Nathanaël Cheriere
email: nathanael.cheriere@irisa.fr

Matthieu Dorier
email: mdorier@anl.gov

Gabriel Antoniu
email: gabriel.antoniu@inria.fr

Is it Worth Relaxing Fault Tolerance to Speed Up Decommission in Distributed Storage Systems?

Keywords: Decommission, Fault Tolerance, Malleable Distributed Storage System, Elastic Distributed Storage System

Efficient resource utilization is a major concern for large-scale computer platforms. One method used to lower energy consumption and operational cost is to reduce the amount of idle resources. This can be achieved by using malleability, namely, the possibility for resource managers to dynamically increase or decrease the amount of resources of jobs while they are running.

Decommissioning (i.e., removing from the cluster) the idle nodes as soon as possible allows the resource manager to quickly reallocate those nodes to other jobs. Challenges appear when such nodes host part of a distributed storage system. Such storage systems may need to transfer large amounts of data before releasing the nodes, in order to ensure data availability and a certain level of fault tolerance.

In this paper, we model and evaluate the performance of the decommission operation when relaxing the level of fault tolerance (i.e., the number of replicas) during this operation. Intuitively, this is expected to reduce the amount of data transfers needed before nodes are released, and thus allow nodes to be returned to the resource manager faster. We quantify theoretically how much time and resources are saved by such a fast decommission strategy compared with a standard decommission that does not temporarily reduce the fault-tolerance level. We establish lower bounds for the duration of the different phases of a fast decommission. We use the lower bounds to estimate when fast decommission would be useful to reduce the usage of core-hours and when not. We implement a prototype for fast decommission and experimentally validate the lower bounds on the duration of the operation and confirm in practice our theoretical findings.

I. INTRODUCTION

Improving the resource utilization of a platform is a challenge for its administrators. It directly links to better cost effectiveness and higher productivity. One approach used to improve resource utilization is to reduce the number of idle nodes. This can be achieved by multiple ways, such as careful scheduling, precise prediction of the required resources, or job malleability.

Job malleability is the possibility for jobs to have their resources resized at run time by the resource manager. Thus, unused resources can be returned to the resource manager and either shut down or reassigned to other jobs. Applications also benefit from malleability; unused resources are released, thus reducing the energy consumption and costs attributed to that application at the same time as the core-hour usage is decreased. Moreover, the capability to dynamically adjust the number of resources available to a job allows the job to match the workloads in order to have constant quality of service, even if the workload is highly volatile. Many solutions have been proposed to add malleability to platforms and applications. Resource managers such as KOALA-F [START_REF] Kuzmanovska | KOALA-F: A Resource Manager for Scheduling Frameworks in Clusters[END_REF] and Morpheus [START_REF] Jyothi | Morpheus: Towards Automated SLOs for Enterprise Clusters[END_REF], are able to manage malleable jobs, and various frameworks [START_REF] Vadhiyar | SRS: A Framework for Developing Malleable and Migratable Parallel Applications For Distributed Systems[END_REF], [START_REF] Kale | A Malleable-Job System for Timeshared Parallel Machines[END_REF], [START_REF] Buisson | A Framework for Dynamic Adaptation of Parallel Components[END_REF] enable the design of malleable applications.

Previous works focused on the malleability of computing resources, however; and no distributed storage systems have been designed to be malleable, even though many of them include both commission (adding nodes) and decommission (removing nodes) operations for maintenance purposes. Indeed, adding nodes to a distributed storage system or removing nodes from it involves many data transfers in order to balance the load. These data transfers are assumed to be too slow for practical use. The implemented rescaling operations (commission and decommission) are thus rightfully designed to limit their impact on application performance as much as possible and are not optimized for speed.

Having a truly malleable distributed storage system with fast rescaling operations would enable many features for applications that need to deploy a distributed storage system colocated with computation resources. In particular, applications could benefit from fast data accesses to a co-deployed distributed storage system and benefit from malleability.

• Reduction of the core-hours cost: When a storage node is not needed by the application anymore, it can be given back to the resource manager instead of being idle. • Ideal scalability: The distributed storage system can expand and contract with the malleable application using it, ensuring consistent storage system performance. • Data isolation: The data manipulated by an application can be located solely on the computing nodes used by the application and does not need to be stored on a shared storage cluster. This is consistent with the recent trends in HPC systems, which increasingly include local storage on computing resources. With a fast decommission, the platform can claim resources back from applications quickly. This action allows the resource managers to mitigate unpredictable events, such as the submission of high-priority jobs or a sudden increase in the workload of some jobs, by quickly allocating new resources to the task.

In our previous work [START_REF] Cheriere | How Fast Can One Scale Down a Distributed File System?[END_REF], we modeled node decommission in replication-based distributed storage systems and provided a theoretical lower bound for the duration of this operation. Knowing a lower bound for this operation can help in several ways.

• The lower bound can be used to evaluate the performance of decommission mechanisms when designing a distributed storage system on a given physical platform: decommission would cost at least that lower bound on that platform. • It can also help the resource scheduler make scheduling decisions. With it, the resource scheduler can anticipate some minimum duration needed to decommission nodes and make them available when they are needed. • With the lower bound, a user can quickly estimate whether to use malleability on a given platform: if the lower bound is too high it means that the performance cost of supporting malleability may not be compensated by its expected benefits, so malleability would not be useful in such a case. • The study of this operation highlights inherent bottlenecks that need to be mitigated for efficient implementations. In our previous work, we assumed that the level of fault tolerance of the storage system should not be weakened during the decommission; if the system is configured to keep k replicas of an object at all times, the number of replicas of that object during the decommission should never be strictly less than k. It also means that the decommissioned nodes can be given back to the resource manager only at the end of the decommission operation, since all objects need to be sufficiently replicated on the remaining nodes. This is an opportunity for optimization. As long as no data is lost, decommissioned nodes can be returned to the resource manager sooner. We denote this strategy as fast decommission. It is composed of three phases. During the data-safekeeping phase, the system ensures that at least one replica of each object is present on the remaining nodes, transferring objects if needed. Then, during the node release phase, the decommissioned nodes are given back to the resource manager. Missing replicas are recreated during the system stabilization phase.

With this strategy, the decommissioned nodes are effectively made available for other jobs faster than they are with standard decommission. However, fast decommission comes at the cost of weakened fault tolerance during the system stabilization phase: not all objects have their required number of replicas until the stabilization finishes.

The idea of trading fault tolerance for performance is not new, and is, in fact, rather intuitive. The main contribution of this paper is to show, mathematically and experimentally, that this intuition is incorrect in many situations. Our goal is to make a step forward in better understanding the actual trade-off that exists between the duration of the decommission and its impact on fault tolerance. To this end, we provide theoretical lower bounds for the two main phases of fast decommission: the data-safekeeping and the system stabilization phases (the node release is assumed to be instantaneous). We also implement fast decommission in Pufferbench [START_REF]Pufferbench[END_REF] [8], a benchmark designed to study the commission and decommission mechanisms of distributed storage systems in practice.

The lower bounds highlight interesting results. As expected, the nodes decommissioned with the fast decommission mechanism are released in a fraction of the time needed by the standard decommission. For distributed storage systems, however, the phase of system stabilization that comes after the release of the decommissioned nodes lengthen the duration of the whole operation. When the bottleneck of the operation is the network, the whole operation lasts as long as the standard decommission. In the case of a storage bottleneck, the duration of the operation is longer than that with the standard decommission: fewer resources are available for the stabilization phase, and thus the operation is longer.

The experimental results obtained with Pufferbench confirm the trends obtained from the lower bounds. In particular, the decommission times obtained are on average within 10% of the lower bounds when the storage is the bottleneck and are on average within 40% of the lower bounds when the network is the bottleneck.

We also compared the number of core-hours needed for the fast decommission and the standard decommission mechanisms. In the case of a network bottleneck, using fast decommission always leads to a reduction in core-hour usage. The gain in core-hours increases with the number of decommissioned nodes. In the case of a storage bottleneck, however, no gain in the usage of core-hours is realized unless many nodes are decommissioned at once.

Overall, fast decommission can be an interesting tradeoff for the designer of distributed storage systems when the network is the bottleneck: the consumption of core-hours is reduced compared with that of standard decommission while the duration stays the same for the distributed storage system. Moreover, depending on the network bandwidth, the stabilization phase, during which the fault tolerance is weakened, can be short. However, the trade-off is less relevant in the case of a storage bottleneck: the whole operation is longer than the duration of the standard decommission, and there is no gain in core-hours unless many nodes are decommissioned at once. Fast decommission may even be detrimental depending on the bandwidth of the storage devices. If the storage is slow, such as a hard drive, the fault tolerance will be weakened for the duration of the long stabilization phase, increasing the probability of losing data.

This paper is organized as follows. Related work is presented in Section II. Assumptions detailed in Section III are used to build the lower bounds in Section IV. In Section V, we compare the experimental run times of an implementation of fast decommission with the lower bounds. We discuss the results in Section VI and present our conclusions in Section VII. II. RELATED WORKS Many distributed and parallel file systems, such as Ceph [START_REF] Weil | Ceph: A Scalable, High-Performance Distributed File System[END_REF] and HDFS [START_REF] Shvachko | The Hadoop Distributed File System[END_REF], include a decommission mechanism. However, it is available primarily for maintenance purposes. Hence it is understandably optimized to reduce the performance impact of the decommission on other jobs. It is not meant to be fast. Some distributed storage systems enable some form of malleability: some of the machines of the cluster on which they are deployed can be shut down to save energy. Rabbit [START_REF] Amur | Robust and Flexible Power-Proportional Storage[END_REF], Sierra [START_REF] Thereska | Sierra: Practical Power-Proportionality for Data center Storage[END_REF], and SpringFS [START_REF] Lianghong | SpringFS: Bridging Agility and Performance in Elastic Distributed Storage[END_REF] are examples of such a system. They have two main limitations. First, the shutdown nodes still store data and may be turned back on at any time; thus they cannot be given back to the resource manager for use by another job. Second, the nodes that can be shut down are determined by the distributed storage system and not by the resource manager.

