Sandrine Kablan

Josef Loening

Is Chad Affected by Dutch or Nigerian Disease?

Keywords: JEL classifications: C30, E32, E61, O11, O13 Natural resource curse, Dutch disease, Chad, Structural VAR

We examine the effects of the 'natural resource curse' on Chad and find little evidence for Dutch disease. Structural vector auto-regression suggests that changes in domestic output and prices are overwhelmingly determined by aggregate demand and supply shocks, and while oil production and high international prices negatively affect agricultural output, the effects are small. Consistent with empirical evidence for neighbouring Cameroon, we observe minimal impact on Chad's manufacturing sector. We associate our findings with structural underemployment and the inefficient use of existing production factors. In this context, increased public expenditures in tradable sectors present the opportunity to make oil revenues an engine of national development.

Introduction

Economic history is replete with examples of natural resource-poor countries outperforming natural resource-rich countries. [START_REF] Sachs | Natural Resource Abundance and Economic Growth[END_REF] find an inverse relationship between natural resource intensity and growth between 1970 and 1990, and their results are supported by the fact that there are very few cases of resource-abundant developing countries sustaining 2% per annum growth during this period. [START_REF] Sala-I-Martin | Addressing the Natural Resource Curse: An Illustration from Nigeria[END_REF] call this phenomenon the Natural Resource Curse.

In the economic literature, while several studies explain the negative relationships between natural resource exploitation and economic growth, there are four plausible explanations that emerge from the previous research (1) Rent-seeking would not encourage governments of such countries to invest in innovative sectors with growth potential, [START_REF] Gelb | Windfall gains: blessing or curse?[END_REF] and [START_REF] Auty | Resource-based industrialization: sowing the oil in eight developing countries[END_REF]. (2) The governments of such countries would rather waste rents through profligate or inappropriate consumption. (3) Price volatility of natural resources leads to greater uncertainty, which can reduce factor accumulation through greater risk. (4) More recent studies, from the 1970s and 1980s, suggest that Dutch disease is responsible, i.e., natural resource production promotes de-industrialisation, Neary and Van Wijnbergen (1986) and [START_REF] Matsuyama | Agricultural productivity, comparative advantage and economic growth[END_REF]. Indeed, booms in natural resource sectors do affect the distribution of employment throughout the economy. For example, one typical African country that is afflicted by the natural resource curse is Nigeria. Nigeria is one of the top ten oil exporters in the world; however, its economy was stagnant and failed to alleviate poverty until very recently when reforms were finally undertaken, Budina, Pang, Van Wijnbergen (2006). [START_REF] Sala-I-Martin | Addressing the Natural Resource Curse: An Illustration from Nigeria[END_REF] show that rather than Dutch disease, waste appreciation of an effective exchange rate. In countries affected by Dutch disease, the positive effects of a natural resource boom are diminished by the negative effects of contraction and stagnation in the country's tradable sectors and by an appreciation of the real effective exchange rate. Consistent with these symptoms, Ndjamena, the capital of Chad, ranked third with respect to cost of living, just behind Tokyo, in a recent survey. 1 This suggests that people living in Ndjamena pay higher living costs, defined as consumption, in non-tradable sectors. This paper examines the features that could result in Chad becoming a natural resource curse country, even when the windfall oil revenue it is experiencing could be an opportunity to boost the nation's economic growth, if properly managed. Moreover, if Dutch disease is diagnosed, economic measures could be taken to guide Chad in taking advantage of oil revenues. For example, Chad's government could invest part of the country's oil revenue in the agricultural or manufacturing sectors, thus serving to avoid further decline in the sector. By doing so, Chad could also avoid being the victim of the natural resource curse. The effects of Dutch disease, however, are more challenging to evaluate in Chad because of data limitations and also because the non-oil sectors are underdeveloped, making it difficult to detect their current and anticipated decline. A good diagnosis of Dutch disease would make economic policies more appropriate and help to avoid the problems associated with the resource curse. Indeed, if measures are taken to counter Dutch disease when the economy does not suffer, it could have disastrous consequences. And if measures are not taken when the economy is threatened, the consequences could be equally destructive. The policy recommendations must complement counter-policies to other channels of the natural resource curse and reinforce them. To the best of our knowledge, this is the first paper to address the issue of the natural resource curse in Chad, and as such, this paper is interesting in the sense that Chad is one of the latest low-income countries to have discovered oil. Therefore, there is concern about the effects of this oil windfall on Chad's economic recovery.

In this paper, we consider four channels through which a country may become a natural resource curse country, and we thoroughly analyse the possibility of Dutch disease through its main channels. Because of some specificities to developing countries that are not taken into consideration in the original model of [START_REF] Corden | Booming Sector and De-Industrialization in a Small Open Economy[END_REF], we deem it relevant to verify our intuition through an empirical analysis. Indeed, when some hypotheses (perfect substitutability between domestic and imported goods, nonexistence of unemployment and efficient use of production factors) are relaxed, some conclusions of the original model do not hold, making it difficult, through an analysis of Chad's economic data, to draw a conclusion regarding Dutch disease. Therefore, we use a structural VAR model to assess whether the agricultural and manufacturing sectors of Chad are affected by Dutch disease.

Introducing structural shocks allows us to take into consideration some specific effects of oil discovery on Chad's economy. Indeed, with only a simple Vector Auto-Regressive (VAR), we were not able to make any substantive conclusions. Our results suggest, however, that there is no evidence of Dutch disease in the manufacturing sector, while evidence of the disease is a bit more obvious in the agricultural sector. We remain cautious in the interpretation of our results, as the data from the manufacturing and agricultural output must be interpolated quarterly due to data limitations. This paper proceeds as follows. Section 2 provides analyses of Chad's economy as a potential natural resource curse country. Further analyses regarding Dutch disease and its main transmission channels are presented in section 3. In Section 4, we conduct some empirical tests, and in the analysis, we establish the impulse response functions and the variance decompositions by estimating a structural vector auto-regression (VAR) model. In Section 5, we summarise the findings of this study and propose some policy recommendations.

Stylised facts: Chad and the natural resource curse

Several studies emphasise the fact that natural resource-rich countries perform far worse than less well-endowed countries. Frankel, 2010, identifies 3.5%. These effects create uncertainty in the investment decisions of the public sector as well as private agents, thus ultimately impeding economic growth.

b) Poor institutions:

The literature indicates that entrepreneurial activity, by creating new and better goods or services, destroys rents accruing to those holding licenses, thus restricting trade in already existing goods or services. Lane and[START_REF] Lane | Power, growth, and the voracity effect[END_REF][START_REF] Lane | The voracity effect[END_REF] show that, in an economy with multiple powerful groups, each group has open access to production and higher productivity may, in fact, force a decline in the rate of return on investment and thus on growth. This scenario is probable because, when productivity increases, each group attempts to acquire a greater share of the production by demanding more transfers, which, at term, increases the tax rate and reduces the net return on capital. Thus, the redistribution effect may outweigh the direct effect of increased productivity. Similarly, Baland and Francois, 2000, show that when a significant number of individuals are already engaged in rent seeking behaviours, an increase in productivity drives the economy towards more rent seeking and may actually lead to a decline in the aggregate income. [START_REF] Torvik | Natural resource, rent seeking and welfare[END_REF], finds that if productivity growth increases profits in rent situations relative to profits in the production sector, the result is lower welfare. Many empirical studies corroborate these facts. Gallagher, 1991, documents the disproportionate rise in rents with increases in income throughout the African continent, while [START_REF] Hilaire | The effects of Trinidad and Tobago's oil boom on relative prices, wages and labour flows[END_REF], describes the decline in Trinidad and Tobago's GDP immediately after oil prices quadrupled, an event that constituted 20% of the country's GDP in 1972 through 1974, and further documents a rise in the premium on import licenses for food. Chad could be one of these bad examples. Indeed, Chad, ranked 171 out of 178 countries by Transparency International, is one of the most corrupt states in the world, and Chad's institutions remain poor, as indicated by its 2.2/6 CPIA index (Country Policy and Institutional Assessment) in 2010. Chad demonstrates very low levels in terms of its management of the four components of CPIA index: economic management, structural policies, policies for social inclusion and public sector management and institutions.

Regulation remains inadequate, particularly with regard to creating or closing a business.

Moreover, the proportion of budgetary resources allocated to priority sectors remain below the levels suggested by the PRSP (Poverty Reduction Strategy Paper), a fact that particularly disadvantages social sectors and rural development. Even though Chad was accepted in April 2010 as an EITI3 (extractive industries transparency initiative) candidate, progress remains slow. Thus, most governance indicators show that much remains to be done at this level.

c) Anarchy:

Statistics show that more and more countries around the world have democratised and succeeded in establishing a peaceful environment. This is not true, however, in natural resource-rich countries. The annual rate for democratisation since 1980 is 3.17 for natural resource non-rich countries and 1.13 for resource-rich countries. Chad is a country that has long been affected by political instability. However, recent negotiations between the government and the rebels have somewhat quieted these tensions, though the last parliamentary and presidential elections were marked by unrest and protest. Furthermore, the management of oil revenues reflected a faster increase in government spending than did other resources, a situation due, in part, to the fact that Chad did not create a sovereign fund to manage the volatility of oil resources and ensure the proper use of the funds. d) Crowding out of the manufacturing sector: This effect is not really observed in Chad, as many investments, the result of oil revenues or foreign direct investment, have been made in new factories and industries. These investments have helped to provide Chad with new industries such as a refinery, a power plant, and a cement factory, which, in turn, have enhanced Chad's industry sector and freed the country from its dependence on foreign refined oil. However, we propose to better analyse this crowding-out effect of manufacturing along with the phenomenon of Dutch disease. e) Transmission channels of Dutch disease: a brief overview [START_REF] Corden | Booming Sector and De-Industrialization in a Small Open Economy[END_REF] original model of Dutch disease considers a small country with full and efficient employment of production factors in the two tradable and non-tradable sectors where there is a mobile production factor transferable between sectors and a perfectly elastic demand for tradable goods. (i) Large resource boom inflows entail an increase in aggregate expenditures for tradable and non-tradable goods, thus influencing the real effective exchange rate (REER). An increased demand for non-tradables, induced by financial inflows, translates into an upward shift in the demand for non-tradables (services and contraction sectors) as well as an increase in their prices. As the price of tradables is fixed, the REER appreciates, which discourages their production. This is the spending effect. (ii) In the non-tradable sector, as the marginal product of labour increases as a result, there is a reallocation of labour from the tradables sectors to the non-tradables sectors. This is the resource transfer effect. (iii) Last, REER appreciation induces a disincentive to buy non-tradables. This is the expenditure-switching effect (the volatility channel). 4 Further, if the relationships between natural resource exports and government expenditures (or government revenues) are correlated, the movement of a real effective exchange rate will be volatile through the spending effect. Serven (2003) finds that high volatility tends to have negative impacts on investment and economic growth. In sum, the transmission channels and the effects of Dutch disease, presented first by [START_REF] Brahmbhatt | Dealing with Dutch Disease[END_REF] 5 , can be summarised as follows. 6

Figure 1: Transmission Channels and Effects of Dutch Disease

Note: REER denotes a real effective exchange rate. Source: Authors.

