Lamiche Chaabane
email: lamiche07@gmail.com

An Efficient Cooperative Method to Solve Multiple Sequence Alignment Problem

Keywords: Cooperative approach, multiple sequence alignment, SPSO, PSO, SA, BaliBASE benchmarks

In this research work, we propose a cooperative approach called simulated particle swarm optimization (SPSO) which is based on metaheuristics to find an approximate solution for the multiple sequence alignment (MSA) problem. The developed approach uses the particle swam optimization (PSO) algorithm to discover the search space globally and the simulated annealing (SA) technique to improve the population leader « gbest » quality in order to overcome local optimum problem. Simulation results on BaliBASE benchmarks have shown the potent of the proposed method to produce good quality alignments comparing to those given by other existing methods.

Introduction

Multiple sequence alignment (MSA) is a crucial tool in molecular biology and genome analysis. It has been considered as one of the important tasks in bioinformatics [1]. It helps to construct a phylogenetic tree of related DNA sequences, to predict the function and structure of unknown protein sequences, by aligning with other sequences whose function and structure is already known, and to allow comparison of the structural relationships between sequences by simultaneously aligning multiple sequences and constructing connections between the elements in different sequences [START_REF] Jiang | On the Complexity of Multiple Sequence Alignment[END_REF].

Discovering optimal alignment in multiple biological sequence data is known as a NP-complete problem [START_REF] Bonizzoni | The Complexity of Multiple Sequence Alignment with SPscore that is a Metric[END_REF]. It has been identified as a combinatorial optimization problem [START_REF] Papadimitriou | Combinatorial Optimization: Algorithms and Complexity[END_REF], which is solved by using an exact or approximate algorithms. These algorithms lead to exploit various genetic information to determine evolutionary relationships among living beings [START_REF] Bonizzoni | The Complexity of Multiple Sequence Alignment with SPscore that is a Metric[END_REF].

Recently, the trend has shifted to the use of iterative algorithms in order to tackle the MSA problem. These approaches are based on the improvement of the given initial alignment through a series of some iterations until a stopping criterion is reached. They include genetic algorithm (GA) [START_REF] Horng | A Genetic Algorithm for Multiple Sequence Alignment[END_REF], simulated annealing algorithm (SA) [START_REF] Hernández-Guía | A new Simulated Annealing Algorithm for the Multiple Sequence Alignment Problem. The approach of polymers in a random media[END_REF], particle swarm optimization (PSO) [START_REF] Lei | A Particle Swarm Optimization Algorithm for Finding DNA Sequence Motifs[END_REF], GA-ACO algorithm [START_REF] Lee | Genetic Algorithm with Ant Colony Optimization (GA-ACO) for Multiple Sequence Alignment[END_REF], Ant Colony Algo-rithm [START_REF] Chen | An Efficient ant Colony Algorithm for Multiple Sequences Alignment[END_REF] and so on. Generally, these metaheuristics are able to find nearly optimal solutions for large instances in a reasonable processing time.

In this study, we propose a hybrid approach called SPSO algorithm to solve the MSA problem. The developed model is the cooperation between both PSO algorithm and simulated annealing technique. The remainder of the paper is organized as follows: section 2 presents a brief review of the researches related to the proposed framework. In section 3, both PSO and SA concepts are described. In section 4, our proposed SPSO algorithm is explained in detail. In section 5, the simulation results are provided. Finally, the study is concluded in section 6.

Background

A brief review of some related works in the multiple sequence alignment field using iterative methods is presented in this section. Rias et al. [START_REF] Riaz | Multiple Sequence Alignment Using Tabu Search[END_REF] presented a tabu search algorithm to align multiple sequences. The framework of his work consists to implement the adaptive memory features typical of tabu searches in order to obtain multiple sequences alignment where the quality of an alignment is measured by the COFFEE objective function. In [START_REF] Xu1 | A Method for Multiple Sequence Alignment Based on Particle Swarm Optimization[END_REF], the authors proposed a novel approach to multiple sequence alignment based on Particle Swarm Optimization (PSO) to improve a sequence alignment previously obtained using Clustal X In Ref. [START_REF] Chen | Multiple Sequence Alignment Based on Genetic Algorithm with Reserve Selection[END_REF], the authors presented an approach to the MSA problem by applying genetic algorithm with a reserve selection mechanism to avoid premature convergence in GA. A better results are obtained compared with those produced by classical GA. The authors in [START_REF] Long | Multiple Sequence Alignment Based on a Binary Particle Swarm Optimization Algorithm[END_REF] proposed an algorithm based on binary PSO algorithm to address the multiple sequence alignment problem. Simulation results using SP score measure and nine BaliBASE tests case showed that the proposed BPSO algorithm has superior performance when compared to ClustalW and SAGA algorithms.

An artificial bee colony algorithm for solving MSA problem is introduced in [START_REF] Lei | Artificial Bee Colony Algorithm for Solving Multiple Sequence Alignment[END_REF]. In Ref. [START_REF] Cutello | Protein Multiple Sequence Alignment by Hybrid Bio-inspired Algorithms[END_REF], V. Cutello et al. presented an immune inspired algorithm (IMSA) to tackle the multiple sequence alignment problem using ad-hoc mutation operators. Experimental results on BALIBASE v.1.0 show that IMSA is superior to PRRP, CLUSTALX, SAGA, DIALIGN, PIMA, MULTIALIGN and PILEUP8. In [START_REF] Ernesto | Simulated Annealing with Previous Solutions Applied to DNA Sequence Alignment[END_REF], simulated annealing technique was applied to solve MSA problem using a set of DNA benchmarks of HIV virus genes of human and simian.

In Ref. [START_REF] Abu-Srhan | A Hybrid Algorithm Using a Genetic Algorithm and Cuckoo Search Algorithm to Solve the Traveling Salesman Problem and its Application to Multiple Sequence Alignment[END_REF], the authors proposed a hybrid algorithm using a GA and cuckoo search algorithm to improve multiple sequence alignment. The obtained results are compared with ClustalW by using five different datasets. Recently, an efficient method by using multi-objective genetic algorithm (MSAGMOGA) to discover optimal alignments is proposed in [START_REF] Kayaa | Multiple Sequence Alignment with Affine Gap by using Multi-objective Genetic Algorithm[END_REF]. Experiments on the BAliBASE 2.0 database confirmed that MSAGMOGA obtained better results than MUSCLE, SAGA and MSA-GA methods.

Particle swarm optimization (PSO) is an adapted algorithm developed the first time by Kennedy and Eberhart [START_REF] Kennedy | Particle Swarm Optimization[END_REF], inspired by bird flocking and fish schooling. PSO used a population of individuals called particles and two primary operators: velocity update and position update. During each generation, each particle moves toward the particles according to its best position and the global best position. In addition, a new velocity value for each particle is calculated based on its current velocity, the distance from its previous best position and the distance from the global best position. The evolution of the swarm is governed by the following equations:

V (k+1) = w.V (k) +c 1 .rand 1 . (pbest (k) -X (k) ) +c 2 .rand 2 .(gbest( k ) -X (k) ) .
( 1)

X (k+1) = X (k) + V (k+1) . ( 2
)
where: X is the position of the particle, V is the velocity of the particle, w is the inertia weight, pbest is the best position of the particle, gbest is the global best position of the swarm, rand1, rand2 are random values between 0 and 1, c1, c2 are positive constants which determine the impact of the personal best solution and the global best solution on the search process, respectively, k is the iteration number.

Concerning the stopping condition, generally PSO algorithm terminates when a set number of times or until a minimum error is achieved. All parameters of PSO algorithm are fixed experimentally in order to have a good compromise between the convergence time of the algorithm and the final solution quality.

