scholarly journals Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Marischa Elveny ◽  
Meysam Hosseini ◽  
Tzu-Chia Chen ◽  
Adedoyin Isola Lawal ◽  
S. M. Alizadeh
Keyword(s):  
Isentropic Compressibility ◽  
Biofuel Production ◽  
Experimental Procedure ◽  
Mean Relative Error ◽  
Production Processes ◽  
Great Performance ◽  
Normal Melting Point ◽  
Input Variables ◽  
Learning Machine ◽  
New Machine

Isentropic compressibility is one of the significant properties of biofuel. On the other hand, the complexity related to the experimental procedure makes the detection process of this parameter time-consuming and hard. Thus, we propose a new Machine Learning (ML) method based on Extreme Learning Machine (ELM) to model this important value. A real database containing 483 actual datasets is compared with the outputs predicted by the ELM model. The results of this comparison show that this ML method, with a mean relative error of 0.19 and R 2 values of 1, has a great performance in calculations related to the biodiesel field. In addition, sensitivity analysis exhibits that the most efficient parameter of input variables is the normal melting point to determine isentropic compressibility.

scholarly journals Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Three Dimensional ◽  
Site Characterization ◽  
Standard Penetration Test ◽  
Support Vector ◽  
Characterization Model ◽  
Svm Model ◽  
Input Variables ◽  
Learning Machine ◽  
A Site

The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values () spread over 220 sq km area of Bangalore. The model is applied for corrected () values. The three input variables (, , and , where , , and are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, , and ε) has been also presented.

scholarly journals Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection

2017 ◽  
Vol 20 (2) ◽  
pp. 520-532 ◽  
Author(s):  
A. B. Dariane ◽  
Sh. Azimi
Keyword(s):  
Wavelet Transform ◽  
Multistep Method ◽  
Test Period ◽  
Fast Method ◽  
Streamflow Forecasting ◽  
Input Selection ◽  
Fuzzy Models ◽  
Monthly Streamflow ◽  
Input Variables ◽  
Learning Machine

Abstract In this paper the performance of extreme learning machine (ELM) training method of radial basis function artificial neural network (RBF-ANN) is evaluated using monthly hydrological data from Ajichai Basin. ELM is a newly introduced fast method and here we show a novel application of this method in monthly streamflow forecasting. ELM may not work well for a large number of input variables. Therefore, an input selection is applied to overcome this problem. The Nash–Sutcliffe efficiency (NSE) of ANN trained by backpropagation (BP) and ELM algorithm using initial input selection was found to be 0.66 and 0.72, respectively, for the test period. However, when wavelet transform, and then genetic algorithm (GA)-based input selection are applied, the test NSE increase to 0.76 and 0.86, respectively, for ANN-BP and ANN-ELM. Similarly, using singular spectral analysis (SSA) instead, the coefficients are found to be 0.88 and 0.90, respectively, for the test period. These results show the importance of input selection and superiority of ELM and SSA over BP and wavelet transform. Finally, a proposed multistep method shows an outstanding NSE value of 0.97, which is near perfect and well above the performance of the previous methods.

scholarly journals An Assessment Of Microalgal Biodiesel With Acetone, Butanol and Ethanol Using LIfe Cycle Assessment (LCA) Methodology

2021 ◽  
Author(s):  
Cherilyn Dignan
Keyword(s):  
Fossil Fuels ◽  
Ghg Emissions ◽  
Primary Energy ◽  
Biofuel Production ◽  
Production Model ◽  
Lca Methodology ◽  
Fuel Production ◽  
Renewable Fuel ◽  
Production Processes ◽  
Microalgal Biodiesel

Canada, as one of the largest producers and consumers of fossil fuels per capita on the planet, is attempting to reduce greenhouse gas (GHG) emissions. In order to accomplish this, fuel alternatives, such as biofuel, are required. Accordingly, this study uses LCA methodology to quantify the GHG impact of a unique biofuel production model. This unique model produces biodiesel (BD), acetone, butanol and ethanol (ABE) from microalgae and assesses the process GHG impact against other microalgal BD production processes. This study’s microalgal BD and ABE production process produces 76 kgCO2e per functional unit, whereas other comparable microalgal BD production processes produce between 3.7 and 85 kgCO2e. Overall, this study clarifies that without the development of versatile infrastructure to accommodate biofuel production, LCA studies will continue to find renewable fuel production processes net GHG positive for the simple reason that fossil resources are still the primary energy source.