4

We can consider the volatility channel an untraditional channel in the sense that most previous studies mainly focus on the spending effect and the resource movement effect.

However, there are many features in the model of [START_REF] Corden | Booming Sector and De-Industrialization in a Small Open Economy[END_REF] that do not fit the characteristics of low income countries (LICs). For instance, most LICs suffer from high structural unemployment and inefficient use of existing production factors. As an illustration, Chad is far from full employment and has much idle capital7 . Again, many importables are domestically produced in LICs, and if one disaggregates the tradables sectors into exportables and importables, the threat of reduction in the tradables sectors is mitigated. [START_REF] Nkusu | Aid and the Dutch Disease in Low-Income Countries: Informed Diagnoses for Prudent Prognoses[END_REF] notes that in LICs, there is imperfect substitutability between domestically produced manufactured goods and imported goods. Furthermore, domestic manufacturers have the ability to raise prices and increase supply in response to domestic market conditions, while manufacturing firms that use sizable amounts of imported inputs can benefit from a relatively appreciated domestic currency. Because of the complementarity between imported and domestically produced inputs, the demand for domestic inputs can also increase. As a result, input supplier firms, as well as firms using domestic and imported inputs, will expand according to the price elasticities of the supply of inputs and the demand for goods. In the end, elasticity-prices mitigate the REER appreciation, and again, the risk of de-industrialisation is mitigated.

According to the same author (Nkusu), in the two-sector model, the resource effect is a corollary of the spending effect because of the assumption of full employment. However, when this hypothesis is relaxed, the resource effect cannot, by itself, trigger Dutch disease effects. As an illustration, Benjamin, Devarajan and Weiner, 1989, using a computable general equilibrium model for Cameroon, show that when there is imperfect substitutability between domestic and imported goods, some of the manufacturing sector could benefit. Moreover, whenever resource inflows are used in the socioeconomic sector, the three-sector model of Dutch disease (which differentiates between importables and exportables in the tradables sector) is more relevant, and Dutch disease effects can be reversed. Indeed, an increased use of available resources allows both the tradables and non-tradables sectors to expand, in which case, the trade balance would not necessarily deteriorate. These cause-effect events are demonstrated in [START_REF] Levy | Public investment to reverse Dutch Disease: the case of Chad[END_REF] general equilibrium model as she simulates the positive effects of agricultural policy (road network for food distribution and irrigation infrastructure) on Chad's economy. The results reveal that using oil revenues to improve water access in Chad's rural areas promotes poverty reduction and, at the same time, generates substantial economic growth. A road network not only improves food distribution of agricultural products and market integration, but it is also believed to improve trade and human and physical capital productivity. Thus, if oil revenues are well spent in the key agricultural sectors, the three most common symptoms of Dutch disease (a decline of specific sectors, an increase in the real exchange rate, worsening of poverty and inequality) may not appear. Based on these contrasting models and simulations, [START_REF] Nkusu | Aid and the Dutch Disease in Low-Income Countries: Informed Diagnoses for Prudent Prognoses[END_REF] and [START_REF] Levy | Public investment to reverse Dutch Disease: the case of Chad[END_REF]), and while taking into consideration developing countries' specificities and the good use of oil revenues, we more thoroughly analyse the relationship between Chad and Dutch disease, that is, whether Chad is affected by Dutch disease.

Chad's economic statistics and Dutch disease channels

Our methodology is to analyse stylised facts that could reveal the three effects of Dutch disease, though data on Chad's economy are very scarce. We were, nevertheless, able to find data using the BEAC (Banque des Etats de l'Afrique Centrale) and the IMF (International Monetary Fund) data sources. While Chad's case reveals some patterns of the spending effect, it is difficult given the availability of the data to conclude the extent of resource and volatility effects.

Spending effect:

Although the data do not allow us to observe the share of spending on non-tradables, data from the table of financial operations of the Chadian government indicate that government spending tripled between 2003 (year in which oil began to be exploited) and 2009 (figure 2). This sharp increase is mainly due to current expenditures, which increased by 332%, and capital expenditures, which increased by 57%. The increased capital expenses mainly served to provide road infrastructure, which placed Chad second behind Cameroon among CEMAC countries. Other investments included those in the social field through the construction of health and education infrastructure. As for current expenditures, the increase is mainly due to the expense of transfers and subsidies (which increased by 1338%), military expenditures (which increased by 203%) and other current expenditures consisting primarily of salaries and materials (which increased by 174%). As noted, these data show a sharp increase in public spending, though the distribution of these expenditures is not necessarily well addressed, as evidenced by the data. The increase in military spending (wages and weapons) is a response to political instability and recurring conflicts that Chad has faced. Furthermore, social spending, which could have been financed by subsidies and transfers, have not yielded significant results. Indeed, Chad remains one of the least developed countries in terms of social development, with a ranking of 175 out of 182 on the Human Development Index of UNDP (2007). Again, assuming that some of these subsidies and transfers have been allocated to key economic sectors, such as oil and cotton, the results remain disappointing and disconcerting. The cotton sector presents a significant decline in production from 2003 to 2009. For example, cotton seeds decreased from 102,000 tons to 40,000 tons, though there was a peak in 2004 of 207,500 tons. Regardless, increased government expenditures do not result in higher prices for non-tradables 8 . The two other channels of Dutch disease do not allow us to conclude with certainty whether

Chad is a victim of this economic pitfall.

Resource movement effect:

Increased personal expenditures in the public sector are due to an increase of 47% in the average wage and an increase in the number of employees in the public sector from 57,000 in 2001 to 117,000 in 2008. However, our data do not allow us determine the origin of this workforce. Therefore we cannot draw a conclusion about the existence of a resource movement effect.

Exchange rate volatility

Similarly, the anchor of the CFA franc to the euro does not allow us to observe the volatility of the real effective exchange rate. Before the discovery of oil in Chad, Chad's main trading partner for exports had been the euro area (approximately 60 percent in exports). After the discovery of oil, however, Chad increased oil exports to the U.S., which then became Chad's main trading partner (approximately 90 percent of Chad's trading is with the U.S., (figure 3)).

Consequently, if the dollar depreciates against the euro, Chad's real effective exchange rate will likely appreciate. 9

9 For example, if we consider that the world consisted only pf Chad, the U.S., and the euro area, Chad's real effective exchange rate could be expressed as REER=W us ×E us ×(P us /P Chad )+ W euro ×E euro ×(P euro /P Chad ). Here, W us and W euro are the weights, that is, the trade flows (both exports and imports) between Chad and the U.S. and Chad and the euro area, respectively. E us and E euro are the nominal exchange rates defined as units of domestic currency per unit of foreign currency. P i is the price level in country i measured by the CPI index. By using these relationships, we estimated Chad's real effective exchange rate (REER) in Figure 4. (% of GDP) SD: 1. 3 (1990-2003) SD: 3.5 (2004)(2005)(2006)(2007)(2008) which this balance decreases (although it does remain positive). This decline can be explained by fewer exports to the U.S. (see Figure 10). Figure 7 shows that the volatility of government consumption, which has been increasing since 2004. This increase could suggest a risk with respect to an increase in the volatility for the real exchange rate and, therefore, a threat to stable economic growth. Furthermore, upon a thorough examination of the composition of exports and imports (figures 8 and 9), we notice that oil exports rise sharply and represent the bulk of the exports (85%, on average, over the period) at the expense of cotton fibre and other exports, whose export quantities have decreased. Meanwhile, imports have almost doubled from 2003 to 2009, an increase that is attributable to the non-oil sector, the oil sector and the public sector. were agricultural products such as cotton and livestock; however, after the discovery of the oil reserves, oil quickly became Chad's dominant export. (Figure 10). Figure 11 shows that oil production has declined gradually since 2004. Figure 12, which depicts the share of the output in the agricultural and manufacturing sectors as a percent of the GDP indicates that the contraction of the agricultural sector is substantial. Therefore, at first glance, Chad seems to be clearly affected by Dutch disease, as a decline of production in agricultural and manufacturing sectors is one of its typical symptoms. Given that Chad's GDP has increased because of oil exports, we are a bit sceptical in drawing this conclusion. Indeed, figure 13 shows that output in the manufacturing sector is increasing, while the output in the agricultural sector declined severely in 2005 and has been sluggish even since. 10

The aforementioned evidences suggest that the agricultural sector could be affected by Dutch disease and that the manufacturing sector is not affected. This is consistent with the results of some previous studies, such [START_REF] Benjamin | The 'Dutch' Disease in a Developing Country. Oil Reserves in Cameroon[END_REF] and [START_REF] Fardmanesh | Dutch Disease Economics and the Oil Syndrome: An Empirical Study[END_REF]. For example, [START_REF] Benjamin | The 'Dutch' Disease in a Developing Country. Oil Reserves in Cameroon[END_REF], conclude that the manufacturing sector in Cameroon could expand output, thus assuming imperfect substitutability between domestic and foreign goods. 10 However, it should be noted that we cannot solely rely on the possibility of Dutch disease for the plunge of the output in the agricultural sector. We should also consider other diverse external factors, such as weather, civil war, and enabling environment. Given the stylised facts, we question whether Chad's economy is actually affected by Dutch disease.

