Product Yield Prediction System and Critical Area Database

2007 ◽  
Author(s):  
Thomas S. Barnett ◽  
Jeanne Bickford ◽  
Alan J. Weger
Keyword(s):  
Product Yield ◽  
Yield Prediction ◽  
Prediction System ◽  
Critical Area

Product Yield Prediction System and Critical Area Database

2008 ◽  
Vol 21 (3) ◽  
pp. 337-341 ◽  
Author(s):  
T.S. Barnett ◽  
J.P. Bickford ◽  
A.J. Weger
Keyword(s):  
Product Yield ◽  
Yield Prediction ◽  
Prediction System ◽  
Critical Area

Comparison of Lumping Approaches to Predict the Product Yield in a Dual Bed VGO Hydrocracker

2012 ◽  
Vol 9 (1) ◽  
Author(s):  
Sepehr Sadighi ◽  
Arshad Ahmad ◽  
Mansoor Shirvani
Keyword(s):  
Product Yield ◽  
Pilot Scale ◽  
Yield Prediction ◽  
Average Deviation ◽  
Hydrogen Consumption ◽  
Product Yields ◽  
The Absolute ◽  
Rigorous Model ◽  
Dual Bed

In this research, to predict the product yields of a pilot scale VGO hydrocracking reactor charged with mono functional hydrotreating and hydrocracking catalysts, two different four-lump models are developed. The first one, called combined bed model, is a simplex in which there is no boundary between hydrotreating and hydrocracking reactions through the reactor. The second one, called dual bed model, is a rigorous model in which hydrogen consumption and hydrotreating reactions are included. In this way, the reactor is subdivided into two different layers, so the effect of hydrotreating reactions on the hydrocracking section can be considered. Results show that the absolute average deviation (AAD%) of the yield prediction for the combined bed and the dual bed models are 8.23 percent and 5.87 percent, respectively. The main reason for the lower average deviation of the dual bed model is its higher accuracy to predict the yield of gas which is also the major advantage of this approach. However, the simplicity of the combined bed model can make it more applicable and attractive, especially when hydrogen consumption as well as sulfur, nitrogen and aromatic specifications of the feed and products are not accessible.

scholarly journals Purwarupa Sistem Prediksi Luas dan Hasil Panen Padi suatu Wilayah menggunakan Pengolahan Citra Digital dengan Metode Sobel dan Otsu

2016 ◽  
Vol 6 (2) ◽  
pp. 187
Author(s):  
Ardya Yunita Putri ◽  
Raden Sumiharto
Keyword(s):  
Image Processing ◽  
Crop Yield ◽  
Paddy Fields ◽  
Yield Prediction ◽  
Prediction System ◽  
Aerial Photo ◽  
Green Color ◽  
White Pixel ◽  
Otsu’S Method ◽  
Paddy Crop

Area and paddy crop yield prediction system of an area using  image processing by Sobel  Otsu’s method is one of  system that utilize aerial photo data for measuring  area and prediction of its crop yield. Otsu’s method is used to thresholding process and  Sobel’s method is used to detect paddy field’s edges that will calculate its area. Then filtering process so that the scanning process white pixels are counted only exist in the desired region. After the amount of white pixel(s) is obtained, their amount is multiplied with the scale that obtained from calibration process and crop yield prediction (kg/m2). Detection of yellow paddy color that ready-to-harvest is successfully performed by processing the HSV color, which is then detected by thresholding HSV. At the time of testing with variety of paddy color, the detected paddy color is the paddy color ready-to-harvest, which is brownish yellow that represented by white pixels, and will be used then to predict its area and crop yield. Thereafter, accuracy calculation test resulting in different error levels in different paddy fields. Error in testing of this system are 3,1 %, 8,7%, 4,9% dan 248%. The highest error value is caused by excessive exposure of light, with the result that the green color on paddy is detected by the system as yellow and some areas are covered by trees that, thereby reducing paddy fields area calculation.

scholarly journals Development of a rice yield prediction system over Bhubaneswar, India: combination of extended range forecast and CERES-rice model

