This research has been performed with the aim to find out the optimum condition of operating variables for the densification process of rice straw as an alternative solid fuel. A response surface methodology with three levels (-1, 0 and +1) was used as the experimental design. Independent variables to be optimized include densifying pressure, particle size and moisture content of the raw materials. The experiment was performed using a laboratory scale hydraulic press machine in which a number of 17 treatments were randomly implemented following the Bob-Behnken design. The experimental data on the density, relaxation and durability of solid fuel treated as the response variables were fitted into a quadratic polynomial model. The simultaneous optimization of the response variables has been implemented using Desirability Function (DF) approach, computed with the use of Design Expert software. The results of the research showed that the optimum conditions to produce solid fuels from rice straw biomass were obtained at a pressure of 3002.8 psi, the particle size of 34.90 mesh and raw material water content of 7.77%. The application of the optimum conditions enables to produce a solid fuel having a density of at least 1.0 g cm-3. However, further research is still required to investigate the combustion performance of the produced solid fuels in the real combustor.
PDF Abstract XML References Citation
How to cite this article
Biomass constitutes the third major natural energy source in the world after oil and coal (Ozbay et al., 2006). Up to present, biomass is utilized to supply energy for more than half of world population which is comparable to 1250 million tonnes oil equivalent (mtoe) (Purohit et al., 2006). If the biomass can be converted into various forms of energy economically and sustainably, it will not only provide a substitution for fossil fuel but also reduce the carbon emission into atmosphere as biomass has the potential to be CO2 neutral. Therefore, the utilization of biomass as an alternative energy, particularly in the under-developed and developing countries can also be related to Clean Development Mechanism (CDM) for the investment of carbon trading.
In Indonesia, agricultural residues have become the most important source of biomass since it is an agricultural country with almost 70% of the population living in rural areas. It is estimated that the country is capable of supplying 146.7 million tons of biomass per year, equivalent to about 470 GJ year-1 (Abdullah, 2009). As the country has around 7-8 million hectares of rice fields, the largest source of the biomass comes from rice field residues, such as rice straws. To the farmers, rice straw residues do not have any economic value and are source of problem. In order to get rid of the residues, farmers either abandon or burn away most of the rice straws not long after harvesting the paddy. However, burning the rice straws not only releases a large amount of pollutants, affecting the ambient air quality but also substantially contribute to the formation of brown cloud which deteriorates the local atmospheric visibility that may cause the traffic accident. Therefore, a clean and an efficient method are required to convert the bulky rice straw residue into an alternative solid fuel to be used as rural household energy source. A mechanical densification process is proposed as a means which can be implemented to produce the solid fuel.
Biomass densification is not new idea and has been practiced for many years in a number of countries. At commercial scale, the densification is usually performed under high briquetting pressure by means of piston press, extrusion screws or by roll presses, supplemented with preheating raw materials (Rhen et al., 2005; Husain et al., 2001; Li and Liu, 2000). High pressure operation provides an advantage of producing strong compacted solid fuel without using binder (Rhen et al., 2005; Li and Liu, 2000). Such an operating condition is however, difficult to implement for a manual scale production in the rural community due to the requirements of high energy. It is therefore necessary to determine the processing and raw materials conditions that lead either to a correct selection or design of machines for densification of biomass based upon manual operation.
A Response Surface Methodology (RSM) approach has been commonly used for optimization studies in recent years. The RSM is a collection of statistical and mathematical techniques useful to empirically study the relationship between a response and several input variables (Myers and Montgomery, 2002). It has been quite common that several response variables are involved in the product or process development. Therefore, the optimization procedure of the independent variables in this case requires a simultaneous consideration of all responses (Jeong and Kim, 2009). The multi-response problem consists of three stages: data collection, model building and optimization. This approach has been applied in many studies, such as mechanical characteristics of polymer concrete (Barbuta and Lepadatu, 2008), surface roughness in turning process of mechanical parts (Doniavi et al., 2007) and optimization of cement clinkering process (Amiri et al., 2008), to name a few. The present study applies the RSM approach with the aim at finding out the levels of factors in the densification of rice straw biomass that provide optimum response from quality of the product and operational cost point of views through an investigation applying a laboratory scale pressing machine.
MATERIALS AND METHODS
The rice straw biomass was collected from rice fields in the rural areas outside the city of Banda Aceh, Indonesia. Prior to reducing its size, the rice straw was dried in the open air for a week. The dried rice straw was then ground in a milling machine and results were screened into three particle size categories of 20, 40 and 60 mesh. The moisture content of the samples was adjusted to 5, 10 and 15% by weight, respectively. A 10% starch was introduced into the sample to promote the binding among particles.
