of bacteria Thiobacillus and Pseudomonas are those used to leach non-sulfide minerals and from the genus of fungi Penicillium and Aspergillus (heterotrophic fungi) are those used in leaching process, a study shows 55–60% leaching rate for nickel and cobalt, some other studies indicates that 95% and 92% leaching rate achieved while using pretreated Aspergillus Niger by ultrasound for 14 and 20 days respectively which increase its stability [4, 11, 20, 22] (Table 1).
Microorganism/ both autotrophic and heterotrophic | Ore sample |
---|---|
Aspergillus Niger, Hyphomicrobium | Flourapatite (phosphorus ore) |
Pseudomonas Oryzihabitans | Magnesite, Dolomite (magnesium ore) |
Bacillus Licheniformis | Silica |
Thiobacillus Ferrooxidans, Acidianus Brierleyi, Sulfobacillus, Thermosul Fidooxidans, Sulfolobus Rivotincti | Chalcopyrite (Low and high grade), Pyrite, Covellite |
Penicillium Simplicissimum, Penicillium Verruculosum, Aspergillus Niger, Acidithiobacillus Ferrooxidans | Iron ore, Hematite, Zinc and nickel Silicates |
Thiobacillus Thiooxidans | Pyrrhotite |
Thiobacillus Caldus | Arsenopyrite |
Metallosphaera Sedula, Sulfolobus Metallicus (BC), | Pyrite |
Paenibacillus polymyxa | Bauxite (low grade) |
Heterotrophic fungi Aspergillus and Penicillium species combined to leach low-grade nickel-cobalt oxide ores, low-grade laterite ores and spudumene (aluminosilicate), these aluminosilicate (spudumene) also leached by heterotrophic yeasts (Rhodotorula rubra), Aspergillus Niger used to leach zinc and nickel silicate [11]. Bacterial leaching can be generalized in three mechanism redoxolysis, acidolysis, complexolysis, and in case fungal leaching bioaccumulation is important mechanism. To solubilize rock phosphorous, Aspergillus Niger has been used in many occasions due to the production of organic acids with low molecular weight and phosphorous is basically essential micronutrients for the growth of bio organism, these microorganism convert insoluble phosphate to soluble, the two filamentous fungi used in phosphate leaching are Aspergillus Niger and some Penicillium, the metabolic fungal reaction produces organic acid that result the formation of acidolysis, complex and chelate [22].
The second group of bacterial genus is Leptospirillum, which is categorized in moderate thermophilic bacteria that can only oxidize ferrous ions; it is dominate iron oxidizer, which is referred as Leptospirillum Ferrooxidans (L. Ferrooxidans). Oxidation process takes place under strong acidity and temperature up to 30°C, L. Ferrooxidans has high affinity to Fe2+ and low affinity to Fe3+ which results a working condition of high Fe3+/Fe2+ ratio, when redox potential is low, L. Ferrooxidans has low growth rate at the initial stage of a mixed batch culture, a native strain of Leptospirillum Ferrooxidans used to leach zinc from low grade sulfide complex from La Silvita and La Resbalosa (Patagonia Argentina) [23]. The leach liquor itself has been a place where microorganism found, higher amount of Leptospirillum Ferriphilum were in a leach liquor, in a study conducted to leach the effect of pH on the bioleaching of a low-grade, black schist ore from Finland using Acidithiobacillus Ferrooxidans and Leptospirillum Ferrooxidans as extractant [24]. The bacteria can relatively resist high concentration of uranium, molybdenum, and silver, this is due to its affinity towards to ferrous ions or resistivity to refractory elements, but it cannot oxidize sulfur or any sulfur related compounds. By combing it with other sulfur- oxidizing acidophiles, sulfur-oxidizing process can be achieved; these are T. Caldus, T. Ferrooxidans, or T. Thiooxidans, to oxidize sulphidic gold concentrate a mixed culture of Thiobacillus and Leptospirillum has been used [11].