Outline of the Simulated Annealing (SA)

Simulated annealing (SA) is a general probabilistic local search algorithm proposed by Kirkpatrick et al. [START_REF] Ben-Ameur | Computing the Initial Temperature of Simulated Annealing[END_REF] to solve difficult optimization problems. It is inspired by the annealing of solids in physics. SA models the slow cooling process of solids to achieve the minimum energy as an analogy, reaching the minimum function value. As a result, it attains an optimal/near-optimal solution by implementing an iterative cooling process from a high temperature, at which solid particles are in the liquid phase.. Simulated annealing utilizes a control parameter, temperature T, for the cooling process. The solid is allowed to attain the thermal equilibrium for every T degree that has its energy E probabilistically distributed, as given in equation ( 3), where k b is the Boltzmann constant. (3)

In the combinatorial optimization context, if we aim to find a good solution then we move from a solution to one of its neighbors in the search space according to a probabilistic criterion. If the cost decreases then the solution is retained and the move is accepted. Otherwise, the move is accepted only with a probability depending on the cost increase and the temperature parameter T [START_REF] Ben-Ameur | Computing the Initial Temperature of Simulated Annealing[END_REF].

Proposed Method

PSO performs excellently in the case of global search but it is not efficient in local search. It suffers from its weak local search ability and the local minima limit. On the other hand, SA is good in local search while less good in global search. However, it takes advantage of the acceptance of candidate solutions by the use of metropolis criteria and must escape from local optimum to search in other solution space.

In order to construct an intelligent algorithm which can be effectively avoid weaknesses and fully use the advantages of both PSO and SA algorithms, we propose a hybrid approach which combines the PSO with simulated annealing, so the new hybrid algorithm called Simulated Particle Swarm optimization (SPSO) conducts both global search and local search in every iteration. According to this hybridization manner, the probability to obtain better solutions significantly increases. At each iteration, the proposed hybrid SPSO algorithm consists of applying PSO algorithm in order to guide global search, and use SA to improve the gbest which helps PSO to escape from local optimum and increase the convergence speed of SPSO algorithm. The flowchart of the proposed SPSO is presented in Fig. 1.

PSO components of MSA problem

Particle Representation. Each particle represents a potential solution to the MSA problem, effectively it corresponds to a sequence alignment. A particle is then represented as a set of vectors, where each vector specifies the positions of the gaps in each one of the sequences to be aligned [START_REF] Rodriguez | Multiple Sequence Alignment Using Swarm Intelligence[END_REF].

Swarm Initialization. The size of the whole swarm is determined by the user. The initial set of particles is generated by adding gaps into each sequence at random position, thus, all the sequences have the same length L in which its value is 1.2 times of the longest sequences [START_REF] Rodriguez | Multiple Sequence Alignment Using Swarm Intelligence[END_REF]. Fitness Evaluation. A parameter to determine which alignment will survive in the next generation is its fitness value. A formal definition of the sum-of-pairs (SPS) of multiple sequence alignment is introduced which is used as a tool to compute fitness. The score assigned to each alignment is the sum of the scores (SP) of the alignment of each pair of sequences. The score of each pair of sequences is the sum of the score assigned to the match of each pair of symbols, which is given by the substitution matrix. The score of a multiple alignment is given as follows:

∑ ∑ - = + = = 1 1 1 ) , ( ) ( k i k i j j i A A S A Score . ( 4
)
where the S(A i ,A j ) is the alignment score between two given sequences A i and A j .

Particle Move: In the PSO algorithm, each particle moves towards the leader at a speed proportional to the distance between the particle and the leader. In this paper, (

) 5
To move particles towards the leader, an operator similar to the crossover operator from genetic algorithms is used [START_REF] Rodriguez | Multiple Sequence Alignment Using Swarm Intelligence[END_REF]. It consists to select a crossover point which divides the alignment into two segments, and then a segment of the particle is replaced with a segment from the leader. This replacement is achieved removing from the particle the gaps that are in the segment, and then adding the gaps from the leader's segment.

SA components of MSA problem

According to the basic elements of simulated annealing cited above, components of our proposed algorithm for MSA problem are as follows :

Cost Function. The cost function is used to evaluate the quality of each alignment in the swarm. In order to retain the same paradigm to measure the quality of the solution, we decide in this study to use the sum-of-pairs (SPS) as a cost function which is the same one of that used in the PSO algorithm.

Initial Solution.

The generation of an initial solution is an important step towards getting a final improved alignment. In our developed method, the initial solution is constructed by insertion of gaps randomly in each sequence of the alignment.

Generation of Neighbors.

In the multiple sequence alignment context, neighbors of a current solution are obtained by employing some perturbations to the gaps positions into the different sequences. In this work, we choose to apply a simple efficient strategy to change positions of these gaps, this mechanism is called LocalShuffle operator, its main idea is as follows : firstly, picks a random amino acid from a randomly chosen sequence in the alignment and checks whether one of its neighbors is a gap. If this is the case, the algorithm swaps the selected amino acid with a gap neighbor. If both neighbors are gaps then one of them is picked randomly [START_REF] Lamiche | An Effective Alignment Technique to Solve MSA Problem[END_REF].

Choice of Cooling Schedule. An effective cooling schedule is essential to reducing the amount of time required by the algorithm to find an optimal solution. Several temperature decreasing schemes have been proposed in the literature, they include static schedules and adaptive schedules [START_REF] Ernesto | Simulated Annealing with Previous Solutions Applied to DNA Sequence Alignment[END_REF]. Here, we propose to use the most common cooling function which is defined by T k+1 = α.T k This function decreases the temperature value by a α factor, where α ∈ [0.70, 1.0[. The pseudo-code of our SA is given below.

Pseudo-code of SA algorithm

Fix 6. Return gbset value and its correspondent particle.

Simulation and Results

The proposed approach is implemented in Java language. All tests have been fulfilled on a PC with an 2.66 GHz Intel Pentium IV processor and 4GB RAM. We conducted some experiments in order to demonstrate the effectiveness of our SPSO algorithm. For that, a set of benchmark sequences with different identities and lengths are chosen from the BAliBase 1.0 library [START_REF] Thompson | BAliBASE: A Benchmark Alignment Database for the Evaluation of Multiple Alignment Programs[END_REF]. Characteristics of the used data and all parameters setting of the algorithm SPSO are summarized in Table 1 and 2 respectively. All obtained results by GA, PSO, ABC and our SPSO algorithm using the best, the worst and the average SPS values of 10 experiments are summarized in table 3, 4 and 5 respectively. [START_REF] Lei | Artificial Bee Colony Algorithm for Solving Multiple Sequence Alignment[END_REF] 0.60.90 0.6829 0..5463 PSO [START_REF] Lei | Artificial Bee Colony Algorithm for Solving Multiple Sequence Alignment[END_REF] 0.6444 0.6543 0.6296 ABC [START_REF] Lei | Artificial Bee Colony Algorithm for Solving Multiple Sequence Alignment[END_REF] 0.6895 0.7654 0.6543 SH3 Our SPSO 0.7698 0.8712 0.8197 GA [START_REF] Lei | Artificial Bee Colony Algorithm for Solving Multiple Sequence Alignment[END_REF] 0.5141 0.5398 0.4891 PSO [START_REF] Lei | Artificial Bee Colony Algorithm for Solving Multiple Sequence Alignment[END_REF] 0.6252 0.6407 0.6116 ABC [START_REF] Lei | Artificial Bee Colony Algorithm for Solving Multiple Sequence Alignment[END_REF] 0.6451 0.6854 0.5922 cytochrome c Our SPSO 0.7206 0.7417 0.6806 GA [START_REF] Lei | Artificial Bee Colony Algorithm for Solving Multiple Sequence Alignment[END_REF] 0.7455 0.8065 0.6921 PSO [START_REF] Lei | Artificial Bee Colony Algorithm for Solving Multiple Sequence Alignment[END_REF] 0.7120 0.7331 0.7008 ABC [START_REF] Lei | Artificial Bee Colony Algorithm for Solving Multiple Sequence Alignment[END_REF] 0 The results plotted in table 3-5 show clearly the considerable improvement of scores by the proposed SPSO approach. Indeed, it produces alignments of much better quality than that of the other cited methods in both specified categories.

