Optimization of Rubber Seed Oil Transesterification to Biodiesel Using Experimental Designs and Artificial Neural Networks

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ABSTRACT

The development of biofuels is driven both by concern about the greenhouse effect and by interest in the opportunities for exploitation of biomass of agricultural origin. In order to improve the yield and quality of biodiesel through modeling and optimization, several studies are in progress. In this paper, biodiesel produced from rubber seed oil in the homogeneous transesterification is studied using a Plackett-Burman experimental design, a full factorial design, a central composite design and an Artificial Neural Network (ANN) coupled with a Genetic Algorithm (GA).Variables such as temperature, stirring speed, reaction time, type of alcohol, and type of catalyst are studied to obtain the best specific gravity and kinematic viscosity. Type of alcohol and type of catalyst have the greatest effect on the two responses, with ethanol (alcohol) and sulphuric acid (catalyst) producing the best results. The specific gravity and kinematic viscosity changes recorded during the transesterification process followed the first and second order polynomial models, respectively. The ANN coupled with GA was used to optimize the two responses simultaneously. Global optimal values of specific gravity (0.883) and kinematic viscosity (6.76 cSt) were recorded when a temperature of 90°C, a stirring speed of 305 rpm, and a treatment time of 141 min were imposed.

Cite this paper

Kouassi, K. , Abolle, A. , Yao, K. , Boa, D. , Adouby, K. , Drogui, P. and Tyagi, R. (2018) Optimization of Rubber Seed Oil Transesterification to Biodiesel Using Experimental Designs and Artificial Neural Networks. Green and Sustainable Chemistry, 8, 39-61. doi: 10.4236/gsc.2018.81004.

1. Introduction

The dramatic growth in global energy demand in recent decades can be traced mainly to the requirements of transport and industry [1] [2] [3] . Oil and hy- droelectric sources are insufficient to meet this demand. Besides, the producer of oil often causes dramatic fluctuations of the price of oil, with worldwide economic consequences, especially in developing countries [4] .

The use of fossil energy also has adverse effects on the environment, including global warming and climate change [1] [2] [5] . These concerns have drawn the attention of researchers to the potential of biofuels [2] [6] [7] [8] . Because they are extensively available in tropical zone and that they result from classic and simple process, vegetable oils have been targeted for exploitation as biofuel [3] [4] [9] [10] [11] . However, the viscosity and density of these oils are critical pa- rameters determining their potential for use as fuel, affecting both the injection system (flow, maximum pressure, injection timing) and the spraying mechanism in the combustion chamber of engines [12] .

The high viscosity of vegetable oils has led some users to heat them before injection, to dilute them in conventional diesel, or to modify the injectors of the engines [13] [14] . Since the direct use of crude vegetable oils is technically impracticable for traditional diesel engines, some form of processing is required to render them suitable for use as biodiesel [2] [5] [7] [14] .

Five paths are available for this purpose: dilution, micro-emulsification, pyro- lysis, co-feeding and co-processing with fossil feedstock material and transesterification. The last approach offers the potential for industrialization, with glycerine as a by-product [10] .

The basic catalysts as the hydroxide of potassium (KOH) and the hydroxide of sodium (NaOH) are generally used. In the other side, sulfuric acid and chlorhydric acid were usually uses as acidic catalysts the transestérification of the vegetable oils [17] .

Indeed, transesterification is a chemical reaction during which the esters are transformed into other esters by exchange of the alkyl group [15] . This reaction can be done either by an alcoholysis (reaction of an alcohol on an ester), or by an acidolysis (reaction of an acid on an ester), or by an esterolysis (reaction of an ester on another ester). These three types of transesterification can take place in the oil [15] . But in order to have alkyl esters with molecular weights similar to diesel, transesterification by alcoholysis was done as part of this work. Thus, the transesterification of vegetable oils is made by reacting a triglyceride with an al- cohol in the presence of a catalyst. A mixture of glycerol and alkyl esters is obtained [16] . Three types of catalysts for transesterification reactions are basic catalysts, acid catalysts and, other catalysts as alkoxides or metal oxides and enzymatic catalysts [17] . Thus, in the context of the transesterifications can be carried out with basic catalysts such as potassium hydroxide (KOH) and sodium hydroxide (NaOH), and acid ones such as sulfuric acid and hydrochloric acid are used [17] .

