Optimization of Calorific Value in Briquette made of Coconut Shell and Cassava Peel by varying of Mass Fraction and Drying Temperature

. Coconut shells and cassava peels are agricultural residues that are abundantly produced in places where coconut and cassava processing activities are prevalent. formerly these waste products have been disposed of through incineration or natural decomposition, hence exacerbating air pollution and triggering degradation of the environment. The objective of this research is to determine the optimum combination of mixed mass fraction and drying temperature for briquettes produced from coconut shell and cassava peel. Observed variable on this research was Mass fraction with the ratio of coconut shell and Cassava peel 75:25 as level 1, 70:30 as level 2 and 65:35 as level 3. Drying temperature has 3 level 150 o C, 200 o C and 250 o C. The lowest calorific value achieved in briquette made of 65% coconut shell mix with 35% Cassava peel and drying temperature is 200 o C on second replication The highest calorific value achieved in briquette made of 65% coconut shell mix with 35% Cassava peel and drying temperature is 150 o C on third replication. Coconut shell has a greater effect than Cassava peel on the calorific value of briquettes, but the chemical content of Cassava peel makes the burning rate longer. According to the outcomes of the normality test, versus fits, histograms, and versus order plots indicate that the data has a normal distribution. based on optimization results using Taguchi L 9 and ANOVA optimizer the optimal combination using rule larger better is the mass fraction of coconut shell 70:30 Cassava peel with a drying temperature of 250 o C.


Introduction
The conversion of waste into biomass performs a crucial role in tackling many environmental concerns and advancing the objective of sustainability [1].The management of waste has emerged as a significant and critical issue on a global scale, since landfills are becoming overwhelmed and pollution levels have been showing an upward trend [2][3].The conversion of waste materials into biomass is an opportunity to address the adverse consequences associated with waste disposal, while concurrently generating a resource of significant value [4][5].Biomass, which is obtained from organic waste materials, can serve as a sustainable energy resource, thereby mitigating our dependence on nonrenewable fossil fuels [6].This practice not only aids in mitigating climate change through the reduction of greenhouse gas emissions, but also enhances air quality and promotes environmental well-being.Moreover, the conversion of trash into biomass serves to advance the concepts of a circular economy by effectively closing the resource loop and diminishing the demand for primary materials [7].The acknowledgment of the significance of converting trash into biomass is an opportunity to foster a future that is both sustainable and robust, so benefiting future generations [8].
Biomass production can be facilitated by the utilization of an extensive variety of waste products, * Corresponding author: kukuh.winarso@trunojoyo.ac.id hence presenting a viable and environmentally friendly means of generating renewable energy [9].The conversion of organic waste, encompassing food leftovers, agricultural residues, and yard waste, into biomass can be achieved through several methods such as composting or anaerobic digestion [10].This process facilitates the decomposition of organic materials, resulting in the production of compost or biogas that is abundant in nutrients and can be utilized for the generation of energy [11].The utilization of forestry residues, such as sawdust and wood chips, as biomass can be achieved by thermal processes such as pyrolysis or gasification [12].These procedures lead to the creation of biochar or syngas.[13] Moreover, it is possible to repurpose industrial waste, including paper waste and manufacturing byproducts, by means of recycling or converting them into fuel pellets, thereby transforming them into biomass [14].Through the utilization of various waste streams, it is possible to mitigate the accumulation of waste in landfills, diminish the release of greenhouse gases into the atmosphere, and provide a valuable resource that may be harnessed for sustainable energy production.
Biomass waste comprises a diverse range of materials, encompassing agricultural residues such as crop stalks and fruit peels, animal waste including manure, forestry residues like wood chips and sawdust, wood waste originating from construction and manufacturing activities, industrial waste from various sectors, municipal solid waste consisting of food scraps and paper, as well as aquatic biomass waste derived from plants and animal farming [15].Various types of waste materials can be transformed into energy through a range of methods, including composting, anaerobic digestion, pyrolysis, gasification, or combustion.Through the utilization of various biomass waste streams, it is conceivable to considerably reduce the accumulation of garbage in landfills, produce sustainable forms of energy, and make valuable contributions to the establishment of a more environmentally conscious and circular economic system [16][17].
Coconut shell is primarily composed of cellulose, lignin, pentosans, and ash.Cellulose is a complex carbohydrate and the main structural component of plant cell walls [18].Lignin is a polymer that provides rigidity and strength to plant cell walls [19].Pentosans are a type of carbohydrate commonly found in plant materials [20].The ash content in coconut shell is typically low [21].Additionally, coconut shell may contain trace amounts of elements such as sodium, potassium, zinc, calcium, iron, and silica [22].However, it's important to note that the exact chemical composition can vary depending on factors such as the specific variety of coconut and processing method [23].Cellulose and lignin play a significant role in the combustion process.It acts as a catalyst, favouring the dehydration and combustion reactions [18].Additionally, the presence of cellulose in biomass particles affects their expansion during the combustion process.Because of the decisive role of cellulose in the composition and combustion process [24].The expansion of biomass particles with lignin content more than 10% and less than 10% during combustion process are caused by lignin and hemicellulose, respectively.