Proximate and ultimate analysis of the rice straw was performed at the Research and Development Center for Mineral and Coal Technology, Bandung, Indonesia. Proximate analysis provides information on the weight percent of the moisture, Volatile Matter (VM) when heated up to 950°C, Fixed Carbon (FC) and ash in a biomass material, whereas the ultimate analysis presents the weight percent of major elements such as carbon (C), hydrogen (H) and oxygen (O), as well as other elements of sulphur (S) and nitrogen (N). Both proximate and ultimate analysis was conducted in accordance with the recommendation of American Society for Testing Materials (ASTM). Determination of moisture content in dry sample was carried out consistent with ASTM D. 3173. Ash and volatile matter were analyzed in line with ASTM D.3174 and ISO 562, respectively. The fixed carbon was obtained through the following calculation: 100-total percentage of moisture content, ash and volatile matter. With respect to the ultimate analysis, elements of C, H and O were determined using ASTM standard method D. 5373 while total sulphur was performed along with ASTM standard D. 4239. Oxygen was calculated following Eq. 1 (Zhen, 1993):
where, C, H, N, S and Ash are carbon, hydrogen, nitrogen, sulphur and ash percentages in the biomass, respectively. Gross calorific value was measured using adiabatic bomb calorimeter following the ASTM standard D. 5685.
The independent variables being studied were densifying pressure X1, particle size X2 and moisture content X3, keeping a 10% starch binder as a constant variable. The dependent variables analyzed were density, relaxation and durability of densified biomass produced. The densification experiments were conducted using a bench type manually operated laboratory hydraulic press having a capacity of 20 ton (Hydraulic Press Shop CMC ISO9002) and a densification die. The densification die was constructed from stainless steel cylinder of 30 mm in internal diameter and 250 mm in length, equipped with a stamp of 30 mm in external diameter. A Bob-Behnken design with three levels, low, medium and high coded as -1, 0 and +1 was applied to this study. The level values of each variable and code investigated in this study is presented in Table 1.
The density of the produced briquettes was found by a simple method as a ratio of weight and volume determined from the briquette geometric shape.
|Table 1:||Experimental range and levels of independent variables|
|Table 2:||Bob-behnken design matrix along with experimental data predicted results|
Relaxation in volume was measured after the briquettes were stored for a week, utilizing a vernier calliper. The durability of the produced solid fuels was measured in accordance with ASAE S269 (ASAE, 1996).
A number of 17 runs were randomly performed to optimize the process variable, as shown in Table 2 together with the experimental and predicted results of the dependent variables: the density, relaxation and durability of the densified rice straw biomass. The experimental data were analyzed by RSM using Design Expert software (Version 6.06, State-Ease Inc. Minneapolis, USA) to fit the second order polynomial relationship, as shown in Eq. 2:
where, Y is the predicted response and X1, X2 and X3 are coded independent variables corresponding to the pressure, particle size and moisture content, respectively. Constants βo, βi and βij are linear term, quadratic term and cross product term coefficients, respectively. The coded values are related to the real values through Eq. 3:
where, Z is the coded value (-1, 0 or +1) and X is the corresponding original un-coded value, while Xo the mid value of the domain, ΔX represents as the increment of X for every unit of Z.
For the purpose of optimizing multiple response variables, it is necessary to establish the optimum criteria in accordance to the Desirability Function (DF) approach, as proposed by Derringer and Suich (1980). The maximum or minimum value of the variable response is determined on the basis of technical and/or economical considerations. The general approach is to first convert each response Yk into an individual desirability function dk = h (Yk) that may vary over the range of 0≤dk =1. If the response Yk meets the goal or target value, then dk = 1 and if the response falls beyond the acceptable limit, then dk = 0. The individual desirability functions are then combined into a single composite response, the so-called Desirability Function (DF), defined in Eq. 4 as the geometric mean of the different dk-values:
It is clear from Eq. 4 that DF will be close to 1.0 if all individual desirability functions are also close to 1.0. Therefore, DF = 1.0 implies that all response variables are at their respective optimum or target value condition. This type of methodology has been successfully applied for the optimization of mechanical densification process of agricultural crop residues for the production cattle feed (Munoz-Hernandez et al., 2006).