In order to further assess the potent of our SPSO algorithm, a second experiment using another datasets is performed, where the goal is to compare our SPSO approach with TLPSO-MSA [START_REF] Lalwani | A Novel Two-level Particle Swarm Optimization Approach for Efficient Multiple Sequence Alignment[END_REF] technique. In this experiment, three different sets of protein coming from the BaliBASE 3.0 database [START_REF] Thompson | BAliBASE 3.0: Latest Developments of the Multiple Sequence Alignment Benchmark[END_REF] are selected. The average SP and TC scores [START_REF] Thompson | A Comprehensive Comparison of Multiple Sequence Alignment Programs[END_REF] are portrayed in table 6. From table 6, it can be seen that our SPSO outperforms clearly TLPSO-MSA method in terms of TC score for all cases. In terms of SP score, it finds results better than TSPSO-MSA for RV11 and RV20 protein families and produces a competitive results on RV12 dataset.

Conclusion

In this work, we contributed to the ongoing research by proposing a hybrid model for finding optimized alignments to the MSA problem. The developed approach uses the characteristics of the random search, global convergence of the PSO and the potent of the simulated annealing to update the global optimal solution. The performance of the proposed SPSO algorithm is judged on a set of BaliBase benchmark problems and is favorably compared with other algorithms in the literature. The results demonstrate that the proposed approach is overall more effective to find better alignments in a reasonable processing time.

In the future, we will revise our score function to make this score more realistic, also, we can use another intelligent heuristic to generate the initial whole swarm in order to increase the convergence speed of the algorithm. In addition, we can incorporate other efficient mechanisms for the neighborhood structure to improve the global solution quality. A comparison of the proposed method with some other aligners such as Clustal W, SAGA or MULTALIGN is possible to verify its effectiveness.

Fig. 1 .

 1 Fig. 1. SPSO algorithm flowchart.

  T initial , T final , α Set T ←T initial , S current ←S initial , score(S current ) ← score(S initial )After the description of the key components of both PSO and SA algorithms, the pseudo-code of our SPSO procedure is summarized as follows:

	While (T > T final ) do S new ←LocalShuffle(S current )
	Compute the alculate score of S new
	d:=score(S new )-score(S current )
	If (d >= 0) then
	S current ← S new
	If score (S new ) > score(S better ) then S better ← S new
	end if
	else
	If exp(d/T ) > random(0,1) then
	S current ← S new
	end if
	end if T= α.T
	end while
	SPSO Pseudo-code
	1. Generate randomly a set of particles
	2. Fix ITMAX value
	3. Set it ← 1
	4. While (it ≤ ITMAX) do
	4.1. Determine the leader particle gbest
	4.2. For each particle in population do
	a) Measure the distance between gbest and the particle
	b) Select randomly a crossover point
	c) Apply particle move mechanism
	End for
	// Calculate the new leader particle gbest
	4.3 Update gbest
	4.4 New_gbest ← SA(gbest) // Apply SA to improve gbest
	4.5 Insert New_gbest into the current population
	4.6. it ← it + 1
	5. End while
	// Output the result.

Table 1 .

 1 Characteristics of benchmark sequences.

	Types	Name	N	(Min,Max)	Identity
		SH3	5	(80,49)	<25%
	Short	cytochrome c	5	(70,87)	20%-40%
		serine protease	5	(66,82)	>35%
		protein kinase	5	(263,276)	<25%
	Medium	anthranilate isomerase	4	(247,259)	20%-40%
		serine protease	5	(222,245)	>35%
		aminotransferase	4	(358,387)	<25%
	Long	glutamyl-trna synthetase taq DNA polymerase	5 5	(438,486) (806,928)	20%-40% >35%

Table 2 .

 2 . Parameter settings for experiments.

	Parameter	Value
	Number of iterations (ITMAX)	1000
	Swarm size	50
	Initial temperature (T initial ), Final temperature (T final )	100, 0.0001
	Score matrix, Gap open	PAM250, 10
	c 1 , c 2 constants	1.49618

Table 3 .

 3 Comparison results for short sequences.

	Sequence name	Algorithm	Average Best	Worst
		GA

Table 4 .

 4 Comparison results for medium sequences.

	serine protease	.7956	0.8168	0.7732
	Our SPSO	0.8878	0.8904	0.8505

Table 5 .

 5 Comparison results for long sequences

	Sequence name	Algorithm	Average Best	Worst
		GA[14]	0.3646	0.3719	0.3548
	aminotransferase	PSO [14] ABC [14]	0.5819 0.5819	0.5903 0.5936	0.5645 0.5741
		Our SPSO	0.6602	0.6378	0.6612
		GA[14]	0.3738	0.3824	0.3648
	glutamyl-trna synthetase	PSO [14] ABC [14]	0.3987 0.4040	0.4154 0.4254	0.3828 0.3857
		Our SPSO	0.4316	0.4449	0.4207
		GA[14]	0.2500	0.2644	0.2441
	taq DNA polymerase	PSO [14] ABC [14]	0.2533 0.2569	0.2588 0.2615	0.2484 0.2520
		Our SPSO	0.2603	0.2882	0.2794

Table 6 .

 6 Comparison results on the selected test cases.

	Sequence set	N	TLPSO-MSA[24]		Our SPSO
			SP	TC	SP	TC
	RV11	38	0.80	0.71	0.84	0.74
	RV12	44	0.92	0.80	0.86	0.81
	RV20	41	0.91	0.64	0.92	0.68
	Overall average		0.87	0.72	0.87	0.74


A Chapuis
email: arnaud.chapuis@mines-albi.fr

J Blin

P Carré

D Lecomte

Separation efficiency and energy consumption of oil expression using a screw-press: The case of Jatropha curcas L. seeds

Keywords:

The performance of oil expression from Jatropha curcas L. (Jatropha) seeds using a pilot scale continuous screw press was studied. The influence of seed pre-treatment, i.e. whole, crushed and deshelled seeds, screw-press operational settings (shaft rotational speed and press cake outlet section) was investigated. For each experiment, the material flows (seeds, press cake and crude oil) were measured and analysed for their oil, water and solids contents. The behaviour of oil expression was very sensitive to seeds preparation. It was shown that for whole seeds, a good reproducibility was obtained, whereas for crushed or deshelled seeds, heterogeneity of the feed led to unsteady pressing conditions and important discrepancies in the performance. The presence of seed shells contributes to build a porous solid matrix which favours oil flow through the press cake. For whole seeds, a correlation between oil recovery and seed throughput was proposed. The mass balance consistency was carefully analysed and oil yield was determined using a direct and an indirect method. A good linear correlation between seed and press cake throughputs was observed: the seed throughput is always divided in a stream of crude oil and a stream of press cake in the same proportion. This important result shows that the residual oil in press cake and the amount of solids carried by the oil are directly related and determine the efficacy of the separation. Thus, for a given screw press and feed material, the oil sediment content can be predicted knowing the oil recovery. The energy consumption during pressing was measured and modelled as a function of oil recovery and seeds oil content. The specific mechanical energy for oil expression was less than 5% of the energy content of the oil and a minimum mechanical energy requirement was generally observed at oil recoveries between 70% and 80%.