The third group thermophic bacteria mainly characterized by oxidation of iron to assure growth chemolithorophically, some are facultative autotrophs that require synergetic effect of other microorganism to like yeasts extract, cysteine, or glutathione. Among the microorganism in this group Sulfolobus species is the major one, these organism categorized as moderate thermophilic at an average values of temperature 40°C -60°C and the second group is extreme thermophilc at an average values of temperature 65–85°C. One of the moderate thermophilic gram positive bacteria, Sulfobacillus Thermosulfidooxidans is facultative autotrophs in which its growth stimulated by yeasts extract, where the presence of CO2, weight and volume ratio (w/v) are factor to facilitate and inhibit growth. From of extreme theremophilic Sulfolobus Acidocaldarius and Acidianus Brierleyi are those in genera Archaebacteria, among the other four genera Sulfolobus, Acidanus, Metallosphaera, and Sulfurococcus act aerobically and categorized in extreme thermophilic acidophilic bacterial which oxidizes ferrous and elemental sulfur and sulphide based minerals. These bacteria grows under all conditions (auto, mixo, heterotrphic) depending on the yeast extract ratio (w/v), found in facultative chemolithotrophic species act in acidic medium and temperature value can be up to 90°C [11].
All the major concepts of bioleaching have been discussed, so what are factors affecting rate of bioleaching and leaching efficiency, the major factors can be summarized as microbiological, mineralogical and physiochemistry factors. A physiochemistry factor includes temperature, pH, redox potential, oxygen content, carbon dioxide content which facilitate mineral oxidation required for cell growth, mass transfer, light, surface tension which mean that the topography of mineral surface that indicate the rate adsorption and crystal structure which has direct relation on the rate of reaction. Microbiological factors includes microbial diversity that is the distinct nature of micro organisms with regard to range of unicellular organisms, variety of microorganism found in an environment suitable for bioleaching, these includes bacteria, fungi, algae, flagellates, and those found in microbial biocenosis, the other microbiological factors are population diversity, metal tolerance, spatial distribution microorganism and adaptation ability of microorganism. The third major factor is the nature of mineral processed, characteristics like grain size which affect rate of dissolution, porosity related to rate of chemical attack and digestion rate, hydrophobicity is another physiochemistry factor to determine the rate leaching, hydrophobicity is differentiating whether the elements are water hating or loving while floatation takes place. Process is the other major factor affecting leaching efficiency, techniques where bioleaching process takes place (heap, dump, in situ) which we will be discussed below, pulp density is the variable which results variation on dissolution rate, a study shows that dissolution metal increases while pulp density increases but it is based on (w/v) ratio that is between 5 and 20%, the other factor is concentration of target mineral, this can inhibit the growth of microorganism, that cause a limitation of pulp density usage [25]. Stirring speed is also another factor affecting rate of dissolution and geometry of the heap during heap leaching process, the other major factor is the presence of fluoride released from the ore sample, which inhibits the process of bioleaching, and when the release decreases the rate of inhibition eventually reduced.
Besides leaching process microorganisms are used for bioremediation of mining sites, treatment of gangue, tailing, and mineral wastes from the industry, contamination of sediments due heavy metals and soil from toxicities, sewage sludge can cured by microorganism in which the process is called bioremediation [26].
The successful bioleaching process is characterize by the intimacy of microorganisms to a mineral surface, strong attachment result high rate of oxidation and dissolution on a substrate (mineral surface), this is achieved by the rate success of bio film formation. In general leaching techniques are two – Percolation leaching – a solution infiltrate through a fixed mineral location, and agitation leaching - mineral bearing ore stirred by a solution but while working in large scale, percolation leaching is usually chosen [7]. The principal commercial methods are aerated stirred-tanks, in situ, dump, heap, vat, bench scale, tank, column, reactor leaching are among the many. It was dump bioleaching process taken as the first commercial bioleaching in 1950 used to leach copper from sulfide minerals, since then bioleaching bloomed by copper oxide heap leaching, industrial microbial leaching process applied for sulfidic gold and bioheap commercial leaching of copper ore (chalcocite and covellite). The high production of bio heap leaching of copper in 1980 established at Lo Aguirre mine in Chile processing 16,000 tones ore/per year at the inception [27], these wipe the way that led to Chile’s industrial bio copper production in large scale especially from the year 1984 [28].