RESULTS AND DISCUSSION
Table 3 presented the results of proximate and ultimate analysis of rice straw from this study and compared with the one of Calvo et al. (2004). With the exception to the volatile matter in the proximate analysis, other data in both analyses of the two studies are comparable. With regard to HHV, the result obtained from Calvo et al. (2004) is slightly higher than that of this study, due to its higher carbon content. However, both HHVs are in the range of normal values, as shown by Huang et al. (2008) who studied 172 rice straw samples from around China and found out that the minimum and maximum values of HHV are 3051 cal g-1 and 4000 cal g-1, respectively.
|Table 3:||Properties of rice straw biomass|
|aThis study, bCalvo et al. (2004)|
Table 2 presented the design matrix in the coded units in conjunction with the experimental data and the predicted values of three response variables, the density, relaxation and durability of densified rice straw. The predicted values of the response were calculated from quadratic model fitting techniques utilizing Design Expert software. The experimental data, the density, relaxation and durability of solid fuel produced were utilized to develop the statistical model using multiple regression analysis method to fit the response function in accordance to Eq. 2. The resulted relationships between each response variable and independent variables of pressure, particle size and moisture content are presented in Table 4, where Y1, Y2 and Y3 are density, relaxation and durability of the solid fuel produced, respectively.
The significance of the statistical model shown in Table 4 was evaluated by the F-test analysis of variance (ANOVA) presented in Table 5. In Table 4, the value of Prob>F less than 0.0500 revealed that the quadratic model of the response variable is significant at 95% confidence level. From inspection of the value of Prob>F, it can be concluded that all models representing the response variables are significant. The model showed a relatively high determination coefficient, R2 and low the coefficient of variation C.V. These values are obtained as follows: R2 = 0.8371 and C.V. = 3.12 for Y1, R2 = 0.9931 and C.V. = 4.82 for Y2 and R2 = 0.9697 and C.V. = 24.21 for Y3. The closer the determination coefficient to unity, the better agreement of the model suits the experimental data, showing less the difference between the calculated and measured values.
Myers and Montgomery (2002) also suggested that the model adequacy can be evaluated not only from R2 but also from adjusted R2, predicted R2 and prediction error sum of squares (PRESS). A good model is indicated by a large R2 and a low PRESS. In this case, R2 = 0.8371; adjusted-R2 = 0.6276; predicted-R2 = -1.6068; adeq precision = 7.761 and PRESS = 0.14 for Y1. A negative "Pred R-Squared" implies that the overall mean is a better predictor of the response than the current model.
|Table 4:||The fitted model equations|
|Y1 = Density, Y2 = Relaxation and Y3 = Dlurability of solid fuel|
|Table 5:||Analysis of variance (ANOVA) of the fitted models|
|Y1 = Density, Y2 = Relaxation and Y3 = Dlurability of solid fuel|
"Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The current study showed ratios of 4.801 for Y1, 37.289 for Y2 and 16.763 for Y3, respectively which indicate an adequate signal, confirming each model can be used to navigate the design space.
It is clearly seen that the residual values are normally distributed on both sides of the line indicating that the experimental data are in excellent agreement with the predicted values. The above findings indicate outstanding adequacy of the proposed quadratic model to represent the variable responses of density, relaxation and durability of the densified rice straw in the range of pressure: 3000-7000 psi, particle size: 20-60 mesh and moisture content: 5-15%, respectively.
The normal probability plots showing the distribution of residual value defined as the difference between the predicted and experimental data for all response variables of density, relaxation and durability of the solid fuel are forming a straight line, as shown in Fig. 1a-c, respectively.
|Fig. 1:||Normal probability plot of residuals for (a) density, (b) relaxation and (c) durability of rice straw briquette|
OPTIMIZATION OF THE RESPONSE VARIABLE
The determination of optimum operating conditions for the density of densified rice straw is aimed at obtaining high quality of solid fuel and minimum operating cost of production. One feature of high quality densified solid fuel is indicated by its density of higher than 1.0 g.cm-3. Minimum operating cost can be achieved when the pressure is at minimum (X1<5000 psi), particle size is at maximum (X2 >40 mesh) and moisture content is at maximum (X3>10%) levels. The second order polynomial model to represent all response variables, namely the density, relaxation and durability of densified rice straw, were utilized to optimize the operating conditions of the independent variables. This model is merely valid in the selected experimental domain. In this study, pressure, particle size and moisture content were chosen in the range of 3000-7000 psi, 20-60 mesh and 5-15%, respectively.
Applying the desirability function (DF) method, the Design Expert software produced a number of 10 solutions of which each has a DF = 1, as shown in Table 6. However, among 10 solutions only three meet the predetermined criteria. The first is 3244.0 psi for pressure, 58.08 mesh for particle size and 14.63% for moisture content. The second is 3002.8 psi for pressure, 34.90 mesh for particle size and 7.77% for moisture content and the last is 3912.4 psi for pressure, 48.39 mesh for particle size and 12.77% for moisture content.
