Damien Bourgeois
email: damien.bourgeois@cea.fr

Valentin Lacanau

Régis Mastretta

Christiane Contino-Pépin

Daniel Meyer

A simple process for the recovery of palladium from wastes of printed circuit boards

Keywords: Palladium, e-waste, Solvent extraction, Malonamide

An efficient process for the recovery of palladium from waste printed circuits boards (PCBs) is detailed. Palladium is employed as an electrode material in multi-layer ceramic capacitors (MLCCs). These components can be easily removed from PCBs by desoldering. As palladium is alloyed with silver, its dissolution is readily achieved using dilute nitric acid. As a result, a solution containing palladium along with base metals, mostly copper and iron, is obtained. This solution is then processed through solvent extraction (SX) with a solvent based on N,N'-dimethyl,N,N'-dibutyltetradecylmalonamide (BDMA), a robust extracting molecule previously developed in the frame of the reprocessing of waste nuclear fuel. The volume of effluents generated during the SX sequence is limited: iron scrubbing is operated with a very low aqueous to organic phase volume ratio, no specific metal chelator is required for palladium stripping, and no shift from acidic to basic media is required. Finally, a ca 1 g/L Pd(II) aqueous solution with 99,4% purity is obtained, from which palladium is directly isolated as dichlorodiammine palladium(II) salt (Pd(NH 3 ) 2 Cl 2 ) after precipitation with ammonia. Overall, palladium is quantitatively recovered from the leaching solution, and no palladium was detected in the remaining solid residue. Purity is high, as no contaminating metal could be detected in the final palladium salt. The proposed approach is simple and complementary to existing hydrometallurgical processes dedicated to gold and copper recovery.

Introduction

Palladium is a precious metal which is principally employed in autocatalysts. The increasing demand in palladium for this application combined with stable production has recently led to a sharp rise in palladium price as the market remains tight (Cowley, 2019).

Palladium price more than doubled over the last three years, and even, in January 2019, reached that of gold. Since then, palladium price regularly reaches ca. 45 k€/kg (1600 $/oz). Palladium market is expected to go further in deficit and production from other secondary resources becomes a key to fulfil palladium needs [START_REF] Sverdrup | A system dynamics model for platinum group metal supply, market price, depletion of extractable amounts, ore grade, recycling and stocks-in-use[END_REF]. Supply from the so-called urban mine appears as a very attractive alternative to traditional mining, especially for Western countries, poorly endowed with natural mineral ores [START_REF] Sun | Recycling of metals from urban mines a strategic evaluation[END_REF]. Collected waste volume is increasing, and largely unexploited resources are regularly disclosed, such as printed circuits boards (PCBs) contained in end of life vehicles [START_REF] Xu | Recycling Potentials of Precious Metals from End-of-Life Vehicle Parts by Selective Dismantling[END_REF]. Gold represents the principal value fraction of metals contained in waste of electric and electronic equipment (WEEE) [START_REF] Diaz | Comprehensive process for the recovery of value and critical materials from electronic waste[END_REF]. Copper and silver represent respectively the highest base-metal and semiprecious metal contents and are present in important quantity. Processes dedicated to the recovery of metals from WEEE have been widely investigated, and mostly optimized for gold and copper recovery [START_REF] Hsu | Advancements in the treatment and processing of electronic waste with sustainability: a review of metal extraction and recovery technologies[END_REF][START_REF] Işıldar | Electronic waste as a secondary source of critical metals: Management and recovery technologies[END_REF][START_REF] Kaya | Current WEEE recycling solutions[END_REF]. Precious metals apart from gold have been generally much less detailed in these studies [START_REF] Lu | Precious metals recovery from waste printed circuit boards: A review for current status and perspective[END_REF]. Existing copper smelters have been successfully adapted for the reprocessing of WEEE, and several industrial companies claim to recover palladium along with gold and copper from WEEE [START_REF] Cui | Metallurgical recovery of metals from electronic waste: A review[END_REF]. This technical solution is well-adapted to the reprocessing of used autocatalysts, as precious metals are supported on an inert aluminosilicate (cordierite) [START_REF] Ding | Recovery of precious metals from electronic waste and spent catalysts: A review[END_REF]. But reprocessing of used PCBs in copper smelters necessitates complicated off-gas treatments [START_REF] Cui | Metallurgical recovery of metals from electronic waste: A review[END_REF][START_REF] Li | Hydrometallurgical recovery of metals from waste printed circuit boards (WPCBs): Current status and perspectives -A review[END_REF]. In complement, hydrometallurgical processes have been developed to recover valuable metals from waste PCBs: These are generally soaked in acidic baths to remove unwanted base metals (copper, iron, nickel…), which leaves a solid residue, enriched in gold, further processed through dissolution/isolation sequences [START_REF] Birloaga | Hydrometallurgical processing of waste printed circuit boards[END_REF]. Very often, the outcome of palladium in these processes is not detailed. Efficient techniques targeting palladium recovery from secondary resources are scarce to our knowledge. Detailed analysis of the proposed approaches reveals the difficulty to design a simple process which enables isolation of pure palladium (Table 1). All approaches rely on poorly selective halide based precious metals leaching agents (chloride or iodide media), and (when operated) the purification of palladium can be tedious. Proposed processes often end up with a mixture, of low or unspecified palladium concentration, and isolation of pure palladium is seldom performed. Finally, some proposed processes clearly raise safety issues (Table 1). As 8 to 9% of the total palladium demand is employed in electronic goods manufacturing, it is clearly understandable that palladium recycling from waste PCBs deserves a much deeper consideration.

Available data suggest that palladium concentration in waste PCBs ranges between 10 and 100 mg/kg [START_REF] Yazici | Extraction of metals from waste printed circuit boards (WPCBs) in H2SO4-CuSO4-NaCl solutions[END_REF][START_REF] Zhang | Selective recovery of palladium from waste printed circuit boards by a novel non-acid process[END_REF]. Higher concentrations have been found in specific cases, though on quite old samples [START_REF] Cui | Metallurgical recovery of metals from electronic waste: A review[END_REF]. In PCBs, palladium is principally employed in multi-layer ceramic capacitors (MLCCs) because of its electrical conductivity and its durability [START_REF] Işıldar | Electronic waste as a secondary source of critical metals: Management and recovery technologies[END_REF][START_REF] Prabaharan | A hydrometallurgical process for recovering total metal values from waste monolithic ceramic capacitors[END_REF]. Palladium is found in the conductive electrode material sandwiched between insulating ceramic layers. Apart from very old MLCCs (more than 20 years old), where 100% Pd electrode material was employed, palladium is found in an alloy with silver [START_REF] Lee | Dielectric Properties and Reliability of Zn 0.95 Mg 0.05 TiO 3 +0.25TiO 2 MLCCs with Different Pd/Ag Ratios of Electrodes[END_REF][START_REF] Wang | Silver-Palladium Thick-Film Conductors[END_REF]. A cheaper alternative to the use of silver-palladium alloys is the use of nickel electrodes, which leads to heavier and less durable MLCCs. As nickel is a ferromagnetic metal, different MLCC classes can be easily discriminated with a simple magnet: Base metal electrodes (BME) MLCCs are magnetic, whereas precious metal electrodes (PME) MLCCs are not. Most of precious metals recovery processes from waste PCBs start with a shredding or grinding step (Table 1). The purpose of this step is to ensure access to the metallic parts for the leaching/dissolving agent, and is directly inspired from the comminution step of natural ores processing. In the case of waste PCBs, a different approach is also sometimes considered as i. metallic parts are often easily accessible (surface layers, continuous contact…) and ii. reducing PCBs into powder also leads to total mixing of the metals. Several studies focusing on gold and copper suggest removing electronic components from boards prior to leaching in order to lower the amount of leaching agent required [START_REF] Choudhary | Recovery of palladium from secondary waste using soluble tannins cross-linked Lagerstroemia speciosa leaves powder: Recovery of palladium from secondary waste using biomass[END_REF][START_REF] Fontana | Palladium recovery from monolithic ceramic capacitors by leaching, solvent extraction and reduction[END_REF]. Components are connected to the boards through solders, mostly made of Sn-Pb based alloys in waste PCBs, which can easily be removed either via thermal or chemical treatment [START_REF] Kaya | Current WEEE recycling solutions[END_REF]. This simple step enables obtaining electronic components separated from naked boards.

The location of palladium in MLCCs pushes towards such an approach, which enables simple separation of palladium and gold/copper flows.

In this work, we describe a complete detailed study which enables the recovery of palladium from used PCBs, through a hydrometallurgical process based on chemical desoldering of components, including MLCCs, followed by selective leaching with nitric acid, selective palladium extraction with a malonamide (N,N'-dimethyl,N,N'dibutyltetradecylmalonamide, BDMA, Figure 1) based solvent, and precipitation of a pure palladium salt using standard techniques. The choice of the extracting molecule was motivated by our previous results which demonstrated its excellent performance in the processing of complex aqueous nitrate solutions containing palladium [START_REF] Mastretta | Palladium Isolation and Purification from Nitrate Media: Efficient Process Based on Malonamides[END_REF][START_REF] Poirot | Metal Recognition Driven by Weak Interactions: A Case Study in Solvent Extraction[END_REF], along with the consequent technical knowledge gathered during process development studies dedicated to spent nuclear fuel reprocessing with same molecule [START_REF] Modolo | DIAMEX Counter-Current Extraction Process for Recovery of Trivalent Actinides from Simulated High Active Concentrate[END_REF]. The adaptation of an existing technology for a new application thus only deserves proof of concept at the laboratory scale, as most process issues have already been tackled out. Overall, the proposed process generates a solid residue which contains most initial gold and copper content, and a controlled volume of aqueous effluents, which can be in part reemployed in the leaching step.

Experimental

Materials and reagents

Used electronic devices were picked up randomly from WEEE collection points and manually dismantled to recover the PCBs contained inside. Concentrated nitric acid, concentrated hydrochloric acid, sodium chloride, toluene and aqueous 28% ammonia solution were purchased from Carlo Erba reagents. N,N'-dimethyl,N,N'dibutyltetradecylmalonamide (BDMA, Figure 1) was kindly provided by the Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA, France).

Dissolution of solders

A 3 M aqueous nitric acid solution (200 mL) was placed in a beaker and heated at 50°C.

Entire PCBs were placed in the solution and regularly gently swirled into the solution until components fall to the bottom of the beaker (maximum 15 min). The boards were removed, and the components recovered after filtration. The solution became turbid, and a white solid settled in the bottom, and it was isolated after centrifugation.

Complete digestion of components

With aqua regia: Components were weighed and placed in a glass vessel. A mixture of 32% HCl and 69% HNO 3 2/1 (v/v) at a 25 g/L solid/liquid ratio (S/L = 1/40) was then added, and the vessel was covered with a glass slip. The resulting mixture was stirred at 80°C for 24 h using an IKA-Mag magnetic stirrer and a teflon coated bar. The resulting suspension was filtered with a 0.20µm filter and the resulting solution analyzed by ICP/AES. The residual solid was analyzed by SEM.

With aqueous nitric acid solution: The same procedure was applied, using a 3 M aqueous HNO 3 solution.

Palladium leaching from MLCCs

Components were weighed and placed in a glass beaker. A 3 M aqueous HNO 3 solution at a 100 g/L solid/liquid ratio (S/L = 1/10) was then added, and the beaker was covered with a glass slip. The resulting mixture was stirred at 80°C for 4 h using an IKA-Mag magnetic stirrer and a teflon coated bar. The resulting suspension was centrifuged to remove fine particles (ceramics, remaining tin(IV) oxide).

