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Optimization of submerged fermentation conditions to overproduce bioethanol using two industrial and traditional Saccharomyces cerevisiae strains

  • Reza Shaghaghi-Moghaddam , Hoda Jafarizadeh-Malmiri EMAIL logo , Parviz Mehdikhani , Reza Alijanianzadeh and Sepide Jalalian
Published/Copyright: April 12, 2018
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Green Processing and Synthesis
From the journal Green Processing and Synthesis Volume 8 Issue 1

Abstract

The present study focuses on the overproduction of bioethanol through submerged fermentation. In a batch-scale submerged bioreactor using a traditional and an industrial Saccharomyces cerevisiae (NCYC 4109 and SFO6) strains, the fermentation was accomplished. The effects of the substrate brix (20.50–24.00 °Bx) and inoculum percentage in the initial fermentation solution (15%–45%) as independent variables on bioethanol production (g/l) as the dependent variable were assessed using the response surface methodology. Using the obtained experimental values for the response variable based on experiments for the fermentation parameters, a general model (second-order) with high coefficient of determination values (R2 > 95%) was generated to predict the bioethanol concentrations that were obtained using both yeast strains. The obtained results indicated that the optimum fermentation conditions to overproduce bioethanol (56.14 g/l) using the SFO6 yeast were at the substrate brix and inoculum percentage values of 24.70 °Bx and 26.35%, respectively. However, a higher concentration of bioethanol (53.1 g/l) using the NCYC 4109 yeast strain was obtained at the substrate brix and inoculum percentage values of 24.68 °Bx and 40.07%, respectively.

Keywords: bioethanol production; inoculum density; optimization; response surface methodology; Saccharomyces cerevisiae

1 Introduction

After the overutilization of fossil fuels in the past, concerns are being raised regarding their exhaustible nature and their negative environmental impacts [1]. In the last decades, there have been many attempts to develop new processes for finding replacements that do not have their negative features but are commercially practical. Bioethanol and biodiesel can be produced from agricultural products and wastes, and have attracted attention because of their huge and diverse raw material availability [2], [3], [4], [5]. Compared to the conventional fossil fuels, bioethanol has numerous advantages such as lower emission of volatile organic compounds and higher specific energy and heat of vaporization [6]. Bioethanol, as with other sustainable energy sources, has got its own advantages and disadvantages. One of the disadvantages of the current production routes is the low productivity of the process in comparison with the current chemical process, which has motivated the manipulation of the process conditions in order to increase productivity [7]. Studies have shown that the performance of fermentation is highly dependent on the type of microorganism strain that is used as the producing agent, the type and volume of the bioreactors, the type and composition of culture media and proper nutrients, and the pH and temperature of the fermentation media [8], [9], [10].

One of the factors that can significantly affect the fermentation efficiency is the initial concentration of the activated yeast in the medium, which should be able to tolerate the higher osmotic pressure of the growth medium and bioethanol concentration [11], [12]. As the initial concentration of the inoculum is increased, the required time for the completion of the fermentation process would decrease, and consequently the amount of alcohol produced will also decrease because of the lesser quantity of substrate present in the medium. Thus, there should be a balance between the speed of production of bioethanol and the appropriate final concentration [10], [13], [14], [15].

Some studies have arrived at appropriate models for describing the produced bioethanol based on process variables such as temperature, substrate type, and concentration. Laluce et al. utilized the response surface methodology (RSM) with central composite design (CCD) as a statistical analysis and design procedure in order to better monitor, model, and optimize the inoculum size, substrate density, and temperature in bioethanol production. Their study revealed such independent variables’ effects on bioethanol production and also showed that statistical procedures could be successfully utilized for modeling to optimize the process [15]. In fact, statistical methods can easily determine the interaction effects of the independent parameters on the responses [16]. On the other hand, the obtained mathematical models generated using experimental data can be used to predict the responses of the process and to find the optimum process conditions [10].

In this work, an industrial and a traditional Saccharomyces cerevisiae strains, which were screened in our previous study, were used to overproduce bioethanol selected through optimization [17]. In order to do that, the substrate brix (°Bx) and the inoculum percentage were used as independent variables and alcohol production (g/l) as the dependent variable. Experimental design and RSM were used to evaluate the effects of changing the variables on the rapid fermentation in bioethanol production.