Some resource managers are able to manage malleable distributed storage systems. The SCADS Director [START_REF] Trushkowsky | The SCADS Director: Scaling a Distributed Storage System under Stringent Performance Requirements[END_REF], for example, is a resource manager designed to ensure servicelevel objectives. It can add or remove storage nodes and move the data, as well as the number of replicas needed for each file. The SCADS Director adds malleability to the SCADS file system [START_REF] Armbrust | SCADS: Scale-Independent Storage for Social Computing Applications[END_REF]. Its authors focus their evaluation on the ability of the system to maintain its service-level objective, however, and not on the performance of the rescaling operations themselves.

Lim, Babu, and Chase [START_REF] Lim | Automated Control for Elastic Storage[END_REF] propose a resource manager based on HDFS. This resource manager chooses when to add and remove nodes and the parameters of the operations. However, it simply uses HDFS "as is" and does not focus on efficiency. Both Trushkowsky et al. [START_REF] Trushkowsky | The SCADS Director: Scaling a Distributed Storage System under Stringent Performance Requirements[END_REF] and Lim et al. [START_REF] Lim | Automated Control for Elastic Storage[END_REF] focus on ways to leverage malleability rather that on improving it. Therefore their work is complementary to this paper.

In a previous work [START_REF] Cheriere | How Fast Can One Scale Down a Distributed File System?[END_REF], we provided lower bounds for the time of a standard decommission in replication-based storage systems. This standard decommission required that the requested number of replicas for each object be maintained throughout the decommission operation. In this paper, we relax this constraint. We accept that the number of replicas of the stored objects drops below its normal value during the decommission. Of course, we still require that no data be lost and that the replication factor be brought back to its normal value at the end of the operation. This method of decommission trades better resource utilization for temporarily higher vulnerability to faults. Indeed, a storage node can be given back to the resource manager as soon as at least one replica of each of its data objects exists on remaining nodes. Until new replicas are created, however, a crash may lead to data loss. While this trade-off is not new, this paper aims to model it precisely.

III. CONTEXT AND PROBLEM STATEMENT

In this section, we start by stating what the targeted storage systems are. We then define the fast decommission operation and the assumptions used in order to compute the lower bounds of its duration.

A. Targeted distributed storage systems

Many similarities exist between the fast decommission mechanism and the crash of a node followed by the recovery of the system. In both cases, some storage nodes become unavailable, and some data has to be recreated on the remaining nodes.

While several methods exist to recreate the missing data, in this paper we consider only the distributed storage systems that use data replication. This crash recovery mechanism is popular: it is used in HDFS [START_REF] Shvachko | The Hadoop Distributed File System[END_REF], Rabbit [START_REF] Amur | Robust and Flexible Power-Proportional Storage[END_REF], and Sierra [START_REF] Thereska | Sierra: Practical Power-Proportionality for Data center Storage[END_REF], among others. It has the advantages of being simple and highly parallel: most of the remaining nodes share some replicas with the crashed nodes and thus can quickly restore the replication level to its initial value. Moreover, little CPU power is required for this technique.

We do not consider full-node replication, in which sets of nodes host exactly the replicas of the same objects, since the recovery mechanism is fundamentally different. Erasure coding, used in systems such as Pelican [START_REF] Balakrishnan | Pelican: A Building Block for Exascale Cold Data Storage[END_REF], is not considered either. With erasure coding, CPU power is needed to recreate missing data. Thus a mathematical model of the CPU would be required in order to compute a lower bound on the duration of such operations.

Another major recovery mechanism, lineage, also requires CPU power. Its principles greatly differ from those of data replication. When a node crashes, the data that is lost is recreated by executing again the jobs that generated it. Modeling the lower bounds of such an operation would require knowledge of the jobs that generated the data, however, and we therefore do not consider this recovery mechanism in this paper. Lineage is used in Tachyon [START_REF] Li | Reliable, Memory Speed Storage for Cluster Computing Frameworks[END_REF] and is tightly coupled to Spark [START_REF] Zaharia | Spark: Cluster Computing with Working Sets[END_REF] in order to regenerate the data.

Despite the similarities between fault tolerance mechanisms and fast decommission, the fault tolerance mechanism cannot be used directly to decommission nodes. The fault tolerance mechanism of a distributed storage system has an upper limit on the number of nodes that can crash simultaneously. The fast decommission mechanism is an intentional operation. Hence, even if more nodes are decommissioned than the number of replicas, this decommission mechanism will prevent the loss of data by first making sure that at least one replica of each object exists in the remaining nodes.

B. Problem definition

We consider a replication-based distributed storage system deployed on a cluster of N nodes. Each node initially hosts an amount of data D. Each of the objects stored in the system is replicated r times. The resource manager requests the decommission of x arbitrarily chosen nodes.

A fast decommission is done in three main steps. 1) Data-safekeeping: During the data-safekeeping phase, the objects that are stored only on the leaving nodes have a replica transferred to remaining nodes to ensure that no data is lost during the operation. 2) Nodes release: The leaving nodes are given back to the resource manager. They no longer participate in the distributed storage. 3) System stabilization: The missing replicas are recreated by the remaining nodes to recreate the target replication degree.

We define the time to availability t avail as the lower bound of the date at which the data-safekeeping terminates. The stabilization time t stab is the lower bound on the time at which the whole process terminates; t stab is obtained assuming that the leaving nodes participated only in the data-safekeeping phase and were all removed from the cluster at time t avail .

C. Assumptions on the cluster infrastructure

We make three assumptions concerning the hardware of the cluster to provide comprehensive lower bounds.

Assumption 1: Homogeneous cluster

All nodes have the same characteristics, in particular the same network throughput (S Net ) and storage write and read throughputs (S W rite , S Read ).

Assumption 2: Ideal network

The network is full duplex, data can be sent and received with a throughput of S Net at any time, and there is no contention.

Assumption 3: Ideal storage system

The writing speed is not higher than the reading speed (S W rite ≤ S Read ). The storage device must share its I/O time between reads and writes and thus cannot sustain simultaneous reads and writes at maximum speed (during any span of time t, if a time t Read ≤ t is spent reading, the storage cannot write for more than tt Read , and conversely).

Assumption 3 holds for most modern storage devices. Moreover, we assume that all resources are available to the decommission and that either the network or the storage is the bottleneck.

The network is the bottleneck if it limits the storage in any situation: S Net < S Read . Conversely, the storage is the bottleneck if it cannot read or write fast enough the data received and sent on the network: S Read S W rite S Read +S W rite < S Net . There exists a situation in which both storage and network are bottlenecks for different nodes. Although not presented in this paper this situation can be studied by extending the presented approach. We leave this for future work.

D. Assumptions on the initial data distribution

The initial data distribution is important for the performance of the decommission. Thus we make some assumptions in this respect.

Assumption 4: Even data distribution

All N nodes initially host the same amount of data D.

Assumption 5: Uniform data replication Each object stored in the storage system is replicated on r ≥ 2 distinct nodes. The probability of finding a given object on a node is uniform and independent of the node.

Assumption 6: Uniform data distribution

The probability of finding a given object on all the nodes in a set of r distinct nodes is uniform and independent of the chosen set.

These assumptions reflect the ideals of the load-balancing policies implemented in many state-of-the-art distributed file systems such as HDFS [START_REF] Shvachko | The Hadoop Distributed File System[END_REF] and RAMCloud [START_REF] Ousterhout | The Case for RAMClouds: Scalable High-Performance Storage Entirely in DRAM[END_REF]. We assume that the data is initially in an ideal load-balanced state.

E. Formalizing the problem

At the end of the decommission operation, the data distribution on the remaining nodes should satisfy the following objectives.

Objective 1: No data loss

No data can be lost during the decommission.

Objective 2: Maintenance of an even data distribution All nodes host the same amount of data D .

Objective 3: Maintenance of a uniform data distribution All sets of r distinct nodes host the same amount of exclusive data, independently of the choice of the r nodes.

These objectives ensure that the load balancing is ideal at the end of the decommission.

All the listed assumptions and objectives are common with standard decommission. The difference between both decommission strategies comes from Objective 4.

Objective 4: Maintenance of the replication factor

Each object stored on the storage system is replicated on r distinct nodes.

The fault tolerance requirements are relaxed during the execution of the decommission. Instead of ensuring the replication factor of the objects at any time during decommission, the replication factor is required to be at its initial level only at the end of the decommission. This relaxation is the main difference between the assumptions of this work and the ones for the lower bounds of the standard decommission established in our previous work [START_REF] Cheriere | How Fast Can One Scale Down a Distributed File System?[END_REF]. The purpose of this paper is to quantify theoretically how many resources are saved by relaxing this constraint and how fast such a decommission process can be, compared with a standard decommission.

IV. LOWER BOUNDS

In this section, we establish the lower bounds for the duration of the data-safekeeping and stabilization phases (the node release phase is assumed to be instantaneous). Because of space limitations, all proofs are provided in a separate research report. 1

A. Data to move

Because data should not be lost during a decommission (Objective 1), a minimum amount of data has to be moved from the leaving nodes to the remaining ones. The objects to move are the ones that have all their replicas on the leaving nodes and that would have been lost had these nodes all been removed at the same time. Thus, we first compute the probability, p i , for an object to have exactly i replicas on the leaving nodes. From it, we deduce the minimum amount of data to transfer to remaining nodes D avail .

p i =    0 if i > r, ( r i )( N-r x-i ) ( N x )
for i ≤ r.

(Def. 1)

D avail = NDp r /r if x ≥ r 0 in other cases. (Def. 2)
D stab is the lower bound of the amount of data to move in order to recreate all replicas from the leaving nodes onto the remaining nodes. It is the amount of data that was initially present on the leaving nodes and includes D avail .

D stab = xD (Def. 3)
Both D avail and D stab are lower bounds of their respective metric, they are obtained assuming that objects stored on the nodes can be divided as needed to perfectly balance the data on each node.

B. Case 1: Bottleneck at network level

In this section we assume that the network is the bottleneck for the data transfers required by the data-safekeeping and stabilization phases. The network is the bottleneck if it limits the storage in any situation (S Net < S Read ).

1) Time to availability: During the data-safekeeping phase, only the leaving nodes send data to the remaining ones. As defined by Assumption 2, the network is ideal without interference, and each node can send and receive data with a bandwidth S Net at the same time. Two possible bottlenecks may appear, however: either sending data from the leaving nodes or receiving the data on the remaining nodes.