scholarly journals Review on possible algal-biofuel production processes

Biofuels ◽  
2012 ◽  
Vol 3 (3) ◽  
pp. 333-349 ◽  
Author(s):  
Michael Kröger ◽  
Franziska Müller-Langer
Keyword(s):  
Biofuel Production ◽  
Algal Biofuel ◽  
Production Processes

Scale-up and Case Studies of Biofuel Production Processes

2019 ◽  
pp. 209-222
Author(s):  
Debabrata Das ◽  
Jhansi L. Varanasi
Keyword(s):  
Case Studies ◽  
Scale Up ◽  
Biofuel Production ◽  
Production Processes

Research on the biodiversity of Western Siberia microalgae for third-generation biofuel production processes

2013 ◽  
Vol 3 (6) ◽  
pp. 487-492 ◽  
Author(s):  
A. V. Piligaev ◽  
K. N. Sorokina ◽  
A. V. Bryanskaya ◽  
E. A. Demidov ◽  
R. G. Kukushkin ◽  
...  
Keyword(s):  
Western Siberia ◽  
Biofuel Production ◽  
Third Generation ◽  
Production Processes ◽  
Generation Biofuel

scholarly journals Data-Driven Dynamic Modeling for Prediction of Molten Iron Silicon Content Using ELM with Self-Feedback

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ping Zhou ◽  
Meng Yuan ◽  
Hong Wang ◽  
Tianyou Chai
Keyword(s):  
Dynamic Modeling ◽  
Silicon Content ◽  
Data Driven ◽  
Modeling Technology ◽  
Learning Speed ◽  
Assay Procedure ◽  
Thermal Status ◽  
Static Modeling ◽  
Input Variables ◽  
Learning Machine

Silicon content ([Si] for short) of the molten metal is an important index reflecting the product quality and thermal status of the blast furnace (BF) ironmaking process. Since the online detection of [Si] is difficult and larger time delay exists in the offline assay procedure, quality modeling is required to achieve online estimation of [Si]. Focusing on this problem, a data-driven dynamic modeling method is proposed using improved extreme learning machine (ELM) with the help of principle component analysis (PCA). First, data-driven PCA is introduced to pick out the most pivotal variables from multitudinous factors to serve as the secondary variables of modeling. Second, a novel data-driven ELM modeling technology with good generalization performance and nonlinear mapping capability is presented by applying a self-feedback structure on traditional ELM. The feedback outputs at previous time together with input variables at different time constitute a dynamic ELM structure which has a storage ability to tackle data in different time and overcomes the limitation of static modeling of traditional ELM. At last, industrial experiments demonstrate that the proposed method has a better modeling and estimating accuracy as well as a faster learning speed when compared with different modeling methods with different model structures.

scholarly journals Continuous Valorization of Glycerol into Solketal: Recent Advances on Catalysts, Processes, and Industrial Perspectives

2021 ◽  
Vol 2 (2) ◽  
pp. 286-324
Author(s):  
Isabella Corrêa ◽  
Rui P. V. Faria ◽  
Alírio E. Rodrigues
Keyword(s):  
Continuous Production ◽  
Oxidation Stability ◽  
Raw Material ◽  
Biodiesel Production ◽  
Biofuel Production ◽  
Fuel Additive ◽  
Production Chain ◽  
Operational Conditions ◽  
Production Processes ◽  
Recent Advances

With the global biodiesel production growing as never seen before, encouraged by government policies, fiscal incentives, and emissions laws to control air pollution, there has been the collateral effect of generating massive amounts of crude glycerol, a by-product from the biodiesel industry. The positive effect of minimizing CO2 emissions using biofuels is jeopardized by the fact that the waste generated by this industry represents an enormous environmental disadvantage. The strategy of viewing “waste as a resource” led the scientific community to propose numerous processes that use glycerol as raw material. Solketal, the product of the reaction of glycerol and acetone, stands out as a promising fuel additive capable of enhancing fuel octane number and oxidation stability, diminishing particle emissions and gum formation, and enhancing properties at low temperatures. The production of this chemical can rely on several of the Green Chemistry principles, besides fitting the Circular Economy Model, once it can be reinserted in the biofuel production chain. This paper reviews the recent advances in solketal production, focusing on continuous production processes and on Process Intensification strategies. The performance of different catalysts under various operational conditions is summarized and the proposed industrial solketal production processes are compared.

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