Prospecting for Dutch disease in manufacturing and agricultural sectors

 Methodology

In this section, we conduct certain empirical tests. Recent studies such as the IMF (2009), [START_REF] Cerutti | Bolivia: The Hydrocarbons Boom and the Risk of Dutch Disease[END_REF] and [START_REF] Oomes | Diagnosing Dutch Disease: Does Russia Have the Symptoms?[END_REF] use an equilibrium real exchange rate method for their empirical analyses, as this method estimates the relationships between a real effective exchange rate and certain influential variables such as terms of trade, productivity differentials, trade openness and government spending. In the analysis of Chad, however, we face a few limitations. First, as mentioned in Section 3, it is difficult to rely on the real effective exchange rate for the analysis. Second, Chad has relatively poor data, and thus, it is difficult to calculate some of the influential variables mentioned within this text.

Therefore, we choose a method that requires a poor theoretical framework: a VAR model.

VAR models are commonly used for forecasting systems of interrelated time series and for analysing the dynamic impact of random disturbances on the system of variables. Hutchison (1994) uses a vector error correction model (VECM), which is a restricted VAR for non stationary, co-integrated series, to assess whether there is Dutch disease in the UK, the Netherlands, and Norway. Taking into consideration the energy prices, energy output, money/credit supply, and manufacturing output, he concludes that there is no specific sign for Dutch disease in the manufacturing sector of these countries. In a more recent paper, [START_REF] Bjorland | The economic effect of North sea oil on the manufacturing sector[END_REF], on the other hand, showed that while Norway presents no evidence of Dutch disease, the UK is subject to Dutch disease in the long run. She concludes that in Norway, energy booms and oil price increases stimulate the economy, which, in turn, leads to increases in manufacturing production. To the contrary, in the UK, energy booms had a small negative impact on manufacturing, thus suggesting that other factors (i.e., oil price shocks, for example) could explain this decline. She was able to perform a refined analysis because of the use of structural VAR. Structural VAR, through the introduction of restrictions, yields some intuitive theoretical economic justifications to the VAR model, thus allowing it to take into consideration some effects of oil discovery on the economy.

In our case, we conduct two VAR models for the agricultural and manufacturing sectors. The first model (model 1) consists of the volume of oil extraction (ov t ), the real oil price (rop t ), the manufacturing production (m t ) and the inflation rate calculated from the GDP deflator ( t ). The second model (model 2), agricultural production (a t ), is the same that is used for manufacturing production. Appendix 4 shows the co-integration tests for the two models. As there is no co-integrating vector among the variables, we conduct a structural VAR model rather than a VECM. Following Bjorland (1994), we identified three other structural shocks in addition to energy booms ( ). They are real oil price shocks ( ), aggregate demand shocks ( ) and aggregate supply shocks ( ).

Therefore, we define a vector of stationary variables compounded from the first difference of the cited macroeconomic variables for each model, such that , for model 1 is devoted to the manufacturing sector and , for model 2, is devoted to the agricultural sector. Indeed, Appendix 2 shows the stationary tests for the level and first difference variables. The reduced form of VAR is estimated as

(1)
where B j (j= 0,1….p) is a matrix of autoregressive coefficients at lag j B 0 is the identity matrix e t , the residual vector, is serially uncorrelated with the covariance matrix Ω.

Following the Wold representation theorem and ignoring the constant term, the VAR can be represented as an invertible distributed lag of serially uncorrelated disturbances:

(2)

 t ES  t ROP  t AD  t AS z t  ov t , rop t , m t ,  t   y t  ov t , rop t , a t ,  t   z t  B 0  B 1 z t1 . . . B p z tp  e t BLz t  B 0  e t z t  CLe t
with and C 0 as the identity matrix.

The elements in e t are contemporaneously correlated and cannot be interpreted as structural shocks. To have those structural shocks, they must be orthogonalised by imposing restrictions. For this purpose, we must impose enough restrictions to identify the orthogonal (structural) components of the error terms. Therefore, a restricted form of the moving average containing the vector of original disturbances as linear combinations of the Wold innovations can be expressed as ,

where is the vector of the orthogonal structural disturbances, with , thus is normalised to have unit variance for convenience.

(2) and (3) imply that , hence, . Therefore, if D 0 is defined, then the MA representation in (3) is also derived. It follows from the normalisation of that . This imposes k(k+1)/2 =10 restrictions on the elements in D 0 (k being the number of variables in the VAR). As the D 0 matrix comprises sixteen elements, we need six more restrictions to orthogonalise the different innovations. These restrictions will, at the same time, allow us to introduce into the model the intuitive economic explanations for the dynamics of the VAR model. Two types of identifying restrictions are considered: short-run and long-run. In our model, we will have one long-run restriction and five short-run restrictions.

As the order of the variable and shocks is important in the structural VAR, we first order the four uncorrelated shocks that we previously defined:

CL  BL 1 z t  DL t  t cov t   I  t e t  D 0  t CLD 0  DL cov t  D 0 D 0   
. Energy booms will be identified from the equation for oil production and are, thus, interpreted as volume changes. To identify these shocks, we impose the restrictions that oil production depends only on energy booms and real oil price at the first period, in which case, the contemporaneous effects of aggregate demand and aggregate supply disturbances on oil production are zero. Rewriting equation ( 3) in terms of the equation of oil production, we have:

(

Oil price shocks in the 1970s are believed to have reduced world manufacturing output, but such shocks have also worked to transfer income from the oil importing countries to the oil exporting countries. [START_REF] Bjorland | The economic effect of North sea oil on the manufacturing sector[END_REF] introduces into the VAR model the real oil price as opposed to the nominal price, allowing for a focus on both effects. Therefore, we will identify real oil price shocks by assuming that changes in real oil price depend only on real oil price shocks at the first period. This means that aggregate demand and supply shocks, as well as energy booms, will influence the real oil price with a lag, which is reasonable as oil price is a financial spot that reacts quickly to news. Rewriting (3), we have:

Finally, manufacturing (or agricultural) output will also be impacted by demand and supply shocks. To identify these shocks, we include inflation together with manufacturing (or agricultural) output. Demand shocks are different from supply shocks in that demand shocks are assumed to have no long-run effects on output (cf. Blanchard and Quah, 1989).

 t   t AD   t AS   t ES   t OP   ov t  D 11 L t AD  D 12 L t AS  D 13 L t ES  D 14 L t ROP D 11,0  D 12,0  0 rop t  D 21 L t AD  D 22 L t AS  D 23 L t ES  D 24 L t ROP D 21,0  D 22,0  D 23,0  0
The long-run effect of the demand shock upon the level of m t (or a t ) is the sum of the infinite number of lag coefficients, . Writing (4) as , where C(1) and D(1) indicate the long-run matrices of C(L) and D(L), respectively, the long run-restriction implies that D 31 (1)=0 or :

Therefore, from (3) the growth rate of manufacturing (or agricultural) output and the inflation can be described as:

(8) (9)
With these six additional restrictions, our structural VAR is identifiable. However, the VAR model results are sensitive to the way in which they are identified, which is why the identifying restrictions should have plausible interpretations. Furthermore, the credibility of the results could be tested using over-identifying restrictions. Therefore, two over-identifying tests will check the respective positive and negative impacts of demand and supply shocks on inflation. Indeed, our last restriction concerned the non-existence of a long-run effect of demand shocks on output, as opposed to the effect of supply shocks. This assumption implies the simultaneous reverse effects of demand and supply on inflation that can be verified by examining the impulse response analysis. The information criteria can be used to determine the optimal lag length. Appendix Demand and supply shocks have a positive impact on manufacturing production, while energy booms and real oil price shocks have no significant impacts. Regarding the response of the GDP deflator, energy booms have a positive but non-significant impact, while real oil price shocks have a positive impact. The responses of GDP deflator to both energy price and volume are consistent with Dutch disease, where increases in demand and production in the economy push prices upward. A demand shock permanently increases prices, as does a supply shock; however, for the supply shock, the inferior band encompasses zero in the long run. Therefore, the response to this shock may not be positive, but it may be null or even negative. In all cases, the over-identification of restrictions according to which demand (supply) shocks increase (reduce) prices is not totally supported by model 1.

Responses of agricultural output and GDP deflator to the four above mentioned shocks are depicted in figures 16 and 17. Here, the over-identifying restrictions are supported by model 2, as shown in figure 17 (we had to consider among the endogenous variables, inflation at t+1 instead of t). Additionally, energy booms are found to have a positive impact on prices after approximately 8 quarters, while real oil price has a negative impact. Again, this is consistent with Dutch disease, even if the results may not be significant given that the standard deviation bands encompass the x-axis. Figure 16 indicates that energy booms have a negative impact on agricultural production after 4 quarters, which corroborates, again, the presence of Dutch disease in the agricultural sector. However, the higher band of the standard deviation is above the x-axis, implying that output production could continue to increase after one year. Real oil price shocks have no significant impact on agriculture production. Finally, demand and supply shocks have a permanent significant positive impact on agriculture production, as expected. Tables 1 and2 present, respectively, the variance decomposition for manufacturing output and the GDP deflator of model 1 and the agricultural output and the GDP deflator for model 2. As oil volume and real oil price are explained by their own variances, we do not present their variance decomposition. Furthermore, demand shocks are less important than supply shocks in explaining variations in manufacturing output, while the reverse holds for inflation.

Concluding remarks

This paper examines whether Chad, one of the last African countries to have discovered oil, could be affected by the natural resource curse. The interest of the study is enhanced by the fact that Chad is one of the poorest countries in the world, and this oil windfall could be used as a powerful means to alleviate poverty and contribute to economic growth. In addition to the stylised facts for analysing channels of the natural resource curse, we empirically test for Dutch disease. Our methodology is more refined than the traditional methods used to assess Dutch disease, and the results on whether the agricultural sector or the manufacturing sector is affected by Dutch disease allow us to better address policy recommendations applicable to Chad as well as other similar countries.

Indeed, there is considerable literature regarding natural resource-rich countries that performed very poorly economically. In Africa, Nigeria failed during four decades of oil exploitation to make substantial economic gains, thus poverty remains a problem in several Nigerian states, and non-oil economic sectors have stagnated. Our analysis shows that Chad's economy presents several patent features that support the existence of the natural resource curse, such as volatility in government resources, poor institutions, recurrent tensions, and mismanagement of oil resources. More specifically, there are symptoms of Dutch disease. However, as the original model of [START_REF] Corden | Booming Sector and De-Industrialization in a Small Open Economy[END_REF] does not take into consideration some specificities of developing countries, we attempt to conduct a more thorough analysis. The descriptive analysis using Chad's economic statistics, suggests that the agricultural sector could be affected by Dutch disease. However, we could not find clear evidence that the manufacturing sector is adversely affected. Furthermore, the features in Chad's revenue and expenditure systems could be the symptom of Dutch disease, and the volatility effect could have negative impacts on investment and economic growth.