Introduction

Screw pressing, also called oil expression, is the most widespread technique for extracting vegetable oils from dry oilseeds in small and medium-sized plants [START_REF] Khan | Expression of oil from oilseeds -a review[END_REF]. Nowadays, most vegetable oil in the food industry is produced in large-scale industrial plant using solvent extraction, and screwpresses are mainly used for prepressing seeds with high oil contents [START_REF] Matthäus | Oil technology[END_REF]. Screw-presses are also widely used for high value vegetable oils (virgin), for small-scale processing in developing countries and for the production of straight vegetable oil (SVO) for fuel purposes. The latter application is the main scope of this study and more specifically the production of SVO from Jatropha curcas L. (Jatropha) seeds.

A screw-press is composed of a barrel made of narrow spaced bars, in which a conical screw (worm shaft) rotates and presses the seeds (see Fig. 1). The pressure increases along the screw due to reduced volume, and squeezes the oil through the seed mixture, termed cake, and out of the barrel through the spaces between the bars. The de-oiled press cake is discharged at the end of the screw. A mobile conical part, called choke, allows the adjustment of the outlet section of press cake. The mechanical strains inside the barrel are high, up to 50-100 MPa [START_REF] Bredeson | Mechanical pressing[END_REF][START_REF] Mrema | Mathematical model of mechanical oil expression from oilseeds[END_REF], and friction phenomena increase the temperature of the cake. The temperature build-up is crucial in the process since it lowers the oil viscosity and enables it to flow more readily through the pores of the cake [START_REF] Khan | Expression of oil from oilseeds -a review[END_REF].

Prior to pressing, the seeds can undergo several preparation steps to facilitate oil expression and increase oil recovery. The most common pre-treatment operations are drying, dehulling, flaking, crushing and cooking. Thermal treatment (cooking) improves oil expression by thermally breaking oil cell walls but it results in higher contents of phospholipids and in some cases, higher contents of free fatty acids in the oil [START_REF] Matthäus | Oil technology[END_REF][START_REF] Veldsink | Heat pretreatment of oilseeds: effect on oil quality[END_REF]. If such pre-treatments are applied for SVO production, the oil will have to undergo purification treatments such as neutralisation and degumming to comply with quality needs for use as fuel in Diesel engines [START_REF] Blin | Characteristics of vegetable oils for use as fuel in stationary diesel engines -towards specifications for a standard in West Africa[END_REF]. That is why cold pressing is usually preferred for SVO production, especially in small-sized installations.

Although screw expellers have been used for decades in the vegetable oil industry, no satisfactory mathematical models are available as is the case for most solid-liquid separation processes. The development and implementation of screw expellers are essentially based on the experience and know-how of manufacturers and operators. Several modelling attempts are reported in the scientific literature, most of them dating back to the 1980s and 1990s. If batch hydraulic oil expression can be satisfactorily simulated using Shirato-type models -based on soil consolidation theory [START_REF] Willems | Hydraulic pressing of oilseeds: experimental determination and modeling of yield and pressing rates[END_REF], it is not the case for continuous expression using screw-presses. [START_REF] Vadke | Mathematical simulation of an oilseed press[END_REF] applied Shirato models to screw expeller with relatively good prediction results of seed throughput and press cake residual oil with a lab-scale equipment but only on a narrow range of processing conditions. [START_REF] Willems | A consolidation based extruder model to explore GAME process configurations[END_REF] improved Vadke's model and applied it to gas assisted mechanical expression (GAME), but the influence of temperature on pressure and residual oil was not satisfactorily predicted. Moreover, these models could not determine the presence of solid impurities in the oil, or the energy requirements. A theoretical model, based on the cellular structures of oilseeds, was developed by [START_REF] Lanoisellé | Modeling of solid/liquid expression for cellular materials[END_REF] but was not applied to continuous oil expression [START_REF] Lanoisellé | Modeling of solid/liquid expression for cellular materials[END_REF].

Only few data are available in the scientific literature on the performance of screw-pressing for Jatropha oil expression and even fewer concerning energy requirements of vegetable oil expression in general. [START_REF] Karaj | Optimizing mechanical oil extraction of Jatropha curcas L. seeds with respect to press capacity, oil recovery and energy efficiency[END_REF] presented experimental results and analysed the links between oil recovery and energy consumption for Jatropha oil expression using a lab-scale cylinder-hole type screw-press. This type of press is commonly used for farm-scale oil production, but on industrial scale strainer presses are far more common. Thus, the work presented in this paper aims to bridge the gap by providing experimental results, including oil expression performance and energy requirements for a pilot scale strainer-type screw-press.

We present an experimental methodology to investigate the performances of continuous oil expression using screw expellers. The present case study deals with the pressing of Jatropha seeds but the methodology could be applied to any type of oilseeds. The main objectives are (i) to investigate the influence of seed preparation on the behaviour and performance of oil expression; (ii) to establish a mass balance of oil, solids and water and (iii) to identify useful relations between oil recovery, specific energy consumption and material throughput.

A series of experiments was conducted on a pilot scale screw press. The parameters studied included seed preparation, i.e. whole, crushed and deshelled seeds, as well as screw-press operational settings, i.e. screw rotational speed and press cake outlet section. For each experimental setting, the mechanical energy consumption was measured and material flows (seeds, press cake and crude oil) were measured and analysed for oil, water and solids contents. The analysis of the results started with a thorough assessment of oil, solids and water mass balance over the press, including the reconciliation of measurement data, which constitutes the basis for determining the separation efficiency. Then, from the mass balance analysis, a systematic correlation between residual oil in the press cake and solids content of expressed oil will be proposed. Finally, the specific energy consumption will be studied with respect to separation efficiency.

Materials and methods

Input materials

The experiments were carried out in the fall of 2012 at the pilot oil plant of CREOL in Bordeaux, France. The seeds used originated from Jakarta, they had been harvested in 2008 and stored in France for 4 years. For these experiments, all seeds were dried in a hot air dryer to reduce the moisture content from 9.5% to about 6% wb. Then five batches were prepared: whole seeds, crushed seeds and deshelled seeds, including three different deshelling levels.

For crushing, an industrial cracking mill was used (200 kg h -1 , Damman-Croes S. A. International, Belgium), made of two couples of corrugated cylinders with a spacing of 3 mm. For deshelling, the seeds first passed through the same cracking mill, but with a larger spacing between the rolls (5 mm) to break the shells. Large shell parts were removed by passing through an air grader (D50, Ets Denis S. A., France) and a specific gravity separator (Kipp Kelly, ArrowCorp Inc., Canada) allowed finer sorting of kernels. The specific gravity separator had 5 outputs with gradual shell mass fractions that were used to prepare deshelled seeds batches.

Whole seeds had an average shell mass fraction of 45%. Three levels of deshelling were used in the experiment, termed "deshelled -low", "deshelled -medium" and "deshelled -high" corresponding to shell mass fractions of 39%, 33% and 26%, respectively (see Section 3.1 for the calculation of shell mass fractions).

Microwave continuous heating tunnel

For some experimental settings, the seeds were preheated to a temperature of 35 • C using a microwave continuous heating tunnel. This equipment is a prototype specially developed for oilseed materials, constituted of microwave applicators and a conveyor belt. It is similar to the one described in [START_REF] Methlouthi | Microwave applicator with conveyor belt system[END_REF] and made by MES International Ltd., United Kingdom.

Instrumented screw-press

The experiments were conducted on a 101 mm diameter screw-press with a nominal throughput of 120 kg h -1 (MBU20, La Mécanique Moderne S. A., France). The electrical motor of 7.5 kW was powered through a frequency converter set in a closed regulation loop with an RPM feedback from an incremental coder. This configuration allowed for torque, speed and power acquisition (2 Hz) from the frequency converter (Altivar 71, Schneider Electric S. A., France) with an accuracy of 5%. For temperature measurements, 9 K-type thermocouples of 1.5 mm diameter (Inconel 600 ® sheath ref. 405-050, TC Ltd., United Kingdom) were inserted in 25 mm depth holes in the 5 cm thick steel bars along the barrel. They were connected to a temperature display. The seeds were fed by gravity through the hopper and a vat allowed for oil collection below the barrel.