Thus, the time to availability t avail depends on the number of nodes leaving the cluster x, the amount of data to move D avail , and the bandwidth of the network S Net . We express t avail as follows.

t avail = NDp r rxS Net if x ≤ N/2 NDp r r(N-x)S Net otherwise.
(Prop. 1)

2) Stabilization time: The safekeeping phase has the priority over the stabilization phase; decommissioned nodes have to be released as fast as possible. However, depending whether the bottleneck of the safekeeping phase is receiving or sending data, the stabilization can happen at the same time as the safekeeping without slowing down the later. Indeed, when the leaving nodes sending data are the bottleneck of the datasafekeeping phase, the remaining nodes do not have their network bandwidth saturated by the reception of the data. Thus, data exchanges needed to stabilize the storage can start before the end of the safekeeping phase without slowing it.

We denote as t over the time gained on the duration of the stabilization phase by starting it before the end of the safekeeping phase.

t over = (N-2x)NDp r rx(N-x)S Net if x ≤ N/2 0 otherwise. (Prop. 2)
From this, we obtain the time needed to stabilize the distributed storage system t stab . The stabilization phase can use all available resources only after the end of the safekeeping phase, however, some overlap between the two phases reduces Decommissioned nodes (out of a 20-nodes cluster) Time to decommission (s) q q q q q q q q q q q q q q q Network bottleneck Standard decommission Data safekeeping Fast decommission Fig. 1. Lower bounds for the duration of the data-safekeeping phase and fast decommission compared with the lower bound of the standard decommission, in case of a network bottleneck. Each node initially hosts 50 GiB of data; the network has a throughput of 1.25 GiB/s (10 Gib/s).

the duration of the stabilization by t over . Thus, t stab is defined by prop. 3.

t stab = t avail -t over + D stab -D avail S Recv = xD (N -x)S Net . (Prop. 3)
3) Observations: The lower bound for the whole operation (t stab ) is exactly the lower bound for the standard decommission established in our previous work [START_REF] Cheriere | How Fast Can One Scale Down a Distributed File System?[END_REF] (in which the replication factor is maintained). Thus, one can relax the fault tolerance to release nodes faster (the fewer the' corehours used, the better the overall platform utilization) without any difference in the length of the operation compared with standard decommission.

We also infer that keeping the leaving nodes after they have transferred the data needed for the fast decommission does not speed the duration of the operation: in all cases, receiving data on the remaining nodes is the bottleneck. It would, however, have an impact on the ability of the cluster to service read requests. The network is completely saturated by the stabilization, servicing any request would slow it.

In Figure 1, we observe the differences between a standard decommission and a fast decommission; with the fast decommission, the nodes are released in a fraction of the time needed to decommission nodes while maintaining the replication factor.

C. Case 2: Bottleneck at storage level

When the storage is the bottleneck, the situation is different because of the limitations of the storage devices (Assumption 3): data cannot be read and written at the same time. The storage is a bottleneck if it cannot read and write all the data received and sent on the network during the same period of time ( S Read S W rite S Read +S W rite < S Net ). 1) Time to availability: The limitations on the storage, however, do not have any impact on the time to availability since leaving nodes only have to read data, and remaining nodes only have to write it. Thus, the time to availability depends on the data to move during the data-safekeeping phase D avail and the reading and writing speeds of the storage devices S Read and S W rite .

t avail = NDp r rxS Read if x ≤ NS W rite S Read +S W rite NDp r r(N-x)S W rite otherwise.
(Prop. 4) 2) Stabilization time: Similar to the first case, when the bottleneck of the operation is reading data from the leaving nodes, the storage of the remaining nodes is not saturated: these nodes can read or write more data without slowing down the data-safekeeping process. Thus, the remaining nodes can exchange data to start the stabilization before the datasafekeeping finishes and without impact on the time to availability.

Each remaining node has some time t over to exchange data with other remaining nodes in the data-safekeeping phase.

t over = (N-x)S W rite -xS Read x(N-x)S Read S W rite if x ≤ NS W rite S Read +S W rite 0 otherwise.
(Prop. 5) We determine S e f f , the effective writing speed on the cluster when the remaining nodes exchange data among themselves. S e f f is not simply the product of the number of remaining nodes by their individual writing speed. Indeed, to exchange data among themselves, remaining nodes also must read data.

To avoid reading multiple times data from storage devices with low read bandwidth, many systems use buffering. The data read is stored in memory (that has a higher bandwidth) and then sent to has many destinations as needed. The buffering relies on the bandwidth of the memory being a few times higher than the bandwidth of the storage device. We denote as R the ratio of data read to data written on the storage device during the stabilization.

R =    1
in case of in-memory storage, ∑ r-1 i=1 p i (r-1)p r +∑ r-1 i=1 ip i otherwise.

(Prop. 6) With the ratio R we deduce S e f f . Storage devices have their operation time divided between reads and writes (they cannot read and write at the same time). The cluster must also avoid imbalances between the data read and written. If too much data is read compared with the data written, the amount of memory needed to store it before writing it will increase. On the contrary, if too little data is read, the system will slow since storage devices will have to wait for data to write. Thus, the ratio of data read on data written during any given duration should be equal to R. From this we deduce S e f f . S e f f = (N-x)S W rite S Read S Read +RS W rite (Prop. 7) From the speed at which data is effectively exchanged on the cluster during the stabilization (Prop. 7), the amount of data to write (Def. 2 and 3), the duration of the overlap of data-safekeeping and stabilization (Prop. 5), and the time to availability (Prop. 4), we deduce the stabilization time t stab . Duration (s) q q q q q q q q q q q q q q q Storage bottleneck Standard decommission Data safekeeping Fast decommission Fig. 2. Lower bounds for the duration of the data-safekeeping phase and fast decommission compared with the lower bound of the standard decommission, in case of a storage bottleneck. Each node initially hosts 50 GiB of data, and the reading and writing speed of the storage is set to 1.25 GiB/s.

t stab = D N -x R S Read + 1 S W rite x - N p r r + NDp r r(N -x)Sw (Prop. 8)
3) Observations: In the case of a storage bottleneck, the data-safekeeping phase and thus the effective decommission of the leaving nodes can be completed a lot faster than with the standard decommission. It is done, however, at the cost of a long stabilization phase: the leaving nodes were reading data in the case of a standard decommission, reading that must be done by remaining nodes in the case of a fast decommission. This situation implies that, contrary to the case of a network bottleneck, the longer the leaving nodes stay in the cluster, the faster the stabilization is. The stabilization cannot be faster than the standard decommission since the standard decommission is the extreme case in which the leaving nodes stay until the end of the stabilization.

During a fast decommission, the storage devices are fully saturated. Thus, servicing any request can only slow the decommission.

In Figure 2, we show the lower bounds for the duration of a standard decommission and of the data-safekeeping and stabilization phases of a fast decommission. Decommissioned nodes are available in a fraction of the time needed for a standard decommission. However, it comes at the cost of having the distributed storage system unable to operate for a longer time.

D. Core-hour usage

We study the core-hours usage of the strategies as it is linked to the financial and energetical cost of the active nodes during the operation. Comparing the consumption of corehours equates to comparing the cost of the strategies.

In Figure 3 we compare the usage of core-hours for the standard decommission and the fast decommission in the case of a network bottleneck. Since the numbers are based on the lower bounds for the duration of the operations, the figure represents the lower bound for the core-hour consumption. We observe that using the fast decommission mechanism Decommissioned nodes (out of a 20-nodes cluster)

Core hours q q q q q q q q q q q q q q q q q Core-hours

Fast decommission Standard decommission

Fig. 3. Lower bounds for the number of core-hours used during a fast decommission compared with the standard decommission in the case of a network bottleneck. Each node initially hosts 50 GiB of data, and the network bandwidth is set to 10 Gb/s to match the values observed on the hardware used for the experiments in Section V. Decommissioned nodes (out of a 20-nodes cluster)

Core hours q q q q q q q q q q q q q q q q Core-hours

Fast decommission Standard decommission

Fig. 4. Lower bounds for the number of core-hours used during a fast decommission compared with the standard decommission in the case of a storage bottleneck. Each node initially hosts 50 GiB of data, and the storage bandwidths are set to 207 MiB/s reading and 199 MiB/s writing to match the values observed on the hardware used for the experiments in Section V. always reduces the core-hours consumption when the network is the bottleneck. Moreover, the gain increases greatly with the number of decommissioned nodes, and more than 50% of the core-hours consumption can be saved when many nodes are decommissioned at once.

In Figure 4 we compare the core-hours needed for the decommission in the case of a storage bottleneck. When few (less than NS W rite /(S Read + S W rite ), less than 8 in this case) nodes are decommissioned at once, there are no benefits in using the fast decommission compared with the standard decommission. When many nodes are decommissioned simultaneously, however, the core-hours consumption can be reduced by more than 50%.

V. EXPERIMENTAL VALIDATION

In this section, we use Pufferbench to study the fast decommission mechanism in practice.

A. Implementing fast decommission in Pufferbench

Pufferbench [START_REF]Pufferbench[END_REF] is a modular benchmark designed to evaluate how fast one can rescale a distributed storage system on a given infrastructure. We implemented the fast decommission mechanism in Pufferbench. Pufferbench computes and recreates on the hardware all the I/O that are requiered for a rescaling operation. It emulates a distributed storage system for the duration of a rescaling operation.

The leaving nodes transfer to the remaining ones only the data that is exclusively on them with high priority. The remaining nodes have to recreate the missing replicas; however, the operation is done with a lower priority. The leaving nodes can leave the cluster only after the data is on the storage device; they cannot leave if the data is only buffered in memory.

We also made sure that the implementation of the fast decommission matches the assumptions presented in Section III in order to be able to safely compare the lower bounds and the practical results.

B. Experimental setup

All measurements were done on Grid'5000 [START_REF] Balouek | Adding Virtualization Capabilities to the Grid'5000 Testbed[END_REF], the French experimental testbed. Experiments were done on the grisou cluster in Nancy. The cluster is composed of 51 nodes: Dell PowerEdge R630 with Intel Xeon E5-2630 v3 Haswell 2.40 GHz (2 CPUs/node, 8 cores/CPU), 128 GiB of RAM, and two 558 GiB HDD. The nodes are all connected with a 10 Gb/s Ethernet network to a common Cisco Nexus 9508.

