Process Optimization for Acid Hydrolysis and Characterization of Bioethanol from Leftover Injera Waste by Using Response Surface Methodology: Central Composite Design

Abstract

In this study, leftover injera waste from the southwestern parts of Ethiopia was used as a raw material for bioethanol production. The conversion of this biomass into ethanol involved processing techniques, which include hydrolysis, fermentation, and distillation. This research focuses on determining optimal parameters that are temperature, acid concentration, and hydrolyzing time in a hydrolysis stage. Using response surface analysis, the suggested model is quadratic and has three independent factors, which had significant effects on the yield of ethanol. In this analysis, the temperature and hydrolyzing time had a positive relationship with the yield of ethanol whereas acid concentration had a negative relation. The optimum yield of ethanol obtained was 79.07%. The yield optimized in g/g was 29.99, which was obtained at a temperature of 109.99°C, at an acid concentration of 1.00%, and hydrolyzing time of 49.59 minutes. For this analysis, the mathematical model equation was developed and the R2 value was 99.9% and its desirability was 0.8867. The property of ethanol was characterized by the many parameters used in different standardization. The density, viscosity, flammability, boiling points, and pH were determined as 0.803 gcm−3, 1.1 cP, 14°C, 80°C, and 6.65, respectively.

Wastewater Treatment Using a Photoelectrochemical Oxidation Process for the Coffee Processing Industry Optimization of Chemical Oxygen Demand (COD) Removal Using Response Surface Methodology

“…Regression models equation S(E1) and aligned diagrams of the interactive relationships between them and the response variable may be used to illustrate the interactive reaction between four independent factors and dependent variables. Diagnostic diagrams, such as the normal probability distribution diagram of residuals and the diagram of projected values vs. real values, can also be used to assess the model's suitability [ 40 – 43 ].…”

Box–Behnken Design-Based Optimization of the Saccharification of Primary Paper-Mill Sludge as a Renewable Raw Material for Bioethanol Production

“…Statistical methods are considered to be more acceptable by many researchers due to their time-saving advantage, ease of optimization with reduced experimental runs, and precision with less error occurrence. Factorial design, response surface methodology (RSM) based on the central composite design (CCD), and the Box-Behnken design (BBD), to name a few, are being employed for process optimization [18][19][20][21]. These statistical optimizations have been employed for the pretreatment, hydrolysis, and fermentation using different biomasses such as switch grass [22], corn stover [23], prairie cordgrass [24], wheat bran [25], teff straw [26], and sugarcane bagasse [27].…”

Optimisation of dilute acid hydrolysis for bioethanol production from selected agricultural wastes

“…In Nigeria, 134 million liters of ethanol are generated annually under the supposition that the factories operate year-round in ideal conditions (Ohimain, 2010). The use of bioethanol as fuel has decreased the overconsumption of crude oil and its impact on the environment (Bayu et al, 2022).…”

“…The complex sugars in lignocellulosic biomass can be efficiently broken down into their monomeric unit by a process called Dilute Acid Hydrolysis (DAH), resulting in higher yields of simple sugars compared to enzymatic hydrolysis and these sugars can then be fermented to produce bioethanol. The two biggest benefits of DAH over enzymatic hydrolysis are its ability to penetrate lignin structure without the need for substantial pretreatment and its speed (Bayu et al, 2022).…”