2 Materials and methods

2.1 Materials

The molasses used as the substrate was supplied by Sahand Sugar Beet Company (Khoy, Iran). It has a brix value, total amount of reduced sugar, pH, ash content, and density of 74.07 (°Bx), 48.65%, 6.16, 9.6% (v/v), and 1.385×103 (kg/m3), respectively. Based on the obtained results of our previous study, two different kinds of dried form of S. cerevisiae strains, namely NCYC 4109 (a traditional bakery yeast from the National Collection of Yeast Cultures, Norwich, UK) and SFO6 (an industrial yeast from Iran Mayeh Company, Tehran, Iran), were selected and provided by the Agricultural Research and Education Natural Resources Center (West Azerbaijan, Urmia, Iran) [17]. Sulfuric acid, diammonium hydrogen phosphate (DHP), and urea as the pH adjuster, P, and N sources, respectively, were purchased from the Dr. Mojallali Company (Tehran, Iran).

2.2 Inoculum preparation and anaerobic fermentation

In order to prepare of the inoculum (biomass), sterile molasses was diluted using distilled water to provide a diluted molasses solution with a brix value of 11.24 (°Bx), and its pH was set to 4.2 using sulfuric acid (30% v/v). Urea (250 ppm) and DHP (500 ppm) were added to the substrate to increase its nutritional value. Each provided dried yeast (0.3 g) was added to 1 l of the prepared substrate. The samples were incubated at 32°C for 14 h and then aerated (1 volume of air per volume of liquid per minute).

In order to produce bioethanol, batches of submerged fermentation molasses with different brix values (20.5–24.00 °Bx) were prepared and used as the fermentation substrates. They were added to different amounts of the provided inoculum (15–30 ml) to obtain 100 ml each of the fermented solution (including the substrate and inoculum). The solution mixtures were then poured into 250-ml sealed bottles and incubated at 32°C for 32 h.

2.3 Analysis

Concentration of bioethanol: At the end of anaerobic fermentation, the concentration of the produced bioethanol was calculated using the technique described by Son et al. [18]. In this method, after bioethanol distillation, the true brix value in the distilled fermentation broth was measured by a refractometer (Index instrument Ltd., Kissimmee, FL, USA) and the bioethanol concentration using a hydrometer.

2.4 Design of experiments and statistical analysis

As a useful statistical method, RSM has numerous advantages including designing experiments that yield sufficient and consistent measurements of the studied responses, achieving a model that best fits the experimental data, and gaining optimum values of the experimental parameters that lead to target values for the responses [16], [19], [20], [21].

According to the literature, the effects of two independent variables, namely the substrate brix (°Bx, X1) and percentage of inoculum (%, X2), in the initial fermentation solutions on the response variable, namely the bioethanol concentration (g/l, Y), were evaluated using RSM [15], [17]. The experiments were designed by a two-factor CCD based on cubic points and using one block, which indicated that the total experiment runs should be 13 with five replicates for the center points [16]. Table 1 shows the levels of the studied variables. A general polynomial model was used to establish the relationship between the response variable of the fermentation process and the two independent variables for each microorganism:

Table 1:

Fermentation conditions and their levels.

Independent variablesUnitLevels
LowMiddleHigh
X1, substrate brix(°Bx)20.5022.2524.00
X2, inoculum percentage(%)153045
(1)Y=β0+β1X1+β2X2+β11X12+β22X22+β12X1X2

where Y and Xi are related to the bioethanol concentration and fermentation parameters, respectively. Furthermore, β is a constant, and βi, βii, and βij represent the coefficients of the main, quadratic, and interaction terms, respectively. Significant differences between the fermentation parameters were analyzed using the analysis of variance (ANOVA) and based on the p-value. In fact, a small p-value (p < 0.05) means a significant effect of the terms (e.g. linear, quadratic, and interaction) on the responses [22]. Adequacy of the generated models was evaluated using the coefficient of determination (R2 and R2-adjusted) [20].