To verify whether there is Dutch disease in Chad's agricultural and manufacturing sectors, we use a structural VAR model and analyse the impulse response functions and variance decompositions. Accordingly we find that the manufacturing sector is not negatively affected by energy booms, while the agricultural sector could, in the long run, react negatively to oil production. The variance decomposition analyses corroborate our analyses of an impulse response function. Indeed, the variation of manufacturing output is better explained by real oil price shocks than by energy booms. And variation in the agricultural sector, while explained in the first year by real oil price shocks, is better explained, in the long run, by energy booms.

It should be noted that in the empirical analysis we used interpolated data due to data limitations. Therefore, caution should be exercised when interpreting the results of the study. Another consideration worthy of mention is that there is a possibility that the decline in the agricultural output is due to the decline in the output of the cotton sector (as mentioned in the descriptive analysis), rather than to a decline in oil production. In addition, as mentioned in Section 2, assuming Chad's country-specific circumstances, such as the existence of large number of unemployed workers and idle capital, there is a possibility that Chad could benefit from the oil boom. In fact, the output in the manufacturing sector is increasing, as Figure 13 indicates. Given the data limitations and potential concerns, we should avoid making hard-line policy prescriptions based solely on the interpretations of our findings, as [START_REF] Nkusu | Aid and the Dutch Disease in Low-Income Countries: Informed Diagnoses for Prudent Prognoses[END_REF] suggests. Further research should be implemented, particularly

  five channels through which the natural resource curse can occur: volatility, institutions, anarchy, the crowding out of manufacturing and Dutch disease. Chad discovered and began exploiting oil in 2003. Since this date, there has been much concern about how Chad can benefit from this new source of revenue. Indeed, in 2001 the World Bank agreed to finance the oil exploitation project under the condition that Chad use the oil revenues to finance social programmes and develop infrastructure. However, some irregularities were recently detected, leading us to analyse Chad as a potential natural resource curse country. a) Volatility: One channel of the natural resource curse is the volatility due to fluctuations in commodities' prices. Commodities' prices fluctuated widely in recent years with a dramatic increase in certain products such as oil, whose price index rose from 47.23 in January 2000 to 188.44 in October 2011. Its standard deviation of 53.47 over the past decade reflects the high volatility of oil prices. This volatility in oil prices is then transmitted to government resources and to the commodities' price level in Chad. Since the discovery of oil, government resources have largely been made up of oil revenues. Thus, public spending as a share of non-oil GDP has increased by 46% between 2003 and 2009, while inflation was 8 to 10% between 2008 and 2009, unlike other CEMAC 2 countries, where it is approximately 2 Economic and Monetary Community of Central Africa.

Figure

  Figure 2: Expenditures

Figure 3 :

 3 Figure 3: Exports to the U.S. (left) and to the Euro Area (right)

Figure

  Figure 6: Revenue and Expenditure Figure 7: Government Consumption

Figure 8 :

 8 Figure 8: Chad FoB exports in billions CFA F Figure 9: Chad imports in billions CFA F

  Figure 10: Exports Figure 11: Oil Production

  Figure 12: Share of Output (% of GDP) Figure 13: Value Added

3

  Figures 14 and 15 depict the cumulative effects of demand shocks, supply shocks,

Figure 14 :

 14 Figure 14: Accumulated response to structural one S.D. innovations ±2 S.E.

Figure 16 :

 16 Figure 16: Accumulated response to structural one S.D. innovations ±2 S.E.

   C 32 1D 21,0  C 33 1D 31,0  C 34 1D 41,0  0 m t  D 31 L t AD  D 32 L t AS  D 33 L t ES  D 34 L t ROP  t  D 41 L t AD  D 42 L t AS  D 43 L t ES  D 44 L t ROP  Data description All variables are quarterly data for the period 1983/Q4 to 2008/Q4. The period depends on the data availability. Specific definitions and sources of the data are provided in Appendix 1. Before the estimation, we must mention a few data limitations. 11 First, we cannot find quarterly data for the GDP deflator or for the agricultural and manufacturing outputs. Therefore, we interpolate yearly data into quarterly data by interpolation cubic spline with the last observation matched to the source data 12 . Second, though Chad discovered oil reserves in 2003, it still lacks ample data for oil production. Therefore, taking these data limitations into consideration, we must be cautious when interpreting the estimation results.

	 j0  D 31,j	C1D 0  D1
	C 31 1D 11,0

Table 2 :

 2 This may reflect a relatively steep short-run supply schedule in terms of a standard aggregate demand and supply diagram, where wages and prices adjust quickly, while energy booms account for less than 1% of the explained variance in manufacturing. Real oil price shocks, on the contrary, explain 2 to 6% of the variance over the first eight years, which is consistent with our analysis of the impulse response function. Again, energy booms explain approximately 1% of the variation in inflation, while real oil price accounts for Variance decomposition for model 2

	approximately 10%.
	With respect to model 2, whose results of variance decomposition are displayed in table 2,
	we observe the same pattern for demand and supply shocks, describing the same mechanism
	for price adjustment. Energy booms are responsible for less than 1% during the first year, but
	for approximately 4% thereafter, while 4% of the explained variance in agriculture is
	accounted for by real oil price shocks during the first quarter, though it is less than 2%
	thereafter. With regard to inflation, energy booms explained less than 1% of the variation,
	while real oil price accounts for more than 5% at all horizons. As a robustness check, we
	introduce rainfall as an exogenous variable into the SVAR models. However, the results do
	not change, probably because the effects of this variable are already captured by agricultural
	output.

See http://www.mercer.com/costoflivingpr in detail. The most expensive city in the world is Luanda in Angola, according to the same survey.

It aims to promote governance by strengthening transparency in the extractive industries. Revenues from the extractive industries should be an important source of economic growth and social development in developing countries.

Overall explanation of the transmission channels can be found in[START_REF] Brahmbhatt | Dealing with Dutch Disease[END_REF].[START_REF] Cerutti | Bolivia: The Hydrocarbons Boom and the Risk of Dutch Disease[END_REF] and[START_REF] Oomes | Diagnosing Dutch Disease: Does Russia Have the Symptoms?[END_REF] also provide clear explanations of the spending effect and the resource movement effect.

A similar summary can be found in[START_REF] Oomes | Diagnosing Dutch Disease: Does Russia Have the Symptoms?[END_REF].

IMF country report, No 09/67, 2009.

IMF country report No 09/67.

In addition to data limitations, data unreliability is also problem. For example, the IMF (2009) notes that "not only are its external sector data unreliable but Chad also has a substantial amount of unrecorded trade with Nigeria and Cameroon that would not be reflected in such statistics as the REER and the terms of trade."

This method assigns each value in the low frequency series to the last high frequency observation associated with the low frequency period, then places all intermediate points on a natural cubic spline connecting all the points.

in the aforementioned fields.

Nonetheless, to avoid the Natural Resource Curse phenomenon, we make the following policy recommendations.

First, the diversification of exports with respect to both goods and trading partners is important. As oil is now its dominant export, Chad's economy is vulnerable to the demand and supply shocks associated with oil. In addition, Chad exports oil mainly to the U.S., and the volatility of these oil exports to the U.S.> continue to increase (figure 7), implying that Chad's exports will suffer significantly if the U.S. suffers a severe recession or stops importing from Chad for some reason. Figure 7 and Figure 10 imply that oil revenue and exports to the U.S. seem to be highly correlated, which could increase the volatility of the real effective exchange rate through the spending effect.

Second, wise spending policies are also indispensable. Revenues received from oil exports, if well managed, can contribute to economic growth. For example, the government should dedicate revenues to the agricultural sector, which used to be the main sector for exports. Indeed, [START_REF] Levy | Public investment to reverse Dutch Disease: the case of Chad[END_REF] shows that investments in the road network for food distribution, irrigation infrastructure and improvements to water access in Chad's rural areas allow for poverty reduction and generate substantial economic growth.

Our last policy recommendation is to focus on the improvement and transparency of Chad's government and its institutions, as Chad is ranked 175th in the Corruption Perception Index (CPI) in 2009. 13 As Sala-i-Martin and Subramanian (2003) note, corruption could be the most influential factor for low economic growth, not Dutch disease.

The quality of institutions is, therefore, a pre-requisite for the appropriate use of oil windfalls to ensure economic growth. 13


N Kanari

E Allain

I Gaballah

A Study of the Chromite Carbochlorination Kinetics

Keywords: Cr, Mg, Si, Al, Fe

The carbochlorination of a chromite concentrate was studied between 500 °C and 1000 °C using boat experiments. The reaction products were analyzed by SEM, XRD and chemical analysis.

The carbochlorination of a chromite concentrate at about 600 °C led to the partial selective elimination of iron thus increasing the Cr/Fe ratio in the treated concentrate. Total carbochlorination of the chromite concentrates and volatilization of the reaction products was achieved at temperatures higher than 800 °C.

Kinetics of the chromite carbochlorination was studied between 750 °C and 1050 °C using thermogravimetric analysis. The results were discussed in terms of the effects of gas flow rate, temperature, partial pressure of Cl 2 +CO and Cl 2 /CO ratio on the carbochlorination process. It was observed that the temperature effect changed significantly with the progress of the reaction. The initial stage of the carbochlorination was characterized by apparent activation energy of about 135 and 74 kJ/mol below and above 925 °C, respectively. While a value of about of 195 kJ/mol was found for the remaining of the carbochlorination process.

I. INTRODUCTION

Although the chromium is found in a variety of minerals [START_REF] Amiel | Anonymous: Thermochemical and Physical Properties (TAPP, version 2.1)[END_REF] (crocoïte PbCrO 4 , vauquelinite Pb 2 Cu[CrO 4 ][PO 4 ], uvarovite Ca 3 Cr 2 [SiO 4 ] 3 , merumite 4 (Cr, Al) 2 O 3 3 H 2 O, etc.), the main ore of chromium is the chromite mineral that is a spinel with the simple formula FeCr 2 O 4 . Chromite belongs to the spinel group and its space group is Fd3m [2] . However, naturally occurred chromite ore is characterized by the substitution of Fe 2+ by Mg 2+ and that of Cr 3+ by Al 3+ and Fe 3+ with the following general formula (Mg, Fe 2+ )(Cr, Al, Fe) 3+ 2 O 4 . Figure I shows the chromite structure where the iron and magnesium occupy the tetrahedral sites while the chromium, aluminum, and iron fill the octahedral ones [3] . The gangue is essentially composed of orthopyroxene, olivine and plagioclase, as well as hydrous alteration products such as serpentine, chlorite and talc.

