Modelling of fermentative bioethanol production from indigenous Ulva prolifera biomass by Saccharomyces cerevisiae NFCCI1248 using an integrated ANN-GA approach

Dave, Niyam; Varadavenkatesan, Thivaharan; Selvaraj, Raja; Vinayagam, Ramesh

doi:10.1016/j.scitotenv.2021.148429

Cited by 39 publications

(10 citation statements)

References 52 publications

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“…This strategy allows trends to be uncovered from the ANN predictions in an attempt to overcome the known drawback of AI methods of lacking physical insights. It also helps identify the variables that have the greatest impact on the model’s predictions by holding all other variables constant . Besides, it is especially useful for ANN models since it captures the relative contributions of each input variable to the model’s overall performance .…”

Section: Resultsmentioning

confidence: 99%

“…It also helps identify the variables that have the greatest impact on the model's predictions by holding all other variables constant. 64 Besides, it is especially useful for ANN models since it captures the relative contributions of each input variable to the model's overall performance. 65 As a result, to determine the effect of individual parameters on the proposed ANN model, a OVAT sensitivity analysis was also conducted by systematically deleting each variable while monitoring the R 2 and RMSE values.…”

Section: Training Of Annmentioning

confidence: 99%

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Uncovering the Effect of Physical Conditions and Surface Roughness on the Maximum Spreading Factor of Impinging Droplets Using a Supervised Artificial Neural Network Model

Choudhury,

Nasir,

Kaiser

et al. 2023

Ind. Eng. Chem. Res.

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Section: Resultsmentioning

confidence: 99%

Section: Training Of Annmentioning

confidence: 99%

Uncovering the Effect of Physical Conditions and Surface Roughness on the Maximum Spreading Factor of Impinging Droplets Using a Supervised Artificial Neural Network Model

Choudhury,

Nasir,

Kaiser

et al. 2023

Ind. Eng. Chem. Res.

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“…Pengukuran gula reduksi dilakukan dengan spektofotometer dengan panjang gelombang 570 nm. Kandungan gula reduksi ditentukan berdasarkan kurva standar glukosa [16] .…”

Section: Analisis Gula Pereduksiunclassified

Optimasi Proses Hidrolisis Rumput Laut Ulva Reticulata dengan Pelarut HNO3 untuk Produksi Bioetanol

Kolo

2023

J.Ris.Kim.

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One alternative to make biofuels and replace petroleum-based fuels is to convert non-food ingredients from Ulva reticulata seaweed into bioethanol. Seventy percent of the earth's surface is covered by microalgae and seaweeds that can be converted into bioethanol. Ulva seaweed contains 50.3% carbohydrates in the form of heteropolysaccharides such as glucose, arabinose, rhamnose and xylose. Optimization of the seaweed hydrolysis catalyzed by HNO3 using Microwave irradiation was done by varying acid concentration (1, 3, 5, 7%), hydrolysis time (30, 40, 50, and 60 minutes), and hydrolysis temperature (75, 100, 125, 150°C). Fermentation was carried out by varying inoculum concentrations (6, 8, and 10% (v/v)) for 5, 6 and 7 days at a temperature of 30°C and a pH of 4.5. Analysis of the surface texture of the sample was carried out by Scanning Electron Microscopy (SEM). The analysis of reducing sugars concentration was carried out using the dinitrosalicylate (DNS) method. Ethanol analysis was carried out by Gas Chromatography (GC). The results of SEM analysis showed that prior to hydrolysis, the surface morphology of the powder was still compact and intact. Whereas after being hydrolyzed with HNO3 it was seen that the surface texture of the powder suffered significant damage. The hydrolysis results showed that the optimum conditions during the pretreatment of U. reticulata powder was at acid concentration of 7%, reaction time of 50 minutes, reaction temperature of 150°C and 250 watts of power which gave hydrolysate with reducing sugar concentration of 86.5 g/L. Fermentation of the hydrolysate using yeast Saccharomyces cerevisiae produced bioethanol with concentration of 37.2% as analyzed using a gas chromatograph.

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“…Although other optimisation methods are reported in the literature in the field of bioethanol production [16], Response Surface Methodology (RSM), using a central composite design (CCD), is the most common and widely used tool in MM hydrolysis optimisation, considering several conditions [5, 7, 9-11, 17, 18]. With reduced experimental time, it identifies linear, quadratic and sometimes cubic effects between variables together with their interactions, and determines the best reaction conditions leading to an optimal release of sugars [5,19].…”

Section: Statement Of Novelty Introductionmentioning

confidence: 99%

Bioethanol Production from Marine Macroalgae Waste: Optimisation of Thermal acid Hydrolysis

Pardilhó,

Oliveira,

Pires

et al. 2024

Waste Biomass Valor

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Marine macroalgae waste, resulting from the accumulation of drifted algal biomass along the coastline, might be a relevant complementary raw material aiming sustainable bioethanol production. In the present study, the optimisation of thermal acid hydrolysis was performed using response surface methodology (RSM) considering the effect of three variables, namely, reaction time (10–60 min), acid concentration (0.1–2.5% (v/v) H2SO4) and biomass:acid ratio (5–15% (w/v)) on sugar concentration and yield. Under the best conditions, the resulting hydrolysates were fermented (7 days, 30 °C, 150 rpm, commercial yeast) to produce bioethanol. A statistically valid second-order model was obtained (r2 = 0.9876; Prob > F lower than 0.05), showing that sugar concentration is mostly influenced by the biomass:acid ratio while reaction time was not significant. The maximum predicted sugar concentration was 18.4 g/L, being obtained at 2.5% H2SO4 concentration and 15% (w/v) biomass:acid ratio, corresponding to a sugars yield of 12.5 g/100 g (less 36% than that obtained using 10% (w/v)). At the best conditions, the hydrolysates were fermented to obtain a bioethanol concentration up to 2.4 g/L and a 21 mgbioethanol/gbiomass yield, emphasizing the biomass potential for bioenergy production. Graphical Abstract

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Modelling of fermentative bioethanol production from indigenous Ulva prolifera biomass by Saccharomyces cerevisiae NFCCI1248 using an integrated ANN-GA approach

Cited by 39 publications

References 52 publications

Uncovering the Effect of Physical Conditions and Surface Roughness on the Maximum Spreading Factor of Impinging Droplets Using a Supervised Artificial Neural Network Model

Uncovering the Effect of Physical Conditions and Surface Roughness on the Maximum Spreading Factor of Impinging Droplets Using a Supervised Artificial Neural Network Model

Optimasi Proses Hidrolisis Rumput Laut Ulva Reticulata dengan Pelarut HNO3 untuk Produksi Bioetanol

Bioethanol Production from Marine Macroalgae Waste: Optimisation of Thermal acid Hydrolysis

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