2.5 Optimization procedure and model verification

To better visualize the interaction effects of the fermentation variables on the bioethanol concentration, three-dimensional surface plots were established for each yeast strain [14]. In order to show graphically the optimum area, the contour plots were used [19]. For obtaining the exact values of the optimized fermentation conditions under which bioethanol with highest concentration was produced, numerical optimization was used [23]. To verify the acceptability of the fitted models, Tukey’s comparison test was performed between the values of the predicted and experimental bioethanol concentration at the obtained optimum fermentation conditions. The Minitab statistical software (Minitab Inc., version 16.2.4, PA, USA) was used for CCD, RSM, ANOVA, Tukey’s test, optimization, and verification procedures.

3 Results and discussion

3.1 Response surface models investigation

The response values for the experimental data (Tables 2 and 3 ) were used to fit and generate second-order polynomial models for studying bioethanol production as a function of the fermentation variables using the SFO6 and NCYC 4109 yeast strains, respectively. The estimated regression coefficients and p-values of all terms are given in Tables 4 and 5 for the fitted models based on SFO6 and NCYC 4109 strains, respectively. As can be seen from Table 4, among all the terms, the main term (i.e. the inoculum percentage) in the fermentation solution and quadratic term (i.e. substrate brix) had only insignificant effects on the bioethanol production using the industrial yeast strain (SFO6). The high value (0.9653) of the coefficient of determination (R2) and the reasonably high value (0.7622) of R2-adjusted confirmed the suitability of the resulting model for predicting the produced bioethanol concentration using the yeast SFO6 within the defined ranges for the fermentation conditions. As can be seen from Table 5, all the terms in the generated model for predicting bioethanol concentration throughout the fermentation process using a traditional bakery yeast (NCYC 4109) had significant (p < 0.05) effects. The higher values of R2 (0.9776) and R2-adjusted (0.8508) also verified the fitness of the proposed model.

Table 2:

Matrix of the CCD for bioethanol production using the SFO6 yeast.

RunSubstrate brix (°Bx)Inoculum (%)Bioethanol concentration (g/l)aBioethanol concentration (g/l)b
124.7230.055.6255.91
222.2530.055.2355.27
320.5015.049.7049.27
424.0045.050.4950.92
522.2530.055.2355.27
622.2530.054.9955.27
722.2530.055.3855.27
822.2530.055.5455.27
920.5045.050.651.43
1019.7830.053.4953.20
1122.2551.248.7847.88
1224.0015.054.4453.60
1322.258.847.3448.24

  1. aExperimental value.

  2. bPredicted value.

  3. CCD, Central composite design.

Table 3:

Matrix of the CCD for bioethanol production using the NCYC 4109 yeast.

RunSubstrate brix (°Bx)Inoculum (%)Bioethanol concentration (g/l)aBioethanol concentration (g/l)b
124.7230.050.3450.14
222.2530.051.6851.05
320.5015.044.9745.07
424.0045.051.2951.26
522.2530.052.0751.05
622.2530.052.4751.05
722.2530.051.6851.05
822.2530.050.8951.05
920.5045.041.2038.23
1019.7830.044.9745.95
1122.2551.239.4540.51
1224.0015.035.4037.96
1322.258.837.0835.94

  1. aExperimental value.

  2. bPredicted value.

  3. CCD, Central composite design.

Table 4:

p-Values and regression coefficients for the generated model based on the yeast strain SFO6.

p-ValueRegression coefficient
ParametersIndependent variablesp-ValueβCoefficient
Constant0.000β0 (constant)55.27
MainX10.008β10.95
X20.638β2−0.12
QuadraticX120.236β11−0.35
X220.000β22−3.60
InteractionX1X20.013β12−1.20
R20.9653
R2-adjusted0.7622

  1. 1, Substrate brix (°Bx); 2, inoculum (%).

Table 5:

p-Values and regression coefficients for the generated model based on the yeast strain NCYC 4109.

p-ValueRegression coefficient
ParametersIndependent variablesp-ValueβCoefficient
Constant0.000β0 (constant)51.05
MainX10.010β11.48
X20.006β21.61
QuadraticX120.012β11−1.50
X220.000β22−6.41
InteractionX1X20.000β125.03
R20.9776
R2-adjusted0.8508

  1. 1, Substrate brix (°Bx); 2, inoculum (%).

