Prediction of Bioethanol from Production of Lignocellulosic Biomass Waste from Agriculture and Livestock Using Regression Analysis Model

Data collection

The data of crops production, ruminant population, and ruminant slaughtered in Indonesia are collected from FAO for prediction of LBW. The calculation of the number of LBW is carried out by considering the proportion of crops: LBW (for agriculture sector) (Kim & Dale, 2004; Ntelok, 2017) and based on animal unit (AU) (for livestock sector) (Felisberto et al., 2011). The values obtained are converted to the production of LBW lignocellulose component (cellulose, hemicellulose, and lignin). One AU equal to 1 cattle weight 450 kg.

Regression model

This study use linear regression analysis model to predict bioethanol production. This model is used to predict bioethanol production from the annual amount of LBW based on production of lignocellulose component include cellulose, hemicellulose, and lignin. This approach is in line with Núñez et al. (2011) method, which prioritize the use of regression model to predict and effect estimation. The following equation describes the linear regression model:

Where y is the dependent variable, x is the independent or explanatory variable, a is the intercept, b is the slope of the line, and ε is error or residue.

Potential of LBW production

Every year, crops production, population of ruminant, and ruminant slaughtered in Indonesia increases. It is linear with increasing LBW production (Table 1). Every year, the average crops production increase 1.99%, population of ruminant 2.85%, ruminant slaughtered 2.23%, and LBW production 2.07%. Indonesian crops consist of 2 types, primary crops (paddy) and secondary crops (maize, cassava, sorghum, and sugarcane). Sorghum is only grown in certain areas in Indonesia.

Indonesia has various types of ruminants, such as beef cattle, goat, sheep, and buffalo. Every day, ruminants are slaughtered in slaughterhouse (abattoir) to meet food demand (especially meat). One of the most common types of waste produced in large amount from slaughterhouses is paunch content waste. According to Pancapalaga et al. (2021) the paunch content waste is incompletely digested of feed consumed and has still nutrient content that can be used by microbes in the gastrointestinal tract. During raising ruminants, they produce feces and urine every day while abattoir produce paunch content. According to Gupta et al. (2016) ratio of feces and urine is 3:1. Feces and paunch content waste contain nutrients such as fiber or lignocellulose.

Maize (corn) is composed of 38% grain, 7% cobs, 12% husks, 13% leaves, and 30% stalk (Ludfiani, 2016). So, the potential of LBW production from maize is around 62%. Compare to cassava, the LBW production from cassava is around 20% of the total cassava weight, specifically cassava peel (Ntelok, 2017). Indonesia also high LBW production in palm oil because palm oil production increase every year and it is potential to be used as biofuel (high potential for biodiesel). In this study is not discussed further about palm oil because in Southeast Asia has only Indonesia and Malaysia produce palm oil.

BPS-Statistic Indonesia reported palm oil production is 44.75 million tons in 2020, 45.12 million tons in 2021, and 45.58 million tons in 2022. FAO reported that Indonesia is the top 1 producer of palm oil in the world and production in 2021 is around 44.75 million tons of palm oil, 10.17 million tons of palm kernel, 253.31 million tons oil of palm fruit, 4.44 million tons of oil of palm kernel. According to Mahlia et al. (2019) palm oil tree biomass can be converted into biofuels and biopower. About 5% of the biodiesel produced from palm oil can be mixed with petroleum diesel as fuel (Hassan et al., 2011). Palm oil plantation residues and residues from the palm oil industry are quite high and also potential to be used fuel. Palm oil empty fruit brunch can produce one third of the power from a direct combustion compare to methane gas for electricity (Kaniapan et al., 2021).

Based on the data in Table 1, the largest amount of LBW production is from livestock sector. Compare to LBW production in Southeast Asia, LBW production in Indonesia is in 1^st position in Southeast Asia from sector of agricultural and livestock (Figure 1). FAO reported that Indonesia is in 3^rd position in the world (after China and India) for rice production, and it is in 8^th position in the world for maize and sugar cane production. This indicates that Indonesia has great potential in producing abundant LBW, and has potential to be used as biofuel such as bioethanol.