Pufferbench emulates a DSS that initially hosts 50 GiB per node. Ten measurements per configuration of Pufferbench were done. The results are represented by using box plots showing the minimum, the first quartile, the median, the third quartile, and the maximum duration of the phases.

In order to create a network bottleneck, the data was stored in memory because it has a bandwidth (6 GiB/s reading, 3 GiB/s writing) significantly higher than the network's bandwidth. Similarly, in order to generate a storage bottleneck, the data was stored on the drives of the nodes (207 MiB/s reading, 199 MiB/s writing).

For each configuration (bottleneck and number of decommissioned nodes), ten measures of decommission times were done for fast decommission and for standard decommission.

In this evaluation, we use a 20-node cluster as the initial size, to show that the lower-bounds match the behavior observed in practice. For other scales of cluster, the analytical results should be used to determine the relevance of the fastdecommission.

C. Decommission when the network is the bottleneck

Figure 5 shows the duration of the data-safekeeping and stabilization phases when the network is the bottleneck. The standard decommission has been added for comparison.

Compared with the lower bounds, the time to availability is on average 37% slower, while the stabilization time is 32% slower. For the same configurations, the standard decommission is, on average, 22% slower than its lower bound. Note that the lower bounds cannot be reached in practice: they assume an absence of latency and permanent maximum throughput from both the storage and the network.

When few nodes are decommissioned (less than 6), the difference in duration between the two strategies is negligible. Decommissioned nodes (out of a 20-nodes cluster) Core hours normalized q q q q q q q q q q q q q q q q Fast decommission -Network Fast decommission -Storage Standard decommission Fig. 6. Core-hours needed to do a fast decommission on a cluster of 20 nodes normalized by the standard decommission with the same bottleneck.

The standard deviation has been added.

When many nodes are decommissioned at once, however, there is a large difference between the standard decommission and the time to stabilization. For example, the fast decommission is 12% slower than the standard decommission when 14 nodes are decommissioned. The reason for this difference is the stress on the network induced by the fast decommission. Indeed, during the fast decommission, the remaining nodes have to send and receive data at the maximum bandwidth speed in order to stabilize the system quickly. During the standard decommission, however, the sending load is distributed not only on the remaining nodes but also on the leaving nodes, reducing the overall load on each node. This difference does not appear on the lower bounds because we assume that the network is ideal (Assumption 2). Figure 6 shows the number of core-hours consumed by decommission normalized by standard decommission. In most cases, using the fast decommission reduce the usage in the number of core-hours. The gain in core-hours increases with the number of decommissioned nodes. When most of the nodes are decommissioned at once, the fast decommission uses only 50% of the core-hours required by the standard decommission. The predictions about the core-hour usage (see Section IV-D) are confirmed by these results.

When the network is the bottleneck, using fast decommission is a relevant choice. Even if fault tolerance is temporarily reduced, the overall operation is slightly slower, but there is a substantial gain in core-hours saved. This gain increases with the number of decommissioned nodes.

D. Decommission when the storage is the bottleneck

The time to availability and the stabilization time obtained with Pufferbench in the case of a storage bottleneck are presented in Figure 7. The duration of the standard decommission has been added for reference.

On average, the time to availability is within 10% of its lower bound, while the stabilization time is within 9% of its lower bound. In comparison, the standard decommission is within 17% of its lower bound. From this, we deduce that the lower bounds are sound and can almost be reached in practice.

Figure 6 shows the number of core-hours needed for the whole operation normalized by the core-hours needed for a standard decommission. Using a fast decommission offers no benefit in core-hours when the number of decommissioned nodes is low. In this case, the core-hours needed to stabilize the system are canceling the benefits of releasing the decommissioned nodes earlier. When a large number of nodes are decommissioned at once, however, the gain in core-hours can reach 50%. These results are in line with the core-hours that lower bounds established in Section IV-D.

Depending on the scenario, using fast decommission when there is a storage bottleneck can be detrimental or risky. If most of the decommission concerns just a few nodes, the fast decommission is detrimental: the fault tolerance is affected, the whole operation is slower than a standard decommission, and there are no gains in core-hours usage. If many nodes are decommissioned at once, the gains in core-hours may be worth the longer operation and the risk taken. However, the whole operation takes a long time, during which the fault tolerance is not ensured; thus there is a greater risk of losing data due to a crash.

VI. DISCUSSION

In this section, we discuss some aspects of the lower bounds.

A. Simple assumptions for general conclusions

The assumptions used in this work are voluntarily few and simple to enable general conclusions, not specific to a particular platform or implementation. These assumptions were used in previous works [6] [8], where they helped closely model the behavior of HDFS.

B. Upper bound versus lower bound

Defining an upper bound for the operation is hardly possible since nothing prevents a node from waiting for an arbitrarily long time before sending any data to its destination. In contrast, a lower bound can be used in many useful ways, as explained in Section I.

C. Using the lower bound as a model

As shown in Section V, an efficient implementation of fast decommission exhibits a performance that varies with the number of decommissioned nodes in a way similar to the lower bounds. Thus, we can use the lower bound as a model for the fast decommission to predict the duration of the different phases. For instance, in the case of the network bottleneck, the lower bounds can be used as models with a coefficient of determination of 0.979 for the time to preservation and 0.995 for the time to stabilization.

Once fitted to the decommission mechanism of a distributed storage system, the model can be useful for resource managers to estimate the duration of a decommission and evaluate whether it is interesting to decommission nodes, when to do so, and which nodes to decommission.

D. Preserving k > 1 replicas

For the lower bounds presented in Section IV, the fault tolerance is simply ignored during the decommission: only one replica of each object is required. However, one may want to be able to tolerate 0 < k -1 < r faults during the decommission. In this case, at least k > 1 replicas of each object must be preserved on the remaining nodes before the leaving nodes are released.

Lower bounds for this situation can be defined. In the case of a network bottleneck (Prop. 9 and Fig. 8), the time to stabilization is the same as the standard decommission which is also the time to stabilization when maintaining only one replica. For the time to availability, we notice that receiving the data is always the bottleneck, indeed, due to the uniform data distribution (Assumption 6), each and every node hosts some objects that are also stored by leaving nodes, and they can replicate them among themselves.

t avail = k ∑ i=1 ip r-k+i ND r(N -x)S Net t stab = xD (N -x)S Net (Prop. 9)
In the case of a storage bottleneck (Prop. 10 and 11, and Figure 9) the time to availability is longer than when keeping Duration (s) q q q q q q q q q q q q q q q Network bottleneck Standard decommission Data safekeeping with k=1 Fast decommission with k=1 Data safekeeping with k=2 Fast decommission with k=2 Fig. 8. Lower bounds for the duration of the data-safekeeping phase and fast decommission compared with the lower bound for the standard decommission in case of a network bottleneck for k = 1 and k = 2. Each node initially hosts 50 GiB of data, and the network bandwidth is set to 1.25 GiB/s. only one replica. On the other hand, the time to stabilization is shorter. Indeed, since the leaving nodes stay for a longer time, their drives are used to read data during a longer duration, eventually reducing the reading load on the drives of the remaining nodes. Note, however, that reaching the lower bound of the stabilization time when k > 1 is hardly possible in practice since all the data transferred during the preservation would have to be kept in a buffer to reduce the reading overhead during the stabilization, which induces very large memory buffers.

Let R avail be the ratio of the amount of data to read on the data to write during the data-safekeeping phase.

VII. CONCLUSION

Efficient decommission is needed to leverage malleability in distributed storage systems. In this work, we study fast decommission, a decommission mechanism that makes the released nodes available to the resource manager as soon as possible by relaxing the fault tolerance. We provide lower bounds for the various steps required for this decommission, Duration (s) q q q q q q q q q q q q q q q Storage bottleneck Standard decommission Data safekeeping with k=1 Fast decommission with k=1 Data safekeeping with k=2 Fast decommission with k=2 Fig. 9. Lower bounds for the duration of the data-safekeeping phase and fast decommission compared with the lower bound for the standard decommission in case of a storage bottleneck for k = 1 and k = 2. Each node initially hosts 50 GiB of data, and the reading and writing speed of the storage is set to 1.25 GiB/s. and we validate them using a prototype implemented in Pufferbench.

We demonstrate that fast decommission allows to return the decommissioned nodes to the resource manager in a fraction of the time required by standard decommission. We show that in case of a network bottleneck, the duration of the whole operation is only slightly longer than for a standard decommission, while the core-hour usage is significantly reduced. In this situation, the choice of using fast decommission is relevant and can be considered by distributed storage system designers, or even decided at run time.

In the case of a storage bottleneck, however, using a fast decommission to release few nodes is detrimental to resource usage. The whole operation lasts longer than standard decommission; and although the released nodes are returned faster to the resource manager, there is no overall gain in core-hours.

In both cases, the more nodes are decommissioned at once, the higher the gain in core-hours. However, this also leads to higher risks since the fault tolerance is weakened for a longer period of time.

Using these lower bounds in order to design a resource manager fully aware of distributed storage system malleability is a challenge left for future work.

  out of a 20-nodes cluster)

Fig. 5 .

 5 Fig. 5. Duration of the data-safekeeping phase, fast decommission and normal decommission obtained with Pufferbench and compared with the lower bounds, in the case of a network bottleneck. Each node initially hosted 50 GiB of data.

Fig. 7 .

 7 Fig. 7. Duration of the data-safekeeping phase, fast decommission and normal decommission obtained with Pufferbench and compared with the lower bounds, in the case of a storage bottleneck. Each node initially hosted 50 GiB of data.

  out of a 20-nodes cluster)

S

  Read +R avail S W rite S W rite S Read if x < R avail (N-x)S W rite S Read ∑ k i=1 ip r-k+i ND r(N-x)S W rite in other cases. (Prop. 10) Let R stab be the ratio of the amount of data to read on the amount of data to write during the stabilization phase. W rite + S Read (Nx)S Read S W rite (Prop. 11)

  out of a 20-nodes cluster)

https://hal.archives-ouvertes.fr/hal-01943964

ACKNOWLEDGMENT

The work presented in this paper is the result of a collaboration between the KerData project team at Inria and Argonne National Laboratory, in the framework of the Data@Exascale Associate team, within the Joint Laboratory for Extreme-Scale Computing (JLESC, https://jlesc.github.io).

Experiments presented in this paper were carried out on the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER, and several universities as well as other organizations (see https://www. grid5000.fr).