Methods
The briquettes in this research are made from coconut oil and Cassava peel.The adhesive material uses tapioca starch and water, use ratio of 1:1.The composition of the pressing material is only 10% of the mass of the briquette.The pre-treatment of processing the cassava peel raw material involves utilising a chopping machine to cut it into smaller pieces, followed by subjecting it to solar drying for a duration of 8 hours.The objective of this endeavour is to decrease the amount of water present.The initial phase of the coconut shell pretreatment involves subjecting the shell to a drying process lasting within 8 hours, during which the outer layer undergoes a colour transformation to a brown hue.Subsequently, the material should be processed using a mechanical chopping apparatus till it is transformed into granular form.In the concluding phase, the granules conduct a subsequent sun-drying process lasting a maximum of 8 hours.
After pre-treatment, the next step is mixing ingredient of briquette based on design of experiment on Table 1.The briquettes that have been mixed according to the design of the experiment are pressed in a briquette machine with a pressure of 2 tons.based on this research, it is optimal for briquetting, resulting in briquettes with high density.The final step is drying briquette, using drying machine.Temperature during drying temperature set show on table 1 3 3 Each combination using three replications, for evaluating the degree of variability or consistency observed in the results.On this research dependent variable is calorific value on briquette.It measuring by boom calorimeter.

Result and discussion
Briquettes on this research are produced by briquette moulding machine.Briquettes produced on this machine are cylindrical shape.Diameter 50 mm and length 80 mm.internal hole is 20 mm, this hole has specific role to isolating heat.There is 1 mm fin along briquette, they spread 6 fins circling surface of briquette.The product of briquette show in Fig. 1.Result of calorific value using boom calorimeter shown on table 3. Table 3 revealed that the lowest calorific value achieved in briquette made of 65% coconut shell mix with 35% Cassava peel and drying temperature is 200 o C on second replication The highest calorific value achieved in briquette made of 65% coconut shell mix with 35% Cassava peel and drying temperature is 150 o C on third replication.Table 3 shown Signal to Noise Ratio (S/N Ratio).Based on this research [25][26], it also determines the maximum possible amount of data that can be transmitted reliably over a given channel.The optimal range for the signal-to-noise ratio (SNR) is typically above 70dB and below 100dB.In terms of statistics, the results obtained inside this range can be considered reliable.Table 3 displays the S/N Ratio data, which exhibits an increase of 70 decibels and is concentrated within the range of 74 to 75.The data obtained from table 3 were subjected to analysis using the Taguchi L9 method and ANOVA.This analysis aimed to determine the optimal combination of parameters, namely mole fraction composition and heating temperature, in coconut shell and cassava peel used as briquette in relation to the response variable of calorific value.Result of Taguchi L9 optimization show on Fig. 2.   3 show optimal parameter by means S/N ratio.Base on that plot the optimal parameter is fraction mass coconut shell 70% and Cassava peel 30%.Heating temperature is 250 o C.This plot show that all plot spot had tendency away from horizontal mean.Its indicate that both of variable has equivalent influence to calorific value.this is also the same as the results of the ANOVA optimizer plot, that shown on Fig. 4. if the results of both plot diagrams show the same combination of parameters this usually indicates that the combination is statistically valid [27].Fig. 4 displays the results of a normal probability plot analysis, which is an effective method for assessing the normality of data distributions.This analysis focuses on two variables, primarily the fraction mass and the heating temperature.The dependent variable in this research is the calorific value.Upon examination of Fig. 4, it becomes evident that the data points depicted therein span a range extending from 6 to 94 percent on the axis.Both have same symmetrical limitation, it mean the data spread normally.In addition to the residual axis ranging from -500 to 410.The values presented in this data set accurately represent the range of the data points.The observed data points exhibit a high degree of alignment with the red line depicted in the plot, indicating a significant and noteworthy discovery in the visual representation.Based on this alignment, the errors or data points exhibit a dispersion pattern that conforms to a normal distribution.The alignment of the data points for the indicated variables with the red line in Fig. 4 provides a consequential outcome.5 presents the results of a comparative analysis conducted on surface roughness, focusing specifically on the impact of three significant variables: gas pressure, power output, and gas cutting.The assessment of homogeneity of variance is an important aspect in determining the consistency and uniformity of variation across data points.In this technique, opposing fits are utilized and they have a significant impact on this evaluation.Upon closer examination, it is evident that the visible dots represented in Fig. 6 lack a discernible pattern or any specific arrangement.However, they are observed to manifest on the graph as an apparently arbitrary arrangement.This discovery indicates that there is no persistent pattern of increase or decrease in the residual variance of the discrepancies between observed and predicted values as we progress along the fitted values [28], [29].Fuhrer more is using 70% coconut shell and 30% Cassava peel, drying temperature is150 o C. Usually, the closest data using as benchmark to generate optimal parameter [30].