About 75 pct of chromite are used for the ferro-chromium production. Moreover, chromite is used for chemical, refractory and foundry applications. Natural characteristics such as the chromium to iron ratio, the chromite grain size and the SiO 2 , Al 2 O 3 , MgO, and P contents can be as important as the Cr 2 O 3 content in determining the suitability of a chromite product for various end uses as well as its market price. Table I [4] represents some chromite specifications for different uses. Thus, the metallurgical chromite is characterized by its high chromium content and a Cr/Fe ratio higher than 2. Conventional mineral processing cannot modify the composition of chromite. This composition can be modified only by using chemical or thermal methods. Chlorination appeared to be a potential approach for beneficiation of poor chromite concentrates and/or ores [5][6][7][8] . This paper deals with the carbochlorination of chromite concentrate to increase its Cr/Fe ratio through selective chlorination of iron compounds. The effect of several parameters on the reaction of chromite mineral with Cl 2 +CO was studied by using thermogravimetric measurements.

The literature is rich with respect to the thermodynamic and kinetic aspects of chlorination of simple oxides contained in the chromite (FeO, Fe 2 O 3 , Cr 2 O 3 , MgO, Al 2 O 3 , and SiO 2 ) using different chlorinating agents. Results of this bibliographic study were summarized in Reference [5] through [8]. However, few publications were devoted to the chlorination of chromite ores and concentrates.

Athawale and Altekar [9] investigated the selective chlorination of iron oxides contained in a chromite concentrate, by using HCl in a fluidized bed. The authors suggested that the selective chlorination could be carried out effectively between 900 °C and 950 °C producing a residue having high Cr/Fe ratio useful for the steel industry.

Table I. Chromite Specifications by Use (Wt pct) [4] . The chlorination of a chromite ore (38.2 Wt pct Cr 2 O 3 ) and its concentrate (51.1 Wt pct Cr 2 O 3 ) with Cl 2 +C between 600 °C and 1000 °C was carried out by Hussein and El-Barawi [10] . Between 800 °C and 1000 °C, they found that the amount of carbon necessary to achieve almost full chlorination and volatilization of products, between 800 °C and 1000 °C, for the chromite ore or its concentrate, was four and five times that of the stoichiometric amounts, respectively.

Metallurgical

The decrease of reaction rate at temperatures higher than 800 °C was explained by possible decomposition of COCl 2 formed in situ. The chlorination rate of iron was higher than that of chromium and attributed to favorable thermodynamic data. Finally, the authors suggested that it is possible to make products having Cr/Fe ratios suitable for the ferro-chromium production by using short time carbochlorination of chromite ores at 600 °C or even at lower temperatures.

Hussein et al. [11] studied the chlorination of Cr 2 O 3 , Al 2 O 3 , MgO, FeO, Fe 2 O 3 , of theirs mixtures and of synthetic compounds having the formula Me 2+ Me 2 3+ O 4 (M = metal) by using Cl 2 +C. The behavior of a chromite ore and its concentrate was also tested using the same chlorination conditions, it was found to be in fair agreement with the results obtained from synthetic compounds.

The European patent N° 0 096 241 [12] described the chlorination of oxidized materials by using C+Cl 2 in a fluidized bed at temperatures higher than 800 °C in order to produce the metal chlorides. The chlorination of chromite was performed between 1000 °C and 1100 °C. The charge was composed of 20.25 kg of upgraded chromite ore and 7.75 kg of coke. A gas mixture of Cl 2 +N 2 of 3 m 3 /h and containing 30 pct Cl 2 was used. About 95 % of all the oxides contained in the chromite were chlorinated and volatilized and the chlorides (CrCl 3 , FeCl 2/3 , AlCl 3 , and MgCl 2 ) were recovered in cyclones, whilst the dusts were composed of chromium and aluminum oxides.

II. MATERIALS AND EXPERIMENTAL PROCEDURE

The sample of chromite concentrate was obtained from an Albanian plant. The scanning electron microscopy 'SEM', X-ray diffraction 'XRD', microprobe and chemical analyses were used to determine the composition of this concentrate. Figure 2 gives some aspects of the chromite concentrate as revealed by SEM and microprobe analyses. Results of chemical and microprobe analyses concerning five major oxides of the chromite concentrate were grouped in Table II. This concentrate contained about 48 % Cr 2 O 3 and was of a metallurgical grade characterized by a Cr/Fe ratio close to 3.2. As it is shown in Figure 2 and Table II, the microprobe analysis suggested the presence of two different bodies in the concentrate. The chromite that is essentially constituted of chromium, iron, magnesium, aluminum oxides, and the gangue containing the oxides of magnesium and silicon along with small amounts of iron and aluminum oxide.

Combining the results of chemical and microprobe analysis, it resulted that chromite constituted about 80 pct of the concentrate. Based on the microprobe analysis data, the general formula of the chromite mineral could be the following : (Fe 2+ 0.30 , Mg 0.70 )(Cr 1.56 , Al 0.37 , Fe 3+ 0.07 )O 4 . This solid can also be formulated as : 30.9 pct FeCr 2 O 4 , 51.0 pct MgCr 2 O 4 , 13.7 pct MgAl 2 O 4 and 4.4 pct Fe 3 O 4 . One may underline that these spinels have the same crystalline structure [13] also mentioned the presence of these compounds in the chromite ores.

Carbochlorination of chromite concentrate was performed using the horizontal experimental set-up schematized in Figure 3. This apparatus is composed of a gas metering unit followed by gas purification one and a horizontal furnace. A carbon furnace was added to eliminate any traces of oxygen contained in the gaseous mixture. The gaseous reaction products were recovered, in the condensers, by cooling and the spent gases were purified before their release to the atmosphere. The reaction products were examined by XRD, SEM and analyzed using conventional chemical methods.

The kinetic study was performed using samples of chromite mineral contained in the concentrate mentioned above. It was separated from the gangue by successive treatments using dense liquors. Kinetics of the carbochlorination of chromite with Cl 2 +CO was determined by thermogravimetric analysis 'TGA' technique schematized in Figure 4. Its main unit is a CAHN 1000 microbalance having a sensitivity of 10 µg. The carbochlorinating gas mixture was dried by H 2 SO 4 and CaCl 2 before its introduction into the reactor. About 40 mg of chromite mineral was used for every test. The sample was uniformly distributed in a quartz crucible having a section of about 0.5 cm 2 . This crucible was hooked to the balance by quartz rods. It was heated in the carbochlorinating gas mixture by an electrical furnace having a heating rate of about 7 °C/min. The evolution of the samples' weight and temperature was continuously recorded. For the TGA isothermal runs, the sample was heated in nitrogen up to the desired temperature before the introduction of the carbochlorination gas mixture into the reactor.

III. THERMODYNAMIC CONSIDERATIONS

The evolution of free standard energy changes (∆G°) as a function of temperature for the carbochlorination reactions (Eqs. 1 through 16) of major compounds contained in chromite concentrate was calculated. Data of Reference [14] and [15] were used for these calculations. Figure 5 traces the evolution of ∆G° versus temperature for the considered reactions. All the considered reactions were thermodynamically feasible in the explored temperature range. However, the carbochlorination reactions of iron oxides (Eqs. 7 to 9) were more favorable than that of chromium compounds (Eqs. 1 to 3) at temperatures higher than 500 °C. The phase stability diagrams of (Cr, Fe, Mg, Al, Si)-O-Cl systems [5,6] suggested that the chlorides were the stable phases during the chlorination of oxides in the presence of a reducing atmosphere. Using carbon monoxide as reducing agent, the main products of the carbochlorination of chromium, iron, aluminum, magnesium and silicon oxides are expected to be CrCl , FeCl 3 , AlCl 3 , MgCl 2 , and SiCl 4 [5,6] , respectively. The chromium oxychloride (CrO 2 Cl 2 ) is formed using a chlorinating gas mixture having high partial pressure of oxygen [16] .

Figure 6 shows the evolution of vapor pressure of chlorides of major elements contained in chromite as a function of the temperature [17,[START_REF] Amiel | Anonymous: Thermochemical and Physical Properties (TAPP, version 2.1)[END_REF] . As shown by this figure, these chlorides are characterized by a wide difference of vapor pressure at a given temperature. Silicon, aluminum and ferric chlorides possess a high vapor pressure at temperature lower or equal to 300 °C. This suggests that the carbochlorination of chromite compounds, at temperatures close to 500 °C, will allows the volatilization of these chlorides leading to a residue rich in chromium compounds.

IV. RESULTS

A. Carbochlorination of Chromite Concentrate in the Horizontal Experimental Set-up

Several grams of the chromite concentrate sample was treated in Cl 2 +CO (Cl 2 /CO = 1) between 500 °C and 1000 °C for a reaction time of 2 hours. Figure 7 gives the evolution of the percent weight loss (pct WL) of the sample versus temperature. The chromite concentrate starts to react with the carbochlorination gas mixture at 500 °C. However, temperatures close to 1000 °C were required to achieve a complete reaction of the sample and volatilization of the reaction products. The residues were analyzed in order to determine their iron content. The extraction curve of iron as function of the temperature is also traced in Figure 7. Clearly, more than 45 pct of iron contained in the chromite was extracted at 500 °C for a reaction time of two hours. At higher temperatures, the extraction of iron increased almost linearly while that of pct weight loss augmented exponentially.

Another series of carbochlorination experiments of chromite concentrate was carried out between 500 °C and 850 °C for a reaction time of 2 and 8 hours. Chromium and iron contents of residues were determined by chemical analysis. In addition, the residues and condensates were characterized by XRD and SEM analyses. Figure 8 (a) traces pct WL of the chromite concentrate versus temperature for the two residence times. Small difference of pct WL was observed at temperatures lower than 700 °C when the reaction time was increased from 2 to 8 hours. The effects of temperature and the reaction time on the extraction of iron and chromium are given in Figure 8 (b). Results suggested that increase of the reaction time had a feeble effect on the extraction of iron in the whole-explored temperature range. The extraction of chromium at temperature lower or equal to 650 °C did not exceed 15 pct and was also slightly dependent on the reaction time. However, the extraction of chromium at temperature higher than 700 °C depended strongly on the temperature and reaction time. These results suggested that low temperatures and short reaction times might allow the selective extraction iron with limited losses of chromium.