This material is based upon work supported by the U.S. Department of Energy, Office of Science under contract DE-AC02-06CH11357.


Angélique Fabre

Pauline Faure

Anne Bréhant

Yvan Wyart

Philippe Moulin

How to test membrane robustness regarding particles

Keywords: membrane, abrasion, particles, hollow fibers

Short Introduction

Thanks to the improvement in membrane resistance, low pressure membranes are now used for the treatment of complex water such as surface water, sea water and produced water in oil & gas applications, with or without pre-treatment. Consequently, the polymeric micro and ultrafiltration membranes are exposed to effluents increasingly concentrated in particles leading to abrasion and/or erosion risks. While membrane chemical and mechanical ageing have been widely studied, only few studies deal with abrasion of hollow fiber membranes [START_REF] Lai | Enhanced abrasion resistant PVDF/nanoclay hollow fibre composite membranes for water treatment[END_REF]. The objective of this study is to define a methodology to classify hollow fibre membranes according to their abrasion resistance.

Material and Methods

Brown corundum (Al2O3, =4 g.cm -3 at 20°C, hardness=9 Mohs) and glass powder (SiO2, =2,5 g.cm -3 at 20°C, hardness=6 Mohs) supplied by Mineralex were used in this study to mimic particles in complex effluents. These particles have been selected for their high abrasive potential (angular shaped particles). They are mainly used as an anti-slip agent in paints. Commercial PVDF membranes were tested corresponding to two homogenous ultrafiltration membranes (i.e. asymmetric membranes), one composite ultrafiltration membrane and one homogeneous microfiltration membrane (i.e. symmetric membrane).

Two accelerated testing methods were compared to evaluate the abrasion resistance of the membranes with synthetic effluents made of particles and water. The first one was based on a biphasic system including water and particles movement along the fibers simulating abrasion [START_REF] Barkoula | Processes and influencing parameters of the solid particle erosion of polymers and their composite[END_REF]. In the second one, the abrasion occurred in a three-phase system with particles, water and air, simulating membrane cleanings. To setup the abrasion test, a tailor-made lab-scale module is filled with the abrasive solution (50 g.L -1 ). Air is continuously introduced at the bottom of the lab-scale module using air compressor. Air bubbles induce turbulence within the lab-scale module and allow the particles to shake in water.

Results and Discussion

Among parameters used to monitor membrane abrasion, Log Removal Value (LRV) appears to be the most promising as presented Figure 1. Moreover, no clear correlation was established between the abrasion rate of the membrane and the other monitored parameters (i.e. permeability, air integrity test, mechanical properties and bubble point), which is consistent with Ji et al (2015) conclusions. Scanning Electron Microscopy (SEM) images show advanced damages on the membrane external layer after 15 days of abrasion using three-phase system. Indeed, the whole surface is modified, with the formation of many folds (Figure 2). Similar modification on the external layer is observed on membranes from industrial site after xxx months (Figure 3).

Conclusions

Different experimental conditions have been tested in order to simulate abrasion with highly variable response time. Three-phase tests (water, air and particles), allowed (i) to obtain a rapid abrasion similar to the membrane behaviours observed on industrial sites and (ii) to classify hollow fibre membranes according to their abrasion resistance.

Figure 1 .

 1 Figure 1. LRV measurement during abrasion test in tree-phase system (UF1 homogeneous ultrafiltration membrane -UF2 composite ultrafiltration membrane)

Figure 2 .Figure 3 .

 23 Figure 2. SEM image of the external layer of the tested membrane after accelerated ageing (X 5000)


Xueru Yan
email: xueru.yan@centrale-marseille.fralexandre.favard@im2np.frjean-luc.seguin@im2np.fr

Alexandre Favard

Jean-Luc Seguin

Marc Bendahan
email: marc.bendahan@im2np.frstephane.anguille@univ-amu.frphilippe.moulin@univ-amu.fr

Stéphane Anguille

Philippe Moulin

CNRIUT'2018, Aix-en-Provence Removal of Humidity by Ionic Liquid Filter to Protect Gas Sensors

Keywords:

Air quality has become a hot issue of common concern, particularly BTEX gases (Benzene, Toluene, Ethylbenzene and Xylene). They often cause serious environmental problems and have negative effect on human health even at very low concentration. Gas sensors have been in use for monitoring flammable as well as toxic gases. Since a few years, metal oxide gas sensor are also used to the control and monitor the air quality The detection of gaseous pollutants under real conditions requires working in a humid environment. It is well known that the humidity reduces the performances of gas sensors, particularly in terms of sensitivity. In this work, we demonstrated the possibility to reduce the humidity impact on a metal oxide sensor by using ionic liquid-based filter, without modification of the sensor sensitivity.

Mots-Clésionic liquid based filter

Introduction

With the development of society and the advancement of technology, human demand on the environment has gradually gone beyond the bearing capacity of the environment. Air quality has become a hot issue of common concern, particularly BTEX gases (Benzene, Toluene, Ethylbenzene and Xylene). They often cause serious environmental problems and have negative effect on human health even at very low concentration. Since a few years, mental oxide gas sensors are used to control and monitor the air quality. It is well known the performance of metal oxide gas sensors is influenced by relative humidity [1]. In this work, we demonstrated the possibility to reduce the humidity impact on a metal oxide sensor using an ionic liquid-based filter, without modification of the sensor sensitivity.

Materials and Methods

Ionic liquid based filter

Ionic liquids (ILs) are salts which form liquid phase at room temperature. More importantly, these are endowed with excellent solvating ability and can be recycled [START_REF] Ibrahim | The role of ionic liquids in desulfurization of fuels: A review[END_REF]. In this study, the 1-butyl-3-methylimidazolium bromide ([Bmim + ][Br -]) [START_REF] Alviz | Bmim][Br]) versus a volative organic solvent in the production of acetylsalicylic acid[END_REF] is selected as absorbent due to its high viscosity hygroscopic properties and availability. The innovation of this filter is to capture the water vapor from a mix of gases without impacting the target gases [START_REF] Anguille | Device for extraction of pollutants by multichannel tubular membrane[END_REF].

Gas Sensor

The gas sensor is composed of a sensitive layer of tungsten trioxide (WO3) thin film (50 nm) deposited by reactive RF magnetron sputtering on a transducer developed in IM2NP laboratory [START_REF] Favard | Highly Sensitive WO3 Thin Films Integrated on Microsensor Platforms for ppb BTEX Detection in a Gas Mixture with High Rate of Humidity[END_REF].

The WO3 is a n-type metal oxide with a large gap and oxygen vacancies and its conductivity depends on the composition of the surrounding gas atmosphere.

The sensor response under BTEX vapors was calculated using the relation (1):

Sensor response (%) = ( R air -R gas R gas ) × 100 (1)
With Rair, the sensor's resistance under air and Rgas, the sensor's resistance under BTEX vapors.

Test bench for electrical characterization under target gases and humidity

We used a fully automated test bench specially designed for the BTEX gases detection in the pressure of different humidity levels. It is composed of a gas dilution and humidification system that generates an output mixture at very low concentrations (1 to 500 ppb) with a variable humidity (0 to 90%), an integrated test cell and an acquisition system to characterize the electrical responses of the sensor.

Results and Discussion

Influence of humidity on sensor response

To study the humidity influence on the sensor response, the gas sensor was exposed at 500 ppb of BTEX under dry air and wet air (relative humidity 50%). With the figure 1 shown, under dry air, the sensor response is 24%. When the introduction of 50% relative humidity in mixture gas, the sensor response is reduced to 12%. As some studies reported that humidity leads to a decrease of the sensor response [START_REF] Qi | Electrical response of Sm2O3-doped SnO2 to C2H2 and effect of humidity interference[END_REF][START_REF] Ionescu | Role of water vapour in the interaction of SnO2 gas sensors with CO and CH4[END_REF].

Influence of filters to sensor under dry air

It is important that the addition of a filter upstream of the sensor does not disturb its response. We therefore studied the sensor response under identical conditions (HR 0%, BTEX: 500ppb) initially without filter and then with filter.

We first designed a filter composed by a mixture of active carbon (AC) and ionic liquid (IL), to capture humidity. However, when we placed this filter upstream the sensor, sensor response decreased to 12% (fig. 2a) compared to sensor response without filter (fig. 2b).

It means that this filter has an influence on gas sensor response. In order to understand this problem, we renewed the experiment with a first filter based only on pure activated carbon and then with a second filter based only on ionic liquid. With the AC filter, the sensor response decreased to 12% (fig. 2c), which is the same as the response of mixed IL-AC filter.

However, when only the IL filter was placed upstream the sensor, the response to 500 ppb of BTEX remains 25% (fig. 2d). Based on this experiment we can conclude that the IL filter does not capture BTEX gases. For the next experiments we choose this filter configuration (IL filter only).

It is well known that AC can remove VOCs from a gas mixture, as already reported in the literature [START_REF] Zhang | Adsorption of VOCs onto engineered carbon materials: A review[END_REF][START_REF] Long | VOCs reduction and inhibition mechanisms of using active carbon filler in bituminous materials[END_REF]. Because of the overwhelming physical adsorption mechanisms, active carbon with large surface area and rich pore structure shows high adsorption capacity to VOCs. Furthermore, the presence of chemical functional groups on AC surfaces makes it a good adsorbent for VOCs through chemical adsorption mechanisms. When the [Bmim + ][Br -] was exposed on wet air, it was found that it caught water vapors because the anions of IL ([Br -]) and water molecules form H-bonding [START_REF] Grishina | Water effect on physicochemical properties of 1butyl-3-methylimidazolium based ionic liquids with inorganic anions[END_REF].

Figure 2 -Compared response to500 ppb of BETX with different filters under dry air (0 % HR, 100 sccm)

Influence of ionic liquid filter on sensor response under wet air

To reduce the humidity influence on the sensor response, according to last experiment, we placed an IL filter upstream the sensor, and the sensor was exposed to 500 ppb of BTEX with 50% relative humidity.

In figure 3, we can observe that with or without IL filter, the sensor response remains 25%. Hence, with this filter, the gas sensor keep its performances, even under 50% of relative humidity.

Conclusions

In this work, we proposed a new solution to protect gas sensors from humidity. We confirmed that with an IL filter, which is comprised by [Bmim + ][Br -], on upstream can reduce the influence on gas sensor. We also demonstrated that the ionic liquid filter does not disturb BTEX detection.