Figure 1. The LBW production of countries in Southeast Asia in 2021.

(A) Production of LBW from agricultural crops; (B) production of LBW from livestock sector.

The LBW from agriculture (agricultural residue) and livestock (feces and paunch content) contain nutrients that can be utilized into value-added products. The LBW contain high fiber (80 to 95%) (Shrotri et al., 2017). The main components of fiber or lignocellulose of LBW are cellulose, hemicellulose, and lignin. The fiber value of LBW depend on management factor and the part of plant. According to Gummert et al. (2019) variations in fiber content of biomass depend on various factors such as the type or part of the plant, species, type of tissue, growth stage, and growth condition.

Table 2 show the potential of LBW production from sector of agriculture and livestock based on lignocellulose component. The average of LBW lignocellulose contents from various types of LBW such as rice straw, corn stover, sugarcane, bagasse, cassava peel, paunch content, and feces are 29.36% of cellulose, 23.76% of hemicellulose, and 16.17% of lignin. Based on these contents, production lignocellulose of LBW can reach 136.99 million tons of cellulose, 112.33 million tons of hemicellulose, and 64.09 million tons of lignin. Feces and rice straw produce higher amount of LBW than other.

Cellulose is the largest fraction in LBW. According to Vasić et al. (2021) forages and agricultural waste contain lignin around 10-30%. Compare to palm oil tree residue, this value is lower. Palm oil tree residue such as palm oil frond, palm oil empty fruit bunch, and palm oil trunk have chemical composition arranged 34.40-40.70% cellulose, 26.10-31.80% hemicellulose, and 12.45-26.20% lignin (Kaniapan et al., 2021).

Cellulose and hemicellulose are carbohydrate which is the main feedstock for producing energy (Kim & Dale, 2004; Zeinaly et al., 2017). Both of them are unavailable carbohydrate (Paxton, 2020). The LBW contain carbohydrate structure which can be used as an energy source (Zeinaly et al., 2017). To optimize the pre-treatment process and expand the usability value of LBW, it is necessary to be evaluated through proximate analysis and energy content (Kaniapan et al., 2021). Cellulose can be synthesized by bacteria and fungi (Vasić et al., 2021), and hemicellulose is an important component and it used in biofuels and various bioproducts (Huang et al., 2021).

Based on the data in Table 2, lignocellulose content of paunch content is in the middle value. The paunch content contains groups of microbes and lignocellulose derived from feed which are being degraded by rumen microbes and enzymes in the gastrointestinal tract during the digestion process. This is a factor that affect the chemical content of paunch content because the lignocellulose value is obtained not only from feed, but also from rumen microbes. According to Elfaki & Abdelatti (2015) and Sarteshnizi et al. (2018) paunch content contain various components, such as saliva, anaerobic microbes (fungi, protozoa, and bacteria), nutrient content (cellulose, hemicellulose, protein, fat, carbohydrates, minerals, and vitamins), and enzymes. It also contain volatile fatty acids (VFAs) such as acetate, propionate, and butyrate (Gleason et al., 2022; Koppolu & Clements, 2004).

According to Partama (2019) ruminant can consume 3.21% of dry matter, and digestibility level of feed is a maximum of 70%. Feed of ruminant consist 60-70% of carbohydrates (cellulose, hemicellulose) and almost 50% of the lignocellulose component is digested by rumen microbes. According to Fasake and Dashora (2020) around 10.86% undigested feed from ruminant feces. The average total digestibility nutrient of fiber from forage is 40-65% in ruminant, so the undigested nutrient will be excreted through the feces (Hartadi, 2019). Feces is the undigested residue of feed consumed and contain nutrients content, microbes, or enzyme (Santoso et al., 2015). Digestibility of buffalo is higher (2-3%) than cattle (Prihantoro et al., 2012).