Table III gives results of SEM and XRD analyses of several carbochlorination residues and condensates obtained at different reaction times and temperatures. Spinel [(Fe,Mg)(Cr,Al) 2 O 4 ] was identified to be the main phase in the residue up to 800 °C. As mentioned earlier, it was difficult to detect the evolution of the spinels by XRD. Moreover, lattice parameters are almost independent from the simultaneous removal of divalent (Fe 2+ , Mg) and trivalent (Fe 3+ , Cr, and Al) elements. On the other hand, the lacunaire spinels could be generated but the current experimental tools don't allow their detection. Chromium trichloride (CrCl 3 ) was identified in the condensate at temperatures higher or equal to 600 °C. As shown by Table III, magnesium was detected in the condensates at 800 °C and it was identified as MgCl 2 . 6H 2 O due to the hygroscopic nature of MgCl 2 . These results confirm the possibility of selective separation of chlorides of Mg, Cr from those of Fe and Al by a careful selective cooling of the gas phase. Spectra of SEM analyses of the carbochlorination residues of chromite along with the semiquantitative analysis are shown in Figure 9. These results agree with those obtained by chemical analysis. Almost all the iron was removed at temperatures higher than 600 °C. Total extraction of chromium was achieved at 800 °C for a reaction time of 8 hours and the residue was essentially composed of Cl, Mg, Si, and O. These results indicate that magnesium chloride (Table III) was only partially volatilized due to its low vapor pressure at 800 °C (Figure 6). Moreover, the presence of silicon in this residue suggests than the magnesium silicates were less reactive towards the Cl 2 +CO gas mixture [6] . Water leaching of this residue allows the separation of magnesium chloride from magnesium silicates.

Table IV summarizes the results obtained during the carbochlorination of chromite concentrate between 500 °C and 800 °C. As shown in this table, the initial Cr/Fe ratio is close to 3.2. This ratio increases as the carbochlorination temperature and time augments. It was doubled during the carbochlorination of the chromite concentrate at 500 °C with limited losses of chromium. This suggests that the carbochlorination of a chromite ore and/or a concentrate having a low Cr/Fe ratio, around 500 °C, will lead to the increase of this ratio to a satisfactory level for the ferro-chromium production specifications. The selective chlorination of iron could be also advantageous for chromite concentrates used for other applications.

In order to obtain an insight of the reaction mechanism of this complex solid, it was worthwhile to carry out a kinetic study of the carbochlorination of the chromite by Cl 2 +CO gaseous mixture using TGA at relatively high temperature. The following paragraphs describe these kinetic parameters as determined by TG analysis. The non-isothermal carbochlorination of chromite mineral was investigated from room temperature to about 975 °C using the apparatus described in Figure 4. Figure 10 gives pct WL as a function of temperature at temperatures higher than 400 °C. The chromite started to react with the carbochlorinating gas mixtures at about 500 °C. Total reaction of the chromite with Cl 2 +CO and volatilization of the reaction products were achieved at about 975 °C. The pct WL curve had a plateau of weight loss (≈ 35 pct WL) observed at about 775 °C prior to a sharp increase of pct WL at temperatures higher than 825 °C. The derivative of the pct WL traced in Figure 10 shows clearly the existence of two regions of distinct kinetics. The pct WL observed corresponds roughly to the calculated weight loss for the carbochlorination of Fe 3 O 4 and FeCr 2 O 4 (see L 1 in Figure 10) both contained in the chromite. This couldn't be attributed to the difference between the rate of MgCl 2 formation and that of its volatilization. Moreover, the slow down of the reaction rate at about 35 pct WL was also observed during the oxychlorination of chromite by Cl 2 +O 2 where the volatilization rate of MgCl 2 was higher than that of its formation [6,8] .

Effect of gas flow rate

It was studied at 1000 °C during the carbochlorination of chromite with a gas mixture having an equimolar content of carbon monoxide and chlorine. The total flow rate of Cl 2 +CO was varied from 8 to 100 L/h. It was interesting to follow the evolution of the reaction rate as function of gas flow rate at different pct WL of the sample. Figure 11 gives a typical example of the reaction rate evolution versus flow rate for the WL range up to 25 pct and between 65 to 90 pct. For a gas flow rate higher than 20 L/h, the initial reaction rate was at least 5 times faster than that obtained for a WL higher than 65 pct. On the other hand, the reaction rate seems to be almost independent of the gas flow rate for values higher than 60 L/h whichever was the pct WL chosen. A gas flow rate of 80 L/h (linear gas velocity 'V g ' = 1.1 cm/s) was used to study the effect of other parameters on the carbochlorination of chromite.

Effect of temperature

The effect of temperature during the isothermal carbochlorination tests of chromite sample was studied between 750 °C and 1050 °C. Results are given in Figure 12 as pct WL versus time. About 400 minutes were required to carbochlorinate 90 pct of the sample at 775 °C, while only 5 minutes were necessary to reach the same pct WL at 1050 °C. On the other hand, the reaction rate up to 35 pct WL was rapid in the investigated temperature range. Different kinetics equations [18] were used to fit the experimental data concerning the evolution of the reaction extent (ratio of weight of the reacted fraction to initial weight) as function of time. The best mathematical fitting of the experimental data was obtained using equations 17 and 18. As mentioned before, the chlorination of (Fe, Mg)(Cr, Al) 2 O 4 proceeds in two steps. The first is up to 35 % of weight loss that corresponds roughly to the chlorination of FeCr 2 O 4 characterized by a high reaction rate. The second step is that of the chlorination of Mg(Cr, Al) 2 O 4 with a weight loss of 35 to 100 % and characterized by a relatively slow rate of reaction.

Figure 13 shows the mathematical fitting of experimental data for a reaction extent of 5 to 35 % weight loss. Up to 850 °C, the reaction progress according to equation [17] with a correlation coefficient of 0.971. Equation [17] represents the pore diffusion control in reaction of porous or nonporous solids with a porous product layer of spheres. This suggests that the diffusion of iron and chromium in the spinel could be the slowest step during the chlorination of the chromite. At temperatures higher than 925 °C, Figure 13 b suggests that the experimental data fit equation [18] with a correlation coefficient of 0.991. Equation [18] is considered to describe a reaction controlled by the chemical reaction in the case of shrinking non-porous particles (with or without a solid porous product) and porous particles with unchanged overall sizes. This suggests that the chlorination of chromite to be controlled by the chemical reaction. This change may be attributed to the presence of non-volatilized CrCl 3 , see figure 6, and/or molten magnesium chloride that offers a privileged access for the chlorination of unreacted portion of chromite [6] . Such hypothesis is partially confirmed by the change of the apparent activation energy from 135 to 74 kJ/mol around 925 °C.

For the chlorination of Mg(Cr, Al) 2 O 4 and the rest of the sample, Eq. [17] seem to be the most appropriate to describe the evolution of the reaction extent as function of time for the temperature range of 750 °C to 1050 °C with a correlation coefficient of 0.996 (Figures 14 a andb).

1-3(1-X) 2/3 +2(1-X) = kt [17] 1-(1-X) 1/3 = kt [18]
where k = constant and t = chlorination time.

The effect of temperature on the carbochlorination was estimated thanks to Arrhenius diagram. Figure 15 gives plots for pct WL ranging from 5 to 30 pct and from 40 to 90 pct. Apparent activation energy 'E a ' of about 135 and 74 kJ/mol were calculated for the carbochlorination of chromite up to 30 pct WL below and above 925 °C. A higher value of E a of about 195 kJ/mol characterizes the carbochlorination process between 40 and 90 pct WL.

Effect of (Cl 2 +CO) partial pressure

To determine this effect a series of the isothermal tests was carried out at 1000 °C. A gas mixture of Cl 2 +CO+N 2 having a linear velocity of 1.10 cm/s was used. The Cl 2 /CO molar ratio was kept constant and equal to one, while (Cl 2 +CO) content was varied from 15 to 100 pct. Figure 15 represents the obtained data plotted as pct WL versus time. Results show that the time required to obtain a given pct WL decreased with the raise of the pct (Cl 2 +CO) in the gas mixtures. The evolution of the reaction rate as a function of the partial pressure of (Cl 2 +CO) for the two pct WL ranges is traced in Figure 15 (b). The apparent reaction orders obtained were 1.30 and 1.40 for 5 ≤ pct WL ≤ 30 and 40 ≤ pct WL ≤ 60, respectively. These non-integer values may reflect a complex mechanism involving probably several intermediate steps.

Effect of Cl 2 /(Cl 2 +CO) ratio

The effect of Cl 2 /(Cl 2 +CO) molar ratio was also studied at 1000 °C using a Cl 2 +CO gas mixture having a V g of 1.10 cm/s. The chlorine content was varied from 10 to 90 pct. Figures 16 (a) and (b) show the data plotted as pct WL against time. About 9 minutes were required to obtain 80 pct WL of the sample using a gas mixture containing 10 or 90 pct Cl 2 . The evolution of the reaction rate as function of Cl 2 /(Cl 2 +CO) for 5 ≤ pct WL ≤ 30 and 40 ≤ pct WL ≤ 60 was illustrated in Figure 16 (c). It indicates that the maximum reaction rate was obtained for a gas mixture having almost an equimolar Cl 2 and CO content.

V. CONCLUSIONS

Carbochlorination of chromite at about 600 °C could be used for upgrading the chromium concentrates and ores. More than 60 pct of iron was extracted during the treatment of a chromite concentrate at this temperature during two hours with a chromium extraction of less than 5 pct.

Consequently, the Cr/Fe ratio was at least doubled. The reaction time and temperature had a slight effect on the kinetics of iron removal. While, total extraction of chromium was achieved at temperatures higher than 750 °C and it depended on the temperature and reaction duration.

Partial removal of magnesium as MgCl 2 occurred at temperatures higher than 800 °C.

Carbochlorination of the chromite concentrates at these temperatures followed by a controlled cooling of the gas phase allows selective separation of chlorides of major elements contained in the chromite.