Solvent Extraction

Extractions were carried out in 50 mL glass vials by putting in contact the aqueous solution resulting from leaching of components with an organic phase containing BDMA in toluene.

The two phases were vigorously shaken at 1000 vibrations per min with an IKA-Vibrax VXR basic shaker in a 20-22°C environment during 1 h. The vials were removed and let to stand for 5 min and the phases were separated. Scrubbing and stripping stages were performed in 50 mL glass vials by putting in contact the organic phase resulting from previous stage, with an adequate aqueous solution (diluted nitric acid solution, pure water of sodium chloride solution, see text). The volume of the aqueous phase was chosen according to the A/O ratio detailed in the text. The two phases were vigorously shaken at 1000 vibrations per min with an IKA-Vibrax VXR basic shaker in a 20-22°C environment for 1 h. The vials were removed and let to stand for 5 min and the phases were separated.

When necessary, 500 µL of each phase were taken for analysis.

Palladium precipitation

To the aqueous sodium chloride solution containing Pd(II) was added dropwise an aqueous 28% ammonia solution until the solution became clear and colorless and the medium became basic (ca. 5% of the initial volume needed). To the resulting solution, a 6 M aqueous hydrochloric solution was added dropwise under constant stirring until precipitation of a yellow solid. The resulting suspension was stirred until it cooled to room temperature, and filtered. The resulting solid was washed with water and dried in an oven at 40°C overnight.

Determination of metal concentration in solutions

Aliquots of aqueous phases were directly diluted into a 2% HNO 3 aqueous solution. Metals contained in the aliquots of the organic phases were back-extracted with 800 µL of an aqueous 0.1 M thiourea solution at 20-22°C for 1 h. Both phases were separated, and 500 µL of the resulting aqueous solution were taken and diluted into a 2% HNO 3 aqueous solution. Concentrations of each metal in both aqueous phases (extraction and stripping phases) were determined by inductively coupled plasma atomic emission spectroscopy (ICP/AES, SPECTRO ARCOS ICP Spectrometer, AMETEK Materials Analysis). Given concentrations are calculated as the means of three replicates on three different wavelengths for each metal; relative standard deviations were determined and lie between 1 and 4%. Quantification limits (LoQ) in each analyzed phase were determined for each metal from the dilution factor applied and from the background equivalent concentration (BEC), calculated by the spectrometer for each optical line.

Powder X-ray diffraction (PXRD)

The PXRD pattern of the obtained solid was recorded at ambient temperature on a Bruker D8 ADVANCE powder diffractometer using Ni filtered CuK  (=1.5406 Å) radiation. Data was collected in the range 5-60° (2θ), with a step of 0.01°, and a scanning rate of 0.1°/min.

2.9 Scanning Electronic Microscopy (SEM) and elemental analysis SEM characterization was obtained with a FEI QUANTA FEG 200 environmental scanning electron microscope (ESEM) equipped with a gaseous secondary electron detector (GSED). Experiments were performed at 100 Pa and an accelerating voltage of 15 kV.

Elemental analysis was performed with X-ray Energy dispersive spectrometry (X-EDS)

analyses using a Bruker AXS X-flash 5010 detector coupled to the ESEM.

Results and discussion

Chemical de-soldering and isolation of MLCCs

Thermal de-soldering of components from PCBs has been implemented at the industrial scale. Its application requires specific equipment and temperature control to enable complete melting without emission of toxic lead fumes. On smaller scale, it may be interesting to turn to chemical de-soldering [START_REF] Zhang | Selective Desoldering Separation of Tin-Lead Alloy for Dismantling of Electronic Components from Printed Circuit Boards[END_REF]. Several media have been employed, and the most efficient technique appears to be immersion into an aqueous nitric acid bath [START_REF] Yang | Leaching copper from shredded particles of waste printed circuit boards[END_REF][START_REF] Yoo | Recovery of Sn, Ag and Cu from Waste Pb-Free Solder Using Nitric Acid Leaching[END_REF]. We evaluated at laboratory scale the removal of electronic components from boards after immersion into 3 M aqueous nitric acid solution. Small pieces (ca. 2 x 2 cm 2 ) of different PCBs were cut off and immersed into 3 M nitric acid. According to the board studied, complete dissolution was either observed after a few minutes at room temperature, or after 10 to 15 min of gentle heating.

This behavior may be related to the composition of solders employed and/or the presence of varnish. Altogether, a 3 M aqueous nitric acid bath was prepared and heated at 50°C in order to achieve removal of components from all PCBs studied. Most small components fall to the bottom of the bath, while some pinned components remain on the board.

Interestingly, MLCCs are not pinned on the boards (Figure 2), and pinned components are generally memory bars or processors, which often contain gold. De-soldered components are then recovered after simple filtration.

Chemical de-soldering has several advantages. First of all, it is a safe operation. Only dilute aqueous nitric acid is employed, no brown NO x fume could be observed. It is a rapid process, as tin, lead and Sn-Pb alloys dissolutions are very easy in nitric acid. Secondly, no gold is leached into nitric acid in the absence of chloride ions, and copper is almost not attacked during this process. The de-soldering solution was analyzed (Table 2) and contains Cu at a g/L level, but this does not represent an important quantity considering the large amount of Cu contained in PCBs. A rough estimate based on an average weight Cu content of 15% in PCBs suggests that more than 95% of Cu remains on the board.

Thirdly, tin can be easily recovered in the process. A white precipitate was observed in the filtrate after few minutes, and its isolation followed by X-EDS analysis revealed that it is exclusively composed of Sn. Actually, oxidation of metallic tin by nitric acid has been described to lead to complete oxidation into Sn(IV) species, which are soluble only in the presence of chloride ions. Otherwise, hydrolysis of the metallic cation occurs, and leads eventually to precipitation of Sn(IV) oxide (as SnO 2 or H 2 SnO 3 ), as long as concentrated nitric acid is not employed [START_REF] Yoo | Recovery of Sn, Ag and Cu from Waste Pb-Free Solder Using Nitric Acid Leaching[END_REF]. Finally, no Ag nor Pd were detected in the solution, so that it is relatively uncomplicated, and contains only Cu, Pb, Ni and Fe as major metallic salts (Table 2). Re-use and reprocessing of such solutions have been already successfully investigated by several authors [START_REF] Lee | Recovery of copper, tin and lead from the spent nitric etching solutions of printed circuit board and regeneration of the etching solution[END_REF][START_REF] Yang | Green Process of Metal Recycling: Coprocessing Waste Printed Circuit Boards and Spent Tin Stripping Solution[END_REF].

As a consequence, we have not fully optimized this step.

Components recovered after filtration are of various natures. It is possible to operate following leaching step on the whole mixture (Figure 2), or to remove magnetic components and/or processors (black components) and plastic parts. It is also possible to isolate large non-magnetic MLCCs to check their integrity and evaluate their Pd content. At laboratory scale, all these steps were performed manually. But physical techniques to operate sorting of WEEEs pieces have been well developed and can easily be envisioned.

Location of Pd in waste PCBs and consequences for leaching

Several non-magnetic MLCCs were selected and collected after de-soldering, and analyzed with optical and scanning electronic microscopy (SEM). The multilayer structure is well visible at low magnification with optical microscopy, but the electrode can only be seen thanks to SEM as its thickness is only in order of 1 µm (Figure 2). Chemical analysis by X-ray Energy dispersive spectrometry (X-EDS) revealed the presence of both Ag and Pd, in a ratio varying between 4/1 and 2/1 according to the considered sample. Pd was always found along with silver, confirming the use of Ag-Pd alloys. Barium and titanium were the main elements found in the thick layer, in full agreement with the well-described use of a BaTiO 3 ceramic as the dielectric material. Ag was also detected in the connecting terminations of the MLCCs, pure in most cases, containing a small amount of Pd in some cases (up to 5% respective to Ag). Similar analysis performed on magnetic MLCCs revealed the use of Ni or Ni-Sn based electrodes, with Ni or Cu based connecting terminations.

In order to evaluate the amount of palladium present in MLCCs, we tried to perform complete digestion with aqua regia of one MLCC reduced to coarse powder after manual grinding in a mortar. Almost no Pd was detected in the leaching solution (4 mg/L). A white solid residue was left, and its analysis by X-EDS revealed the presence of both Ag and Pd in the solid, along with Ti from the ceramic. This result is quite surprising since aqua regia is often proposed for complete dissolution of precious metals present in waste PCBs.

Previous studies give however contradictory information about the leaching of Pd and Ag by aqua regia: Metallic palladium leads to soluble chloro complexes in aqua regia even in the presence of Ag [START_REF] Jung | Processing of high purity gold from scraps using diethylene glycol di-N-butyl ether (dibutyl carbitol)[END_REF][START_REF] Prabaharan | A hydrometallurgical process for recovering total metal values from waste monolithic ceramic capacitors[END_REF]. The reduction of nitric acid into NH 4 + could lead to the precipitation of stable red Pd(NH 4 ) 2 Cl 6 without the need for stronger oxidizing agent to stabilize the Pd(IV) in the system. This has been observed in the presence of Zn(II) or Ni(II) ions [START_REF] Park | Recovery of high purity precious metals from printed circuit boards[END_REF]. Ni(II) has been quantified in our study (550-1100 mg/L), but no red precipitate was observed. Silver is not soluble in dilute chloride media, but can be solubilized by concentrated aqua regia as AgCl 2 -, and this has been observed also using a low 2/1 (v/v) ratio of hydrochloric to nitric concentrated acids [START_REF] Wang | Determination of gold and silver in geological samples by focused infrared digestion: A re-investigation of aqua regia digestion[END_REF]. Surface passivation by AgCl has also been described during attack of metallic Ag alone in aqua regia even at low concentration, using a 1/40 S/L ratio [START_REF] Park | Recovery of high purity precious metals from printed circuit boards[END_REF]. It is thus reasonable to assume that no dissolution of the Ag-Pd alloy occurred, and that Pd dissolution was prevented by the presence of Ag. Two hypotheses can be put forward: the temperature may be too low, as sometimes complete digestion is performed at higher temperature in pressure vessels, or the S/L ratio may be still too high.

We therefore turned to the use of nitric acid alone. Ag is readily soluble in nitric acid and 3 to 6 M aqueous nitric acid solutions have been successfully employed for efficient Ag leaching from metals mixtures [START_REF] Petter | Evaluation of gold and silver leaching from printed circuit board of cellphones[END_REF]. Dissolution of Pd in the absence of chloride ions may be sluggish, but its oxidation is accelerated through alloying with Ag [START_REF] Cole | Oxidation and Reduction of Palladium in the Presence of Silver[END_REF]. Although available literature regarding selective leaching of palladium alloys, especially Ag-Pd alloys, is scarce, we were pleased to find that dissolution using 3 M aqueous nitric acid instead of aqua regia led to efficient Pd dissolution, even after only 4 h.

Although it is not possible to state whether Pd leaching is complete, no Pd was detected in the remaining solid, and Pd content in the MLCCs studied was determined to be between 0,5 and 3% wt (5 to 30 g/kg). Latter value demonstrates that Pd content in MLCCs can be very high, and that focusing a recovery process on MLCCs should enable to recover substantial Pd contained in waste PCBs. Finally, these results demonstrate that the use of aqua regia for the dissolution of complex matrices containing precious metals is not so simple. It would be possible, using a higher temperature or a lower S/L ratio (i.e. a higher volume of liquid), but this approach does not appear as a good process solution.

