Abstract:In this research, Capparis spinosa oil was used as a feedstock for biodiesel production in the presence of NaOH/NaX as a nano heterogeneous catalyst. The NaOH/NaX catalyst was characterized by XRD, SEM, and BET analyses. The transesterification reaction was optimized through response surface methodology (RSM) based on the central composite design (CCD) of experiments. The effects of key variables of catalyst weight, methanol to oil molar ratio, and time on the reaction were studied, and a precise discussion ab… Show more
“…Globally, the energy requirement is elaborating progressively because of population growth and industrial development. Thus, raising energy demand and fossil fuel prices along with the concern of the environment motivated the investigators to look for alternative fuel energy [1,2]. Biodiesel is considered as an alternative fuel having properties such as nontoxic, renewable energy, sulfur-free, clean-burning, ester-based, and low emission as compared to fossil fuels [2].…”
The production of biodiesel from vegetables or fruits waste oils has high potential as renewable energy. The Cucurbita maxima wastes are massive source of oils, which are believed to indicate the possible sources of renewable energy whose biodiesel can be produced. Hence, the study explores the potential of the Cucurbita maxima wastes, for the production of biodiesel. In this study, the Soxhlet extraction method was used to extract Cucurbita maxima waste oil using an organic solvent. Through Box-Behnken design (BBD), the effects of methanol to oil molar ratio (6–10), catalyst concentration (2–6%), and reaction time (45–75 min) on the transesterification efficiency of methyl esters were investigated. The oil contents of Cucurbita maxima waste was found to be
44.6
±
0.21
%. This oil was characterized, and after obtaining the pure characterized oil, biodiesel was produced using microwave assisted by the transesterification process. The optimum conversion efficiency of the Cucurbita maxima waste oil to fatty acid methyl ether was 97.76%, at the optimal parameters, methanol to oil ratio (8.4 : 1), catalyst concentration (3.14%), and reaction time (57.12 min). The results revealed that all parameters have a significant effect on the yield of biodiesel (
p
<
0.0001
). The physicochemical properties reveal that the Cucurbita maxima waste oil could be applied as a potential source of material for methyl ester production. The fatty acid profile of the oil indicated that it was mainly composed of unsaturated fatty acid, which ensures good flow properties of the fuel. The results of these studies showed the prospective of Cucurbita maxima wastes as a new potential feedstock for biodiesel production.
“…Globally, the energy requirement is elaborating progressively because of population growth and industrial development. Thus, raising energy demand and fossil fuel prices along with the concern of the environment motivated the investigators to look for alternative fuel energy [1,2]. Biodiesel is considered as an alternative fuel having properties such as nontoxic, renewable energy, sulfur-free, clean-burning, ester-based, and low emission as compared to fossil fuels [2].…”
The production of biodiesel from vegetables or fruits waste oils has high potential as renewable energy. The Cucurbita maxima wastes are massive source of oils, which are believed to indicate the possible sources of renewable energy whose biodiesel can be produced. Hence, the study explores the potential of the Cucurbita maxima wastes, for the production of biodiesel. In this study, the Soxhlet extraction method was used to extract Cucurbita maxima waste oil using an organic solvent. Through Box-Behnken design (BBD), the effects of methanol to oil molar ratio (6–10), catalyst concentration (2–6%), and reaction time (45–75 min) on the transesterification efficiency of methyl esters were investigated. The oil contents of Cucurbita maxima waste was found to be
44.6
±
0.21
%. This oil was characterized, and after obtaining the pure characterized oil, biodiesel was produced using microwave assisted by the transesterification process. The optimum conversion efficiency of the Cucurbita maxima waste oil to fatty acid methyl ether was 97.76%, at the optimal parameters, methanol to oil ratio (8.4 : 1), catalyst concentration (3.14%), and reaction time (57.12 min). The results revealed that all parameters have a significant effect on the yield of biodiesel (
p
<
0.0001
). The physicochemical properties reveal that the Cucurbita maxima waste oil could be applied as a potential source of material for methyl ester production. The fatty acid profile of the oil indicated that it was mainly composed of unsaturated fatty acid, which ensures good flow properties of the fuel. The results of these studies showed the prospective of Cucurbita maxima wastes as a new potential feedstock for biodiesel production.