The effect of temperature on the reaction of the chromite mineral with Cl 2 +CO between 750 °C and 1050 °C changes with the reaction progress. The average value of the apparent activation energy was about 135 and 74 kJ/mol for reaction extents up to 0.35 at temperatures lower and higher than 925 °C, respectively. The carbochlorination process for reaction extent higher than 0.4 proceeded with a value of E a of about 195 kJ/mol.

The mean apparent reaction order of the chromite carbochlorination was about 1.35 with respect to Cl 2 +CO at 1000 °C. At this temperature, a maximum reaction rate was obtained with a carbochlorinating gas mixture having a Cl 2 /(Cl 2 +CO) ratio of 0.5. Figure : Evolution of standard free energy changes as function of temperature for the carbochlorination of compounds of chromite concentrate [14,15] . Figure : Evolution of vapor pressure of several chlorides [17,[START_REF] Amiel | Anonymous: Thermochemical and Physical Properties (TAPP, version 2.1)[END_REF] .

FIGURE CAPTIONS

TABLE CAPTIONS

Table I : Chromite Specifications by Use (Wt pct) [4] Table II for the carbochlorination of compounds of chromite concentrate [14,15] Figure 6 : Evolution of vapor pressure of several chlorides [17,[START_REF] Amiel | Anonymous: Thermochemical and Physical Properties (TAPP, version 2.1)[END_REF] .

Figure :

 : Figure : Spinel chromite structure[3] . Figure:Results of microprobe and SEM analyses of chromite concentrate. Figure : Horizontal experimental set-up. Figure : Thermogravimetric experimental set-up.Figure:Evolution of standard free energy changes as function of temperature for the carbochlorination of compounds of chromite concentrate[14, 15] . Figure:Evolution of vapor pressure of several chlorides[17,[START_REF] Amiel | Anonymous: Thermochemical and Physical Properties (TAPP, version 2.1)[END_REF] . Figure : Evolution of pct WL and extraction of iron as a function of temperature during carbochlorination of chromite concentrates for two hours. Figure : Evolution of pct WL (a) and extraction of Cr and Fe (b) as a function of temperature and reaction time during carbochlorination of chromite. Figure : Semi-quantitative analyses of the carbochlorination residues of chromite concentrate at different temperatures for a reaction time of 2 and 8 hours.Figure 10 : Evolution of pct WL of the sample and derivative of WL versus temperature during nonisothermal carbochlorination of chromite. Figure 11 : Effect of gas flow rate on the carbochlorination of chromite at 1000 °C.Figure 12 : Isotherms of carbochlorination of chromite from (a) 750 °C to 875 °C and (b) 900 °C to 1050 °C. Figure 13 : Mathematical fitting of the carbochlorination data ( X ≤ 0.35) using Eq. [17 and 18].Figure 14 : Mathematical fitting of the carbochlorination data (0.4 ≤ X ≤ 0.9) using Eq. [17]. Figure 15 : Arrhenius diagrams of the chromite carbochlorination.Figure 16 : Isotherms of carbochlorination of chromite at 1000 °C (a) using various Cl 2 +CO contents and (b) apparent reaction orders with respect to Cl 2 +CO. Figure 17 : Carbochlorination of chromite at 1000 °C. (a) and (b) : pct WL versus temperature for different Cl 2 contents in Cl 2 +CO gas mixture (c) : reaction rate against Cl 2 /(Cl 2 +CO)molar ratio.

  Figure : Spinel chromite structure[3] . Figure:Results of microprobe and SEM analyses of chromite concentrate. Figure : Horizontal experimental set-up. Figure : Thermogravimetric experimental set-up.Figure:Evolution of standard free energy changes as function of temperature for the carbochlorination of compounds of chromite concentrate[14, 15] . Figure:Evolution of vapor pressure of several chlorides[17,[START_REF] Amiel | Anonymous: Thermochemical and Physical Properties (TAPP, version 2.1)[END_REF] . Figure : Evolution of pct WL and extraction of iron as a function of temperature during carbochlorination of chromite concentrates for two hours. Figure : Evolution of pct WL (a) and extraction of Cr and Fe (b) as a function of temperature and reaction time during carbochlorination of chromite. Figure : Semi-quantitative analyses of the carbochlorination residues of chromite concentrate at different temperatures for a reaction time of 2 and 8 hours.Figure 10 : Evolution of pct WL of the sample and derivative of WL versus temperature during nonisothermal carbochlorination of chromite. Figure 11 : Effect of gas flow rate on the carbochlorination of chromite at 1000 °C.Figure 12 : Isotherms of carbochlorination of chromite from (a) 750 °C to 875 °C and (b) 900 °C to 1050 °C. Figure 13 : Mathematical fitting of the carbochlorination data ( X ≤ 0.35) using Eq. [17 and 18].Figure 14 : Mathematical fitting of the carbochlorination data (0.4 ≤ X ≤ 0.9) using Eq. [17]. Figure 15 : Arrhenius diagrams of the chromite carbochlorination.Figure 16 : Isotherms of carbochlorination of chromite at 1000 °C (a) using various Cl 2 +CO contents and (b) apparent reaction orders with respect to Cl 2 +CO. Figure 17 : Carbochlorination of chromite at 1000 °C. (a) and (b) : pct WL versus temperature for different Cl 2 contents in Cl 2 +CO gas mixture (c) : reaction rate against Cl 2 /(Cl 2 +CO)molar ratio.

Figure 10 :

 10 Figure : Spinel chromite structure[3] . Figure:Results of microprobe and SEM analyses of chromite concentrate. Figure : Horizontal experimental set-up. Figure : Thermogravimetric experimental set-up.Figure:Evolution of standard free energy changes as function of temperature for the carbochlorination of compounds of chromite concentrate[14, 15] . Figure:Evolution of vapor pressure of several chlorides[17,[START_REF] Amiel | Anonymous: Thermochemical and Physical Properties (TAPP, version 2.1)[END_REF] . Figure : Evolution of pct WL and extraction of iron as a function of temperature during carbochlorination of chromite concentrates for two hours. Figure : Evolution of pct WL (a) and extraction of Cr and Fe (b) as a function of temperature and reaction time during carbochlorination of chromite. Figure : Semi-quantitative analyses of the carbochlorination residues of chromite concentrate at different temperatures for a reaction time of 2 and 8 hours.Figure 10 : Evolution of pct WL of the sample and derivative of WL versus temperature during nonisothermal carbochlorination of chromite. Figure 11 : Effect of gas flow rate on the carbochlorination of chromite at 1000 °C.Figure 12 : Isotherms of carbochlorination of chromite from (a) 750 °C to 875 °C and (b) 900 °C to 1050 °C. Figure 13 : Mathematical fitting of the carbochlorination data ( X ≤ 0.35) using Eq. [17 and 18].Figure 14 : Mathematical fitting of the carbochlorination data (0.4 ≤ X ≤ 0.9) using Eq. [17]. Figure 15 : Arrhenius diagrams of the chromite carbochlorination.Figure 16 : Isotherms of carbochlorination of chromite at 1000 °C (a) using various Cl 2 +CO contents and (b) apparent reaction orders with respect to Cl 2 +CO. Figure 17 : Carbochlorination of chromite at 1000 °C. (a) and (b) : pct WL versus temperature for different Cl 2 contents in Cl 2 +CO gas mixture (c) : reaction rate against Cl 2 /(Cl 2 +CO)molar ratio.

Figure 12 :

 12 Figure : Spinel chromite structure[3] . Figure:Results of microprobe and SEM analyses of chromite concentrate. Figure : Horizontal experimental set-up. Figure : Thermogravimetric experimental set-up.Figure:Evolution of standard free energy changes as function of temperature for the carbochlorination of compounds of chromite concentrate[14, 15] . Figure:Evolution of vapor pressure of several chlorides[17,[START_REF] Amiel | Anonymous: Thermochemical and Physical Properties (TAPP, version 2.1)[END_REF] . Figure : Evolution of pct WL and extraction of iron as a function of temperature during carbochlorination of chromite concentrates for two hours. Figure : Evolution of pct WL (a) and extraction of Cr and Fe (b) as a function of temperature and reaction time during carbochlorination of chromite. Figure : Semi-quantitative analyses of the carbochlorination residues of chromite concentrate at different temperatures for a reaction time of 2 and 8 hours.Figure 10 : Evolution of pct WL of the sample and derivative of WL versus temperature during nonisothermal carbochlorination of chromite. Figure 11 : Effect of gas flow rate on the carbochlorination of chromite at 1000 °C.Figure 12 : Isotherms of carbochlorination of chromite from (a) 750 °C to 875 °C and (b) 900 °C to 1050 °C. Figure 13 : Mathematical fitting of the carbochlorination data ( X ≤ 0.35) using Eq. [17 and 18].Figure 14 : Mathematical fitting of the carbochlorination data (0.4 ≤ X ≤ 0.9) using Eq. [17]. Figure 15 : Arrhenius diagrams of the chromite carbochlorination.Figure 16 : Isotherms of carbochlorination of chromite at 1000 °C (a) using various Cl 2 +CO contents and (b) apparent reaction orders with respect to Cl 2 +CO. Figure 17 : Carbochlorination of chromite at 1000 °C. (a) and (b) : pct WL versus temperature for different Cl 2 contents in Cl 2 +CO gas mixture (c) : reaction rate against Cl 2 /(Cl 2 +CO)molar ratio.

Figure 14 :

 14 Figure : Spinel chromite structure[3] . Figure:Results of microprobe and SEM analyses of chromite concentrate. Figure : Horizontal experimental set-up. Figure : Thermogravimetric experimental set-up.Figure:Evolution of standard free energy changes as function of temperature for the carbochlorination of compounds of chromite concentrate[14, 15] . Figure:Evolution of vapor pressure of several chlorides[17,[START_REF] Amiel | Anonymous: Thermochemical and Physical Properties (TAPP, version 2.1)[END_REF] . Figure : Evolution of pct WL and extraction of iron as a function of temperature during carbochlorination of chromite concentrates for two hours. Figure : Evolution of pct WL (a) and extraction of Cr and Fe (b) as a function of temperature and reaction time during carbochlorination of chromite. Figure : Semi-quantitative analyses of the carbochlorination residues of chromite concentrate at different temperatures for a reaction time of 2 and 8 hours.Figure 10 : Evolution of pct WL of the sample and derivative of WL versus temperature during nonisothermal carbochlorination of chromite. Figure 11 : Effect of gas flow rate on the carbochlorination of chromite at 1000 °C.Figure 12 : Isotherms of carbochlorination of chromite from (a) 750 °C to 875 °C and (b) 900 °C to 1050 °C. Figure 13 : Mathematical fitting of the carbochlorination data ( X ≤ 0.35) using Eq. [17 and 18].Figure 14 : Mathematical fitting of the carbochlorination data (0.4 ≤ X ≤ 0.9) using Eq. [17]. Figure 15 : Arrhenius diagrams of the chromite carbochlorination.Figure 16 : Isotherms of carbochlorination of chromite at 1000 °C (a) using various Cl 2 +CO contents and (b) apparent reaction orders with respect to Cl 2 +CO. Figure 17 : Carbochlorination of chromite at 1000 °C. (a) and (b) : pct WL versus temperature for different Cl 2 contents in Cl 2 +CO gas mixture (c) : reaction rate against Cl 2 /(Cl 2 +CO)molar ratio.