Acknowledgment

The author wish to thank Mr. Alain COMBES for technical support throughout this work.

Figure 1 -

 1 Figure 1 -Sensors response to 500 ppb of BETX, a) in dry air (0 %), b) in wet air (50 % RH)

Figure 3 -

 3 Figure 3 -Sensors response under 500 ppb of BTEX, a) in dry air (0 % RH), b) wet air (50 % RH) with ionic liquid filter


M Le Hir

Y Wyart

G Georges

L Siozade

P Sauvade

P Moulin

Effect of salinity and nanoparticle polydispersity on retention and UF membrane fouling

Keywords: Ultrafiltration, nanoparticles, fouling, fluorescence, polydispersity, salinity

Introduction

Nanoparticles (NPs) retention by ultrafiltration (UF) membrane presents a great interest since few decades due to the emergence of this new pollution in natural waters used to produce drinking water. Some studies already showed that specific NPs are efficiently retained by UF [START_REF] Troester | Vulnerability of Drinking Water Supplies to Engineered Nanoparticles[END_REF]). However, this retention is strongly dependent of water composition and chemistry. Presence of salts can affect the stability of NPs suspensions and lead to aggregation or repulsion phenomenon which can induce a modification of NP retention. In this study, the NP retention have been studied with the filtration of NP suspension with size close to or smaller than membrane pore size. The influence of salinity and polydispersity of the feed suspensions on NP retention was analyzed and compared to simplified NP suspension. To deepen this work, the location of membrane fouling after filtration experiment was determined by Confocal Laser Scanning Microscopy (CLSM). This location of the fluorescent signal allowed to identify the membrane retention zone(s), to estimate fouling mechanisms operating during the filtration and to determine the penetration profile. Moreover, it is possible to distinguish the fouling contribution of each size of NP with or without salt. Microscopic observations were compared to fouling mechanisms found with application of Hermia models on flux data observed during the experiment.

Material and Methods

Ideal suspensions of fluorescent calibrated NPs with size of 10 (NP-10) and 1.5 nm (NP-1.5) were filtered until a volume concentration factor of 200. Filtrations were realized with NP-10 and NP-1.5 filtered individually, simultaneously and with the adding of 50 mmol.L -1 of NaCl. Ultrafiltration membranes used are PES multichannel hollow fiber (ALTEON I, Aquasource, SUEZ, France) with nominal pore size of 20 nm. The efficiency of the membrane and the quantity of NP remained blocked in and/or on the membrane were estimated by NPs retention measurements. Location of the fluorescence signal emitted by NPs in and/or on the membrane was visualized by CLSM and penetration profiles of NPs have been determined. The use of NPs with different fluorescent emission wavelengths allowed to distinguish each size of NPs in membrane material. Location of fluorescent signal of NPs have been correlated with macroscopic permeate flux data to validate fouling mechanism(s) operated during the filtration experiment. Finally, influence of transmembrane pressure (TMP) was studied on retention rate and fouling establishment and location.

Results and Discussion

Retention rate of NP-10 filtered individually is not modified with the adding of NP-1.5, salinity or both whatever the TMP applied. This retention rate stays superior or equal to 99% while the NPs size is twice smaller than nominal pore size. However, the adding of NP-1.5 or/and salinity changes the location of NP-10, leading to a strongly increase of NP-10 quantity remained blocked at the membrane like the increase of TMP. Retention rate of NP-1.5 has been modified by each factor added (salinity and/or NP). Increase of TMP, adding of NP-10 and adding of salinity lead to a decrease of NP-1.5 retention. Repulsion charge effects facilitate the passage of NP-1.5 through the membrane. Retention rate estimated by mass balance shows an increase of retention rate of each NPs size versus the time. The establishment of membrane fouling which lead to a better selectivity can explain this higher retention. Visualization of fouled membrane by CLSM and measurement of penetration profile show that the retention of NP-10 is operated mainly on membrane surface and in the membrane skin while NP-1.5 fluorescent signal is detected on membrane surface into the skin and the support. Concerning NP-1.5, the adding of NP-10 and NaCl leads to a cake formation including NP-1.5 on membrane material. The participation of NP-1.5 to the deposit on membrane surface is increased with presence of NP-10 and salinity due to the retention of NP-1.5 by the cake formed by NP-10.

Conclusions

The adding of salinity (50 mmol.L -1 ) or/and NP-1.5 not strongly involve the NP-10 retention by UF membrane but the quantity remained blocked on membrane surface was increased importantly: the fouling model, cake filtration, appears earlier. At the opposite, retentions of NP-1.5 were decreased by adding of NP-10, salinity and TMP increase. The part of NP-1.5 which stays blocked in the membrane is decreased and the recovery in the permeate is increased when salinity or/and NP-10 are added in feed suspensions. It is the first time that the fouling contribution of each size of NP is detailed on the penetration profile of NP.

Figure 1 .

 1 Figure 1. MCBL imagery of membrane fouled by NP-10 (in green) and NP-1,5 (in red) and penetration profiles obtained after a filtration of monodisperse (a, b) and polydisperse suspension (c, d)

Acknowledgment

"The project leading to this publication has received funding from Excellence Initiative of Aix-Marseille University -A*MIDEX, a French "Investissements d'Avenir" programme. It has been carried out in the framework of the Labex MEC."


Alexandre Favard

Xueru Yan

Stéphane Anguille

Philippe Moulin

Jean-Luc Seguin

Khalifa Aguir

Marc Bendahan

Humidity Impact Reduction on WO 3 Gas Microsensor Response Using New Filters Based on Ionic Liquid 1

Keywords: Gas sensor, humidity removal, tungsten trioxide, ionic liquids based filters

The detection of gaseous pollutants such as Benzene, Toluene, Ethylbenzene and Xylene (BTEX) under real conditions requires working in a humid environment. It is well known that the humidity reduces the performances of gas sensors, particularly in terms of sensitivity. Ionic liquids (ILs) are a new class of solvent with attractive properties for the extraction of volatile organic compounds (VOCs) or gases. Moreover, the ILs are particularly interesting because of their recycling ability and without atmospheric pollution. Using a filter based on a mixed ionic liquid ([Bmim][Br]-[Bmim][PF6]), low concentrations of BTEX are detected with a tungsten trioxide (WO3) thin film (50 nm) deposited by reactive RF magnetron sputtering on a transducer developed in our laboratory. With this filter upstream, we are able to detect 500 ppb of BTEX gases without a sensor sensitivity decrease with 50 % relative humidity.

Introduction

Air quality monitoring has become an important health and societal issue, since the European directive 2008/50/EC, particularly. Several toxicological and epidemiological studies have shown that air pollution causes respiratory problems, such as asthma, cardiovascular disease and cancer. Air pollution is estimated to be responsible for several hundred thousand premature deaths per year [START_REF] Jantunen | Promoting actions for healthy indoor air (IAIAQ)[END_REF][START_REF] Seigneur | Effets sanitaires de la pollution atmosphérique[END_REF] Benzene, Toluene, Ethylbenzene and Xylenes (BTEX) are among the most harmful polluting gases due to their effect on health even at very low concentrations. BTEX is a useful suite to measure. In typical urban environments, there are a lot of VOCs present. Typical GC-MS analysis can resolve many BTEX, but (i) some of these may be below detection limits of the analysis and (ii) the measurement is not obtained immediately. So metal oxide gas sensor can detect online very small BTEX concentrations. Moreover, the detection of gaseous pollutants under real conditions requires working in a humid environment. The humidity often limits the performances of metal oxide gas sensors, particularly in terms of sensitivity. All metal oxide gas sensors are subject to interfering effects derived from water molecules that are still present in most of cases due to ambient humidity. Almost all types of metal oxides have a strong tendency to adsorb moisture to their surfaces, which leads to the degradation of the detection performance. [START_REF] Wang | Metal Oxide Gas Sensors: Sensitivity and Influencing Factors[END_REF].

Ionic liquids (ILs) have emerged as a promising green option to replace traditional organic solvents for gas separation in recent years because of their desirable properties like low vapor pressure [START_REF] Kim | Application of ionic liquids for metal dissolution and extraction[END_REF], thermal stability [START_REF] Rykowska | Modern approaches in dispersive liquid-liquid microextraction (DLLME) based on ionic liquids: A review[END_REF], and tunable structures, which endow ILs the advantages of low energy consumption, less loss of absorbents and so on [START_REF] Lei | Gas Solubility in Ionic Liquids[END_REF][START_REF] Zhang | Carbon capture with ionic liquids: overview and progress[END_REF][START_REF] Mohshim | A study on carbon dioxide removal by blending the ionic liquid in membrane synthesis[END_REF]. They have proven to be effective polar separation solvents with high selectivity and stability [START_REF] Berthod | Recent advances on ionic liquid uses in separation techniques[END_REF][START_REF] Hallett | Room-Temperature Ionic Liquids[END_REF]. Thus, these compounds have been successfully applied for heavy metal removal [START_REF] Platzer | Task-specific thioglycolate ionic liquids for heavy metal extraction: Synthesis, extraction efficacies and recycling properties[END_REF][START_REF] Leyma | Novel thiosalicylate-based ionic liquids for heavy metal extractions[END_REF], carbon oxide recovery [START_REF] Zhu | Effect of immobilization methods and the pore structure on CO2 separation performance in silica-supported ionic liquids[END_REF][START_REF] Wang | Benzyl substituted imidazolium ionic liquids as efficient solvent-free catalysts for the cycloaddition of CO2 with epoxides: Experimental and Theoretic study[END_REF], humidity removal [START_REF] Fan | In situ monitoring the moisture absorption of three ionic liquids with different halogen anions by ATR-FTIR spectroscopy[END_REF], and coupled with membrane processes [START_REF] Mohammadi | Molecular-level understanding of supported ionic liquid membranes for gas separation[END_REF][START_REF] Malankowska | Microfluidic devices as gas -Ionic liquid membrane contactors for CO2 removal from anaesthesia gases[END_REF]. One of the most important characteristic of ILs is that they can be tailor-made designed for specific applications [START_REF] Marullo | Ionic liquids gels: Soft materials for environmental remediation[END_REF][START_REF] Wu | Turning the solubility and lubricity of ionic liquids by absorbing CO2[END_REF]. IL's properties can be adjusted via chemical alteration of the cation and/or anion to produce application specific compounds [START_REF] Wang | Structural Effects of Anions and Cations on the Aggregation Behavior of Ionic Liquids in Aqueous Solutions[END_REF]. This result in a large variety of ILs that can be adapted to given process. The mainly cations used in the synthesis of ILs are ammonium, pyridinium, phosphonium and imidazolium. The structures of those cations are shown Figure 1. These cations can be functionalized with alkyl chains (Rn) and they can be associate to common anions such as halides, BF4 -, PF6 -, and NTf2 -.