Aerated, stirred-tank bioreactors, used in mineral concentrate feeds, involve a series of stages that can have lots of tanks connected in parallel depending on the retention of the concentrate [7] a study conducted to check Na-chloride can possibly enhances the chemical and bacterial leaching of chalcopyrite uses three bioreactors engaged with inoculum of the bacteria [29]. Other tanks needed for value adding purposes which are usually single tanks might be connected in series, since these tanks subject to chemical attack, air, heat and sulfide mineral, they should be relatively resistant to corrosion, chemical attack, and soon, in order to have these character tanks can be lined with rubber, galvanized, or other corrosion protection method like using sacrificial anode or using high grade material like stainless steel, aluminum or copper.
Temperature maintained at optimum level by cooling coil or some time tanks are equipped with water jackets depending on the required temperature by the bacteria, these values can be conditioned based of the mineral to be leached, and sometimes the chemical used to enhance the leaching process [29]. Several tanks can be continuously arranged, named as continuous stirred tank reactor (CSTR), as per the above it can be followed by a series of small equal sized reactors [16]. Example of bioleaching of sulfidic gold concentrates, that the discharge from the final stage is subjected to water washing and solid/liquid separation in thickeners. Even though there is less power consumption basically used for agitator and blower, it has linear relationship with the amount of sulfide -sulfure which is required to oxidize and recover the target metal from the parent ore, rate of recovery depends up on the metal grade also.
The main advantages of these tank over other conventional methods like pressure autoclave, roasting, smelting, calcination and soon are; it has low capital and operational cost, relatively less construction period, less complicated requiring less skilled man power and most importantly it is environmental friendly. In general Australia, Chile, USA, Brazil and South Africa are among those countries involved in bio oxidation by stirred tank [7, 16].
Dump (run of mine) [18] leaching involves uncrushed waste rock and low grade ore is piled up or changing a pit to dump by blasting it. Conventional methods would be very expensive to process these type of ores samples, except dump leaching, dump can be very huge, containing in excess of 10 million tons of waste rock, up to 60 m deep [7, 30].
In order to digest some of unwanted minerals like silicate and to promote the growth of acidophilic microorganism, acid water solution is spread on the top surface, the acid water solution percolate through the dump, the more acidic the environment the more growth of microorganisms that oxidize minerals to be recovered. The pregnant leach liquor or acid run-off is collected at the bottom of the dump, from where it is pumped to a recovery station. After collection the process followed by solvent extraction, electro wining for the metal production but dump aeration is vital for the microorganism to growth, tailing from solvent extraction recycled on the top of dump. Escondida mine found Chile is the biggest bio dump leach in world [26, 30].
Heap leaching (crushed and agglomerated) [18] is composed of air, acid and microorganism where commutation takes place on rock samples to turn it to smaller particles which increases the surface area for acid digestions and conditioning it to microorganisms, particle should not be very fine and should be piled allowing a simplifies aeration pipe placed to facilitate air flow. To improve drainage of the mineral containing solution from the bottom of the ore, conditioned ore is spread on specially engineered pads (lined with high-density polyethylene (HDPE)), which consist of perforated plastic drain lines and air also supplied to optimize the growth of microorganism [7]. Heap can be large up to kilometer long, but commonly less than 500 m wide and 10 m long, the size and height of a heap depends up on air (for bacterial to grow) water, acid, heat generated due to the process and its dissipation [31]. Heap surface should be permeable enough for the sulfuric acid to infiltrate and dissolves iron to ferric solution producing ferric ion that react with copper sulfide results ferrous ion and copper solutions. Acidithiobacillus Ferrooxidans oxidize iron where the bacterial can be inoculated and works by attaching itself to ore, with having free movement. After collecting PLS (pregnant leach solution), then solvent extraction is followed where the target mineral recovered and formed into cathodes.
This aerobic bacteria works only in the presence of oxygen in the heap, those bacteria consume it from the solution where oxygen is in liquid phase. This process enhances the conversion of ferrous to ferric ions as per the reaction below.