“…7 a,b,f, an enhancing in this independent factor causes that biodiesel yield increases, with a maximum of 95.88% achieved at a ratio of 10.52:1. However, by enhancing methanol to oil molar ratio higher than optimum condition lead to biodiesel yield decreases 52 Figure 7 The 3D surface and 2D contour plots of interaction between ( a ) catalyst weight and methanol to oil molar ratio, ( b ) reaction time and methanol to oil, ( c ) catalyst weight and reaction time, ( d ) ultrasonic frequency and catalyst weight, ( e ) ultrasonic frequency and reaction time, ( f ) Ultrasonic frequently and methanol to oil. …”
Section: Resultsmentioning
confidence: 99%
“…7a,b,f, an enhancing in this independent factor causes that biodiesel yield increases, with a maximum of 95.88% achieved at a ratio of 10.52:1. However, by enhancing methanol to oil molar ratio higher than optimum condition lead to biodiesel yield decreases 52 The catalyst weight (B) is a primary factor in transesterification reaction. The value of the weight of NaOH@ GO-Fe 3 O 4 was changed from 3 to 5wt%.…”
Section: Effects Of Operating Independent Factors On Transesterificat...mentioning
Burning fossil fuels causes toxic gas emissions to increase, therefore, scientists are trying to find alternative green fuels. One of the important alternative fuels is biodiesel. However, using eco-friendly primary materials is a main factor. Sustainable catalysts should have high performance, good activity, easy separation from reaction cells, and regenerability. In this study, to solve the mentioned problem NaOH@Graphene oxide-Fe3O4 as a magnetic catalyst was used for the first time to generate biodiesel from waste cooking oil. The crystal structure, functional groups, surface area and morphology of catalyst were studied by XRD, FTIR, BET, and FESEM techniques. The response surface methodology based central composite design (RSM-CCD) was used for biodiesel production via ultrasonic technique. The maximum biodiesel yield was 95.88% in the following operation: 10.52:1 molar ratio of methanol to oil, a catalyst weight of 3.76 wt%, a voltage of 49.58 kHz, and a time of 33.29 min. The physiochemical characterization of biodiesel was based to ASTM standard. The magnetic catalyst was high standstill to free fatty acid due to the five cycle’s regeneration. The kinetic study results possess good agreement with first-order kinetics as well as the activation energy and Arrhenius constant are 49.2 kJ/min and 16.47 * 1010 min−1, respectively.
“…The vitality required to recover solid adsorbents is ordinarily lower than that for watery amine arrangements; however, the reactivity between solid sorbent and fluid should be caught on for evaluating the ideal response enthalpy in capturing CO 2 8 . Understanding these details could be accommodating in planning the next-generation adsorbents with lower recovery vitality requirements 9 .…”
Designing a model to connect CO2 adsorption data with various adsorbents based on graphene oxide (GO) which is produced from various forms of solid biomass, can be a promising method to develop novel and efficient adsorbents for CO2 adsorption application. In this work, the information of several GO-based solid sorbents were extracted from 17 articles aimed to develop a machine learning based model for CO2 adsorption capacity prediction. The extracted data including specific surface area, pore volume, temperature, and pressure were considered as input parameter, and CO2 uptake capacity was defined as model response, alsoseven different models, including support vector machine, gradient boosting, random forest, artificial neural network (ANN) based on multilayer perceptron (MLP) and radial basis function (RBF), Extra trees regressor and extreme gradient boosting, were employed to estimate the CO2 adsorption capacity. The best performance was obtained for ANN based on MLP method (R2 > 0.99) with hyperparameters of the following: hidden layer size = [45 35 45 45], optimizer = Adam, the learning rate = 0.003, β1 = 0.9, β2 = 0.999, epochs = 1971, and batch size = 32. To investigate CO2 uptake dependency on mentioned effective parameters, three dimensional diagrams were reported based on MLP network, also the MLP network characteristics including weight and bias matrices were reported for further application of CO2 adsorption process design. The accurately predicted capability of the generated models may considerably minimize experimental efforts, such as estimating CO2 removal efficiency as the target based on adsorbent properties to pick more efficient adsorbents without increasing processing time. Current work employed statistical analysis and machine learning to support the logical design of porous GO for CO2 separation, aiding in screening adsorbents for cleaner manufacturing.
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