Optimization of biodiesel production using sulfonated carbon-based catalyst from an amazon agro-industrial waste

Bastos, Rafael Roberto Cardoso; Corrêa, Ana Paula da Luz; Luz, Patrícia Teresa Souza da; Filho, Geraldo Narciso da Rocha; Zamian, José Roberto; Conceição, Leyvison Rafael Vieira da

doi:10.1016/j.enconman.2019.112457

Cited by 108 publications

(41 citation statements)

References 44 publications

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“…The XRD spectrum ( Figure 2 ) of the OMSC catalyst maintained the two broad diffraction peaks at 2θ = 10–30° and 40–50°, corresponding to the amorphous carbon structure comprising polycyclic aromatic carbon sheets randomly oriented with some content of crystalline graphite [ 36 ]. The findings from the XRD result were further confirmed by the Raman spectrum ( Figure 3 ), which exhibited two narrow bands in the first-order region (1000–2000 cm −1 ) with maxima at 1353 and 1589 cm −1 , respectively.…”

Section: Resultsmentioning

confidence: 99%

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Optimization and Characterization of Mesoporous Sulfonated Carbon Catalyst and Its Application in Modeling and Optimization of Acetin Production

et al. 2020

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In this study, an optimized mesoporous sulfonated carbon (OMSC) catalyst derived from palm kernel shell biomass was developed using template carbonization and subsequent sulfonation under different temperatures and time conditions. The OMSC catalyst was characterized using acid-base titration, elemental analysis, XRD, Raman, FTIR, XPS, TPD-NH3, TGA-DTA, SEM, and N2 adsorption–desorption analysis to reveal its properties. Results proved that the OMSC catalyst is mesoporous and amorphous in structure with improved textural, acidic, and thermal properties. Both FTIR and XPS confirmed the presence of -SO3H, -OH, and -COOH functional groups on the surface of the catalyst. The OMSC catalyst was found to be efficient in catalyzing glycerol conversion to acetin via an acetylation reaction with acetic acid within a short period of 3 h. Response surface methodology (RSM), based on a two-level, three-factor, face-centered central composite design, was used to optimize the reaction conditions. The results showed that the optimized temperature, glycerol-to-acetic acid mole ratio, and catalyst load were 126 °C, 1:10.4, and 0.45 g, respectively. Under these optimum conditions, 97% glycerol conversion (GC) and selectivities of 4.9, 27.8, and 66.5% monoacetin (MA), diacetin (DA), and triacetin (TA), respectively, were achieved and found to be close to the predicted values. Statistical analysis showed that the regression model, as well as the model terms, were significant with the predicted R2 in reasonable agreement with the adjusted R2 (<0.2). The OMSC catalyst maintained excellent performance in GC for the five reaction cycles. The selectivity to TA, the most valuable product, was not stable until the fourth cycle, attributable to the leaching of the acid sites.

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Section: Resultsmentioning

confidence: 99%

“…In heterogeneous catalysis, catalyst reusability is an important factor for its economic viability and is considered a significant advantage when compared with homogeneous catalysis [ 6 , 12 , 36 ]. Therefore, catalyst reusability in glycerol acetylation with acetic acid was conducted by consecutive batch cycles under optimal conditions.…”

Section: Resultsmentioning

confidence: 99%

Optimization and Characterization of Mesoporous Sulfonated Carbon Catalyst and Its Application in Modeling and Optimization of Acetin Production

et al. 2020

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“…Therefore, the use of RES in production allows an agroindustrial enterprise to partially or fully provide itself with the resource in accordance with the technological process. At such entities, this type of equipment acts as a fixed asset transferring its value to products during more than one production cycle (Bastos et al 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of the Economic Profitability of Using Renewable Energy Sources in Agro-Industrial Companies

Syromyatnikov

Druzyanova

Beloglazov

et al. 2021

Int. J. Renew. Energy Dev.

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The study of the impact of the availability of renewable energy sources (RES) on the competitiveness and cost reduction opportunities is an actual objective for agro-industrial enterprises in the present time. The purpose of this paper was to develop a methodological approach based on a questionnaire survey, an integrated indicator of enterprise competitiveness, a model for assessing energy conservation costs, and regression analysis combined with a hierarchical synthesis of 20 Russian agro-industrial companies. The solution of the problem of nonlinear optimization of agro-industrial companies made it possible to determine the excess of the competitiveness criterion over the criterion of energy conservation cost reduction as a result of the use of RES. The conducted regression analysis showed a close relationship between the competitiveness of the companies under study and the availability of RES. Modeling and hierarchical synthesis of the study results confirmed that the use of RES in the activities of agro-industrial companies stimulates their competitive potential reflected by the competitiveness index and minimizes energy conservation costs. It was also found that there is an inverse regression relationship with a high degree of correlation between the provision of RES and the cost of energy-saving measures. Hence, it can be argued that a greater emphasis on the provision of Russian agro-industrial enterprises with RES can lead to their more sustainable and efficient development due to increased competitiveness and better cost reduction strategies.

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“…Biodiesel is a kind of nontoxic and biodegradable fuel, which is aromatic and sulfur free. The production of biodiesel is due to the transesterification reaction between the waste oil and different alcohols like methanol or ethanol [1,2]. In recent years, the importance of biodiesel has been increasing due to the reduction of total petroleum reserves in the world and the increase of global warming issues.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective

et al. 2021

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Owing to the ever-growing impetus towards the development of eco-friendly and low carbon footprint energy solutions, biodiesel production and usage have been the subject of tremendous research efforts. The biodiesel production process is driven by several process parameters, which must be maintained at optimum levels to ensure high productivity. Since biodiesel productivity and quality are also dependent on the various raw materials involved in transesterification, physical experiments are necessary to make any estimation regarding them. However, a brute force approach of carrying out physical experiments until the optimal process parameters have been achieved will not succeed, due to a large number of process parameters and the underlying non-linear relation between the process parameters and responses. In this regard, a machine learning-based prediction approach is used in this paper to quantify the response features of the biodiesel production process as a function of the process parameters. Three powerful machine learning algorithms—linear regression, random forest regression and AdaBoost regression are comprehensively studied in this work. Furthermore, two separate examples—one involving biodiesel yield, the other regarding biodiesel free fatty acid conversion percentage—are illustrated. It is seen that both random forest regression and AdaBoost regression can achieve high accuracy in predictive modelling of biodiesel yield and free fatty acid conversion percentage. However, AdaBoost may be a more suitable approach for biodiesel production modelling, as it achieves the best accuracy amongst the tested algorithms. Moreover, AdaBoost can be more quickly deployed, as it was seen to be insensitive to number of regressors used.

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Optimization of biodiesel production using sulfonated carbon-based catalyst from an amazon agro-industrial waste

Cited by 108 publications

References 44 publications

Optimization and Characterization of Mesoporous Sulfonated Carbon Catalyst and Its Application in Modeling and Optimization of Acetin Production

Optimization and Characterization of Mesoporous Sulfonated Carbon Catalyst and Its Application in Modeling and Optimization of Acetin Production

Evaluation of the Economic Profitability of Using Renewable Energy Sources in Agro-Industrial Companies

Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective

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