Heap some time can be crushed 19 mm with rotating drum with acidified water [29] aeration can be conducted using low pressure fans those directing air through piping on the pad [26]. It is clear that heap leaching requires the preparation of the ore, primarily size reduction, so as to maximize mineral-lixiviant interaction and lay of an impermeable base to prevent lixiviant loss and pollution of water bodies. Heap leaching basically used to leach low-grade ore of copper and zinc, even in the case of copper grade level can be (0.2–2%). To have an effective heap leaching process a mathematical model has been developed by taking heat, mass transfer, liquid, gas flow and chemical process in to consideration [31]. Heap also employed to bioleach silicate mineral, in a study where two microorganism were tested ‘Ferroplasma acidarmanus or the common Acidithiobacillus ferrooxidans against the amenity of silicate minerals. Beside oxidation process energy was generated from flat plate solar energy collectors where heap is designed by HeapSim, heap bioleach simulation tool was used to simulate the heap and process occurring in the heap, even calculating the copper output [32].
In situ leaching requires making the ore permeable for a solution and air to be circulated through the ore body. It does not require metal containing material to be removed from the ground [18]. It employs a method of recovering target minerals from the leach solution. The acid solution percolates until it reaches to impermeable layer. In situ includes recovery of minerals from the intact ore. The resulting metal-enriched solutions are recovered through wells drilled below the ore body. In case of in situ leaching the main concern is pollution of ground water, with this regard there are three types of ore bodies generally considered for in situ leaching: surface deposits above the water table, surface deposits below the water table and deep deposits below the water table. It is burden materials, establish permeability allowing air to pass in which metal bearing solution collected in the sumps [7]. It is combined with mineral recovery operation time and again to pull out the minerals from recovered fluid or pregnant solution or leachant. Acidified leach solutions, applied to the top surface of the entire ore zone, infiltrate through the fragmented ore due to the blast. The leaching bacteria become established and facilitate metal extraction. Metal-rich solutions or large volume of solution is circulated with the aid of gravity flow and pumped and recovered in sumps then again pumped to the surface for metal recovery, the returning fluids to the extraction operation are known as “barren solution”. Metal recovery depends on two major things first the bacteria used (Acidithiobacillus Ferrooxidans) and permeability of the ore-body, which can be increased by fragmenting of ores in place, called “rubblizing”. Due to the ground water pollution this leaching process becomes less used and less popular [18] on the contrary it has been said that it is a best substitute for open pit and shaft mining operation, basically when in situ leaching is applied, no gangue or tailing is byproduct, it also called green mining or mine of the future [33].
Recent study shows that elements like uranium, copper, gold, zinc and other elements are commercial focus of bioleaching and biooxidation [34]. Many studies indicate microbial leaching is more important in low-grade ore, ore sample collected from Mianhuakeng uranium mine located in northern Guangdong province in China, leached by heap, by mixed microorganism of Acidithiobacillus Ferrooxidans and Leptospirillum Ferriphilum with 88.3% leaching efficiency [35]. Uranium leaching takes places by indirect mechanism, as Acidithiobacillus Ferrooxidans does not directly interact with uranium minerals. The role of Acidithiobacillus Ferrooxidans in uranium leaching is the best example of the indirect mechanism. Bacterial activity is limited to oxidation of pyrite and ferrous iron. The process involves periodic spraying or flooding of worked-out stops and tunnels of underground mines with lixiviant [4]. The pH of lixiviant was optimized during the bioleaching of uranium from low grade Indian silicate-apatite ore with 0.024% of U3O8. This study uses Acidithiobacillus Ferrooxidans for leaching and biochemically generated ferric ions as an oxidant, optimizing particle, pulp density and redox potential results 98% uranium bioleaching. In this indirect bioleaching of uranium, the bacteria generate ferric sulfate and pyrite is oxidized by a lixiviant, within acidic environment the oxidations of ferrous ion to ferric ions process executed by the bacteria is fasters than chemical oxidation [36]. In case of uranium bioleaching the main drawback is to oxidize uranium (IV) since it insoluble but on this bioleaching process when ferrous sulfate produced in the process, then re-oxidized to ferric sulfate which enzymatically oxidize uranium (IV) to uranium (VI) by the energy produced by this reaction. A case study in India at Jaduguda mines proofs that use of biogenic ferric sulfate produced by the strain which was then used for efficient uranium extraction and cause no harm to the environment, while extracting uranium, use of reduced MnO2 in Bacfox process to generate biogenic ferric sulfate, results passed air saturated ferrous sulfate solution over Acidithiobacillus Ferrooxidans which is absorbed on