It is conceivable to use the same nitric acid solution to perform first chemical dissolution of solders then leaching of Pd from components. But such a strategy leads to a solution rather diluted in Pd (3 to 5 mg/L). It appears thus judicious to employ a first bath for desoldering, regenerate it according to existing techniques, and then to perform leaching of Pd from components with another aqueous solution, of limited volume. It is also possible to select from the mixture of components obtained after de-soldering only non-magnetic MLCCs. Preliminary leaching experiments (not optimized) led to Pd containing solutions of relatively simple composition (Table 2). On the other hand, dissolution of magnetic components led to mixtures containing no Pd, but relatively important concentration of Cu, Fe and Ni instead (Table 2). As in both case contaminating metals are the same, and only their concentrations differ, the selection of specific MLCCs from the components mixture is not necessary. Indeed, leaching of metals from a mixture of components not selected (Figure 2) led to a solution containing ca. 100 mg/L Pd along with Ag, Cu, Fe, Ni and Pb at higher concentration, and low amount of Sn and Zn (Table 2).

Separation and purification of Pd from a nitric acid solution

Palladium recovery from nitrate media resulting from the dissolution of used nuclear fuel in nitric acid has been largely documented [START_REF] Kolarik | Recovery of value fission platinoids from spent nuclear fuel -Part II: Separation processes[END_REF]. However, we recently highlighted that the proposed solutions are not adequate for a mixture containing common base metals such as Fe, Cu, Pb, Zn, Ni, etc…, and turned to the use of malonamide based solvents [START_REF] Mastretta | Palladium Isolation and Purification from Nitrate Media: Efficient Process Based on Malonamides[END_REF]. Malonamides make up a family of extracting molecules developed in the frame of the reprocessing of spent nuclear fuel [START_REF] Musikas | Potentiality of Nonorganophosphorus Extractant in Chemical Separations of Actinides[END_REF]. Although dedicated to the actinide(III)/lanthanide(III) separation in advanced purification process, e.g. DIAMEX [START_REF] Facchini | Transient-and Steady-State Concentration Profiles in a DIAMEX-like Countercurrent Process for An(III) + Ln(III) Separation[END_REF][START_REF] Sypula | Use of Polyaminocarboxylic Acids as Hydrophilic Masking Agents for Fission Products in Actinide Partitioning Processes[END_REF], their affinity for Pd(II) was also evidenced [START_REF] Poirot | Palladium Extraction by a Malonamide Derivative (DMDOHEMA) from Nitrate Media: Extraction Behavior and Third Phase Characterization[END_REF]. The optimal process in the nuclear fuel cycle is based on N,N'-dimethyl,N,N'-dioctylhexylethoxymalonamide (OEMA, Figure 1), but the previous candidate, BDMA, showed a higher affinity for Pd(II) [START_REF] Mastretta | Palladium Isolation and Purification from Nitrate Media: Efficient Process Based on Malonamides[END_REF][START_REF] Serrano-Purroy | Recovery of minor actinides from HLLW using the DIAMEX process[END_REF]. Latter molecule is also simpler to prepare, as the tetradecyl chain is commercially available. Both molecules are very robust, stable after prolonged contact with concentrated nitric acid, and even resistant to radiolysis [START_REF] Le Caër | Experimental and theoretical study of the degradation of malonamide extractant molecules under ionizing radiation[END_REF].

Process studies performed with continuous feeds in mixer-settlers revealed nice phase splitting [START_REF] Modolo | DIAMEX Counter-Current Extraction Process for Recovery of Trivalent Actinides from Simulated High Active Concentrate[END_REF], probably thanks to low viscosity of the solvent (less than 10 cP). Benchmarking with commercially available extractants recently demonstrated that malonamides are very well positioned and should be considered for the recovery of palladium from complex mixtures such as those arising from the dissolution of electronic waste [START_REF] Mastretta | Palladium Isolation and Purification from Nitrate Media: Efficient Process Based on Malonamides[END_REF].

Regarding the present study, two key points need to be addressed in order to validate the malonamide technology in Pd recovery processes: i. the possibility to generate low amount of effluents during solvent extraction step, and ii. the effective isolation of Pd from waste PCBs at laboratory scale. Considering the composition of the leaching solution obtained beforehand, we evaluated the impact of each metal separately on the extraction step (Table 3). It is clear that Fe is the impurity of concern, and that Cu should be also controlled. All other metals have a lower distribution coefficient and/or a lower concentration in aqueous phase, so that their concentration in organic phase is lower, and that scrubbing of both Fe and Cu will guarantee their elimination from organic phase.

Suitable Pd distribution coefficients and Pd/Fe selectivity have been achieved using BDMA in toluene [START_REF] Poirot | Metal Recognition Driven by Weak Interactions: A Case Study in Solvent Extraction[END_REF]. Modelling a counter current process based on Pd extraction step, Fe scrubbing step and Pd stripping step, each of one with 6 stages, leads to quantitative Pd recovery [START_REF] Mastretta | Palladium Isolation and Purification from Nitrate Media: Efficient Process Based on Malonamides[END_REF]. As it is much simpler to operate crosscurrent processes at laboratory and pilot scale, we evaluated a cross-current process at the laboratory scale. The key step in the Pd isolation sequence is the Fe scrubbing step, where minimal Pd loss has to be achieved, along with complete Fe scrubbing generating minimum amount of aqueous effluents. One of the interesting features of the malonamide technology is that no extra scrubbing or stripping agent is required: only careful control of the HNO 3 concentration in the aqueous phase is required. The distribution coefficients (D M ) of both Pd and Fe directly depend on aqueous HNO 3 concentration, and as long as final aqueous HNO 3 concentration is kept above 1,5 M, Pd distribution coefficient is well above 1, and Pd/Fe selectivity well above 100 (Table 4). This enables quantitative Pd extraction, along with residual Fe, as this metal can be in much higher concentration in the initial aqueous phase than Pd. This residual Fe has to be scrubbed from organic phase to ensure recovery of pure Pd. The concentrations of metals back-extracted from the organic phase into the aqueous phase depend on distribution coefficients, along with the organic to aqueous (O/A) phase volume ratio, according to equation ( 1):

(1)

As a consequence, for a metal with a high distribution coefficient such as Pd, its loss during a scrubbing step will directly depend on O/A and D Pd , following equation ( 2):

(2)

And for a metal with a very low distribution coefficient such as Fe, its scrubbing from organic phase will be almost quantitative, as long as its concentration in aqueous phase does not exceed saturation. Its final concentration in aqueous phase can be approached using equation ( 3):

(3)

From a practical point of view, there is a compromise to find between the O/A ratio and the nitric acid concentration of the aqueous phase employed for scrubbing. A high O/A ratio will enable a low Pd loss, along with low generation of effluents. A low O/A ratio will enable an optimal Fe scrubbing. A low aqueous HNO 3 concentration will enable efficient Fe scrubbing but will also increase Pd loss. In a first instance, we evaluated both Fe scrubbing (elimination) and Pd loss after contacting an organic phase loaded with both metals with an aqueous phase at different O/A ratios (Figure 3). Using only water leads to almost quantitative Fe scrubbing, albeit with important Pd loss (up to 5% at a O/A ratio of 5). Using 1,5 M HNO 3 leads to lower Pd loss (less than 2% when O/A ratio exceeds 7), but also lower Fe elimination (70 to 80% only). Altogether it appears clearly that at least two stages have to be set in place, and different strategies based on sequential two stage scrubbing were evaluated (Figure 4). The best option consists in a first scrubbing with water only, followed by a 1 M aqueous HNO 3 scrubbing, both with an O/A ratio of 10. In these conditions Pd purity is 96% with 3% Pd loss. These screening results demonstrate that an efficient Fe scrubbing step can be obtained with minimum generation of effluents:

the total volume of waste aqueous phase generated represents only 20% of the volume of the organic phase. The final organic phase containing Pd can then be processed in order to recover Pd as a Pd(II) salt with classical techniques.

Impact of chlorides and consequences for Pd recovery

Evaluation of the behavior of each metal in the solvent extraction step drove us also to evaluate the impact of the presence of chloride ions in the aqueous phase. Sn is poorly soluble in pure nitrate media, as Sn(IV) precipitates as mentioned beforehand. As a consequence it is difficult to evaluate its distribution coefficient with precision as concentration of Sn in the organic phase after extraction is below quantification limit. On the other hand, similar experiment performed with a Sn(IV) chloride solution leads to almost quantitative extraction of Sn. The influence of chloride ions was evaluated on 4 key metals (Pd, Ag, Fe and Sn, Table 5). It is clear that chloride have a negative impact on the process: i. they inhibit the leaching of Pd (see above) and lead to the precipitation of Ag, ii.

they decrease the efficiency of Pd extraction, and the Pd/Fe selectivity as Fe is extracted more easily into organic phase, iii. they increase the solubility of Sn and the subsequent extraction of Sn in the organic phase. Altogether, the use of aqua regia is not only poorly suitable for leaching, but also for Pd recovery. Of course other extraction agents could be envisioned [START_REF] Fontana | Palladium recovery from monolithic ceramic capacitors by leaching, solvent extraction and reduction[END_REF], but Sn will require deeper studies as it is employed in the extraction of platinum group metals to increase their extraction efficiency: Sn(II) chloride is a labilizing agent which is easily co-extracted with Pd or Pt in chloride media [START_REF] Mojski | Extraction of platinum metals from hydrochloric acid medium with triphenylphosphine solution in 1,2-dichloroethane[END_REF]. Furthermore, the use of a chloride solution to strip Pd from organic phase appears as a very interesting option for malonamides: With low distribution coefficient in the presence of chloride no specific Pd complexing agent will be required to strip Pd, from organic phase. Afterwards, Pd(II) salts can be easily precipitated from aqueous chloride solutions as Pd(NH 3 ) 2 Cl 2 salt through classical process.

Altogether, the complete sequence is the following: i. chemical de-soldering, ii. Pd leaching from components, iii. Pd isolation through solvent extraction, iv. Pd precipitation as Pd(NH 3 ) 2 Cl 2 . The two last points are detailed in Figure 5 and Figure 6. A selected solution containing a high amount of Fe and Cu (along with other metals Ag, Ni, Pb, Sn, Zn and traces Al, Ba, Nd, Ti and Y, discarded for clarity) was employed and led to an aqueous solution containing 85% of the initial Pd amount with a 99,4% purity: traces of Fe were still quantified in this final Pd solution, Cu was below quantification limit, and other metals were not detected (Figure 6). This aqueous solution was further processed through successive addition of an ammonia solution and hydrochloric acid solution to lead to quantitative precipitation of Pd(NH 3 ) 2 Cl 2 of characteristic yellow color. SEM, X-EDS and powder X-ray diffraction (PXRD) analysis confirmed the structure and the purity of the salt (Figure 5). No residual Fe was detected in the solid.

Regeneration of solvent

We chose not to fully back-extract (strip) Pd from the organic phase in order to minimize the volume of aqueous phase employed: 15% of the Pd remains in the organic phase.