Analytical methods

Oil content analysis using pulsed NMR spectroscopy

All measurements of oil contents in solid materials (seeds, press cake, kernels and shells) were made in triplicate using a pulsed nuclear magnetic resonance spectrometer (Minispeq MQ20/10, Bruker Corp., United States) following the standard method NF EN ISO 10565. Prior to measurements, the spectrometer had to be calibrated with Jatropha materials of known oil contents. This was done using two reference samples, one of seeds and one of press cake, that were previously analysed for oil contents using the Soxhlet extraction method NF V03-908. This method provides a measurement of pure oil mass fraction, excluding moisture, with a precision of about ± 0.1% (m/m) [START_REF] Krygsman | Simple methods for measuring total oil content by benchtop NMR[END_REF].

Seeds and press cake moisture content measurements

The moisture content of seeds and press cake was determined by weighing samples before and after drying in an oven at 103 • C for 24 h, following the standard method NF V03-909. This method provides a precision of about ± 0.2% (m/m).

Oil properties analyses

The water content of crude vegetable oil was measured using the Karl-Fischer titration method described by standard ISO 8534, with a precision of approximately ± 0.1% (m/m).

Foots are gross solid particles contained in crude oil, larger than 0.8 mm. They are assumed to be free of oil and content moisture. The method to measure this solid content consisted in passing the crude oil through a 0.8 mm sieve, and weighing it before and after the operation. This gives the mass of foots impregnated with oil, m foot . Then, the mass fraction of foots free of oil, termed FOOT was deduced using Eq. ( 1), assuming they had an average oil content of 50% (O foot = 0.5) [START_REF] Beerens | Screw-pressing of Jatropha seeds for fuelling purposes in less developed countries[END_REF].

FOOT = m foot • (1 -O foot ) m co (1)
The sediment content of crude oil was analysed after foot removal, by gravimetry following the standard NF E 48-652. This measurement provided a sediment mass concentration, termed SED vol expressed in mg L -1 , that was further converted to a mass fraction SED, relative to crude oil mass, using Eq. ( 2). Sediments were assumed to be free of oil but they contain moisture.

SED = SED vol × 10 -3 co • m co -m foot m co (2)
where co = 920 kg m -3 [START_REF] Akintayo | Characteristics and composition of Parkia biglobbossa and Jatropha curcas oils and cakes[END_REF].

In further calculations, foots and sediments will be grouped in a single term TS, for total solids expressed as TS = SED + FOOT.

Experiments

Experimental settings

The influence of four independent variables was investigated: screw rotational speed at 9, 18 and 26 rpm (when it was technically achievable), choke ring adjustment (i.e. open, medium and tight), seed crushing and seed deshelling. Due to the seed preparation process, deshelled seeds were necessarily coarsely crushed. By combining different values of these independent variables, 19 experimental settings were defined, out of which three were in triplicate, giving 25 experiments. Two settings appeared technically undoable (see Section 4.1.1), three other settings were added, and finally giving 26 experiments completed (see Table 1).

Experimental protocol

A complete measurement was made for each operational setting. Prior to pressing, three samples of 150 g were taken from the seed batch for oil and moisture content measurement, so that for each experiment, the oil and moisture contents of input material were known. Then the press was gradually brought to a stable operating regime. The process was considered in steady state when cage temperatures and electric power values were stable for 5 min.

Once the steady state was achieved, the measurements were taken on a 15 min run. At t = 0, the oil and press cake containers were set in place and the acquisition of mechanical power measurement was triggered. The 9 temperature values from thermocouples were recorded twice during the experiment.

After 15 min, produced press cake and crude oil were weighed. About 200 g of press cake were sampled for oil and moisture content analyses. The collected oil was passed through a 0.8 mm sieve to remove the foots. Afterward, 200 mL of oil was sampled and sent to laboratory for sediment and moisture content analyses.

Crushed and deshelled seeds were slightly preheated to a temperature between 30 • C and 35 • C prior to pressing using a microwave continuous heating tunnel. This was necessary to achieve a proper temperature and pressure build-up during pressing (see Section 4.1.1). Crushed and deshelled seeds are indeed more difficult to process and moreover the room temperature had dropped from 20 • C to about 14 • C between the period when the experiments with whole seeds were conducted and the period when crushed and deshelled seeds were processed. Table 1 gives the measurement results for each experimental setting.

Calculations

Calculation of shell mass fraction

As mentioned above, the effect of seeds deshelling on the expression performance was investigated in the experiments. Therefore, the shell mass fraction of deshelled seeds batches had to be characterised. It was calculated from the oil content measurement of the seed batch, assuming that kernels and shells had constant oil contents. Reference values of oil contents in kernels and shells were determined by manually deshelling 20 entire seeds and measuring separately kernels and shells oil contents by pulsed NMR spectrometry (see Section 2.4.1). Average oil contents of kernels and shells were 55.3% and 1.4% on wet basis (at 6% moisture content) respectively.

Eq. ( 3), established from the oil mass balance in a seed, allowed to calculate the shell mass fraction s of a given seed batch, provided its oil content was known.

s = O ker -O batch O ker -O shell (3)
with O ker = 0.553 and O shell = 0.014.

Mass balance calculations

The calculation of mass balance was crucial for determining the separation efficiency of the process and it was also helpful in appreciating the quality of the measurements.

The different variables used for mass balance calculation are presented in Fig. 1.

The following assumptions were made:

• Crude oil (co) is the mass flowrate coming directly from the press, which contains solids and water. • Pure oil (po) is a fictive oil mass flowrate free of solids and water, as if the crude oil had undergone a perfect separation of solids and water. • Crude oil moisture content is measured on supernatant oil and we assume it is representative of crude oil water content, including solids, as shown in Fig. 1.

Four equations of mass conservation can be written, corresponding to overall matter, oil, water and solids, presented in Eqs. ( 4)-( 7) respectively.

The overall mass balance is expressed as:

ṁs = ṁco + ṁpc + ṁvap (4)
The water mass balance is given by:

ṁs • M s = ṁco • M co + ṁpc • M pc + ṁvap (5)
The oil mass balance comes as:

ṁs • O s = ṁco • (1 -TS) • (1 -M co ) + ṁpc • O pc (6)
that can also be written as: ṁs

• O s = ṁpo + ṁpc • O pc
, where ṁpo is the pure oil mass flowrate. Eventually, the solids mass balance is expressed as:

ṁs • (1 -O s -M s ) = ṁpc • (1 -O pc -M pc ) + ṁco • TS • (1 -M co ) (7)
The seed throughput is calculated from Eqs. ( 4) and ( 5). As the evaporated water mass flowrate is not measured, the calculation has to be iterated in order to converge to seed throughput and water mass flowrate values that verify both Eqs. ( 4) and (5).

Pure oil mass flowrate determination

The pure oil mass flowrate may be derived from the following equation (direct calculation):

ṁD po = ṁco • (1 -TS) • (1 -M co ) (8)
Following oil mass balance equations, pure oil mass flowrate may also be determined indirectly:

ṁI po = ṁs • O s -ṁpc • O pc (9)
Important uncertainties arise from measurements with both methods of calculation. Among the quantities involved, i.e. seeds, raw oil and press cake mass flowrate, residual oil in press cake, water, foots and sediment content, sediment content appears as the most prone to measurement errors. Even if the analytical method is standardised, experience shows that the results are difficult to reproduce, especially for high sediment contents [START_REF] Chirat | Etude de la qualité de carburants dérivés des huiles végétales: approche méthodologique[END_REF]. Therefore, the indirect calculation equation was assumed more reliable and taken as a reference.