Fig. 1. The mainly cations of ILs

The task specific ionic liquids (TSILs) [START_REF] Turgis | Ketone functionalized task specific ionic liquids for selective tantalum extraction[END_REF] have been developed to enhance the selective solubility or/and recyclability [START_REF] Raja Shahrom | CO2 capture by task specific ionic liquids (TSILs) and polymerized ionic liquids (PILs and AAPILs)[END_REF]. The imidazolium based ILs can reduce air pollution, also they can increase the recycling and reuse potential [START_REF] Zheng | A review of imidazolium ionic liquids research and development towards working pair of absorption cycle[END_REF]. More importantly, imidazolium based ILs show the high absorption capacity of water [START_REF] Francesco | Water sorption by anhydrous ionic liquids[END_REF][START_REF] Ziobrowski | The influence of water content in imidazolium based ILs on carbon dioxide removal efficiency[END_REF]. 1-butyl-3methylimidazolium bromide ([Bmim][Br]) was selected as the ionic liquid in this study because it is stable and recyclable [START_REF] Alviz | Comparative life cycle assessment of the use of an ionic liquid ([Bmim]Br) versus a volatile organic solvent in the production of acetylsalicylic acid[END_REF].

The focus of this work is on the humidity removal which is one of the most important parameter affecting the sensor response. We developed an ILs based humidity removal filter reduces the relative humidity. Moreover, we demonstrated the possibility to detect low BTEX concentration (500 ppb) with 50 % relative humidity, without loss of the sensor sensitivity, using a WO3 gas sensor with this filter upstream [START_REF] Favard | Humidity Impact Reduction on WO3 Gas Microsensor Response Using New Filters Based on Ionic Liquid[END_REF]. It is necessary to demonstrate that the ionic liquid only captures the humidity from air and does not affect the sensor response.

In section Ⅱ, we describe the WO3 microsensor, the test bench and the IL filter. In section Ⅲ, we present and comment experimental results showing the effect of the IL filter on the detection of low BTEX a concentration under 50 % relative humidity.

Material and methods

Microsensor design and sensitive layer

Our device is composed of an integrated Pt heater and Pt electrodes for the measurements, deposited by sputtering on a Si/SiO2 substrate (Figure 2) [START_REF] Aguir | Heated sensitive layer gas sensor[END_REF]. The sensitive layer of tungsten trioxide (50 nm) was deposited on the top of these electrodes, by reactive RF magnetron sputtering. After the sensitive layer's deposition on the Pt electrodes, the films were annealed at 743 K for 90 minutes in air to stabilize the chemical composition and the crystalline structure.

WO3 is n-type semiconductors with a large gap and oxygen vacancies. The main charge carriers are free electrons. The conductivity of this oxide depends on the composition of the surrounding gas atmosphere [START_REF] Guérin | Modeling of the conduction in a WO3 thin film as ozone sensor[END_REF]. The physical phenomena for the gas detection are surface reactions between the oxygen ions adsorbed on the surface and the target gas. When the sensitive layers are under air, oxygen molecules come to be chemisorbed on the surface in ion form (𝑂 2 -, 𝑂 -𝑜𝑟 𝑂2-) [START_REF] Li | Synthesis and gas sensing properties of hierarchical meso-macroporous SnO2 for detection of indoor air pollutants[END_REF] and electrons will be trapped on the surface of the sensitive layer. Thus, depletion layer will be created and the resistance of the sensitive layer will be changed. After the BTEX exposition, the oxydo/reduction reaction will take place on the surface of the sensitive layers as follows eq (1) [START_REF] Wang | Highly selective n-butanol gas sensor based on mesoporous SnO2 prepared with hydrothermal treatment[END_REF]:

𝑉𝑂𝐶𝑠 + 𝑂 -→ 𝑉𝑂𝐶𝑠 -𝑂 + 𝑒 -, (1)
The equation [START_REF] Jantunen | Promoting actions for healthy indoor air (IAIAQ)[END_REF] shows that an electron is released in the surface of the grain during the reaction, which will lead to the increase of the charge carrier density. This will induce the change of the depletion layer and so the decrease of the sensitive layer resistance allowing the detection of the target gas.

During all our studies, we used an operating temperature of 513 K. The electrical measurements were made under a constant gas flow of 100 sccm.

Test bench for electrical characterization under BTEX gases and humidity

We used a test bench specially designed for the BTEX detection in the presence of different humidity levels (Figure 3). It is composed of a gas dilution and humidification system that generates an output mixture at very low concentrations (1 to 500 ppb) with a variable humidity (0 to 90 %), an integrated test cell and an acquisition system to characterize the electrical responses of the gas micro sensor.

Humidity is generated starting from pressurized liquid water, which is vaporized through a microporous membrane. The water vapor is injected into the dry gas mixture by means of a proportional valve. This valve, controlled by a humidity sensor placed at the humidifier outlet, makes it possible to keep the hygrometry of the mixture constant. The vapor pressure is kept sufficient by heating and regulating the temperature of the vaporization cell. A second humidity sensor is placed at the output of the filter.

Ionic liquid based filter

To limit the effect of the humidity on the performances of metal oxide gas sensors, it is necessary to develop an ionic liquids based filter that only adsorb the water molecules from atmosphere without capturing the target gas molecules [START_REF] Anguille | Device for extraction of pollutants by multichannel tubular membrane[END_REF]. In this work, we consist of immobilizing ILs into support filter, which allows the amount of active phase needed for a given process to be minimized and greatly facilitates the recovery and reusability of ILs.

The In order to eliminate the influence of a solid matrix or any other forms of support, the filter is made by bubbling the gas mixture into a glass container filled with mixed ILs (Figure 5).

When the [Bmim][Br] was exposed on the wet air, it was found that it caught water vapors because the anions of IL (Br -) and water molecules form Hbonding [START_REF] Grishina | Water effect on physicochemical properties of 1-butyl-3-methylimidazolium based ionic liquids with inorganic anions[END_REF].

Results and discussion

Influence of wet air on sensor response to BETX

Almost all metal oxides have a greater or lesser sensitivity to moisture, which often leads to the degradation of their detection performance. This influence of moisture is well known and is widely described in the literature [START_REF] Korotcenkov | Effect of air humidity on gas response of SnO2 thin film ozone sensors[END_REF].

To study the humidity influence on the sensor responses, the sensors were exposed to 500 ppb of BTEX for 4 minutes (Figure 6) under dry air and wet air (50 % RH). The sensor response was calculated using the relation (2):

𝑆𝑒𝑛𝑠𝑜𝑟 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒 (%) = ( 𝑅 𝑎𝑖𝑟 -𝑅 𝑔𝑎𝑠 𝑅 𝑔𝑎𝑠 ) * 100 (2)
where Rair, the sensor resistance in air before gas exposure and Rgas, the sensor resistance in presence of BTEX. Under dry air, the sensor response to 500 ppb of BTEX is 24 %. The introduction of 50 % of relative humidity in the atmosphere reduces the sensor response to 12 %. The sensor response is divided by 2 under 50 % of relative humidity. This behavior is well known and has been already reported in the literature. Several hypotheses have been made about this behavior [START_REF] Favard | Highly Sensitive WO3 Thin Films Integrated on Microsensor Platforms for ppb BTEX Detection in a Gas Mixture with High Rate of Humidity[END_REF]. Firstly, the adsorption of the water molecules leads to a decrease in the chemical adsorption of the oxygen species on the surface of the metal oxide [START_REF] Qi | Electrical response of Sm2O3-doped SnO2 to C2H2 and effect of humidity interference[END_REF]. Secondly, The BTEX molecules compete with the water molecules to react on the same adsorption sites [START_REF] Ionescu | Role of water vapour in the interaction of SnO2 gas sensors with CO and CH4[END_REF]. In both cases, the introduction of relative humidity in the atmosphere leads to a decrease of the sensor response.

The result showed that the sensor is vulnerable to the errors occur due to the presence of humidity. Thus, a humidity removal device is developed in order to minimize the effect of humidity.

Influence of ionic liquid based filter

In the previous section, it was shown that the effect of humidity of the sensor response. Therefore, a humidity removal filter is used here to practically eliminate the humidity influence on the sensor response. We placed the IL filter upstream to the sensor and we exposed the sensor to 500 ppb of BTEX for 4 minutes (Figure 7) under wet air (50 % RH). As the gas goes through the IL filter, the humidity of the gas is lowered, and hence the humidity effect on the response is eliminated. In figure 7, we can observe that when the IL filter is placed upstream the sensor, the response to 500 ppb of BTEX remains 25 %. Hence, with this filter, we keep the BTEX detection performance of our sensor, even in presence of 50 % of relative humidity in the atmosphere.

The main objective of this work being to validate the filter ability to trap water vapor, we first chose to work with a 50 % humidity rate, which is considered as a good reference for gas sensors tests. Now that we have shown the performance of this type of filter, it will be interesting to study, in future experiments, its behavior at higher humidity levels. This will help to understand at which humidity the sensor response significantly declines. Furthermore, figure 7 shows that the response of the BTEX sensor with the filter inserted into the gas flow is slightly higher and has a larger slope compared to the response under dry air. This behavior is reproducible. It may be related to the high viscosity of the ionic liquid, which causes pressure and flow rate variations at the gas sensor. Further experiments are needed to validate this hypothesis.

Conclusion

In this work, we proposed a new solution to protect metal oxide gas sensors from the influence of humidity. We show the possibility to detect low BTEX concentrations under wet air with a WO3 gas sensor associated with a filter based on an ionic liquid. A low-cost and reliable humidity removal filter is used to eliminate the effect of humidity on a metal oxide gas sensor. The developed filter reduced the level of humidity and stabilized it using environmental friendly compounds. We also demonstrated that the ionic liquid based filter didn't disturb the BTEX detection. These results show that the proposed humidity removal significantly enhances the performance of the sensor.