3.2 Influence of the fermentation conditions on the bioethanol concentration

Effects of the fermentation parameters, namely substrate brix and percentage of inoculum in the initial fermentation solution, on the concentration of the produced bioethanol using SFO6 and NCYC 4109 yeast strains are presented in Figure 1A and B, respectively. As can be seen in Figure 1A, during bioethanol production using the yeast strain SFO6, at a lower substrate brix, by increasing the inoculum percentage the concentration of produced bioethanol increased. However, at higher substrate brix, with an increase in the amount of inoculum, the concentration of the produced bioethanol increased and then decreased. This can be described by the fact that at low substrate brix, a high amount of inoculum can drastically decrease the duration of the yeast lag phase of growth, which in turn could initiate the bioethanol production. On the other hand, at high substrate brix, it seems that by increasing the percentage of inoculum, the concentration of biomass and bioethanol increased, which in turn increased the osmotic pressure of the fermentation broth and inhibited the growth of the yeast. Therefore, the bioethanol production decreased, as can be seen at higher substrate brix and inoculum percentage (Figure 1A). As can be seen in Table 2, the concentration of bioethanol produced by the yeast SFO6 changed from 47.34 to 55.62 g/l, while concentration of the produced bioethanol using the yeast NCYC 4109 varied from 35.40 to 52.47 g/l (Table 3). As can be seen in Figure 1B, at higher substrate brix, by increasing the percentage of inoculum (NCYC 4109) in the initial fermentation solution, the concentration of the produced bioethanol increased. The opposite trends in the produced bioethanol concentration using both yeasts at higher substrate brix and the amount of inoculum revealed that the traditional bakery yeast (NCYC 4109) was tolerant to the high osmotic pressure of the fermentation broth. However, the concentration of the produced bioethanol using this yeast was lower than that produced using the industrial yeast (SFO6), as can be observed in Tables 2 and 3.

Figure 1: Surface plots of the concentration of the produced bioethanol (g/l) as function of the substrate brix (°Bx) and inoculum percentage (%), during fermentation using the yeast strains (A) SFO6 and (B) NCYC 4109.
Figure 1:

Surface plots of the concentration of the produced bioethanol (g/l) as function of the substrate brix (°Bx) and inoculum percentage (%), during fermentation using the yeast strains (A) SFO6 and (B) NCYC 4109.

3.3 Optimization of the submerged fermentation conditions to overproduce bioethanol

Graphical optimizations of the fermentation conditions using the yeast strains SFO6 and NCYC 4109 to overproduce bioethanol are indicated in Figure 2A and B, respectively. As can be seen in Figure 2A, the minimum concentrations for the produced bioethanol using the industrial yeast (SFO6) were obtained at lower substrate brix and inoculum percentage and higher substrate brix and percentage of inoculum. However, the maximum concentrations of the produced bioethanol using the yeast of SFO6 were obtained at the substrate brix ranging from 19 to 35 (°Bx) and an inoculum percentage value of higher than 22%. Numerical optimization indicated that the maximum concentration of the produced bioethanol using SFO6 (56.14 g/l) was obtained at the substrate brix and inoculum percentage values of 24.70 °Bx and 26.35%, respectively. As can be seen in Figure 2B, the minimum concentrations for the produced bioethanol using the traditional bakery yeast (NCYC 4109) were obtained at lower substrate brix and higher inoculum percentage and at lower inoculum percentage and higher substrate brix, while higher concentrations of the produced bioethanol, using the yeast of NCYC 4109, were obtained at both higher substrate brix (>21 °Bx) and inoculum percentage (>25%). Numerical optimization illustrated that the maximum concentration of the produced bioethanol using NCYC 4109 (53.1 g/l) was obtained at the substrate brix and inoculum percentage values of 24.68 °Bx and 40.07%, respectively. The nonsignificant differences between the values of the experimental and predicted concentration of the produced bioethanol at the obtained optimum fermentation conditions using the yeasts strains SFO6 and NCYC 4109 verified the adequacy of the generated models. The experimental values for the concentrations of the produced bioethanol at the obtained optimum conditions using SFO6 and NCYC 4109 were 55.2±2 and 54.23±2 g/l, respectively.