Conclusion
This research providing optimum calorific value of briquette made out of coconut shell and Cassava peel.Basen on Main effect plot and ANOVA optimizer plot the optimum combination is the mass fraction of coconut shell 70% and 30% Cassava peel with a drying temperature of 250 o C. The highest calorific value achieved in briquette made of 65% coconut shell mix with 35% Cassava peel and drying temperature is 150 o C on third replication.Main effect plot by S/N ratio reveal that variable fraction mass had more influence than heating value.Data spread and independent based on normality of data test, versus fits test, versus order and histogram test prove that data on this research spread well statistically.Recommendation for future study is using material with different carbon and brass compositions steel material, further more application cryogenic cooling system in laser cutting.

Fig.
Fig.3show optimal parameter by means S/N ratio.Base on that plot the optimal parameter is fraction mass coconut shell 70% and Cassava peel 30%.Heating temperature is 250 o C.This plot show that all plot spot had tendency away from horizontal mean.Its indicate that both of variable has equivalent influence to calorific value.this is also the same as the results of the ANOVA optimizer plot, that shown on Fig.4.if the results of both plot diagrams show the same combination of parameters this usually indicates that the combination is statistically valid[27].

Fig.
Fig.5presents the results of a comparative analysis conducted on surface roughness, focusing specifically on the impact of three significant variables: gas pressure, power output, and gas cutting.The assessment of homogeneity of variance is an important aspect in determining the consistency and uniformity of variation across data points.In this technique, opposing fits are utilized and they have a significant impact on this evaluation.Upon closer examination, it is evident that the visible dots represented in Fig.6lack a discernible pattern or any specific arrangement.However, they are observed to manifest on the graph as an apparently arbitrary arrangement.This discovery indicates that there is no persistent pattern of increase or decrease in the residual variance of the discrepancies between observed and predicted values as we progress along the fitted values[28],[29].

Fig. 5 .
Fig. 5. Result of versus fits plot on calorific value response variable by ANOVA General Linear Model.

Fig. 6
Fig. 6 shows the histogram of the calorific value data in this research.Histogram form is simple multi modal data, it meaning the data usually classified as sub data category.There is 3 bar height in 1.0 frequency, and the rest is on 2.0 frequency.The higher and wider bars on the histogram indicate the spread of surface roughness values from each combination of experiments in this research.The most surface roughness data spread in the range -700 to +500 from the mean [28-29].

Fig. 6 .
Fig. 6.Result of histogram plot on calorific value response variable by ANOVA General Linear Model.

Fig. 7
Fig.7shows the residuals versus the order of the data.Use the residual versus order plot to verify the assumption that the residuals are not correlated with

Fig. .
Fig. .Result of Versus order plot on calorific value response variable by ANOVA General Linear Model.