Theoretically, a 15% Pd hold-up in the extraction solvent should lead to a maximum 17%

(1 -1 / 0,85) increase in the Pd loading in the organic phase after re-use of the organic starting from one volume of aqueous phase, the same volume of organic phase is needed and can be re-employed in successive cycles, and effluents generated account for only 30% of the initial volume to be treated.

The choice of solvent is the direct consequence of our former studies on such systems [START_REF] Mastretta | Palladium Isolation and Purification from Nitrate Media: Efficient Process Based on Malonamides[END_REF][START_REF] Poirot | Palladium Extraction by a Malonamide Derivative (DMDOHEMA) from Nitrate Media: Extraction Behavior and Third Phase Characterization[END_REF]. Using BDMA in toluene, we were able to tackle key issues related to the development of a solvent extraction process, i.e. low generation of effluents and solvent regeneration. Working on the worst possible case, i.e. a leaching solution of high Fe concentration, we obtained very satisfactory results. Our previous results demonstrate that the use of aromatic diluent is only motivated by the optimization of Pd/Fe selectivity [START_REF] Poirot | Metal Recognition Driven by Weak Interactions: A Case Study in Solvent Extraction[END_REF], and that otherwise Pd distribution coefficient and solubility in the diluent is marginally affected by shift to an aliphatic diluent [START_REF] Poirot | Palladium Extraction by a Malonamide Derivative (DMDOHEMA) from Nitrate Media: Extraction Behavior and Third Phase Characterization[END_REF]. As toluene is volatile, it would be much safer to use a higher boiling point diluent, such as an aromatic cut (e.g. Solvesso 150). Mixed aliphatic-aromatic cuts could also be employed, and it would also be interesting to investigate malonamide in purely aliphatic diluents when physical sorting of the waste guarantees low Fe content in the leachate. Any of these solutions would also definitely limit inhalation exposure to the toluene, a harmful substance.

Conclusion

Through this study, we demonstrated that efficient Pd recovery from waste PCBs can be performed at laboratory scale via an approach based on successive de-soldering, Pd leaching with dilute nitric acid, solvent extraction then precipitation. This approach takes profit of the quasi-exclusive location of Pd in MLCCs, and the fact that it is alloyed with Ag.

It demonstrates that the systematic recourse to shredding and aqua regia is inappropriate for the recovering of Pd, as it will lead to more complex mixtures and leaching difficulties.

Furthermore, the process benefits from previous work, e.g. regeneration of de-soldering baths, and complements existing strategies performed at small industrial scale to recover Au and Cu. Usually, when WEEEs are processed through hydrometallurgy, principal target metal is Au, and other precious metals and base metals are eliminated during a preliminary step in order to maximize Au leaching yield (Figure 7). The proposed process generates a final solid residue which contains still all (or almost all) the Au initially present in the product. This solid residue can be processed according to existing techniques, either directly in the Au leaching step, or with classical base metals dissolution (or even through pyrolysis and grinding for smelting processes). The process also enables selective recovery of Sn as a solid precipitate after dissolution of the solders. We demonstrated that the organic solvent can be re-employed without loss of performance and that the amount of aqueous waste generated is low (ca. 30% of the initial volume to be treated). We have not explored the possibility to recover Ag from this waste, but this option will be explored in a near future. Pd is isolated as a Pd(II) salt, dichlorodiammine palladium(II) Pd(NH 3 ) 2 Cl 2 , with an excellent purity (no other trace metals detected). This salt is a key intermediate in Pd chemistry as its burning at ca. 1000°C leads to Pd sponge, basis for Pd fine chemistry.

To our knowledge, this study is the first one to detail each step from waste PCBs to a key Pd intermediate at the laboratory scale, including outcome of by-products and effluents.

Considering the forecasted increasing demand in Pd, such a process could be further developed and implemented at small industrial scale, i.e. on 10-100 kg of waste PCBs. This is the scale at which regional industrial players operate in the sector of precious metals recovery from waste. These are fully complementary to bigger actors that operate pyrometallurgical processes (Cu smelters). Of course further scale-up of the proposed process is conceivable, and operation of the solvent extraction steps with counter-current continuous mixer-settlers will lead to better performances. But the technological investment is higher, and the access to available waste PCBs may be a limit for costeffective operation rate. Thus we suggest in a first instance not to start at a large industrial scale. Finally, this work, along with others mentioned in this article, strongly suggest players to seek for alternatives to systematic shredding and complete leaching. This approach, inspired by extraction of metals from natural ores, is not adapted to the management of wastes of high content in several metals, of much lower tonnage and diversity.

1 Notes: a. LoQ = 0,001 in the determination of Fe distribution coefficient.

  phase for a new solvent-extraction cycle. Such a slight increase in Pd concentration should not modify distribution coefficient nor extraction yield. Actually, re-use of this solution for a new extraction cycle led to quantitative Pd extraction from same initial aqueous phase. Organic phase Pd content is higher, and those of Fe and Cu do not vary significantly. Interestingly, no regeneration of the organic phase was required: usually, stripping the metal of interest with a strong chelator requires washing of the organic phase in order to perform a new extraction cycle. In the present case, possible traces of chloride did not alter Pd extraction. No other aqueous effluents are thus generated. Altogether,

Figure 1 :

 1 Figure 1: Malonamide extractants: BDMA (also referred to as DMDBTDMA), employed in

Figure 2 :Figure 3 :Figure 4 :Figure 5 :Figure 6 :

 23456 Figure 2: Details of printed circuit boards (PCBs) employed in the study, and location of

Figure 7 :

 7 Figure 7: Comparison between existing processes and proposed process: the proposed

Table 1 :

 1 Summary of previously published studies dedicated to palladium recovery from waste PCBs.

	Starting material	Physical treatment	Chemical pre-treatment	Leaching step	Pd purification	Pd compound obtained	Remarks	Reference
	Disasembled	Crushing,	Enrichment	CuSO 4 + NaCl	SX with di-	Pd(II) solution	97% Pd recovery	(Zhang and
	components	enrichment of	through Cu	(Cu/Cu 2+ < 0.9)	isoamyl sulfide,	in ammonia	during the	Zhang, 2014)
	from waste PCBs.	metallic components with centrifugal air separator, grinding into metallic particles (94% Cu, 304	dissolution with CuSO 4 and NaCl.	Solution obtained: Pd 20 mg/L, Cu 7260 mg/L + traces Pb, Zn, Fe, Al, Ni.	stripping with NH 3 solution	(ca. 100 mg/L)	dissolution-extraction-stripping process, possibility to regenerate the solvent demonstrated.
		mg/kg Pd).
	Disasembled	Shredding into	none	CuSO 4 + NaCl	Not performed Multimetallic	58% Pd leaching.	(Yazici and
	components	metallic particles		+ H 2 SO 4 .		solution (Cu,		Deveci,
	from waste	(18.5% Cu, 97				Fe, Ni, Ag),		2013)
	PCBs.	mg/kg Pd, 86				Pd).
		mg/kg Au).
	MLCCs.	Grinding, sieving Base metals	Concentrated	Au precipitation	Not mentioned,	Max. 90% Pd	(Prabaharan
			leaching with	HCl/HNO 3 at	with urea and	probably	leaching.	et al., 2016)
			concentrated	80°C.	Na 2 S 2 O 5 , then	metallic Pd
			HCl, then with		Pd precipitation
			HCl/H 2 O 2 .		with Zn dust.
					Then aqua
					regia
					redissolution,
					and sodium
					formate
					precipitation.

Table 2 :

 2 Summary of concentrations (mg/L) of principal metals of interest found during the dissolution/leaching steps of the process (Al, Ba, Nd, Ti, and Y were all found below quantification limit and were removed for clarity).

	Composition of solution	Ag	Cu	Fe	Ni	Pb	Pd	Sn	Zn
	Quantification limit (LoQ)	0,2	0,1	0,2	0,15	0,2	0,3	0,2	0,25
	Bath after dissolution of solders <LoQ 1400	70	180	730	<LoQ	40	<LoQ
	After leaching of selected non-magnetic MLCC	205-968	0-4	0-135 5-41	59-300	38-193	2-24 <LoQ
	After dissolution of selected magnetic components	1	2500 2650 3500	700	<LoQ	5	<LoQ
	After leaching of components
	recovered after dissolution of	481	1940	970	350	172	98	33	37
	solders

Table 3 :

 3 Distribution coefficients and maximum concentrations expected in organic phase after solvent extraction for unwanted metals present in leaching solution, of which HNO 3 concentration is 3 M.

	Metal	Distribution	Maximum organic
		coefficient (D)	concentration
			(mg/L)
	Ag	0,04	7
	Cu	0,015	32
	Fe	0,24	88
	Ni	0,01	2
	Pb	0,02	12
	Sn	(0,04) a	<LoQ
	Zn	0,03	1
	Note: a. Solubility of tin is very limited in pure nitrate media

Table 4 :

 4 Distribution coefficients of Pd and Fe between a 0,6 M BDMA in toluene organic phase and aqueous phases of variable HNO 3 concentrations, and corresponding Pd/Fe selectivity.

	Aqueous [HNO 3 ]	1 mol/L	1,2 mol/L	1,5 mol/L	2 mol/L	3 mol/L
	D(Pd)	1,7	2,5	3,7	6,1	27
	D(Fe)	<LoQ a	0,001	0,003	0,024	0,24
	S Pd/Fe	n.a.	2500	1230	250	110

Table 5 :

 5 Impact of the presence of chloride anions on the distribution coefficients of key metals present in leaching solution. Conditions: a controlled quantity of NaCl was added to a 3 M aqueous HNO 3 phase containing dissolved ions.

	Conditions	D(Pd)	D(Fe)	D(Ag)	D(Sn)
	No chloride	27	0,24	0,04	(0,04) a
	1 M chloride	0,06	1,0	n.a. b	11,2

Notes: a. Solubility of tin is very limited in pure nitrate media; b. precipitation of silver chloride.

Acknowledgements

The authors are grateful to the LaBex ChemiSyst for funding the PhD thesis grant of V.

Lacanau (ANR-10-LABX-05-01), and to the Commissariat à l'Energie Atomique et aux

Energies Alternatives (CEA) for financial support of the study. 491


Kunal K Dayal

John E Cater

Michael J Kingan

Gilles D Bellon

Rajnish N Sharma

Wind resource assessment and energy potential of selected locations in Fiji

Keywords: Fiji, wind resource assessment, power density, energy potential, WAsP. 1

come   L'archive ouverte pluridisciplinaire

Introduction

Wind energy plays an integral role in providing environmentally friendly, renewable electrical energy in many developed and developing countries around the globe. In the year 2018, the worldwide installed capacity of wind energy grew to 591 GW from 59 GW in 2005 [START_REF]Renewables 2019 Global Status Report[END_REF][START_REF]Global Wind Statistics[END_REF]. This growth is by an order of magnitude, which highlights the global move towards clean energy from traditional fossil fuel-based power generation. It is likely that wind energy will continue with this steady growth in the near future [START_REF]Renewables 2019 Global Status Report[END_REF][START_REF]Global Wind Statistics[END_REF]. Consequently, new resource assessments, construction permits, and developments are currently underway in many countries [START_REF]Renewables 2019 Global Status Report[END_REF][START_REF]Global Wind Statistics[END_REF].