Figure 2: Contour plots for concentration of the produced bioethanol (g/l) as function of the substrate brix (°Bx) and inoculum percentage (%), during fermentation using the yeast strains (A) SFO6 and (B) NCYC 4109.
Figure 2:

Contour plots for concentration of the produced bioethanol (g/l) as function of the substrate brix (°Bx) and inoculum percentage (%), during fermentation using the yeast strains (A) SFO6 and (B) NCYC 4109.

4 Conclusions

Based on the above study, it can be concluded that RSM can be an effective method to predict, model, and optimize bioethanol production by manipulating the process variables. In this regard, the influence of the interaction of the substrate brix and the inoculum percentage was remarkable on bioethanol production for two selected (industrial and traditional bakery) yeast strains in order to maximize the productivity. However, the findings of the present study revealed that the industrial strain had higher resistance against osmotic pressure of the fermented broth than the traditional bakery yeast strain.

Acknowledgments

The authors would like to thank Bidestan Company for analysis and material support.

  1. Conflict of interest statement: The authors declare no conflict of interest.

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Received: 2018-02-07
Accepted: 2018-02-20
Published Online: 2018-04-12
Published in Print: 2019-01-28

©2019 Walter de Gruyter GmbH, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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  69. Selenium supplementation during fermentation with sugar beet molasses and Saccharomyces cerevisiae to increase bioethanol production
  70. Biosynthetic potential assessment of four food pathogenic bacteria in hydrothermally silver nanoparticles fabrication
  71. Investigating the effectiveness of classical and eco-friendly approaches for synthesis of dialdehydes from organic dihalides
  72. Pyrolysis of palm oil using zeolite catalyst and characterization of the boil-oil
  73. Azadirachta indica leaves extract assisted green synthesis of Ag-TiO2 for degradation of Methylene blue and Rhodamine B dyes in aqueous medium
  74. Synthesis of vitamin E succinate catalyzed by nano-SiO2 immobilized DMAP derivative in mixed solvent system
  75. Extraction of phytosterols from melon (Cucumis melo) seeds by supercritical CO2 as a clean technology
  76. Production of uronic acids by hydrothermolysis of pectin as a model substance for plant biomass waste
  77. Biofabrication of highly pure copper oxide nanoparticles using wheat seed extract and their catalytic activity: A mechanistic approach
  78. Intelligent modeling and optimization of emulsion aggregation method for producing green printing ink
  79. Improved removal of methylene blue on modified hierarchical zeolite Y: Achieved by a “destructive-constructive” method
  80. Two different facile and efficient approaches for the synthesis of various N-arylacetamides via N-acetylation of arylamines and straightforward one-pot reductive acetylation of nitroarenes promoted by recyclable CuFe2O4 nanoparticles in water
  81. Optimization of acid catalyzed esterification and mixed metal oxide catalyzed transesterification for biodiesel production from Moringa oleifera oil
  82. Kinetics and the fluidity of the stearic acid esters with different carbon backbones
  83. Aiming for a standardized protocol for preparing a process green synthesis report and for ranking multiple synthesis plans to a common target product
  84. Microstructure and luminescence of VO2 (B) nanoparticle synthesis by hydrothermal method
  85. Optimization of uranium removal from uranium plant wastewater by response surface methodology (RSM)
  86. Microwave drying of nickel-containing residue: dielectric properties, kinetics, and energy aspects
  87. Simple and convenient two step synthesis of 5-bromo-2,3-dimethoxy-6-methyl-1,4-benzoquinone
  88. Biodiesel production from waste cooking oil
  89. The effect of activation temperature on structure and properties of blue coke-based activated carbon by CO2 activation
  90. Optimization of reaction parameters for the green synthesis of zero valent iron nanoparticles using pine tree needles
  91. Microwave-assisted protocol for squalene isolation and conversion from oil-deodoriser distillates
  92. Denitrification performance of rare earth tailings-based catalysts
  93. Facile synthesis of silver nanoparticles using Averrhoa bilimbi L and Plum extracts and investigation on the synergistic bioactivity using in vitro models
  94. Green production of AgNPs and their phytostimulatory impact
  95. Photocatalytic activity of Ag/Ni bi-metallic nanoparticles on textile dye removal
  96. Topical Issue: Green Process Engineering / Guest Editors: Martine Poux, Patrick Cognet
  97. Modelling and optimisation of oxidative desulphurisation of tyre-derived oil via central composite design approach
  98. CO2 sequestration by carbonation of olivine: a new process for optimal separation of the solids produced
  99. Organic carbonates synthesis improved by pervaporation for CO2 utilisation
  100. Production of starch nanoparticles through solvent-antisolvent precipitation in a spinning disc reactor
  101. A kinetic study of Zn halide/TBAB-catalysed fixation of CO2 with styrene oxide in propylene carbonate
  102. Topical on Green Process Engineering
https://doi.org/10.1515/gps-2018-0044
Keywords for this article
bioethanol production; inoculum density; optimization; response surface methodology; Saccharomyces cerevisiae
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Articles in the same Issue