Performance indicators

The main indicator of separation efficiency is the oil recovery defined as the ratio of pure oil expressed to seeds oil content, which can be calculated either from directly or indirectly calculated pure oil mass flowrate, presented respectively in Eqs. ( 10) and ( 11).

Á D = ṁD po ṁs • O s (10) Á I = ṁI po ṁs • O s (11)
All calculation results are available in Table 1.

Results and discussions

In this section, the efficacy and reproducibility of oil expression using a screw-press is described in regard of operational parameters. Then, the consistency of the mass balance is thoroughly analysed and a relation between press cake residual oil content and solids content of crude oil is presented. Finally, a model linking the specific energy consumption of the process to the oil recovery is proposed.

Influence of operational settings on process performance and behaviour

Description of process operation and difficulties

During the experiment, it was observed that temperature and pressure build-up were closely linked and crucial in obtaining proper oil expression. While the press was gradually brought to a steady operation regime, the barrel temperature raised together with the mechanical power delivered by the motor. Maximum barrel temperatures varied between 75 • C and 120 • C, depending on the experimental setting. Below 75 • C, no proper oil expression occurred. The choke adjustment appeared to have no significant effect on operating conditions and process performance.

Several difficulties were encountered when pressing crushed and deshelled seeds. Instead of clean oil, a thick mixture of oil and fine solids was extracted. With deshelled seeds, it was difficult to obtain proper pressure and temperature build up, so that the seeds were merely extruded and no oil was expressed. This phenomenon is known to occur when pressing seed materials with an insufficient structure to allow a proper pressure build up, such as deshelled or over-cooked seeds [START_REF] Boeck | Edible Oil Processing and Production -Expanding and Expelling[END_REF]. To overcome these issues, it was decided to preheat the seeds to a temperature close to 35 • C, using a microwave continuous heating tunnel, to facilitate temperature build-up, which proved to be quite effective. However, even with this precaution, pressing 50% deshelled seeds was impossible at a shaft speed higher than 4 or 5 rpm.

The difficulty of pressing deshelled oilseeds was previously reported in literature by several authors. [START_REF] Zheng | Screw pressing of whole and dehulled flaxseed for organic oil[END_REF] observed that the screw pressing of dehulled flaxseed presented lower oil yields than whole seeds and required a special configuration of the worm shaft because of the softness of dehulled seeds. A Japanese research group reported the same observation for sunflower seeds and developed a twin-screw press for the oil extraction from dehulled seeds [START_REF] Isobe | A new twin-screw press design for oil extraction of dehulled sunflower seeds[END_REF]. Finally, [START_REF] Xiao | Inversion algorithm for permeability of rapeseed cake and rapeseed dehulled cake[END_REF] compared the permeability of dehulled and undehulled rapeseeds under various pressures and found greater permeability in undehulled material. Thus, the difficulties in pressing deshelled Jatropha seeds might be explained by the reduction of permeability and the lack of solid structure caused by the deshelling.

Then, it was observed during the pressing of crushed and deshelled seeds that the process is never totally in steady state, especially at high rotational speed. In particular, large fluctuations of mechanical power and temperature are observed. This is attributed to the lack of homogeneity of input material: shells and fines always tend to separate from kernel parts. At the best, a quasi-periodicity is observed and the regime is self-maintained.

In some other cases, the regime is not steady and starts drifting: either the temperature and power increase until the worm shaft gets stuck, or the temperature and power drop and oil expression turns to seed extrusion.

Thus, operation at 26 rpm was only possible with whole seeds. With deshelled seeds, such high speed systematically prevented pressure build up and with crushed seeds, pressure and temperature build up was too high and the shaft got stuck.

Reproducibility of the results

The reproducibility of the experiment is appreciated by analysing the results of triplicated experimental settings. Fig. 2 shows the results of these three settings in terms of seed throughput and residual oil in press cake. The graph shows that seed throughput results are fairly reproduced with any type of seeds, the worst case being with crushed seeds with less than ± 10% gap around average. In terms of residual oil in press cake, the results are well reproduced only with whole seeds. It is much more difficult to reproduce the performances when pressing crushed or deshelled seeds. This is attributed to the unsteady state phenomena described in Section 4.1.1.

The lack of reproducibility observed reflects that, in some cases, the control of operational parameters (rotational speed, choke ring adjustment) of the present experimental apparatus is not sufficient to govern the process conditions. Parameters such as feed material homogeneity, cake porosity and temperature cannot be controlled. Consequently, the variable results of triplicated settings with deshelled and crushed seeds correspond to different process conditions, but under no circumstances are linked to measurement errors.

This means that any analysis of the links between controlled parameters and residual oil will present high uncertainty for crushed and deshelled seeds. However, the lack of reproducibility does not impede the analysis of the mass balance for each experiment and the relations between separation efficiency and energy consumption.

Relation between oil recovery and material throughput

Fig. 3a presents the relation between oil recovery and seed throughput with respect to seed preparation: the oil recovery tends to decrease with increasing material throughput. This is physically meaningful, since the increase of material throughput corresponds to a lower residence time and thus a lower oil extraction. Moreover, it can be shown from the results presented in Table 1 that the material throughput is strictly proportional to the screw rotational speed for a given seeds preparation.

Although the residence time is a crucial factor influencing oil recovery, the influence of processing conditions such as temperature and pressure cannot be ignored. Yet, we had observed and explained previously that the processing conditions cannot be reproduced for crushed and deshelled seeds with the present experimental apparatus. Then, no model regression can be made on these data, apart for whole seeds results, which are fairly reproducible.

A non-linear regression was performed on whole seeds, following an asymptotic model defined as:

Á = k 1 + k 2 • exp(k 3 • ṁs ) (12)
The regression gives an R 2 = 0.68 and the curve corresponding to the model is presented in Fig. 3b. The values of the coefficient k 1 , k 2 and k 3 are respectively 0.88, -0.01 and 0.02. Additional experiments with whole seeds at different screw rotational speeds would be required to improve this correlation. The same model was published by [START_REF] Karaj | Optimizing mechanical oil extraction of Jatropha curcas L. seeds with respect to press capacity, oil recovery and energy efficiency[END_REF], but with an R 2 = 0.78.

Mass balance assessment

Interpretation of results and data reconciliation

According to Eqs. ( 8) and ( 9), the direct and indirect calculation methods of the pure oil mass flowrate should give similar results. Fig. 4a illustrates the indirect versus direct calculation of pure oil mass flowrate. Results from both methods are very close, but with random variations and a systematic error revealed by the linear regression that slightly deviates from equality. Indeed, the direct calculation method provides a result significantly higher than the indirect method at p < <0.05 (t-test).

The random variations can be attributed to unavoidable measurement and sampling errors. The systematic deviation is attributed to crude oil sampling for sediment content measurement. The mass flowrate calculated indirectly, taken as the 12)). reference (see Section 3.3), is systematically lower than with the direct method, which means that the sediment content is always under-estimated. The under-estimation of sediment content measurement may be explained by the sampling method. For each experiment, 5-15 kg of raw oil is extracted, from which a raw oil sample of 200 mL is retrieved from the top of the bucket using a beaker while manually agitating the mixture. Thus, even with manual agitation, the sediment content of the sample is certainly lower than the overall sediment content.

This systematic error is corrected by applying a coefficient to the total solids content of crude oil, which is provided by the linear regression (˛= 0.9324).