The oils that can be used as the raw material for biodiesel production can be classified into edible oils, non-edible oils and waste oils and fats. To minimize both food security concerns [18] and the costs of biodiesel production, current research focuses on the non-edible and waste oils and fats ones [11] [12] [18] [19] .

A number of studies [19] [20] [21] [22] have examined the potential to use the transesterification process to enhance rubber seed oil (RSO) [23] [24] for biofuel. Rubber trees are cultivated for the production of natural rubber latex, and the wood from the trees is used for furniture or as firewood. Usually, most of seeds are unused and rot in the field. Annual rubber seed production is estimated to be 800 to 1200 kg/ha/year. A seed contains approximately 40% to 50% oil [19] , representing a yield of approximately 500 L/ha/year [23] [24] [25] . According Zhu et al.’s study [19] of RRIM600 and GT1 clones in Southeast Asia, the age and size of trees influence seed yield. FAO statistical data show that about 10 million hectares of rubber plantations exist worldwide, representing a potential oil production of as much as 5 billion litres per year. This constitutes a significant source of raw material for the synthesis of biodiesel. In most cases, however, this oil has a high level of free fatty acids [20] [21] [22] [26] , requiring a two-step process to produce biodiesel: acid esterification followed by basic transesterification or directly a transesterification in acid catalysis.

An experimental design is a statistical analysis method that determines the relationship between a dependent variable (the response Y) and explanatory variables (influencing factors Xi) according to Equation (1) [27] :

Y = b 0 + b i X i + b i j X i X j + b i j k X i X j X k + b i i X i 2 + (1)

where b0 is the average coefficient, bi is the main coefficient, bij is the second interaction coefficient, bijk is the third interaction coefficient, bii is the quadratic coefficient and Xi, Xj, Xk are the coded variables ( 1 X i + 1 ; 1 X j + 1 ; 1 X k + 1 ).

The coefficients were calculated using a least square method [27] . It permits to highlight the interactions between factors [27] . The mathematical model being found, optimization is defined like being the research of the values of the factors that gives according to the sought-after goal, the best value possible of the response [28] . Several works aiming to optimize the reaction of transestérifica- tion for an industrial implementation have been achieved [29] [30] [31] [32] . In most these works, the reaction yield is the main studied response. This does not only require expensive dosing methods, but also certifications of the final products. Moreover, in other works, the yield is simply expressed by the ratio of the mass of biodiesel to that of the oil [33] [34] . However, in homogeneous catalysis, the separation of the glycérol and the biodiesel not being always clean, a part of the biodiesel meets in the phase glycérineuse and vice versa, blemishing mistake the evaluation of the yield. It is why, we consider in this work a new approach of optimization while studying as responses the parameters of quality of the biodiesel (the specific gravity and the kinematic viscosity) closely bound to the yield of the reaction [35] .

A number of approaches have been considered for modeling and optimizing biodiesel production from rubber seed oil, but knowledge of the actual weight of the different factors affecting the quality of the final product remains incom- plete. The goal of this study was to elucidate the effect of each of these factors, individually and in combination, on the quality of the product. The factors involved in the synthesis of biodiesel [27] [28] were first screened using a Hadamard matrix. A full factorial matrix and central composite design response surface methodology were then used to model and optimize the synthesis, focusing on specific gravity and viscosity. Finally, an Artificial Neural Network (ANN) coupled with a Genetic Algorithm (GA) was used to produce a global optimization of the process.