  1. Regular Articles
  2. Studies on the preparation and properties of biodegradable polyester from soybean oil
  3. Flow-mode biodiesel production from palm oil using a pressurized microwave reactor
  4. Reduction of free fatty acids in waste oil for biodiesel production by glycerolysis: investigation and optimization of process parameters
  5. Saccharin: a cheap and mild acidic agent for the synthesis of azo dyes via telescoped dediazotization
  6. Optimization of lipase-catalyzed synthesis of polyethylene glycol stearate in a solvent-free system
  7. Green synthesis of iron oxide nanoparticles using Platanus orientalis leaf extract for antifungal activity
  8. Ultrasound assisted chemical activation of peanut husk for copper removal
  9. Room temperature silanization of Fe3O4 for the preparation of phenyl functionalized magnetic adsorbent for dispersive solid phase extraction for the extraction of phthalates in water
  10. Evaluation of the saponin green extraction from Ziziphus spina-christi leaves using hydrothermal, microwave and Bain-Marie water bath heating methods
  11. Oxidation of dibenzothiophene using the heterogeneous catalyst of tungsten-based carbon nanotubes
  12. Calcined sodium silicate as an efficient and benign heterogeneous catalyst for the transesterification of natural lecithin to L-α-glycerophosphocholine
  13. Synergistic effect between CO2 and H2O2 on ethylbenzene oxidation catalyzed by carbon supported heteropolyanion catalysts
  14. Hydrocyanation of 2-arylmethyleneindan-1,3-diones using potassium hexacyanoferrate(II) as a nontoxic cyanating agent
  15. Green synthesis of hydratropic aldehyde from α-methylstyrene catalyzed by Al2O3-supported metal phthalocyanines
  16. Environmentally benign chemical recycling of polycarbonate wastes: comparison of micro- and nano-TiO2 solid support efficiencies
  17. Medicago polymorpha-mediated antibacterial silver nanoparticles in the reduction of methyl orange
  18. Production of value-added chemicals from esterification of waste glycerol over MCM-41 supported catalysts
  19. Green synthesis of zerovalent copper nanoparticles for efficient reduction of toxic azo dyes congo red and methyl orange
  20. Optimization of the biological synthesis of silver nanoparticles using Penicillium oxalicum GRS-1 and their antimicrobial effects against common food-borne pathogens
  21. Optimization of submerged fermentation conditions to overproduce bioethanol using two industrial and traditional Saccharomyces cerevisiae strains
  22. Extraction of In3+ and Fe3+ from sulfate solutions by using a 3D-printed “Y”-shaped microreactor
  23. Foliar-mediated Ag:ZnO nanophotocatalysts: green synthesis, characterization, pollutants degradation, and in vitro biocidal activity
  24. Green cyclic acetals production by glycerol etherification reaction with benzaldehyde using cationic acidic resin
  25. Biosynthesis, characterization and antimicrobial activities assessment of fabricated selenium nanoparticles using Pelargonium zonale leaf extract
  26. Synthesis of high surface area magnesia by using walnut shell as a template
  27. Controllable biosynthesis of silver nanoparticles using actinobacterial strains
  28. Green vegetation: a promising source of color dyes
  29. Mechano-chemical synthesis of ammonia and acetic acid from inorganic materials in water
  30. Green synthesis and structural characterization of novel N1-substituted 3,4-dihydropyrimidin-2(1H)-ones
  31. Biodiesel production from cotton oil using heterogeneous CaO catalysts from eggshells prepared at different calcination temperatures
  32. Regeneration of spent mercury catalyst for the treatment of dye wastewater by the microwave and ultrasonic spray-assisted method
  33. Green synthesis of the innovative super paramagnetic nanoparticles from the leaves extract of Fraxinus chinensis Roxb and their application for the decolourisation of toxic dyes
  34. Biogenic ZnO nanoparticles: a study of blueshift of optical band gap and photocatalytic degradation of reactive yellow 186 dye under direct sunlight