2014 ◽  
Vol 22 (3) ◽  
pp. 525-533 ◽  
Author(s):  
K. Ghosh ◽  
Ankita Singh ◽  
U. C. Mohanty ◽  
Nachiketa Acharya ◽  
R. K. Pal ◽  
...  
Keyword(s):  
Rice Yield ◽  
Yield Prediction ◽  
Prediction System ◽  
Extended Range

scholarly journals Formulation of Flowering Index, Morphological Relationships, and Yield Prediction System in True Garlic Aerial Seed Bulbil Production

HortScience ◽  
2005 ◽  
Vol 40 (7) ◽  
pp. 2036-2039 ◽  
Author(s):  
Deepu Mathew ◽  
Zakwan Ahmed ◽  
N. Singh
Keyword(s):  
Leaf Area ◽  
Plant Height ◽  
Growth Period ◽  
Multiple Regression Model ◽  
Leaf Number ◽  
Yield Prediction ◽  
Prediction System ◽  
Flower Stalk ◽  
Head Diameter ◽  
100 Seed Weight

The phenomenon of flowering and aerial bulbil production in Asiatic garlic was observed under long photoperiodic conditions of Ladakh, India. Flowers were sterile and the bulbils produced on the umbel were true to type. Observations on a large number of flowering and nonflowering plants have led to the formulation of a precise flowering index (FI) in garlic. Plants with a minimum leaf number of 7, height 25 cm, collar width 0.6 cm, bulb diameter 3.7 cm, bulb weight 22.5 g, and functional leaf area of 182.4 cm2 had only shown the flowering. The flowering index formulated was a product of leaf number, plant height, functional leaf area, and bulb weight. For flowering, FI should be more than 788, and availability of a minimum photoperiod of 4020 hours during a growth period of 11 months was another prerequisite. Nonfulfillment of any one of the factors of flowering, although FI and photoperiod were satisfactory, led to nonflowering. Garlic aerial bulbil yield was positively correlated with leaf number, plant height, bulb weight, bulb diameter, length of flower stalk, 100 seed weight, and head diameter. Following the multiple regression model y = –11.9 – (0.00031 × number of bulbils) + (0.147 × 100 bulbil weight) + (4.95 × head diameter) + (0.0460 × length of flower stalk), aerial bulbil yield prediction was possible at a mean accuracy of 87%.

scholarly journals The HADES Yield Prediction System – A Case Study on the Turkish Hazelnut Sector

2021 ◽  
Vol 12 ◽  
Author(s):  
Simone Bregaglio ◽  
Kim Fischer ◽  
Fabrizio Ginaldi ◽  
Taynara Valeriano ◽  
Laura Giustarini
Keyword(s):  
Machine Learning ◽  
Predictive Ability ◽  
Simulation Models ◽  
Machine Learning Techniques ◽  
Weather Data ◽  
Climatic Data ◽  
Yield Prediction ◽  
Prediction System ◽  
Alternate Bearing ◽  
Biennial Bearing

Crop yield forecasting activities are essential to support decision making of farmers, private companies and public entities. While standard systems use georeferenced agro-climatic data as input to process-based simulation models, new trends entail the application of machine learning for yield prediction. In this paper we present HADES (HAzelnut yielD forEcaSt), a hazelnut yield prediction system, in which process-based modeling and machine learning techniques are hybridized and applied in Turkey. Official yields in the top hazelnut producing municipalities in 2004–2019 are used as reference data, whereas ground observations of phenology and weather data represent the main HADES inputs. A statistical analysis allows inferring the occurrence and magnitude of biennial bearing in official yields and is used to aid the calibration of a process-based hazelnut simulation model. Then, a Random Forest algorithm is deployed in regression mode using the outputs of the process-based model as predictors, together with information on hazelnut varieties, the presence of alternate bearing in the yield series, and agro-meteorological indicators. HADES predictive ability in calibration and validation was balanced, with relative root mean square error below 20%, and R2 and Nash-Sutcliffe modeling efficiency above 0.7 considering all municipalities together. HADES paves the way for a next-generation yield prediction system, to deliver timely and robust information and enhance the sustainability of the hazelnut sector across the globe.

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