Wind resource assessment is fundamental to determine whether a particular site or region has potential for installing an economically viable wind farm [START_REF] Sathyajith | Wind Energy: Fundamentals, Resource Analysis and Economics[END_REF][START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF]. An assessment of the wind characteristics is vital in terms of the average wind speed, dominant wind direction, wind distribution, diurnal and annual wind speed patterns, wind shear coefficient, turbulence intensity, wind power density, potential annual energy production from a turbine and appropriate wind availability in terms of usable wind for generating electricity using wind turbines, amongst other factors [START_REF] Sathyajith | Wind Energy: Fundamentals, Resource Analysis and Economics[END_REF][START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF].

A number of methods can be employed for wind resource assessment, from the traditional method of setting up a mast with anemometers and wind vanes at different heights, to the use of reanalysis datasets and satellite estimates, as well as the latest technology of using SODAR and LIDAR [START_REF] Sathyajith | Wind Energy: Fundamentals, Resource Analysis and Economics[END_REF][START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF]. For a preliminary understanding of the wind characteristics and the energy potential for a site, at least one full year of ground-based measured wind data is necessary [START_REF] Sathyajith | Wind Energy: Fundamentals, Resource Analysis and Economics[END_REF][START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF].

Small developing countries with limited financial resources face numerous challenges in setting up masts specifically for wind-resource monitoring, thus the long time series of groundbased wind measurements from existing meteorological stations provide an opportunity to estimate the wind-resource parameters.

Fiji is a small island developing state (SIDS) located in the South-West Pacific Ocean between the latitudes of 12 °S -22 °S and longitudes of 177 °E -178 °W. There are more than 332 islands with a total land area of 18,333-km 2 spread out over a sea surface area of 1.3 million km 2 . One-third (110) of these islands are inhabited. Around 87 % of the total land area is takenup by the two largest islands of Viti Levu (10,400 km 2 ) and Vanua Levu (5,540 km 2 ). The islands are of volcanic origin, mountainous and with maximum peaks of around 1300 m.

Fiji is located on the margin of the South Pacific Convergence Zone (SPCZ), a rainy region that extends southeastward from the equatorial west Pacific [START_REF] Rapaport | The Pacific Islands: Environment and Society, Revised Edition[END_REF]. As a result, Fiji has a tropical marine climate with a dry, cool season in austral winter when the SPCZ is small and weak, and a warm, wet season in austral summer when the SPCZ expands and intensifies. In terms of wind, Fiji is in the region of southeasterly trade winds. The stable, regular breeze power the existing wind turbines. Additionally, Fijian coasts experience a strong diurnal cycle of landsea breeze circulation.

The Fiji Department of Energy manages the off-grid electrification on the islands. Fiji targets to produce 100 % electricity from renewable energy resources by 2030 [START_REF] Efl | Energy Fiji Limited[END_REF][START_REF]Fiji's Intended Nationally Determined Contribution[END_REF]. Energy Fiji Limited (EFL) is a corporate statutory body responsible for electricity generation, transmission and distribution to the majority of the population in Fiji [START_REF] Efl | Energy Fiji Limited[END_REF]. Currently, EFL has about 90 % of the total population of 884,887 connected to its electricity grid network with a target of connecting 100 % population by 2030 [START_REF] Efl | Energy Fiji Limited[END_REF]. The EFL grid network supplies electricity to Viti Levu, Vanua Levu, Ovalau and Taveuni only, whereby the overall peak load fluctuates from 80 -180 MW [START_REF] Efl | Energy Fiji Limited[END_REF]. Hydropower and the conventional power plants support the base load while other sources provide support on an intermittent basis. Other islands are dependent on diesel generators, solar home systems, micro-hydro systems and biofuel-based power generation.

Electricity generation in Fiji is from numerous sources including hydro, industrial diesel, heavy fuel oil, biomass and wind [START_REF] Efl | Energy Fiji Limited[END_REF]. From the most recent statistics in 2018, these sources generated 1032.94 GWh of electricity in total with 59 % (609 GWh) from renewable and 41 % (423.7 GWh) from conventional power plants. The hydropower plants contributed 54.9 % (566.7 GWh); independent power producers contributed 3.9 % (39.94 GWh) and 0.25 % (2.6 GWh) was contributed from wind [START_REF] Efl | Energy Fiji Limited[END_REF]. Six hydropower stations are connected to the electricity grid network with a total installed capacity of 131 MW, several industrial diesel and heavy fuel oil power plants with a total installed capacity of 147 MW, a 10 MW wind farm and around 40 MW of biomass fired co-generation power plants operated by Independent Power Producers (IPPs) [START_REF] Efl | Energy Fiji Limited[END_REF].

Fiji has a 10 MW onshore wind farm located on the Butoni hills in Sigatoka on Viti Levu. It consists of 37 × 275-kW Vergnet wind turbines, which operate at an annual average wind speed of 5.47 m/s at a hub height of 55 m [START_REF] Efl | Energy Fiji Limited[END_REF].

The Fiji Department of Energy has carried out a number of wind measurements over time with the aim to assist in electrification of rural inland regions and the outer islands where there is no grid access [8]. Some researchers have used these measurements to assess the potential of wind energy in a few locations [9 -12], but the data has not gone through a proper quality-control process and some of it lacks consistency. It was reported via the analysis done using the Wind Atlas Analysis and Application Program (WAsP) that average yearly wind speeds for Fiji is between 5 to 6 m/s with an average power density of 160 W/m 2 [START_REF] Kumar | Examining wind quality and power prospects on Fiji Islands[END_REF]. Others have measured the wind parameters by setting up masts funded via international donor agencies such as KOICA [14 -16]. Similar studies have been performed in other regions such as Mexico [START_REF] Carrasco-Díaz | An assessment of wind power potential along the coast of Tamaulipas, northeastern Mexico[END_REF],

Kiribati [START_REF] Aukitino | Wind energy resource assessment for Kiribati with a comparison of different methods of determining Weibull parameters[END_REF] and Brazil [START_REF] Lima | Wind energy assessment and wind farm simulation in Triunfo -Pernambuco, Brazil[END_REF], using WAsP. WAsP is a common tool for wind resource assessment and estimation of energy production from selected wind turbines for microsite locations in the range of 5 -15 km. New wind resource knowledge has been created even though it has not yet led to any new wind farm installations in the areas studied [13 -16, 17 -19]. This can be useful for future wind energy planning and development.

In the Fiji Group, the wind resources for the remote island of Kadavu and the urban Suva Peninsula have been previously analysed using WAsP [START_REF] Sharma | Wind energy resource assessment for the Fiji Islands: Kadavu Island and Suva Peninsula[END_REF]. A wind resource map developed at a height of 34 m for both sites, earmarked locations for future wind-farm development. It was reported that the overall wind speed in these regions is 3.5 -6.4 m/s at heights of 20 -34 m above ground level (AGL), predominantly south-easterly. The wind speed was categorised as being in the low-medium wind speed regime, which is suitable for small-medium scale wind turbines like the Vestas V27 225-kW. The analysis indicated promising wind-power development potential for the windward side of the Kadavu ridge in the eastern region with an average net AEP of around 650 MWh and for Laucala Bay (Suva Peninsula) with an average net AEP around 400 -500 MWh [START_REF] Sharma | Wind energy resource assessment for the Fiji Islands: Kadavu Island and Suva Peninsula[END_REF]. Onsite measurement analysis of Laucala Bay showed a prevailing south-easterly wind direction with a mean wind speed of 5.03 m/s and a power density of 131 W/m 2 at 55 m AGL, corresponding to an average energy output of 344 MWh for a 275-kW Vergnet wind turbine [START_REF] Kumar | Examining wind quality and power prospects on Fiji Islands[END_REF].

In addition, the wind quality and power availability for the two major islands in Fiji were examined using NASA's MERRA reanalysis at a height of 50 m [START_REF] Kumar | Examining wind quality and power prospects on Fiji Islands[END_REF]. A comparison of modelled wind speed with ground-based measurement for Laucala Bay showed that the MERRA dataset overestimated wind speeds by more than 25 %. This shows that reanalysis may be unsuitable for assessment in the vicinity of an island because it is produced by a general circulation model, and the spatial resolution was too coarse to properly represent the details of the island's geography. Satellite estimates from WindPRO database were also utilized for offshore wind resource assessment of Bligh Waters, north of Viti Levu [START_REF] Dayal | Offshore wind resource assessment, site suitability and Technology selection for Bligh Waters Fiji using WindPRO[END_REF]. The mean wind speeds and the power density for these studies [9 -16, 21] were found to range from 4.6 m/s and 160 W/m 2 respectively, at 10 m of altitude to 10.8 m/s and 1128 W/m 2 at 55 m. These correspond to a low-to-medium wind speed regime that is suitable for small to medium-scale wind turbines.

In summary, only a few studies and only a few locations have been considered to date in Fiji.

Consequently, the wind resource potential across the Fiji Group is mostly unknown, thus this study aims to carry out a wind-resource assessment of selected locations, across the Fiji Group, where automatic weather stations (AWSs) provide long-term measurements of near surface wind. The analysis evaluates the wind characteristics in terms of mean wind speed, dominant wind direction, mean power density, diurnal and annual wind speed patterns, average wind shear coefficient and the energy potential via resource mapping of a 275-kW Vergnet wind turbine using WAsP analysis. In addition, the potential for future utility-scale wind power development is assessed. Section 2 of this work provides background material, section 3 describes the methodology, and the results are presented in section 4 then discussed in section 5. The conclusions are presented in section 6.

Background

This section contains all the mathematical definitions of the wind parameters computed by WAsP together with the wind shear coefficient and the capacity factor computation [START_REF] Mortensen | Wind Atlas Analysis and Application Program: WAsP 12 Help Facility[END_REF].

Weibull Distribution

In most places, surface wind speed distribution is well approximated by a Weibull distribution [START_REF] Sathyajith | Wind Energy: Fundamentals, Resource Analysis and Economics[END_REF][START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF][START_REF] Mortensen | Wind Atlas Analysis and Application Program: WAsP 12 Help Facility[END_REF]. The Weibull distribution fitting is therefore the method most commonly used in wind energy studies to obtain a smooth distribution despite under-sampling of wind speed due to limited observation periods. The Weibull distribution is a two-parameter function, mathematically represented by 𝑓𝑓(𝑢𝑢) while the cumulative distribution function is represented by 𝐹𝐹(𝑢𝑢):

𝑓𝑓(𝑢𝑢) = 𝑘𝑘 𝐴𝐴 � 𝑢𝑢 𝐴𝐴 � 𝑘𝑘-1 𝑒𝑒 -� 𝑢𝑢 𝐴𝐴 � 𝑘𝑘 (1)
𝐹𝐹(𝑢𝑢) = 1 -𝑒𝑒 -� 𝑢𝑢 𝐴𝐴 � 𝑘𝑘 , ( 2
)
where u is the reference wind speed at the height of measurement, 𝑘𝑘 the non-dimensional shape parameter, and 𝐴𝐴 the scale parameter whose dimensions coincides with that of u (m/s).

𝑘𝑘, 𝐴𝐴, 𝑢𝑢 > 0. In the observed wind climate analysis of WAsP, the Weibull distribution function of wind speed is represented with an estimated Weibull 𝐴𝐴 parameter which indicates on average how windy the site is, the Weibull shape parameter 𝑘𝑘 which indicates how peaked the distribution is, Weibull mean 𝑈𝑈 which indicates the long-term mean wind speed and P which indicates the available power density at the sites.