  35. Leached compounds from the extracts of pomegranate peel, green coconut shell, and karuvelam wood for the removal of hexavalent chromium
  36. Enhancement of molecular weight reduction of natural rubber in triphasic CO2/toluene/H2O systems with hydrogen peroxide for preparation of biobased polyurethanes
  37. An efficient green synthesis of novel 1H-imidazo[1,2-a]imidazole-3-amine and imidazo[2,1-c][1,2,4]triazole-5-amine derivatives via Strecker reaction under controlled microwave heating
  38. Evaluation of three different green fabrication methods for the synthesis of crystalline ZnO nanoparticles using Pelargonium zonale leaf extract
  39. A highly efficient and multifunctional biomass supporting Ag, Ni, and Cu nanoparticles through wetness impregnation for environmental remediation
  40. Simple one-pot green method for large-scale production of mesalamine, an anti-inflammatory agent
  41. Relationships between step and cumulative PMI and E-factors: implications on estimating material efficiency with respect to charting synthesis optimization strategies
  42. A comparative sorption study of Cr3+ and Cr6+ using mango peels: kinetic, equilibrium and thermodynamic
  43. Effects of acid hydrolysis waste liquid recycle on preparation of microcrystalline cellulose
  44. Use of deep eutectic solvents as catalyst: A mini-review
  45. Microwave-assisted synthesis of pyrrolidinone derivatives using 1,1’-butylenebis(3-sulfo-3H-imidazol-1-ium) chloride in ethylene glycol
  46. Green and eco-friendly synthesis of Co3O4 and Ag-Co3O4: Characterization and photo-catalytic activity
  47. Adsorption optimized of the coal-based material and application for cyanide wastewater treatment
  48. Aloe vera leaf extract mediated green synthesis of selenium nanoparticles and assessment of their In vitro antimicrobial activity against spoilage fungi and pathogenic bacteria strains
  49. Waste phenolic resin derived activated carbon by microwave-assisted KOH activation and application to dye wastewater treatment
  50. Direct ethanol production from cellulose by consortium of Trichoderma reesei and Candida molischiana
  51. Agricultural waste biomass-assisted nanostructures: Synthesis and application
  52. Biodiesel production from rubber seed oil using calcium oxide derived from eggshell as catalyst – optimization and modeling studies
  53. Study of fabrication of fully aqueous solution processed SnS quantum dot-sensitized solar cell
  54. Assessment of aqueous extract of Gypsophila aretioides for inhibitory effects on calcium carbonate formation
  55. An environmentally friendly acylation reaction of 2-methylnaphthalene in solvent-free condition in a micro-channel reactor
  56. Aegle marmelos phytochemical stabilized synthesis and characterization of ZnO nanoparticles and their role against agriculture and food pathogen
  57. A reactive coupling process for co-production of solketal and biodiesel
  58. Optimization of the asymmetric synthesis of (S)-1-phenylethanol using Ispir bean as whole-cell biocatalyst
  59. Synthesis of pyrazolopyridine and pyrazoloquinoline derivatives by one-pot, three-component reactions of arylglyoxals, 3-methyl-1-aryl-1H-pyrazol-5-amines and cyclic 1,3-dicarbonyl compounds in the presence of tetrapropylammonium bromide
  60. Preconcentration of morphine in urine sample using a green and solvent-free microextraction method
  61. Extraction of glycyrrhizic acid by aqueous two-phase system formed by PEG and two environmentally friendly organic acid salts - sodium citrate and sodium tartrate
  62. Green synthesis of copper oxide nanoparticles using Juglans regia leaf extract and assessment of their physico-chemical and biological properties
  63. Deep eutectic solvents (DESs) as powerful and recyclable catalysts and solvents for the synthesis of 3,4-dihydropyrimidin-2(1H)-ones/thiones
  64. Biosynthesis, characterization and anti-microbial activity of silver nanoparticle based gel hand wash
  65. Efficient and selective microwave-assisted O-methylation of phenolic compounds using tetramethylammonium hydroxide (TMAH)
  66. Anticoagulant, thrombolytic and antibacterial activities of Euphorbia acruensis latex-mediated bioengineered silver nanoparticles