Further work is in progress to study the behavior of the IL filter under high level of humidity and to insert the IL into a solid matrix to make a more convenient filter.

Fig. 2 .

 2 Fig. 2. Design of the sensor device[START_REF]Ionic Liquid in Synthesis[END_REF]

Fig. 3 .

 3 Fig. 3. Diagram of the test bench.

  [Bmim][Br] (The structure shows in Figure 4a) has been selected because it is one of the most hygroscopic IL. However, [Bmim][Br] is not a Room Temperature Ionic Liquid (RTIL) and the high melting point of [Bmim][Br] (80 ℃) not allow to use it in a bubbling process. In this case, we mixed 10 % of [Bmim][PF6] (The structure shows in Figure 4b) into [Bmim][Br] to ensure the mixed ILs can be use in bubbling process. The [Bmim][PF6] is one of RTIL with hydrophobicity. In addition, it is not interfering with the process of [Bmim][Br] absorption humidity.

Fig. 4 .

 4 Fig. 4. The structures of (a) [Bmim][Br] and (b) [Bmim] [PF6].

Fig. 5 .

 5 Fig. 5. The ionic liquids based filter and bubbling process.

Fig. 6 .

 6 Fig. 6. Sensors response under 500 ppb of BTEX, a) Dry air (0 %), b) wet air (50 %) without IL filter.

Fig. 7 .

 7 Fig. 7. Sensor response under 500 ppb of BTEX, a) Dry air (0 %), b) wet air (50 %) with IL filter.

mm

Acknowledgements

The authors would like to acknowledge Mr. A. Combes for his technical support throughout this work.


Rostand Moutou Pitti

Valérie Nsouami

Nicaise Manfoumbi

Emilio Bastidas-Arteaga

A database for spatial variability assessment of mechanical parameters of Ozigo specie under long-term bending tests in Gabon

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Since 1950, almost all Gabonese forest production is exported. Before 2010, the law required that 75% of the logs were treated locally but the analyzes show that only 25% to 35% were treated before this date. It is why the Gabonese government, in the perspective of an emerging Gabon, decided to ban log exports since January 2010, paving the way for local processing and extension of tropical species in local and sub-regional buildings. However, the mechanical behavior of tropical woods in their environment is a real challenge for the prediction of the behavior of tropical timber structures subject to thermo-hygro mechanical loads [START_REF] Nziengui | Impact of cyclic compressive loading and moisture content on the mechanical behavior Aucoumea Klaineana Pierre[END_REF].

Several studies found that the consideration of the spatial variability is crucial to improve the reliability. The specimens were extracted from three wooden beams Dacryodes buettneri (89mm×176 mm×3090 mm) previously subjected to a creep test in tropical atmospheres and three previously mentioned environments during 9 years (Figures 2a to 2c). These beams were sawn into nine sections 340 mm long. These sections are subdivided into three portions in height referred here as upper, middle and lower, (Figures 2d and2e). Among these specimens, only three (right side) were tested by section (81 test pieces of section 15mm×15mm and 300mm long). A database for spatial variability assessment of mechanical parameters of Ozigo specie under long-term bending tests in Gabon ABSTRACT: This paper focuses on the mechanical behavior of Ozigo wood beams subjected to long-term creep in three environments (air-conditioned inside a building, outside unsheltered and outside sheltered). The purpose is to develop a database that will be useful to evaluate the influence of spatial variability of mechanical parameters/properties on the reliability of beams with or without cracks. To this aim, three points bending tests were performed on 81 specimens at laboratory scale. The results obtained show a significant spatial variability for specimens belonging to the same beam. It was also found that the mean modulus of elasticity (MOE) and the failure stress (FS) of the air-conditioned beam are greater than the same parameters measured at unsheltered and sheltered environments. Figure 3 presents the spatial distribution of the values of MOE obtained from results such as given in Figure 4. We note that the average MOE (for all the specimens) is 11697.05 MPa; this result is slightly higher (15%) than the MOE reported in Manfoumbi (2012) 10700 ± 1550MPa but it is lower (9%) than the value given in CIRAD (2011) which is 13820 ± 2273 MPa. The MOE (10460.48 MPa) of the sheltered outdoor environment is respectively 2% and 24% lower than those reported by [START_REF] Manfoumbi | Contribution à l'adaptation de l'Eurocode 5 aux essences tropicales dans leur environnement[END_REF] and CIRAD (2011).

MATERIAL AND METHODS

CONCLUSIONS AND OUTLOOK

The spatial variability of MOE and the FS after threebending test of specimens submitted to long-term loading in specific environment have been presented. The results show that the MOE of the beam in airconditioned medium is greater to the MOE of the beam in unsheltered and sheltered outdoor environments. This drop-in rigidity is due to the aging of the material, the duration of loading, climatic effects and exposures of beams to environments exteriors.

The overall results indicate that there is a real spatial variability of the studied parameters. The results obtained in this phase will be completed with more 3-points binding tests as well as compression and traction test to complete the database. This database will be useful for characterizing and then generating "random fields" taking into account the uncertainties and spatial variability of these parameters. mechanics Laboratory and housing put at their disposal.

Figure 1 .

 1 Figure 1. Experimental device for three-point bending tests

Figure 2

 2 Figure 2. a) Air-conditioned environment. b) Unsheltered outdoor environment. c) Sheltered outdoor environment d) Alignment of the beam. (e) Test specimens for the characterization of spatial variability.

Keywords:

  Spatial variability; Timber; Ozigo, Modulus of Elasticity; Failure Stress; Dacryodes buettner

Figure 3 .

 3 Figure 3. Force vs central arrow results for specimens at upper part in air-conditioned environment.

Figure 4 .Figure 5 .Figure 5 .

 455 Figure 4. Spatial distribution of MOE for the air-conditioned environment

Figure 6 .

 6 Figure 6. Spatial distribution of MOE for the sheltered outdoor environment

ACKNOWLEDGEMENTS

The authors thank the direction of the National School of Water and Forests (ENEF) of Gabon for the


Julie Guilbaud

Yvan Wyart

Klaas Kaag

Philippe Moulin

Ballast water treatment by membrane processes

Keywords: Ballast water treatment, ultrafiltration, industrial scale, on-board processing, barge-based processing, economic viability

Introduction:

Non-native aquatic species can be introduced in new areas through emptying of the ballast tanks. These introductions induce a high impact on health, economy and environment. This is considered by the International Maritime Organization (IMO): the Ballast Water and Sediments (BMW Convention) entered into force on 8 September 2017 and opens the market for ballast water treatment. The major contribution and novelty of this study is to show successful ballast water treatment using an ultrafiltration process at industrial scale with a high technological readiness level, in order to show the applicability of the ultrafiltration processes for ballast water treatment. Another aim is to compare UV and ultrafiltration treatments applied to fresh water and seawater, from an economic perspective. This comparison accounts for the positioning of the treatment and the specific constraints associated to this positioning. The best membrane processes and the operating conditions were determined for fresh and seawater.

Material and Methods:

The study was conducted during 18 months. The first part, using seawater, was conducted at the Marine Station of Sète (Thau Lagoon, Experimental Marine Ecology Center MEDIMEER UMS 3301, University of Montpellier-2, France) and the second part, using fresh water, was conducted at the Wageningen Marine Research Ballast Water Test Facility, Den Helder (the Netherlands). Constant flux in dead-end ultrafiltration experiments were performed in a mobile unit containing an automatic pilot plant at semi-industrial scale (Figure 1). ballast water treatment has to retain all microorganisms whatever water quality while being compact and cheap. This first study highlights the high potential of ultrafiltration, compared to microfiltration, for microorganisms retentions compared to UV treatment. A correlation between sequence duration, fouling rates, CEB efficiency and the variation of concentrations of microorganisms smaller than 1 µm (picoplankton and bacteria) is observed. Microorganism (<1 µm) concentration increases induce a decrease of sequence duration and irreversible fouling rate and an increase of irreversible fouling rate which seems to be more or less temporary impacted. Indeed, fouling rates decrease with microorganisms concentrations. With seawater runs, the conditions did not reach threshold conditions in the first sequences of filtration at 5 m 3 .h -1 ; 30 min despite seawater quality variation. Contrary to seawater runs, critical conditions of filtration seem to appear with fresh water for 3 m 3 .h -1 . The ultrafiltration process was assessed using 2 different types of water of different quality, and over a long time-scale, as required by the IMO for approval of ballast water treatment processes onboard ships. It should be noted that, during those studies in real waters, planktonic blooms and large variations of turbidity were met. Concerning seawater, with investment costs from 2.3 to 6.5 times higher for ultrafiltration than for UV, CAPEX for the membrane process is far too high to equip a ship. On-board ultrafiltration (Onb) for the treatment of ballast water is not competitive enough compared to the UV system. However, as the production cost of barge-based ultrafiltration during ballast loading is from 5 to 5.4 times lower than the cost of on-board UV treatment for seawater. It could be the subject of an in-depth study which would show the economic interest of the process. With a production cost (Figure 2) from 2.5 to 2.7 times lower for treatment of ballast water with barge-based (B) semi-continuous or continuous ultrafiltration than for UV treatment, ultrafiltration treatment during ballast discharge seems to be the more economically viable and the most efficient solution. Trials in fresh water were carried out with raw water with no pre-treatment as in the case of treatment during ballast loading. If the same experimental conditions as for ultrafiltration had been applied to the UV system, i.e. treating raw water without settling, treatment would have been more difficult or even impossible because of the very low transmittance. Barge-based membrane treatment during ballast loading and ballast discharge seems to be a viable solution, both economically and in terms of retention.

Conclusion:

To conclude on the trials on seawater and fresh water, the quality of the water produced by ultrafiltration is, as expected, of excellent quality, which will remove without a doubt the problem of ballast cleaning-out when the membrane treatment is carried out during ballast loading. However, the on-board ultrafiltration installation is not economically viable, whereas the barge-based installation is very competitive and could considered as new services proposed by ports to meet the international an/or national standards.

Figure 1 .

 1 Figure 1. UF pilot plant [2 industrial modules 84m2, MWCO = 100kDa] Results and Discussion: This paper emphasizes the challenges of the application:

Figure 2 :

 2 Figure 2: Cost productions per cubic meter treated volume for each case [Seawater]