Potential of bioethanol production by regression analysis model

A linear regression model can be used to predict bioethanol production based on the lignocellulose content of LBW (Table 3). The analysis result show the regression model based on production of cellulose, hemicellulose, and lignin each have correlation coefficient (r) 0.9962, 0.9924 and 0.9641, determination coeffisient (R²) 0.9925 (99.25%), 0.9848 (98.48%) and 0.9294 (92.94%), and regretion coefficient 1005.6, 957.16 and 6708.1. The correlation coefficient value shows that there is a strong relationship between the production of cellulose, hemicellulose and lignin with ethanol production. The higher the production of cellulose, hemicellulose and lignin, the higher the ethanol production. The correlation coefficient value in the regression model based on cellulose production is the highest. This shows that the relationship between cellulose production and ethanol production in this regression model is stronger than in other regression models. According to Santoso (2000), a correlation coefficient value >0.50 indicates that the relationship between the dependent and independent variables is strong. Meanwhile, the determination coefficient value shows that 99.25%, 98.48% and 92.94% of cellulose, hemicellulose and lignin production each influences ethanol production. The determination coefficient value in the regression model based on cellulose production is the highest. This shows that the amount of cellulose production that is converted into ethanol is closer to the production than in other regression models. The regression coefficient value of the regression model based on lignin production is higher than that based on cellulose and hemicellulose production. As well as the regression coefficient value of the regression model based on cellulose production is higher than that based on hemicellulose production. This value shows that for every 1 ton increase in cellulose, hemicellulose or lignin production, the predicted increase in ethanol production based on lignin production is higher than that based on cellulose and hemicellulose production. As well as the predicted increase in ethanol production based on selulosa production is higher than that based on hemicellulose production. Regression analysis can estimate the correlation between the dependent variable and the independent variable. Linear regression is used to predict the linear correlation between predictors (Maulud & Abdulazeez, 2020). This study employs linear regression to identifiy factors influencing bioethanol production.

Table 4 presents the predicted bioethanol production based on the lignocellulose content of LBW, noting the conversion process uses enzymatic saccharification. Average production of lignocellulose LBW in Indonesia in 2021 can reach 104.47 million tons and has potential to bioethanol produce of 59.98 billion gallons (227.01 billion liters). This value is quite high than bioethanol production from grain (1G bioethanol). United State produces 15.25 billion gallons of bioethanol from corn (Mohanty & Swain, 2019). The major producer of bioethanol in the world is the United State (55%) and followed Brazil (27%) in 2021 (Broda et al., 2022). United State produces ethanol from corn (grain) and Brazil from sugarcane (Kim & Dale, 2004). Bioethanol has the potential to become a prime candidate for gasoline substitution (Halder et al., 2018). This is because the energy content of bioethanol is higher than gasoline, and it can be blended with gasoline about 5-24% (Sánchez & Cardona, 2008).

Based on Figure 1, Indonesia, Thailand, and Viet Nam have high potential to LBW production. If we calculate the prediction of bioethanol production based on lignocellulose LBW production, Indonesia, Thailand, and Viet Nam have significant potential to produce quite high amount of bioethanol. Currently, Thailand has been producing bioethanol from corn (1G bioethanol) and it is around 1% (over 1,02 million liters) of worldwide bioethanol production. Indonesia has also produced bioethanol (from sugarcane) but in small quantities and has not been able to supply domestic demand yet. Ministry of Energy and Mineral Resources Indonesia reported that bioethanol production just reached 40 million liters, but bioethanol fuel demand in Indonesia reaches 696 million liters.

Based on Table 4, the prediction of bioethanol production from lignocellulose LBW production can meet domestic bioethanol demand. This value is expected to continuously increase every year linear with food demand and LBW production. From the analysis result, the predictive value bioethanol production based on lignin production is predicted to reach the highest. It is because any lignin that is released from the lignocellulose bonds will release cellulose and hemicellulose which can be converted into glucose. However, based on the correlation and determination coefficients value, the prediction of ethanol production is closer to the actual are prediction based on cellulose and hemicellulose production. The best prediction of ethanol production is based on cellulose production because it has the highest correlation and determination coefficient values. According to Zhong et al. (2021) lignin is the main contributor to the structure of roughage, but lignin can be broken down or degrades by rumen fungi (Vahidi et al., 2021).