ṁI po = ˛• ṁD po = ˛• ṁco • (1 -TS) • (1 -M co ) (13)
We introduce a corrected value of total solids TS ˛, such as:

1 -TS ˛= ˛• (1 -TS).
This corrected value of total contamination content is set as reference for further analyses and the direct calculation of pure oil mass flowrate becomes:

ṁD po = ṁco • (1 -TS ˛) • (1 -M co ) (14)
The determination of oil recovery is crucial for appreciating the efficiency of the solid-liquid separation and directly depends on pure oil mass flowrate. Thus, two values can be calculated using direct and indirect pure oil mass flowrate calculation methods (see Eqs. ( 10) and ( 11)). Fig. 4b illustrates the matching between both methods. It is clear that the random error is exacerbated when pure oil mass flowrate is divided by input oil. However, the linear least squares regression exhibits a fair R 2 = 0.78 and the regression coefficient is very close to 1 (interception was forced to 0).

Correlation between seed, press cake and crude oil mass flowrates

A very well-correlated linear relation is observed between seed throughput and press cake output. The press cake throughput is always strictly proportional to the seed input. This result can also be observed in the results published by [START_REF] Karaj | Optimizing mechanical oil extraction of Jatropha curcas L. seeds with respect to press capacity, oil recovery and energy efficiency[END_REF] on Jatropha oil expression experiments but was not highlighted by the authors. We call ˇthe linear regression coefficient relating press cake throughput to seed throughput, as showed in Eq. ( 15). Fig. 5 shows linear regressions between seeds and press cake throughput Seed throughput m s , kg h -1 Press cake output m pc , kg h -1 Fig. 5. Relations between seed and press cake throughput with respect to seed preparation. Lines are linear least squares regression for each modality of seed preparation.

Table 2

Values of coefficient ˇ(linear regression coefficient relating seedcake to seed throughput in Eq. ( 15)) and square residues of linear regressions.

Seed preparation ˇR2

All The value of the coefficient ˇis mostly related to the design of the press, especially the volume generated by the profile of the worm shaft, which is the same for all experiments. However, it also depends on the seeds characteristics, in particular bulk density and oil content, which will influence the input mass flowrate conveyed by the screw and the proportion in which the material is divided between press cake and oil outlets. Indeed, ˇcan be precisely evaluated with respect to seed preparation as shown in Fig. 5. Table 2 summarises the value of ˇfor each seed preparation and for [START_REF] Karaj | Optimizing mechanical oil extraction of Jatropha curcas L. seeds with respect to press capacity, oil recovery and energy efficiency[END_REF] data. These values are specific to Jatropha seeds, and to the pressing equipment used in these experiments.

Crushed and entire seeds have approximately the same oil content; deshelled seeds have higher oil content and are only coarsely crushed compared to crushed seeds. The highest value of ˇrelates to whole seeds. For crushed seeds, the ˇvalue is slightly lower, which can be explained by a higher bulk density. Then the even lower ˇvalue for deshelled seeds might be explained by higher oil content, resulting in less press cake and more oil.

This important result shows that, whatever the operational parameters, the seed throughput is always divided in a stream of crude oil and a stream of press cake in the same proportion (ˇ) for a given input material. Then, the residual oil in press cake and the amount of solids carried by the oil are directly related and determine the efficacy of the separation, i.e. the oil recovery. When the oil recovery is high, the press cake oil content is low, as well as the solids in crude oil, and conversely. This means that the separation efficacy of screw-pressing cannot be evaluated only by measuring crude oil mass flowrate, the knowledge of solids content or residual oil in press cake is required. This result allows writing the relations between oil recovery, press cake oil content and solids in crude oil using the coefficient ˇand the mass balance equations. This information could be very useful for choosing the screw-press design best suited to the type of seeds to process, especially given their oil contents.

Total solids prediction from oil recovery

Using ˇ, oil recovery can be expressed as a simple function of seeds and press cake oil content:

Á = 1 - ˇ• O pc O s (16)
Then, in the perspective of process modelling, provided that the value of ˇis known, the total solids in the extracted crude oil can be predicted. Combining Eq. ( 16) with the oil mass balance Eq. ( 6), total solids contamination can be expressed as a function of oil recovery as:

TS I ˛= 1 - Á • O s (1 -ˇ) • (1 -M co )
Fig. 4c illustrates the calculated solids content versus the measured one. Of course, here the random errors are strongly increased by the several ratios and multiplications.

In practice, the equation for total solids calculation can be simplified by ignoring oil moisture content. The water content of oil is indeed very low: in this case the maximum measured value is 1.1% and the average 0.7%. However, these moisture contents are actually high compared to usual values for vegetable oils, because this oil is degraded (high free fatty acid 9%) due to the poor storage conditions and the age of the seeds. Normally, even after being washed with water, vegetable oil has moisture content up to 0.5% (w/w) after phase separation [START_REF] Lusas | Animal and vegetable fats, oils, and waxes[END_REF]. In comparison, following the standard DIN 51605, the moisture level required for using vegetable oil as a fuel is 0.075% maximum.

Relation between oil recovery and specific energy consumption

The oil expression is a solid-liquid separation process and as such, the specific energy consumption should be linked to the efficiency of the separation. This intuition is confirmed by observing Fig. 6a, which shows a scatterplot of seed-specific energy consumption versus press cake residual oil content.

Using an exploratory data analysis methodology as described in NIST/SEMATECH (2013), a model for seed-specific energy consumption was built stepwise. The basic procedure consists in identifying and fitting a first model including only the main explanatory variable -press cake residual oil in this case. The form of the equation should be determined according to the physics of the process. Then, the residues of this model are plotted against other potential explanatory variables and if there is a strong correlation, the variable is integrated in the model -seeds oil content in this case. In order to ensure that the model describes the data well enough and that there is no missing term, the residues were tested for normality using Shapiro-Wilk test (NIST/SEMATECH, 2013).

The final model is presented in Eq. ( 17); it explains 87% of seed-specific energy consumption variations -R 2 = 0.87. It includes two explanatory variables, oil recovery and seeds oil content and only three parameters, ˇ1, ˇ2 and ˇ3 -press cake residual oil was replaced with oil recovery using Eq. ( 16). The values of ˇ1, ˇ2 and ˇ3 are 1.075, -11. correlation is suitable to describe the process and to adapt the parameters values.

E s = ˇ1 O s • (1 -Á) + ˇ2 1 + ˇ3 • O s (17)
Then, it is useful to observe oil-specific energy consumption, given by Eq. ( 18).

E po = E s Á • O s (18)
This is also very well-correlated to experimental data with an R 2 = 0.86. It is relevant to compare the energy required to extract vegetable oil from the seeds with the oil heating value. Assuming an average heating value of 38.8 MJ kg [START_REF] Blin | Characteristics of vegetable oils for use as fuel in stationary diesel engines -towards specifications for a standard in West Africa[END_REF], we calculated the embodied energy of oil as a percentage of its energy content. This value is plotted against oil recovery in Fig. 6b, for several values of seeds oil contents. The energy spent for oil expression is small (<5%) compared to the heating value of the oil, which makes it an energy efficient separation process. Of course, the production of the mechanical energy and the energy required for seeds preparation, transport and production should also be estimated for a complete determination of the overall embodied primary energy.

The specific energy consumption is strongly sensitive to seeds oil content, especially at low oil content. A minimum energy requirement is generally observed at oil recoveries between 70% and 80%. [START_REF] Karaj | Optimizing mechanical oil extraction of Jatropha curcas L. seeds with respect to press capacity, oil recovery and energy efficiency[END_REF] presented similar results for cylinder-hole type screw-press but with significantly higher energy consumption levels, up to 400 Wh kg -1 of seeds. This shows that strainer-type screw-press is much more energy efficient than cylinder-hole type.

