scholarly journals Yield Prediction of Renewable Diesel From Hydrocracking Process as a Function of Pressure and Temperature Using Analytical Semi Empirical Model (ASEM)

2018 ◽  
Vol 67 ◽  
pp. 02019
Author(s):  
Handrianto Wijaya ◽  
Bambang Heru Susanto
Keyword(s):  
Empirical Model ◽  
The Other ◽  
Yield Prediction ◽  
Trial And Error ◽  
Time Cost ◽  
Renewable Diesel ◽  
Renewable Fuel ◽  
Hydrocracking Process ◽  
Using Data ◽  
Semi Empirical

The development of renewable fuels from biomass is very rapid, and becomes the main alternative to replace petroleum-derived fuels that are limited in stock. There has been a lot of experiments to optimize the production of renewable diesel, but it takes time, cost and a lot of trial and error in order to produce a good result. On the other hand, optimization using simulation is more cost and time effective. One of the processes in the production of this renewable fuel is hydrocracking. This experiment aims to study the effect of pressure and temperature in the hydrocracking process using the Analytical Semi Empirical Model (ASEM) method in representing the yield of the product. Mathematical models will be modified and validated using data from existing research. The results show that Analytical Semi Empirical Model can be used to predict the yield of product from hydrocracking, with all of the models show R2 higher than 0.95 and SSE lower than 3.

A Hybrid Semi-empirical Model for Lean Blow-Out Limit Predictions of Aero-engine Combustors

2014 ◽  
Vol 137 (3) ◽  
Author(s):  
Bin Hu ◽  
Yong Huang ◽  
Jianzhong Xu
Keyword(s):  
Experimental Data ◽  
Numerical Simulation ◽  
Empirical Model ◽  
The Other ◽  
Model Parameters ◽  
Model Parameter ◽  
Other Hand ◽  
Aero Engine ◽  
Semi Empirical

According to the Lefebvre's model and flame volume (FV) concept, an FV model about lean blow-out (LBO) was proposed by authors in early study. On the other hand, due to the model parameter (FV) contained in FV model is obtained based on the experimental data, FV model could only be used in LBO analysis instead of prediction. In view of this, a hybrid FV model is proposed that combines the FV model with numerical simulation in the present study. The model parameters contained in the FV model are all estimated from the simulated nonreacting flows. Comparing with the experimental data for 11 combustors, the maximum and average uncertainties of hybrid FV model are ±16% and ±10%.

scholarly journals Methods based on a semi-empirical model for simulating scroll compressors with HFC and HFO refrigerants

2021 ◽  
Vol 1 ◽  
pp. 148
Author(s):  
George Meramveliotakis ◽  
George Kosmadakis ◽  
Sotirios Karellas
Keyword(s):  
Empirical Model ◽  
The Other ◽  
Fine Tuning ◽  
Optimization Process ◽  
Computational Time ◽  
Small Subset ◽  
Model Parameters ◽  
The Third ◽  
Discharge Temperature ◽  
Semi Empirical

The aim of this work is to evaluate three methodologies regarding semi-empirical scroll compressor modeling for different refrigerants and conduct a comparative analysis of their results and accuracy. The first step is to improve a semi-empirical model for scroll compressors based on established techniques, and further enhance the physical background of some of its sub-processes leading to more accurate predictions. Focus is then given on the compressor operation when changing the refrigerant, proposing three methods in total. The first method refers to the standard model, requiring an optimization process for the calibration of all the model parameters. The second method relies on a reference refrigerant, and also uses optimization procedures, but for the fine-tuning of a small subset of the parameters. The third method is more generalized, without the need of any optimization process for the parameters identification, when fluid change occurs, leading to a very fast approach. Το evaluate the accuracy and verify the applicability of each method also related to the necessary computational time, two scroll compressors each with three different refrigerants are considered (HFCs and HFOs and their blends). The model is evaluated with the available manufacturer data, using R134a as reference refrigerant. The results show that the first method predicts the key indicators with a very high accuracy, with the maximum discrepancy of 2.06%, 4.17% and 3.18 K for the mass flow rate, electric power and discharge temperature respectively. The accuracy of the other two methods is dropping, but within acceptable levels in most of the cases. Therefore, in cases that reduced accuracy can be accepted, the third method is preferred for compressor performance prediction when changing the refrigerant, which provides results at a small fraction of time compared with the other two methods, once the parameters are calibrated for a reference case.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

scholarly journals A Semi-Empirical Model for Predicting Frost Properties

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 412
Author(s):  
Shao-Ming Li ◽  
Kai-Shing Yang ◽  
Chi-Chuan Wang
Keyword(s):  
Thermal Conductivity ◽  
Linear Programming ◽  
Numerical Model ◽  
Empirical Model ◽  
Quantitative Method ◽  
Predictive Ability ◽  
Dimensionless Temperature ◽  
Crystal Shape ◽  
Proposed Model ◽  
Semi Empirical

In this study, a quantitative method for classifying the frost geometry is first proposed to substantiate a numerical model in predicting frost properties like density, thickness, and thermal conductivity. This method can recognize the crystal shape via linear programming of the existing map for frost morphology. By using this method, the frost conditions can be taken into account in a model to obtain the corresponding frost properties like thermal conductivity, frost thickness, and density for specific frost crystal. It is found that the developed model can predict the frost properties more accurately than the existing correlations. Specifically, the proposed model can identify the corresponding frost shape by a dimensionless temperature and the surface temperature. Moreover, by adopting the frost identification into the numerical model, the frost thickness can also be predicted satisfactorily. The proposed calculation method not only shows better predictive ability with thermal conductivities, but also gives good predictions for density and is especially accurate when the frost density is lower than 125 kg/m3. Yet, the predictive ability for frost density is improved by 24% when compared to the most accurate correlation available.

Foreign Aid and its Role in Maintaining the Exploitation of the Agricultural Sector: Evidence from a Case Study in Africa

1983 ◽  
Vol 13 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Claudio Schuftan
Keyword(s):  
Rural Development ◽  
Foreign Aid ◽  
Local Governments ◽  
Agricultural Sector ◽  
National Development ◽  
The Other ◽  
Sizable Proportion ◽  
Using Data ◽  
State Of Affairs

Today most foreign aid donors are genuinely committed to the idea that development in Third World countries should start with rural development. Therefore, a sizable proportion of their development funds are invested in rural projects. However, donors channel these funds through local governments (most often representing local bourgeois interests) that are not as committed to the principle of rural development. These governments are often also embarked in policies that are actually—directly or indirectly—expropriating the surpluses generated by agriculture and investing them in the other sectors of the economy. The peasants are therefore footing most of the bill of overall national development. This paper contends that, because of this state of affairs, foreign aid directed toward rural development is actually filling the investment gap left by an internal system of unequal returns to production in agriculture. In so doing, foreign aid is indirectly financing the development of the other sectors of the economy, even if this result is unintended. This perpetrates maldevelopment without redressing the basic exploitation process of peasants which lies at the core of underdevelopment. Evidence to support this hypothesis is presented using data from a primarily agricultural exporting country: the United Republic of Cameroon.

scholarly journals Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 340
Author(s):  
Ewa Panek ◽  
Dariusz Gozdowski
Keyword(s):  
Linear Regression ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Early Spring ◽  
European Countries ◽  
Yield Prediction ◽  
Country Level ◽  
Using Data

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

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