Figure 16 :

 16 Figure : Spinel chromite structure[3] . Figure:Results of microprobe and SEM analyses of chromite concentrate. Figure : Horizontal experimental set-up. Figure : Thermogravimetric experimental set-up.Figure:Evolution of standard free energy changes as function of temperature for the carbochlorination of compounds of chromite concentrate[14, 15] . Figure:Evolution of vapor pressure of several chlorides[17,[START_REF] Amiel | Anonymous: Thermochemical and Physical Properties (TAPP, version 2.1)[END_REF] . Figure : Evolution of pct WL and extraction of iron as a function of temperature during carbochlorination of chromite concentrates for two hours. Figure : Evolution of pct WL (a) and extraction of Cr and Fe (b) as a function of temperature and reaction time during carbochlorination of chromite. Figure : Semi-quantitative analyses of the carbochlorination residues of chromite concentrate at different temperatures for a reaction time of 2 and 8 hours.Figure 10 : Evolution of pct WL of the sample and derivative of WL versus temperature during nonisothermal carbochlorination of chromite. Figure 11 : Effect of gas flow rate on the carbochlorination of chromite at 1000 °C.Figure 12 : Isotherms of carbochlorination of chromite from (a) 750 °C to 875 °C and (b) 900 °C to 1050 °C. Figure 13 : Mathematical fitting of the carbochlorination data ( X ≤ 0.35) using Eq. [17 and 18].Figure 14 : Mathematical fitting of the carbochlorination data (0.4 ≤ X ≤ 0.9) using Eq. [17]. Figure 15 : Arrhenius diagrams of the chromite carbochlorination.Figure 16 : Isotherms of carbochlorination of chromite at 1000 °C (a) using various Cl 2 +CO contents and (b) apparent reaction orders with respect to Cl 2 +CO. Figure 17 : Carbochlorination of chromite at 1000 °C. (a) and (b) : pct WL versus temperature for different Cl 2 contents in Cl 2 +CO gas mixture (c) : reaction rate against Cl 2 /(Cl 2 +CO)molar ratio.

Figure 3 :Figure 5 :

 35 Figure 3 : Horizontal experimental set-up.

Figure 7 :

 7 Figure 7 : Evolution of pct WL and extraction of iron as a function of temperature during carbochlorination of chromite concentrates for two hours.

Figure 8 :

 8 Figure 8 : Evolution of pct WL (a) and extraction of Cr and Fe (b) as a function of temperature and reaction time during carbochlorination of chromite.

Figure 10 :

 10 Figure 10 : Evolution of pct WL of the sample and derivative of WL versus temperature during non isothermal carbochlorination of chromite.

L 1 : 1 Figure 11 :Figure 12 :Figure 13 :

 11111213 Figure 11 : Effect of gas flow rate on the carbochlorination of chromite at 1000 °C.

Figure 14 :

 14 Figure 14 : Mathematical fitting of the carbochlorination data (0.4 ≤ X ≤ 0.9) using Eq. [17].

Figure 16 :

 16 Figure 15 : Arrhenius diagrams of the chromite carbochlorination.

Table II .

 II Composition of Chromite Concentrate (Wt pct).

		Chemical Analyses	Microprobe
	Oxide	(Average)	Chromite	Gangue
	Cr 2 O 3	47.7	59.4	0.7
	FeO	13.4	13.9	3.7
	MgO	17.8	14.2	42.0
	Al 2 O 3	8.8	9.5	1.4
	SiO 2	7.1	0.1	39.7
	and lattice parameters making their identification, by XRD, almost impossible. References [11] and

Table III :

 III SEM and XRD Results of Carbochlorination Products of Chromite Concentrate for Different Temperatures and Reaction Time.

	T t	Residues		Condensates
	°C h	SEM	XRD	C 1

Table IV :

 IV Evolution of Cr/Fe Ratio in the Carbochlorination Residues of Chromite Concentrate as a Function of the Treatment's Conditions.

	B. Kinetics of Carbochlorination of Chromite Mineral
	1. Non isothermal TG analysis
	T, °C	Time, h	Cr, Wt Pct Fe, Wt Pct	Cr/Fe
	Raw Sample	-	34.2	10.8	3.2
	500	2	40.9	6.3	6.5
		8	35.7	6.6	5.4
	600	2	38.5	4.4	8.8
		8	36.7	3.4	10.8
	700	2	39.9	3.7	10.8
		8	35.2	2.9	12.1
	800	2	38.7	1.5	25.8

Mg, Fe) 2 SiO 4 & Mg 3 Si 2 O 5 (OH) 4 (Fe 2+ , Mg)(Cr, Al, Fe 3+ ) 2 O 4

  : Composition of Chromite Concentrate (Wt pct). Table III : SEM and XRD Results of Carbochlorination Products of Chromite Concentrate for Different Temperatures and Reaction Time. Table IV : Evolution of Cr/Fe Ratio in the Carbochlorination Residues of Chromite Concentrate as a Function of the Treatment's Conditions

										: O 2-;	: Fe 2+ , Mg 2+ ;
										: Cr 3+ , Al 3+ , Fe 3+
					Figure 1 : Spinel structure of chromite [3] .
	2000	Mg Si		Cr2O3 Oxide W pct 0.15					2000	Mg	Si			Cr2O3 Oxide W pct 0.61
	COUNTS	O			FeO MgO Al2O3 SiO2 NiO	2.67 40.67 0.00 35.09 0.44					COUNTS	O				FeO MgO Al2O3 SiO2 NiO	0.76 40.14 0.37 39.75 0.11
				Fe											Fe
	0	0.00		ENERGY (KEV)	10.24					0	0.00		ENERGY (KEV)	10.24
	2000		Mg		Oxide W pct					2000					Oxide W pct
	COUNTS	O	Si	Fe	Cr2O3 FeO MgO Al2O3 SiO2 NiO	0.14 4.96 52.27 0.02 42.17 0.52	000001	20 KV	X 50	0.60 mm	COUNTS	Mg O Al	Cr	Fe	Cr2O3 FeO MgO Al2O3 SiO2 NiO	60.76 13.22 14.52 9.34 0.00 0.00
	0	0.00		ENERGY (KEV)	10.24					0	0.00		ENERGY (KEV)	10.24
			Figure 2 : Results of microprobe and SEM analyses of chromite concentrate.
			1		2	3		4 2	5	6	7		8				9
																	Purified
																	Gases
	Cl2 N2 CO
			1. Flowmeters			2. P 2 O 5 columns		3. Carbon furnace
			4. Drying column		5. Quartz reactor		6. Furnace
			7. Sample boat			8. Condenser		9. NaOH column

(

  Figure 9 : Semi-quantitative analyses of the carbochlorination residues of chromite concentrate at different temperatures for a reaction time of 2 and 8 hours

	T,	°C
		2000
									Cr	E	% A % W					Cr		E	% A	% W
										Cr	18.9	38.4							Cr	18.5	38.4
		COUNTS		O	Mg Al	Si	Cl	Cr Fe	Fe Mg Al Si Cl O Ca	1.5 10.9 3.2 5.4 1.3 58.7 0.2	3.2 10.3 3.4 5.9 1.8 36.8 0.3	O	Mg Al	Si	Cl	Cr	Fe	Fe Mg Al Si Cl O Ca	1.0 10.4 3.7 4.6 0.6 61.1 0.1	2.3 10.1 4.0 5.2 0.8 39.0 0.2
			0
		2000
										E	% A	% W					Cr		E	% A	% W
										Cr	18.1	37.8							Cr	17.7	37.2
		COUNTS	0	O	Al Mg	Si	Cl	Cr Cr Fe	Fe Mg Al Si Cl O Ca	0.8 10.2 3.5 6.2 0.5 60.6 0.2	1.7 10.0 3.8 6.9 0.7 38.8 0.4	O	Al Mg	Si	Cl	Cr	Fe Mg Al Si Cl Fe O Ca	0.4 9.3 3.3 6.9 1.3 60.9 0.1	0.9 9.1 3.6 7.9 1.8 39.4 0.1
		2000
								Cl		E	% A	% W				Cl			E	% A	% W
										Cr	11.3	24.4							Cr	0.1	0.3
		COUNTS		O	Mg Al Si		Cr Cr	Fe Mg Al Si Cl O Ca	0.2 12.8 1.7 4.3 11.4 58.1 0.1	0.5 12.9 1.9 5.0 16.8 38.5 0.1	O	Si Mg				Fe Mg Al Si Cl O Ca	0.0 10.4 0.0 2.5 21.8 65.0 0.2	0.0 11.7 0.0 3.3 36.0 48.3 0.4
			0					Ca								Ca
				0.00				ENERGY (KEV)			10.24	0.00		ENERGY (KEV)		10.24

E : Element, % A : Atomic pct, % W : Weight pct.

ACKNOWLEDGMENTS

This work was performed in the frame of contract N° BRE2-CT92-0173 thanks to the financial support of the European Union (DG-XII). The authors thank Dr. H. L. Schmidt for discussion, suggestion and help. They also would like to thank Dr. J. C. Mugica, Dr. R. Solozabal (INASMET, San Sebastian, Spain) and Dr. M. Coelho (INETI, Lisbon, Portugal) for technical discussions. Authors are indebted to Dr. A. Bonazébi, Dr. M. Djona, Dr. S. Ivanaj, Dr. N. Menad, Dr. N. Mirghaffari for discussions and help on different subjects and to Mrs. C. Richard for technical and administrative support.