2. Materials and Methods

2.1. Raw Material and Chemicals

The crude oil was extracted from rubber seeds collected from private and indus- trial plantations in Côte d’Ivoire. The seeds were dried, crushed, and pressed with a mechanical press. The composition of the brown oil obtained is shown in SM 1. The main chemicals used in the study (potassium hydroxide (KOH) pellets of 85 wt.% purity, sulphuric acid (H2SO4) of 96 wt.% purity, methanol (MeOH) of 99.7 wt.% purity, ethanol (EtOH) of 99 wt.% purity, and magnesium sulphate (MgSO4) of 99 wt.% purity) were provided by Merck Ltd (Germany).

2.2. Transesterification Procedure

The transesterification was carried out in liquid phase under various conditions according to the experimental design. In alkaline conditions, the synthesis of biodiesel was carried out with a molar ratio MeOH/oil (or EtOH/oil) of 6:1 and 1% (w/w) of KOH relative to the oil, while in acidic conditions, a molar ratio MeOH/oil (or EtOH/oil) of 6:1 and 2.25% (mol/mol) of the mixture were used according to Mohamad et al. (2002) [36] . Two alcohols were used, MeOH and EtOH. The experimental set-up used was a 500 mL batch reactor magnetically stirred and equipped with a heater This reactor is surmounted by a coolant to prevent the alcohol from evaporating, especially when the reaction reaction takes place beyond the boiling temperature of the alcohol. At the end of the reaction, the mixture was collected and subjected to settling. The upper layer was then collected and washed with hot distilled water. The moisture in the washed biodiesel was subsequently removed using anhydrous MgSO4.

SM 1. Fatty acid profile of crude rubber seed oil (RSO) (analysis by gaz chromatogrphy coupled with a Detector of Flame ionization (GC/DFI) Hewlett-Packard® 5890 II set provided with an automatic sample ferryman of Agilent 6890).

2.3. Experimental Design

Among existing experimental designs [26] , the Plackett and Burman model [37] is the most suitable for screening. In this study, the transesterification was examined using a Hadamard matrix with five factors: temperature (U1), stirring speed (U2), reaction time (U3), type of solvent (U4), and type of catalyst (U5). Specific gravity and kinematic viscosity were considered as the responses.

Analysis of the effects of the factors on the two responses was performed using Equation (2). In this equation Pi is the contribution of each factor on the response.

P i = 100 × ( b i 2 b i 2 ) ; i 0 (2)

where bi is the estimate of the main effect of factor i.

A full factorial matrix (2k, k being the number of factors) was used for quantitative optimization of the factors. Full factorial matrices examine two or more factors, each with discrete possible values or levels, across all possible combinations of levels and factors. A full factorial design (FFD) may also be called a fully crossed design. The effect of each individual factor, and the effects of interactions between factors, can be examined using this approach. In this study, the effects of temperature (U1), stirring speed (U2), and reaction time (U3) were evaluated.

Response surface methodology (RSM) using a central composite design (CCD) with five levels and two factors (stirring speed and reaction time) was used for to model and optimize the relation between these factors and specific gravity and viscosity. The experimental runs were randomized to minimize the effects of unexpected variability in the observed responses. The methodology employed enables the formulation of a second-order polynomial that describes the process. To correlate the response variable to the independent variables, multiple regressions were used to fit the coefficient of the second-order polynomial model of the response [34] .

Table 1 summarizes the experimental range and levels of the independent process variables studied for each part of this work. For screening, the main fac- tors involved in biodiesel synthesis and their limits were chosen according to the literature [38] . The experimental domain of the full factorial design is defined from the results of the screening. Similarly, the experimental domain of central composite design is defined from the results of the full factorial design. The experimental design and responses observed during the screening are shown in Table 2.

The main interactions, correlation coefficients, variance analysis, residuals, and standard deviations were calculated using the NEMROD-W program (design NEMROD-W, version 9901 Française, LPRAI-Marseille Inc., France).