The first, second and the third acceptable solutions yield the density of densified rice straw of 1.1856, 1.0390 and 1.1378 g cm-3, respectively. As the pressure of the third solution is the highest among the available solutions, coupled with somewhat higher in moisture content, this solution yields the highest density of the product and the highest operational cost. The first solution has a higher pressure and finer particle size in comparison to the second solution. As a consequence, the first solution requires higher operational cost than that of the second solution.
|Table 6:||Alternative solutions that meet DF=1 for optimization of process parameter|
Although the second solution produces the lowest briquette density, it gives the lowest operational cost. In addition, the density produced by the second solution is still higher than the minimum density of 1.0 g cm-3 required for producing a solid fuel briquette. It is therefore appropriate to select the second acceptable solution as the optimum.
A desirability function approach has been utilized to optimize the process variables of pressure, particle size and moisture content on the multi-response variables of density, relaxation and durability of solid fuel produced through a mechanical densification of rice straw biomass. The optimum conditions to produce solid fuels from rice straw biomass were obtained at a pressure of 3002.8 psi, the particle size of 34.90 mesh and raw material water content of 7.77%. With a minimum number of experimental runs, this technique is an efficient way for solution of optimization problems.
The authors wish to express their gratitude to Directorate General of Higher Education (DGHE), Ministry of National Education (MONE) of Republic of Indonesia for providing travel grant under PAR-C project to the first author for the preparation of the manuscript at the University of Leeds, UK.
- Calvo, L.F., M. Otero, B.M. Jenkins, A. Moran and A.I. Garcıa, 2004. Heating process characteristics and kinetics of rice straw in different atmospheres. Fuel Process. Technol., 85: 279-291.
- Derringer, G. and R. Suich, 1980. Simultaneous optimization of several response variables. J. Qual. Technol., 12: 214-219.
- Huang, C., L. Han, Z. Yang and X. Liu, 2008. Prediction of heating value of straw by proximate data and near infrared spectroscopy. Energy Conversion Manage., 49: 3433-3438.
- Husain, Z., Z. Zainac and Z. Abdullah, 2002. Briquetting of palm fibre and shell from the processing of palm nuts to palm oil. Biomass Bioenergy, 22: 505-509.
- Jeong, I.J. and K.J. Kim, 2009. An interactive desirability function method to multi-response optimization. Eur. J. Operat. Res., 195: 412-426.
- Li, Y. and H. Liu, 2000. High-pressure densification of wood residues to form an upgraded fuel. Biomass Bioenergy, 19: 177-186.
- Munoz-Hernandez, G., J. Dominguez-Dominguez and O. Alvarado-Mancilla, 2006. An easy laboratory method for optimizing the parameters for the mechanical densification process: An evaluation with an extruder. CIGR J. Sci. Res. Dev., 8: 1-20.
- Myers, R.H. and D.C. Montgomery, 2002. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. 1st Edn. John Wiley and Sons, New York, ISBN: 0471412554.
- Ozbay, N., B.B. Uzun, E.A. Varol and A.E. Putun, 2006. Comparative analysis of pyrolysis oils and its subfractions under different atmospheric conditions. Fuel Process. Technol., 87: 1013-1019.
- Purohit, P., A.K. Tripathi and T.C. Kandpal, 2006. Energetics of coal substitution by briquettes of agricultural residues. Energy, 31: 1321-1331.
- Rhen, C., R. Gref, M. Sjostorm and I. Wasterlund, 2005. Effects of raw material moisture content densification pressure and temperature on some properties of Norway spruce pellets. Fuel Process. Technol., 87: 11-16.
- Zhen, F., 1993. Physical and chemical properties of corncob for thermal conversion. Petroleum Sci. Technol., 11: 1037-1045.
- Amiri, M., A.A. Najafi and K. Gheshlaghi, 2008. Response surface methodology and genetic algorithm in optimization of cement clinkering process. J. Applied Sci., 8: 2732-2738.
- Barbuta, M. and D. Lepadatu, 2008. Mechanical characteristics investigation of polymer concrete using mixture design of experiments and response surface method. J. Applied Sci., 8: 2242-2249.
- Doniavi, A., M. Eskandarzade and M. Tahmasebian, 2007. Empirical modeling of surface roughness in turning process of 1060 steel using factorial design methodology. J. Applied Sci., 7: 2509-2513.