solid surface [36]. Since the permeability of the ore surface is a factor, the above study uses a process called “rubblizing” that increase fragmenting of ore in place which can be applied in the extraction of sulfide mineral, gold and uranium. While isolating the bacteria from mine water, the isolation media and H2SO4 consumption during isolation, pH variation and temperature were determinate factors, the microbial cell count and the growth of (A. Ferrooxidans) determines by rate of oxidation of iron from Fe2+ to Fe3+, so while leaching if the amount of Fe2+ decrease means the bacteria is using it as energy source to convert it to Fe3+, uranium bioleaching depends on the synergic effects Fe3+ and proton produced by the bacterial [37] that process uses either of the two energy sources to growth iron or sulfur. The reaction of making insoluble uranium to soluble form is as follows [38].
Studies indicate that microbial cell count and pulp density ranges 5–30% (w/v), particle size <75 μm has brought an optimum ore leaching but it should be clear that each ore has its own distinct behavior and no size fits all, meaning results indicated here might be different for another ore sample due to ore elemental composition, crystal structure, grade, topography and surface tension.
The ore is loaded on a water-resistant surface or ore is piled on an impermeable surface until a dump of suitable dimension forms. After leveling the top, then spraying a leach solution onto the dump is followed [4]. These dump is a habitat of heterogeneous microorganism. Dump can have variety particles sizes, where the bacterial annexation, which is anaerobic (microaerophilic), thermophilic begins from the top.
Dump leaching used to pretreat low-grade, refractory- sulfidic gold ores and to leach copper from chalcocite ores while ore grade is low with values ranges between 0.1–0.5%. Copper can be obtained from ore rocks from the mound then washed with dilute H2SO4 to facilitate the oxidation process of mineral by acidophiles, which is followed by cementation process where copper is precipitated from the drainage with scrap iron since it primary iron oxidizing process [39]. Check the leaching process of copper sulfide chalcocite (Cu2S), which occurs with pyrite (FeS2), leaching is due to ferric ion reacts with copper sulfide mineral processes ferrous and copper ions in solution.
In these regions indirect leaching by ferric sulphate also prevails. The exterior of the dump is at ambient temperature and undergoes changes in temperature reflecting seasonal and diurnal fluctuations. Many different microorganisms have been isolated from copper dumps, some of which have been studied in the laboratory. These include a variety of mesophilic, aerobic iron and sulfur oxidizing microorganisms; thermophilic iron and sulfur oxidizing microorganisms; and anaerobic sulphate reducing bacteria. In copper leaching the concentration of target metal by itself is an important variable, copper concentration (100–300 mM range) is values cause difficulty for the microorganism to operate, selecting the microorganism is one of the mechanisms of copper resistant, Acidithiobacillus Ferrooxidans can resist copper concentration and strong acidic environment [40]. Thiobacillus Ferrooxidans was the main product observed after a culture study, from an ore or leach solution for the identification of composition of bacterial population and incase of low ferrous ions, it was Leptospirillum Ferrooxidan was observed, the study shows that utilization of ferrous iron as energy source is dominated by the previous bacteria as the culture shows. Pseudomonas aeruginosa, where heterotrophic bacteria produce various organic acids in an appropriate culture medium is used in copper leaching [41]. The addition of salt in bioleaching of copper resulted process enhancement, after designing the bioreactor the bioleaching of copper was enhanced in both stirred tank or shack flask by adding sodium chloride in leach solution, increasing the dissolution of Fe3+ that eventually reduces precipitation [29] addition of some elements might result inhibition of bioleaching process, fluorine in solution increase the viscosity of leach liquor that result inhibition of bioleaching [42]. It is important to understand the microbiology, which is responsible or identify a means to study bulk activity of microorganism, these features are oxygen uptake in solid and liquid samples, redox potential, pH, ferrous iron concentration and temperature. Microbial leaching has also direct relation with enrichment and culture from solution of ores. Acidithiobacillus Thiooxidans, Acidithiobacillus Ferrooxidans, and Leptospirillum Ferrooxidans have been cultured where the process run at an ambient temperature and the strain of bacterial related to the microorganism mentioned here [27, 43]. Leach solutions enriched with copper exit at the base of the dump and are conveyed to a central recovery facility. In most large-scale operations the leach solution, copper-bearing solution pumped into large cementation units containing iron scrapings for cementation and then electrolysis followed [4]. It was in Chile and Australia the commercial bio heap leaching of copper started mass production. And the first bioleach heap copper extraction plant is in China [44]. The copper extracted percentage can be calculated as,
E = Copper content in the solution/copper content in the sample X 100% [41].