Power and Energy Production from a Wind Turbine

The power production 𝑃𝑃 (in units of 𝑊𝑊) from a wind turbine is estimated using the wind turbine power curve 𝑝𝑝(𝑢𝑢) and the probability density function of the wind speed at the hub height of the wind turbine as follows:

𝑃𝑃 = ∫ 𝑓𝑓(𝑢𝑢) 𝑝𝑝(𝑢𝑢)𝑑𝑑𝑢𝑢 ∞ 0 . ( 3
)
The annual energy output from a wind turbine E (in units of Wh) is estimated using the product of the wind power production with the time and the efficiency as follows:

𝐸𝐸 = 𝜂𝜂𝜂𝜂 ∫ 𝑓𝑓(𝑢𝑢) 𝑝𝑝(𝑢𝑢)𝑑𝑑𝑢𝑢 ∞ 0 , ( 4
)
where 𝑢𝑢 is the wind speed at the hub height in m s -1 , 𝜂𝜂 is the number of hours in one year and 𝜂𝜂 is the efficiency of the wind turbine.

Wind Speed calculation using WAsP

WAsP is a linear wind simulation model and takes into account the following factors when calculating wind speed:

1. The geostrophic balance, where the geostrophic drag law gives the geostrophic wind 'G':

G = 𝑈𝑈 * 𝐾𝐾 � �𝑙𝑙𝑙𝑙 𝑈𝑈 * 𝐹𝐹𝑧𝑧 0 -𝒜𝒜(𝜇𝜇)� 2 + 𝐵𝐵 2 (𝜇𝜇) , ( 5
)
where 𝒜𝒜, 𝐵𝐵 are dimensionless functions of stability, 𝑈𝑈 * is the friction velocity, 𝐾𝐾 is the von Karman's constant (= 0.4), 𝑧𝑧 0 is the surface roughness length and 𝐹𝐹 is the Coriolis parameter.

2. The modified logarithmic wind profile.

𝑢𝑢(𝑧𝑧) = 𝑈𝑈 * 𝑘𝑘 �𝑙𝑙𝑙𝑙 𝑧𝑧 𝑧𝑧 0 -𝜓𝜓 � 𝑧𝑧 𝐿𝐿 �� (6)
where 𝑢𝑢(𝑧𝑧) is the wind speed at height 𝑧𝑧, 𝜓𝜓 is the stability dependent function which is positive for unstable and negative for stable conditions and 𝐿𝐿 is the Monin-Obukhov Length [START_REF] Mortensen | Wind Atlas Analysis and Application Program: WAsP 12 Help Facility[END_REF].

3. A specific (but uniform) stability, roughness variations and height variations.

Vertical Profile of Wind Speed and Wind Shear

A power law or a log law are often used to describe the wind profile near the surface and are commonly used to model the vertical profile of wind speed over regions of homogeneous, flat terrain and prairies. The two laws are:

𝑢𝑢(𝑧𝑧) = 𝑈𝑈 * 𝑘𝑘 𝑙𝑙𝑙𝑙 � 𝑧𝑧 𝑧𝑧 0 � (7) 𝑢𝑢(𝑧𝑧) 𝑢𝑢(𝑧𝑧 𝑟𝑟 ) = � 𝑧𝑧 𝑧𝑧 𝑟𝑟 � 𝛼𝛼 , (8)
where 𝑢𝑢(𝑧𝑧 𝑟𝑟 ) is the reference wind speed at height 𝑧𝑧 𝑟𝑟 and ∝ is the power law exponent or the wind shear coefficient.

The wind shear is the vertical variation of wind speed with height. It results from the surface drag which slows down the wind at the surface. It depends on how the effect of this drag is transported upwards by turbulence, and in particular on the surface roughness, which modulates turbulence. It is an important parameter not only for power production from wind turbines, but also for aerodynamic loadings on the wind turbine blades. The wind shear coefficient can be computed using known wind speeds at two heights, as in equation ( 9):

∝ = 𝑙𝑙𝑙𝑙 � 𝑢𝑢(𝑧𝑧) 𝑢𝑢(𝑧𝑧 𝑟𝑟 ) � 𝑙𝑙𝑙𝑙 � 𝑧𝑧 𝑧𝑧 𝑟𝑟 � (9)

Capacity Factor

The capacity factor (CF) is the ratio of the actual energy output (𝐸𝐸 𝑇𝑇 ) produced by a wind power system to the output that would have been generated if the system operated full-time at its full rated power (𝑃𝑃 𝑅𝑅 ) over a given time period (𝜂𝜂). The dimensionless CF is given by:

𝐶𝐶𝐹𝐹 = 𝐸𝐸 𝑇𝑇 𝑃𝑃 𝑅𝑅 𝜂𝜂 (10)

Methodology

Fiji Meteorological Services (FMS) provided wind data for all Automatic Weather Stations (AWSs) in Fiji, since their installation. This wind data is for periods of between 2 and 8 years from 2011 to 2018 for this study. Thirty AWSs average wind speed and wind direction datasets Figure 1 shows a map of Fiji with the locations of the thirty stations, and Table 1 shows station details in terms of their latitude, longitude, altitude, observation periods and the total number of observations and data availability of these locations. Fourteen stations are located around Viti Levu (Keiyasi, Korolevu, Lomaivuna, Momi, Nadarivatu, Nadi, Nausori, Rakiraki, Rarawai, RKS Lovoni, Sigatoka, Suva, Tokotoko and Yaqara), six located around Vanua Levu subject to the assumption that there is the same overall weather regime for the reference and predicted sites, the weather conditions are close to neutrally stable, the reference wind data is reliable, the surrounding terrain is sufficiently gentle and smooth, and the topographical inputs 234 are adequate and reliable [START_REF] Mortensen | Wind Atlas Analysis and Application Program: WAsP 12 Help Facility[END_REF]. The estimated wind climatology is presented sector wise in the form of a wind rose and a Weibull distribution function. The wind rose indicates the relative frequency of occurrence of wind direction grouped in 12 sectors. The annual value of the Weibull parameters from the real data for the available years was calculated using the WAsP Weibull method [START_REF] Mortensen | Wind Atlas Analysis and Application Program: WAsP 12 Help Facility[END_REF]. Equation [START_REF] Singh | A Pre-feasibility Study of Wind Resources in Vadravadra, Gau Island, Fiji[END_REF], which comes from the power law, computes the wind shear coefficient α using the average wind speeds at 10 m and 55 m above ground level (AGL) as the reference and calculation height for wind shear coefficient respectively. These elevations have been used because WAsP processed the 10 m wind speed from AWSs and WAsP modelled the wind speed at 55 m AGL, and measurements are not available at any other heights.

The topography and land roughness maps for the 30 locations were generated on the WAsP Map Editor 12.1. The topography maps were imported from the SRTM ver.2 Database (http://dds.cr.usgs.gov/srtm/version2_1/SRTM3) at a horizontal resolution of 3 arc seconds (about 90 m). A land roughness of 0.03 at a height of 10 m was used in the topography map aided with additional 4 to 5 roughness line changes from 0.03 -1.5 via digitizing by closely studying the land roughness of the sites using Google Earth. This completed the land roughness maps in the WAsP Map Editor for a 6 km × 6 km grid, for each location. WAsP 12.1 was then used to analyse all sites for their wind energy resource in terms of mean wind speed, power density, orography, and the AEP using a Vergnet 275-kW wind turbine on a resource grid of 6 km × 6 km at a height of 55 m AGL. The obstacles and sheltering effects from nearby vegetation and buildings were taken into account in the WAsP analysis. To consider the impact of air density in the wind energy calculation, the WAsP model uses the WAsP Air Density Calculator to calculate the air density (kg/m 3 ) as a function of altitude (elevation in meters above sea level) and mean air temperature at the same altitude. A lapse rate of 6.5 K/km and a sea level pressure of 1013.25 hPa are assumed and the estimate is based on a standard atmosphere. High-resolution micro-scale resource maps for the resource grid of 6 km × 6 km at 50 m horizontal resolution was developed for each resource parameter. Finally, locations with the potential for supporting future utility-scale wind power development were identified and an evaluation of the minimum installed capacity and the corresponding energy production using Vergnet 275-kW wind turbines were computed using WAsP 12.1.

The Vergnet 275-kW wind turbine has been selected for this study as these turbines can be lowered to the ground and secured in place during extreme wind conditions, such as tropical cyclones, which are not uncommon in Fiji. Vergnet wind turbines are in use at an existing wind farm in Fiji and in other Southwest Pacific island countries prone to tropical cyclones such as Samoa, Vanuatu and New Caledonia. Table 2 presents the specifications of the Vergnet 275-kW wind turbine.

Resource Maps

Resource maps are generated using WAsP on a 6 km × 6 km domain each, for the surroundings of Rakiraki, Nabouwalu and Udu AWSs, which have been identified as suitable for potential wind power sites. The mean wind speed is the first important parameter in determining whether a particular site has potential wind speed resource to rotate a site-specific wind turbine to generate electricity to feed into an electrical grid network. The wind power density is the second most important parameter in determining whether a particular site has potential wind power resource in terms of the available power in the wind. The annual energy production is another important parameter in determining whether a particular site has potential wind energy resource for a specific wind turbine placed at any location within the resource grid. Wind speed and wind power density in the range of Class 3 or higher and AEP of 0.5

GWh/year or higher are considered feasible for utility-scale wind power development [4, 22 and 23].

Results

This section presents the results as follows: wind speed frequency, wind direction and wind speed patterns. In addition, there is a more detailed analysis of the three sites with the potential for future utility-scale wind power development.

Wind Speed Frequency and Wind Direction

The wind speed frequency and direction distributions provide site-specific information on wind resources, in particular the average power density available at the height of measurement. The Weibull A parameter is 5.6 m/s at Nabouwalu at a height of 5 m, and 7.1 m/s and 6.1 m/s at Rakiraki and Udu respectively, at a height of 10 m. A lower Weibull A parameter means lower mean wind speed at a site, while a higher Weibull A parameter means higher mean wind speed at a site. A higher Weibull A parameter also indicates an increase in the dispersion of wind around the mean wind speed [START_REF] Sathyajith | Wind Energy: Fundamentals, Resource Analysis and Economics[END_REF][START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF]. The Weibull A parameter observed at all 3 sites are considered higher. The Weibull shape parameter k is 1.91 at Nabouwalu at a height of 5 m, and 2.53 and 2.49 at Rakiraki and Udu respectively, at a height of 10 m. A lower Weibull shape parameter k (of 1.5 or 2) indicates a greater deviation away from the mean wind speed, while a higher Weibull shape parameter k (> 2.5) indicates that the variation of mean wind speed is small [START_REF] Sathyajith | Wind Energy: Fundamentals, Resource Analysis and Economics[END_REF][START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF]. The Weibull shape parameter k observed at sites of Rakiraki and Udu are considered on the higher side, according to this measure. The percentage of the time when the wind speeds is greater than 3.5 m/s (cut-in wind speed of the Vergnet 275-kW wind turbine) is approximately 73 %, 58.5 % and 71.4 % for Rakiraki, Nabouwalu and Udu, respectively.

The wind power class is a function of wind speed, and the corresponding wind power densities at heights of 10 m and 50 m, are as presented in Table 3 [22]. are outer islands that are located away from the central grid network, so are not suitable for utility-scale wind power development. Nevertheless, these results suggest that wind power could be a reliable renewable power source for these islands.