  67. Volcanic ash as reusable catalyst in the green synthesis of 3H-1,5-benzodiazepines
  68. Green synthesis, anionic polymerization of 1,4-bis(methacryloyl)piperazine using Algerian clay as catalyst
  69. Selenium supplementation during fermentation with sugar beet molasses and Saccharomyces cerevisiae to increase bioethanol production
  70. Biosynthetic potential assessment of four food pathogenic bacteria in hydrothermally silver nanoparticles fabrication
  71. Investigating the effectiveness of classical and eco-friendly approaches for synthesis of dialdehydes from organic dihalides
  72. Pyrolysis of palm oil using zeolite catalyst and characterization of the boil-oil
  73. Azadirachta indica leaves extract assisted green synthesis of Ag-TiO2 for degradation of Methylene blue and Rhodamine B dyes in aqueous medium
  74. Synthesis of vitamin E succinate catalyzed by nano-SiO2 immobilized DMAP derivative in mixed solvent system
  75. Extraction of phytosterols from melon (Cucumis melo) seeds by supercritical CO2 as a clean technology
  76. Production of uronic acids by hydrothermolysis of pectin as a model substance for plant biomass waste
  77. Biofabrication of highly pure copper oxide nanoparticles using wheat seed extract and their catalytic activity: A mechanistic approach
  78. Intelligent modeling and optimization of emulsion aggregation method for producing green printing ink
  79. Improved removal of methylene blue on modified hierarchical zeolite Y: Achieved by a “destructive-constructive” method
  80. Two different facile and efficient approaches for the synthesis of various N-arylacetamides via N-acetylation of arylamines and straightforward one-pot reductive acetylation of nitroarenes promoted by recyclable CuFe2O4 nanoparticles in water
  81. Optimization of acid catalyzed esterification and mixed metal oxide catalyzed transesterification for biodiesel production from Moringa oleifera oil
  82. Kinetics and the fluidity of the stearic acid esters with different carbon backbones
  83. Aiming for a standardized protocol for preparing a process green synthesis report and for ranking multiple synthesis plans to a common target product
  84. Microstructure and luminescence of VO2 (B) nanoparticle synthesis by hydrothermal method
  85. Optimization of uranium removal from uranium plant wastewater by response surface methodology (RSM)
  86. Microwave drying of nickel-containing residue: dielectric properties, kinetics, and energy aspects
  87. Simple and convenient two step synthesis of 5-bromo-2,3-dimethoxy-6-methyl-1,4-benzoquinone
  88. Biodiesel production from waste cooking oil
  89. The effect of activation temperature on structure and properties of blue coke-based activated carbon by CO2 activation
  90. Optimization of reaction parameters for the green synthesis of zero valent iron nanoparticles using pine tree needles
  91. Microwave-assisted protocol for squalene isolation and conversion from oil-deodoriser distillates
  92. Denitrification performance of rare earth tailings-based catalysts
  93. Facile synthesis of silver nanoparticles using Averrhoa bilimbi L and Plum extracts and investigation on the synergistic bioactivity using in vitro models
  94. Green production of AgNPs and their phytostimulatory impact
  95. Photocatalytic activity of Ag/Ni bi-metallic nanoparticles on textile dye removal
  96. Topical Issue: Green Process Engineering / Guest Editors: Martine Poux, Patrick Cognet
  97. Modelling and optimisation of oxidative desulphurisation of tyre-derived oil via central composite design approach
  98. CO2 sequestration by carbonation of olivine: a new process for optimal separation of the solids produced
  99. Organic carbonates synthesis improved by pervaporation for CO2 utilisation
  100. Production of starch nanoparticles through solvent-antisolvent precipitation in a spinning disc reactor
  101. A kinetic study of Zn halide/TBAB-catalysed fixation of CO2 with styrene oxide in propylene carbonate
  102. Topical on Green Process Engineering
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