Table 1. Experimental range and levels of independent process variables.

In order to find a global optimum, a single database was established bymerg- ing the observed responses of the full factorial and central composite design analyses. This database, described in Table 3, was used to model the process with an artificial neural network. The resulting models were then used to search for a global optimum with a genetic algorithm.

The genetic algorithm (GA) was used to determine a global optimum mini- mizing equations (x) and (y) under constraint. GAs are stochastic search tech- niques whose theoretical bases were defined by J. H. Holland [39] . They are based on a natural biological process: the evolution of living species. They evolve through two mechanisms: natural selection and reproduction. Selection ensures that only the fittest individuals survive, while reproduction recombines parental characteristics to create descendants with new possibilities. The combination of these two phenomena (selection and reproduction) leads, generation after generation, to populations that are better and better adapted to the environment in which they live.

Artificial neural network (ANN) is a powerful modeling tool, used in various fields. In this study, specific gravity and kinematic viscosity variation were predicted using the Multilayer Perceptron (MP) [40] [41] . The ANN architecture included an input layer with three neurons (representing temperature, stirring speed and reaction time) and an output layer with two neurons (representing specific gravity and kinematic viscosity). The optimal network topology was determined using MATLAB programm (Matlab R2015a).

The main objective was to simultaneously minimize the specific gravity and kinematic viscosity. Such an optimization was performed via aggregation of the multiple objectives into a single objective function [40] [41] , as expressed by Equation (3).

Table 2. Experimental design and responses of the preliminary screening study.

Table 3. ANN data base.

Min Y ; with Y = Y 1 + Y 2 70 X 1 90 ˚ C , 100 X 2 400 rpm , 105 X 3 165 min . (3)

The computational parameters of the GA were the following: 1) population size = 200; 2) Elite count = 2; 3) Number of Generation = 1500; 4) Fitness scaling function = @fitscalingrank; 5) Selection function = Selection function; 6) Crossover function = @crossoverscattered; 7) Mutation function = @mutationuni- form; 8) Mutation probability = 0.05.

2.4. Analytical Techniques

An ABBE WYA-IS refractometer was used to determine the refraction index. Kinematic viscosity was measured using a capillary tube viscometer (HVU 482). Specific gravity was determined with a DMA 4500 M densimeter. A digital scale (OHAUS) with a precision of 0.001 g was used to measure weight. Physico- chemical characteristics such as acid, ester, peroxide, iodine, and saponification index, water and volatile matter content, specific gravity, viscosity, cetane index, refractive index, and heating value were determined according to the AFNOR [42] [43] and ASTM norms or by calculation [33] [44] [45] . The fatty acid pro- file of crude rubber seed oil is determined by gaz chromatogrphy coupled with a Detector of Flame ionization (GC/DFI) Hewlett-Packard® 5890 II set provided with an automatic sample ferryman of Agilent 6890.

3. Results and Discussion

3.1. Physicochemical Characteristics of the Studied Products

The physicochemical properties determined and compared to those of the gasoil are presented in SM 2.

SM 2. Some physicochemical characteristics of rubber seed oil compared to diesel.

From the foregoing, it should be noticed that the density, viscosity, and the acidity of the rubber seeds oil are limiting factors for the direct use in a diesel engine. This conclusion is consistent with many authors [2] [4] [15] [33] [38] [46] - [51] when they argue that among the physicochemical characteristics of oils, these parameters cause practical problems. As part of this study, their value far removed from the specifications show that it is not opportune to use such oil in a diesel engine. Therefore, a transformation is needed to be carried out, as it is undertaken using the transesterification method according to Hadamard Matrix.

3.2. Factor Screening

The standard deviations of specific gravity and viscosity, estimated by NEMROD-W sofware, were 0.003 and 2.5 cSt respectively. These values validate the chosen experimental field. It thererocessing. Applied Energy, 87, 1815-1835.
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