The sites of Rakiraki, Nabouwalu and Udu meet the criteria of having sufficient mean wind speed and power density for future utility-scale wind power developments at a height of 10 m.

The three sites have higher wind speeds and greater power density at lower heights compared to studies done at higher elevations [15, 13, and 18]. According to the present results, other mainland sites tested cannot be utilized for future utility-scale wind power development as the wind speed and wind power density fall below Class 3. Note that on small outer islands, wind power can be utilized for rural electrification purposes in stand-alone hybrid power systems despite having lower wind speeds.

Wind Speed Patterns

The diurnal wind speed pattern is the average diurnal cycle of the wind composed of the longterm averages of the wind at each hour of the day.

Wind Shear

Table 4 shows the average wind shear coefficients for the 30 AWS locations. They vary from a minimum of 0.11 to a maximum of 0.35. We can compare these with published wind-shear coefficients [START_REF] Patel | Wind and Solar Power Systems: Design, Analysis and Operation[END_REF]: the terrain type in Keiyasi, Korolevu, Nausori, Rotuma, Tokotoko and Yaqara is tall crops, hedges, and shrubs while in Momi, Nabouwalu, Saqani, Udu and Viwa, it is foothigh grass at ground level. In Lomaivuna, Nadarivatu, Rarawai, Sigatoka and Wainikoro, it is wooded country with many trees; Koro Island, Labasa, Nadi, RKS Lovoni, Seaqaqa, Suva,

Vanuabalavu and Vunisea has a small town with few trees and shrubs; Lakeba, Levuka, Matuku, Ono-I-Lau, Rakiraki, and Yasawa-I-Rara has smooth-hard ground as terrain types. The wind shear depends on the terrain type for each location that determines its roughness. A wind shear coefficient of α ≤ 0.2 is sufficient for normal wind turbine operation for all wind speed classes [START_REF] Smith | Evaluation of Wind Shear Patterns at Midwest Wind Energy Facilities[END_REF]. The locations of Lakeba, Levuka, Matuku, Momi, Nabouwalu, Nausori, Ono-I-Lau, Rakiraki, Saqani, Udu, Viwa, and Yasawa meet this requirement, while other sites are outside the range of normal working conditions of wind turbines. height of 55 m. These sites have the potential for future utility-scale wind power developments and this potential could be further explored using a comprehensive resource assessment once wind resource data is available for at least 10 years [START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF][START_REF] Bailey | Wind Resource Assessment Handbook: Fundamentals for Conducting a Successful Monitoring Program[END_REF].

Resource Maps of Potential Sites

Discussion

The three sites, Rakiraki, Nabouwalu and Udu, have higher wind speeds, power densities and AEP in comparison with the findings from previous studies done in Fiji and Kiribati [15, 13 and 18]. The differences between the present study and other studies [START_REF] Sharma | Wind energy resource assessment for the Fiji Islands: Kadavu Island and Suva Peninsula[END_REF][START_REF] Aukitino | Wind energy resource assessment for Kiribati with a comparison of different methods of determining Weibull parameters[END_REF] is the height at which the wind resource is analysed, its geographical location and the duration of the data used.

The wind turbines in the wind farms modelled have a minimum-maximum energy production range of 0.851 to 1.477 GWh, 0.966 to respectively. The proportional wake losses for the three sites is less than 2.74 % for all the three sets of 37 wind turbines. The capacity factor of the wind farms modelled ranges from 0.42 -0.48 while the wind farm efficiency ranges from 97.26 -98.33 %. Current operational onshore wind farms around the world have a capacity factor of 0.20 -0.54 whereas in the Oceania region the capacity factors are in the range of 0.31 -0.48 [START_REF]Renewables 2019 Global Status Report[END_REF]. The capacity factor for the modelled wind farms falls within the range of operational wind farms in this region.

A total AEP of 121.99 GWh is possible from the 3 × 10 MW wind farms modelled. A minimum of 30 MW of wind energy in terms of installed capacity, could be added to the existing grid network at an estimated cost of approximately $102 million FJD (using 3.4 million FJD/MW based on data from existing wind farm installation [START_REF]$34 M Failure: FEA admits lack of study in the wind farm project[END_REF]). This estimated cost of installation is less than the fuel bill for the local utility providers to run diesel generators for 2017 and 2018, which was reported to be 252.2 million FJD [START_REF] Efl | Energy Fiji Limited[END_REF]. The cost comparison does not compare the amount of energy produced by fossil fuel-based power plants.

Comparing the AEP of each of the 10 MW wind farms modelled with the existing wind farm in Butoni, which had an AEP of 2.56 GWh in 2018, indicates that these three sites have good wind energy potential. The analysis presented in this paper suggests that these sites provide a better wind energy utilization than the existing wind farm site. Another possibility is the transfer of under-performing wind turbines from the existing wind farm to the sites identified in the present study, for better utilization of the wind turbines in terms of energy production.

Furthermore, the AEP of 121.99 GWh from the 30 MW modelled wind farms at these sites is larger than the 108.739 GWh of production from the new 40 MW Nadarivatu Hydropower plant in 2018. The new hydropower plant cost approximately $300 million FJD to build and commission [START_REF] Efl | Energy Fiji Limited[END_REF]. Appropriate decision-making on investment by EFL, government, private sector and international investors can turn out to be profitable while contributing significantly to reducing the carbon footprints for Fiji.

Conclusions

This study summarizes an assessment of the wind resource at selected locations in Fiji for the potential of future utility-scale wind-power development. We Each site can accommodate a minimum installed capacity of 10 MW to support the electricity grid network with clean and environmentally friendly renewable electrical energy. Modelling a 10 MW wind farm at each site yields a net annual energy production of 43 GWh, 42 GWh and 37 GWh for Rakiraki, Nabouwalu and Udu respectively, with capacity factors of 0.42 -0.48 and wind farm efficiencies of 97 -98 %. This research promotes the idea that the SIDS in the Pacific can utilize the wind resource to ease its dependence on diesel-based power generation using small-medium sized wind turbines and provides an initial pathway to future wind energy investment opportunities for government, private sector and international investors.

  were recorded using mechanical anemometers and wind vanes. The anemometer used to measure wind speed at the 30 AWSs is Vector A100M, which has a range of 0 to 75 m/s, resolution of 0.1 m, accuracy of 1 % of readings between 10 and 55 m/s, 2 % above 55 m/s, 0.1 m/s for 0.1 to 10 m/s and threshold of 0.15 m/s. The wind vane used to measure wind direction at the 30 AWSs is Vector W200P, which has a range of 0 to 360° degrees, distance constant of 2.3 m, accuracy of ±3° and threshold of 0.6 m/s. Twenty-nine AWSs record wind measurements at a height of 10 m above ground while one station records at 5 m. The altitudes of the AWSs range from 2 m to 791 m above sea level. The temporal resolution of the wind data ranges from 10 minutes to 3 hours.

(

  Labasa, Nabouwalu, Saqani, Seaqaqa, Wainikoro and Udu) and the remaining ten are located on outer islands (Koro, Lakeba, Levuka, Matuku, Ono-I-Lau, Rotuma, Vanuabalavu, Viwa, Vunisea and Yasawa-I-Rara). Almost all the sites have a data availability of more than 75 % over the study period except Nabouwalu that has 27 % data availability. All the sites have data records at 10-minutes intervals except Nabouwalu that records the data at 3-hour intervals.

Figure 1 .

 1 Figure 1. Map of Fiji showing the locations of the 30 AWSs (Original Map Source: Google Earth).

Figures 2 -

 2 Figures 2 -4 present the elevation maps with respective roughness changes incorporated for the three potential sites of Rakiraki, Nabouwalu and Udu respectively with a contour interval of 10 m. The elevation for the three sites varies from sea level to 250 m, 530 m and 160 m with an average of 23.6 m, 163.8 and 15.7 m respectively.

Figure 2 .

 2 Figure 2. Topography and elevation map of Rakiraki (x denotes the location of the AWS).

Figure 3 .

 3 Figure 3. Topography and elevation map of Nabouwalu (x denotes the location of the AWS).

Figure 4 .

 4 Figure 4. Topography and elevation map of Udu (x denotes the location of the AWS).

Figures 5 (

 5 Figures 5 (a) -(c) show the wind speed frequency distribution and the wind direction analysis of the 3 suitable AWS sites. As for the other AWS sites, it is provided in the form of supplementary data. The mean wind speed is 5.01 m/s at Nabouwalu at a height of 5 m, 6.31 m/s and 5.45 m/s at Rakiraki and Udu respectively, at a height of 10 m. Wind direction is dominated by the southeasterly trade winds. In addition, the wind rose also shows wind direction in other sectors caused by island-scale circulation as well as local sheltering effects from nearby buildings and vegetation.

Figure 5 (

 5 Figure 5 (a). Wind rose and wind speed frequency distribution at Nabouwalu at 5 m AGL.

Figure 5 (

 5 Figure 5 (b). Wind rose and wind speed frequency distribution at Rakiraki at 10 m AGL.

Figure 5 (

 5 Figure 5 (c). Wind rose and wind speed frequency distribution at Udu at 10 m AGL.

  10 m. Nabouwalu has a mean wind speed of 5.01 m/s and a power density of 154 W/m 2 at the height of 5 m. In terms of wind power class at a height of 10 m for utility-scale wind power development, Rakiraki is categorised as Class 5 where as Udu and Nabouwalu are categorised as Class 3. Yasawa-I-Rara is Class 5, while Ono-I-Lau and Vanuabalavu are Class 3, but these

   Figure 6 (a). Diurnal wind speed patterns for the 8 AWS sites on the leeward side of Viti Levu at 10 m.

Figure 6

 6 Figure 6 (b). Diurnal wind speed patterns for the 6 AWS sites on the windward side of Viti Levu at 10 m.

Figure 6 (

 6 Figure 6 (c). Diurnal wind speed patterns for the 6 AWS sites in Vanua Levu at 10 m.

Figure 6 (

 6 Figure 6 (d). Diurnal wind speed patterns for the 10 AWS sites in outer islands at 10 m.

  speed pattern is the average, climatological seasonal cycle as represented by monthly means. Figures 7 (a) -(d) show the annual wind speed patterns for the 30 different AWS sites around Fiji. The four figures show the annual patterns of wind speed for AWSs on the leeward side of Viti Levu, the windward side of Viti Levu, Vanua Levu and the outer islands. Wind speeds vary seasonally, with weaker winds during the austral summer (November to April). The sites of Rakiraki and Udu have an annual wind speed of 3.8 -7.0 m/s and 4.2 -6.4 m/s respectively at a height of 10 m. Nabouwalu has an annual wind speed of 3.3 -6.6 m/s at a height of 5 m.

Figure 7 (

 7 Figure 7 (a). Annual wind speed patterns for the 8 AWS sites on the leeward side of Viti Levu at 10 m.

Figure 7 (

 7 Figure 7 (b). Annual wind speed patterns for the 6 AWS sites on the leeward side of Viti Levu at 10 m.

Figure 7

 7 Figure 7 (c). Annual wind speed patterns for the 6 AWS sites in Vanua Levu at 10 m.

Figure 7 (

 7 Figure 7 (d). Annual wind speed patterns for the 10 AWS sites in Outer Islands at 10 m.

Figure 8

 8 Figure 8 presents the monthly average wind speed for the three potential sites of Rakiraki, Nabouwalu and Udu with the corresponding 95 % confidence intervals. At 95 % confidence interval, the wind speed varies from 2.8 m/s to 8 m/s, 1.8 m/s to 7.8 m/s, 3.2 m/s to 7.4 m/s for Rakiraki, Nabouwalu and Udu, respectively. Looking at the 95 % confidence interval plots for the three sites, Nabouwalu is significantly different in comparison with Rakiraki and Udu.