The relation of energy efficiency to oil recovery is an important consideration for optimising the processing strategy of oilseeds, depending on the final uses of the products, their economic values and energy prices. For instance, if the oil and the press cake are used for energy purposes, it might be beneficial to limit the oil recovery in order to minimise the oil expression cost and increase the energy value of the press cake. In this context, the approach applied in this work is particularly relevant and should be validated for other types of seeds and pressing equipment. Additional experiments would be necessary to determine if the correlation in Eq. ( 17) can be generalised to any type of mechanical oil expression process.

Conclusion

The oil expression from crushed and deshelled seeds appeared to be unstable due to a lack of homogeneity in input material, resulting in important discrepancies in the pressing efficiency. A high fraction of shells in the feed allows to build a solid permeable matrix which favours oil flow through the press cake. For a given feed material, the press cake mass flowrate is strictly proportional to the seed throughput, which enables to establish a direct relation between oil recovery and solids content in crude oil. A correlation between oil recovery and specific energy consumption was proposed.

Fig. 1 .

 1 Fig. 1. Side view of an oilseed screw-press and mass balance assessment terms. With authorisation of La Mécanique Moderne.

Fig. 2 .

 2 Fig. 2. Graphical illustration of the results for triplicate experimental settings with respect to seed preparation. (a) Seed throughput; (b) residual oil in press cake.

Fig. 3 .

 3 Fig. 3. Relation between oil recovery and seed throughput. (a) All seed preparation; (b) whole seeds only, line is the regression model (Eq. (12)).

Fig. 4 .

 4 Fig. 4. Validation graphs for the consistency direct and indirect calculation methods of pure oil mass flowrate (a), oil recovery (b) and total solids (c).

Fig. 6 .

 6 Fig. 6. (a) Scatterplot of seed-specific energy consumption versus press cake residual oil content, with respect to seed preparation. (b) Graphical representation of the proposed energy model. Mechanical energy requirement for pressing, expressed as percentage of oil heating value, versus oil recovery, for different values of seeds oil content.

Table 1

 1 Detailed results for all experiments. (Abbreviations: indir.: indirect calculation method; dir. corr.: direct calculation method corrected with coefficient ˛, see Sections 3.3, 3.4 and 4.2.1 for details.).

	)
	Á (dir. corr.) Es -1 (Wh kg		0.79 56.4	0.48 47.3	0.78 66.8	0.63 51.7	0.76 53.9	0.80 51.4	0.80 66.0	0.80 64.0	0.77 55.1	0.79 53.0	0.79 51.5	0.65 30.7	0.72 38.9	0.79 58.7	0.86 53.5	0.80 33.8	0.78 44.3	0.85 42.6	0.69 23.5	0.78 35.1	0.62 25.7	0.74 28.5	0.80 31.5	0.85 57.9	0.81 37.5	0.63 28.4
	Á (indir.)		0.77	0.50	0.85	0.65	0.86	0.86	0.88	0.87	0.81	0.81	0.79	0.52	0.66	0.85	0.90	0.77	0.80	0.82	0.65	0.84	0.57	0.70	0.79	0.88	0.76	0.62
	)
	Osc Msc ṁvap -1 (kg h		0.10 0.068 0.786	0.20 0.067 1.217	0.07 0.062 1.030	0.15 0.065 2.253	0.07 0.059 0.973	0.06 0.057 1.765	0.06 0.053 1.257	0.06 0.047 2.645	0.09 0.064 1.528	0.09 0.063 2.075	0.10 0.062 2.248	0.23 0.060 1.686	0.16 0.062 1.587	0.07 0.050 2.730	0.05 0.070 0.863	0.12 0.075 0.913	0.11 0.072 0.674	0.10 0.070 1.649	0.24 0.070 0.492	0.12 0.073 0.404	0.24 0.069 0.843	0.18 0.073 0.948	0.12 0.067 1.308	0.06 0.064 1.127	0.13 0.075 0.569	0.20 0.075 0.572
		)
	ṁsc	(kg h -1	41.8	126.6	39.8	125.9	46.1	80.3	45.1	80.4	79.2	81.1	81.7	95.7	96.1	94.4	46.5	86.6	41.2	83.4	26.0	19.6	66.3	64.3	61.2	50.5	46.2	91.5
		)
		-1
	ṁpo (dir.	corr.) (kg h	14.57	24.15	13.88	33.89	16.56	29.94	17.16	30.20	28.44	29.46	29.73	29.06	32.33	34.63	21.66	37.30	17.94	39.77	12.48	10.99	23.06	28.65	29.32	20.73	20.34	29.42
	) Mco TS ′ ṁpo (indir.)	) (kg h -1	0.0040 0.13 14.31	0.0075 0.28 24.97	0.0035 0.14 15.13	0.0075 0.24 35.15	0.0060 0.20 18.62	0.0065 0.25 32.35	0.0050 0.18 19.05	0.0070 0.25 33.10	0.0075 0.18 30.25	0.0055 0.19 30.09	0.0060 0.19 29.63	0.0085 0.31 23.07	0.0085 0.30 29.78	0.0065 0.24 37.20	0.0045 0.16 22.73	0.0095 0.23 36.28	0.0085 0.27 18.26	0.0065 0.20 38.18	0.0065 0.24 11.77	0.0055 0.22 11.85	0.0080 0.27 21.16	0.0065 0.24 26.81	0.0060 0.18 29.23	0.0050 0.14 21.40	0.0080 0.22 19.00	0.0110 0.32 28.82
	ṁco -1 (kg h		16.8	33.7	16.2	45.2	21.0	40.2	21.1	40.7	35.1	36.4	37.0	42.2	46.3	46.0	26.0	48.9	24.8	50.3	16.5	14.2	32.1	38.0	36.1	24.3	26.3	43.8
	)
	Settings N (rpm) Choke Seed preparation Ms Osc ṁs -1 (kg h		1.1 9 Open Whole 0.062 0.31 59.4	1.2 26 Open Whole 0.062 0.31 161.5	1.3 9 Tight Whole 0.062 0.31 57.0	1.4 26 Tight Whole 0.062 0.31 173.3	1.5 9 Open Crushed 0.056 0.32 68.1	1.6 16 Open Crushed 0.054 0.31 122.3	1.7 9 Tight Crushed 0.056 0.32 67.4	1.8 16 Tight Crushed 0.054 0.31 123.8	1.9 18 Medium Whole 0.059 0.32 115.8	1.A 18 Medium Whole 0.062 0.31 119.5	1.B 18 Medium Whole 0.062 0.31 121.0	1.C 18 Medium Crushed 0.056 0.32 139.5	1.D 18 Medium Crushed 0.055 0.31 144.0	1.E 18 Medium Crushed 0.054 0.31 143.2	2.1 9 Open Deshell. -L 0.058 0.34 73.4	2.2 18 Open Deshell. -L 0.058 0.34 136.4	2.3 9 Tight Deshell. -L 0.058 0.34 66.8	2.4 18 Tight Deshell. -L 0.058 0.34 135.4	2.5 5 Open Deshell. -H 0.056 0.42 43.0	2.7 4 Tight Deshell. -H 0.056 0.41 34.2	2.9 13 Medium Deshell. -M 0.057 0.37 99.1	2.A 13 Medium Deshell. -M 0.057 0.37 103.3	2.B 13 Medium Deshell. -M 0.057 0.37 98.6	3.1 11 Open Whole 0.059 0.32 76.0	3.2 9 Medium Deshell. -L 0.058 0.34 73.1	3.3 18 Medium Deshell. -L 0.058 0.34 135.8

Acknowledgments

This work was only possible thanks to the technical and financial support of CETIOM and CREOL and also received the assistance of the European Union. Its authors are solely responsible for its content, which does not represent the point of view of these institutions.