Acidophilic bacteria are able to oxidize gold containing sulphidic ore, such a process can be ameliorated by conventional process of cyanidation, these basically reduces the complexation by increasing the capability of microorganisms to reach to the target metal. Certain sulphidic ores containing encapsulated particles of elemental gold, resulting in improved accessibility of gold to complexation by leaching agents such as cyanide. Relative to other conventional process and pretreatments like roasting, smelting and pressure oxidation, bio-oxidation demands less cost and no harm to nature [7]. Though it is under study a commercial bio-oxidation and bio heap leaching of gold prior cyanide extraction. It is the bacteria, Acidithiobacillus Ferrooxidans used to oxidize the sulphide matrix for gold recovery. Prior to extraction, gold ore must be bio-oxidize by the bacteria. In this process refractory sulphidic gold ores contain mainly two types of sulphides: pyrite and arsenopyrite where silver ion was used as a catalyst in acidic environment. Since gold is usually finely disseminated in the sulphide matrix, the objective of biooxidation of refractory gold ores is to break the sulphide matrix by dissolution of pyrite and arsenopyrite and extract 95% of iron and arsenic, the residue of both filtered through a vacuum pump. The consumption of cynide is much higher while biooxidation, the study suggested that using thiourea instead of cyanide is much less toxic but since the process require high consumption of thiourea cost increase steadily, consumption of thiourea reduced by using different agents like SO2, bisulfite, cystine, cystine with oxygen during extraction process [45].
The mesophilic tank leaching is the most common bioleaching process in the world; thermophilc tank is favored while the temperature is high, among such tanks BioCop™ well known, In order to have effective thermophilc tank the following are basic requirements, microbial catalyzed reaction which is needed to facilitate metal dissolution by microbial oxidizing of ferrous iron to ferric iron, initial solublization of ferrous ion takes place using acid solution, oxidation of mineral sulfide takes place by the combination effects of ferric iron and acid solution followed by oxidization of reduced sulfur to sulfate by microorganisms. Reactor configuration is the other factor where the six equal size continuous reactor, three arranged in parallel considered as primary reactors, and the other three arranged in series considered as secondary reactors, in this case reactors are considers as a large continues stirred tank supplied with aeration and agitation. The other factors are oxygen, carbon dioxide, pulp density and finally even though the operational cost is much less plant location, construction material, blower or compressor to supply oxygen to the microbes, high power agitator in case of oxygen plant for oxygen dispersal in the reactor. Growth of industries results the demand of metals in very high quantity and likely go further in the years to come. This brings diminution of high grade ore with effluents and solid wastes that needs to be treated to recover the important elements and protect the environment.
Regarding to environment biohydrometallurgy is vital process, the fact that bioprocess is conducted without the presence of toxic chemical and relatively required low cost makes it most needed. The direct implication of microorganisms in the reduction of uranium is of considerable interest because of its potential application in bio remediating of contaminated sites, in pretreating radioactive wastes, bioleaching is becoming a promising technology.
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