Figure 8 .

 8 Figure 8. Monthly average wind speed for the three potential sites. Error bars represent plus and minus two standard deviation.

Sections 4 .Figures 9

 49 Figures 9 (a) -(c) present high-resolution maps of mean wind speed, wind power density and the AEP using the Vergnet 275-kW wind turbine for the site of Rakiraki at 55 m AGL.

Figure 9 (

 9 Figure 9 (a). Mean wind speed map of Rakiraki at 55 m AGL.

Figure 9 (

 9 Figure 9 (b). Wind-power density map of Rakiraki at 55 m AGL.

Figure 9 (

 9 Figure 9 (c). Annual Energy Production map of Rakiraki at 55 m AGL.

4. 4 . 2 Nabouwalu

 42 Figures 10 (a) -(c) present the high-resolution maps of mean wind speed, wind power density and the AEP using the Vergnet 275-kW wind turbine for the site of Nabouwalu at 55 m AGL.

Figure 10 (

 10 Figure 10 (a). Mean wind speed map of Nabouwalu at 55 m AGL.

Figure 10 (

 10 Figure 10 (b). Wind-power density map of Nabouwalu at 55 m AGL.

Figure 10 (

 10 Figure 10 (c). Annual Energy Production map of Nabouwalu at 55 m AGL

Figure 11 (

 11 Figure 11 (a). Mean wind speed map of Udu at 55 m AGL.

Figure 11 (

 11 Figure 11 (b). Wind-power density map of Udu at 55 m AGL.

  use 2 -8 years of near surface wind observations (2011 -2018) from thirty automatic weather stations. The standard windindustry software, WAsP is used to simulate the wind resource in terms of mean wind speed, dominant wind direction, power density and annual energy production (AEP) using a Vergnet 275-kW wind turbine.The results of the WAsP analysis reveal three potential sites for future utility-scale wind power development: Rakiraki, Nabouwalu and Udu. High-resolution 6 km × 6 km resource maps were produced for these wind resource parameters at a horizontal resolution of 50 m at these sites. Rakiraki, Nabouwalu and Udu have an average wind speed of 7.6 m/s, 7.1 m/s and 7.0 m/s with an average power density of 401 W/m 2 , 512 W/m 2 and 294 W/m 2 , and an average annual production of 0.907 GWh, 0.804 GWh and 0.716 GWh using a 275-kW wind turbine, respectively, at a height of 55 m. The dominant wind direction is southeasterly. The wind shear coefficient computed for the potential sites are suitable and within the normal working conditions stipulated for standard wind turbines.

235

  WAsP Climate Analyst 3.1 generated an estimate of the wind climatology for all 30 locations 236 using the raw data provided by FMS. It uses average hourly data for the analysis, and since

	237
	238	Nabouwalu has records every 3 hours, WAsP regarded the hourly data as missing. This is one
	239	of the reasons for the lower percentage availability in comparison to the other locations. The
	240	data at Nabouwalu are also 10-minute averages of wind measurements but they are recorded
	241	as an average every 3 hours.

Table 1 .

 1 Location and Data Records for the 30 AWSs 242

		Latitude	Longitude	Height	Elevation		# of
	Station	(degrees)	(degrees)	(m)	(m)	Data Period	Records Availability
	Keiyasi	-17.879	177.755	10	89.8	2015 -2018 206458	97.11%
	Koro Is	-17.345	179.418	10	108.8	2014 -2018 276573	99.75%
	Korolevu	-18.212	177.730	10	25.7	2015 -2018 184615	77.45%
	Labasa	-16.433	179.400	10	8.5	2016 -2018 154081	94.02%
	Lakeba	-18.236	-178.804	10	8.9	2012 -2018 347776	90.55%
	Levuka	-17.640	178.818	10	41.5	2015-2018	209922	99.77%
	Lomaivuna	-17.871	178.360	10	122.1	2014 -2018 262525	92.76%
	Matuku	-19.126	179.738	10	15.1	2012 -2018 349434	91.12%
	Momi	-17.895	177.266	10	43.8	2015 -2018 225886	97.24%
	Nabouwalu	-16.993	178.689	5	25.3	2013 -2017	13684	27.27%
	Nadarivatu	-17.567	177.963	10	824.1	2014 -2018 276248	97.25%
	Nadi	-17.759	177.444	10	20.7	2013 -2018 278708	98.86%
	Nausori	-18.046	178.559	10	5.7	2013 -2017	43815	98.20%
	Ono-I-Lau	-20.655	-178.720	10	18.2	2012 -2018 383119	99.83%
	Rakiraki	-17.340	178.221	10	8.1	2013 -2018 320194	97.30%
	Rarawai	-17.556	177.681	10	9.3	2011 -2018 397017	92.10%
	RKS Lovoni	-17.726	178.555	10	17.1	2014 -2018 275930	97.60%
	Rotuma	-12.499	177.046	10	22.1	2014 -2018 204793	89.33%
	Saqani	-16.474	179.708	10	30	2016 -2018 158452	97.60%
	Seaqaqa	-16.475	179.157	10	101.8	2016 -2018 155983	90.67%
	Sigatoka	-18.142	177.503	10	6.7	2013 -2018 286189	89.39%
	Suva	-18.147	178.453	10	6.6	2015 -2017 1048575	95.58%
	Tokotoko	-18.218	178.170	10	4.9	2013 -2018 426425	98.99%
	Udu	-16.141	-179.994	10	43.7	2013 -2018 325924	98.95%
	Vanuabalavu -17.241	-178.952	10	66.1	2012 -2018 297033	76.81%
	Viwa	-17.149	176.911	10	2	2013 -2018 294573	88.38%
	Vunisea	-19.046	178.165	10	31.9	2014 -2018 257896	96.79%
	Wainikoro	-16.304	179.558	10	15.1	2016 -2018 152692	88.96%
	Yaqara	-17.433	177.977	10	20	2014 -2018 284646	99.92%
	Yasawa	-16.698	177.574	10	40	2013 -2018 295182	89.30%
	243

Table 2 .

 2 Vergnet 275-kW turbine specifications

	Rotor	Hub	Cut-in	Cut-out	Rotation	Rated	Rated wind
	Diameter	Height	speed	speed	rate	Power	speed
	32 m	55 m	3.5 m/s	25 m/s	31 rpm	275-kW	12 m/s

Table 3 .

 3 Wind Power Class [22]

	Wind Power	10 m Wind Power	10 m Wind	50 m Wind Power	50 m Wind
	Class	Density (W/m 2 )	Speed (m/s)	Density (W/m 2 )	Speed (m/s)
	1	0 -100	0 -4.4	0 -200	0 -5.6
	2	100 -150	4.4 -5.1	200 -300	5.6 -6.4
	3	150 -200	5.1 -5.6	300 -400	6.4 -7.0
	4	200 -250	5.6 -6.0	400 -500	7.0 -7.5
	5	250 -300	6.0 -6.4	500 -600	7.5 -8.0
	6	300 -400	6.4 -7.0	600 -800	8.0 -8.8
	7	400 -1000	7.0 -9.4	800 -2000	8.8 -11.9
	A Wind Power Class of 3 or above is considered suitable for most utility scale wind power
	developments [4, 22 and 23]. The sites of Rakiraki and Udu have mean wind speeds of 6.31
	m/s and 5.45 m/s and power densities of 241 W/m 2 and 157 W/m 2 respectively, at a height of

Table 4 .

 4 Mean Wind Shear Coefficient at 30 Locations in Fiji

		Wind speed @ 10 m	Wind speed @ 55 m	Mean wind shear
	Location	[m/s]	[m/s]	coefficient [α]
	Matuku	2.45	2.92	0.10
	Ono-I-Lau	5.24	6.33	0.11
	Rakiraki	6.31	7.57	0.11
	Yasawa	6.56	7.87	0.11
	Levuka	2.83	3.48	0.12
	Lakeba	2.69	3.4	0.14
	Nabouwalu	5.01	7.14	0.15
	Udu	5.45	7.01	0.15
	Momi	4.19	5.5	0.16
	Saqani	2.58	3.45	0.17
	Viwa	3.76	5.11	0.18

Table 5

 5 presents a summary of the minimum installed capacity and the wind energy resources for each modelled wind farm in the three sites of Rakiraki, Nabouwalu and Udu. Each of these sites can accommodate a minimum of 10 MW of wind turbine installations with each turbine occupying an area of approximately 804 m 2 . A typical 10 MW wind farm using 37 × 275-kW wind turbines with 5-rotor diameter spacing modelled in Rakiraki, Nabouwalu and Udu would yield a net AEP of 42.869 GWh, 42.116 GWh and 37.005 GWh respectively. The 5-rotor diameter spacing is used to minimize the effects of one turbine working in the wake or disrupted flow of another turbine.

Table 5 .

 5 Modelled Wind Farm Analysis

	Location	Rakiraki	Nabouwalu	Udu
	Installed Capacity [MW]	10	10	10
	Wind Turbine	37 x 275-kW	37 x 275-kW	37 x 275-kW
	Total gross AEP [GWh]	43.602	42.832	38.048
	Mean gross AEP [GWh]	1.178	1.158	1.028
	Min gross AEP [GWh]	0.854	0.994	0.801
	Max gross AEP [GWh]	1.480	1.262	1.259
	Total net AEP [GWh]	42.869	42.116	37.005
	Mean net AEP [GWh]	1.159	1.138	1.000
	Min net AEP [GWh]	0.851	0.966	0.749
	Max net AEP [GWh]	1.477	1.257	1.215
	Total proportional wake loses [%]	1.68	1.67	2.74
	Mean speed [m/s]	8.71	9.75	8.21
	Mean power density [W/m 2 ]	608	1300	485
	Wind farm capacity factor	0.48	0.48	0.42
	Wind farm efficiency (%)	98.32	98.33	97.26

  Nabouwalu and Udu respectively. The mean wind speed and the mean power density at the hub height of the wind turbines modelled at Rakiraki, Nabouwalu and Udu is 8.71 m/s, 9.75 m/s and 8.21 m/s with an average wind power density of 608 W/m 2 , 1300 W/m 2 and 485 W/m 2

1.257

GWh and 0.749 to 1.215 GWh with an average of 1.159 GWh, 1.138 GWh and 1.000 GWh estimated per wind turbine for Rakiraki,

Acknowledgments

This study was conducted as part of a PhD scholarship project funded by the NZ Ministry of Foreign Affairs and Trade (NZMFAT). The authors would like to acknowledge the Fiji Meteorological Services for providing the ground-based wind data measurements and the Department of Wind Energy of the Denmark Technical University for providing a student license for the wind simulation software package WAsP.

In the present study, the wind resources are simulated at 55 m AGL with 5 -6 years of wind data, while for other studies it was simulated at 34 m altitude with 12 -18 months wind data and using a smaller wind turbine.

All three sites identified in the present study meet the criteria of having sufficient mean wind speed, power density and energy in the wind, using a Vergnet 275-kW wind turbine at the hub-