A Predictive Model for Assessment of the Risk of Mold Growth in Rapeseeds Stored in a bulk as a Decision Support Tool for Postharvest Management Systems

2020 ◽  
Vol 97 (8) ◽  
pp. 915-927 ◽  
Author(s):  
Jolanta Wawrzyniak
Keyword(s):  
Decision Support ◽  
Predictive Model ◽  
Decision Support Tool ◽  
Management Systems ◽  
Mold Growth ◽  
Support Tool ◽  
Postharvest Management

Riser Management System Performance in Ultra-Deep Water With Rapidly Changing Current

2011 ◽  
Author(s):  
Gerhard Gundersen ◽  
Gullik A. Jensen
Keyword(s):  
Decision Support ◽  
System Performance ◽  
Decision Support Tool ◽  
Management Systems ◽  
Joint Angles ◽  
Support Tool ◽  
Deep Waters ◽  
Wear And Tear ◽  
Loop Currents ◽  
And Control

The largest loading in terms of bending for a top tensioned riser in ultra-deep waters, subject to strong and rapidly changing ocean currents, are found to be close to the upper and lower extremeties. This has been demonstrated by the analyses of various dynamic current scenarios including strong cross currents and currents with rapidly changing direction, resembling eddies and loop currents based on metocean data from Brazilian waters. The riser response in terms of deflections and bending is not found to be critical at any location along the riser for the investigated load cases. Monitoring a riser based on the upper and lower flex-joint angles are hence sufficient to safely operate and control the drilling riser under such conditions. This implies that Riser Management Systems (RMS) that are widely used in intermediate and deep waters with slowly varying currents can safely be applied for top tensioned risers in ultra-deep waters with strong and rapidly changing currents. In fact they may prove to be more important under these conditions, to reduce the wear and tear, improve safety, and as a decision support tool for when to safely disconnect the riser.

scholarly journals A decision support tool for planning biowaste management systems

2020 ◽  
Vol 242 ◽  
pp. 118460 ◽  
Author(s):  
Mónica Delgado ◽  
Ana López ◽  
Miguel Cuartas ◽  
Carlos Rico ◽  
Amaya Lobo
Keyword(s):  
Decision Support ◽  
Decision Support Tool ◽  
Management Systems ◽  
Support Tool

scholarly journals A Framework for Big Data Analytical Process and Mapping—BAProM: Description of an Application in an Industrial Environment

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6014
Author(s):  
Giovanni Gravito de Carvalho Chrysostomo ◽  
Marco Vinicius Bhering de Aguiar Vallim ◽  
Leilton Santos da Silva ◽  
Leandro A. Silva ◽  
Arnaldo Rabello de Aguiar Vallim Filho
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Predictive Model ◽  
Real World ◽  
Decision Support Tool ◽  
Exploratory Analysis ◽  
Process Mapping ◽  
Support Tool ◽  
Analytical Process ◽  
Real World Application

This paper presents an application of a framework for Big Data Analytical Process and Mapping—BAProM—consisting of four modules: Process Mapping, Data Management, Data Analysis, and Predictive Modeling. The framework was conceived as a decision support tool for industrial business, encompassing the whole big data analytical process. The first module incorporates in big data analytical a mapping of processes and variables, which is not common in such processes. This is a proposal that proved to be adequate in the practical application that was developed. Next, an analytical “workbench” was implemented for data management and exploratory analysis (Modules 2 and 3) and, finally, in Module 4, the implementation of artificial intelligence algorithm support predictive processes. The modules are adaptable to different types of industry and problems and can be applied independently. The paper presents a real-world application seeking as final objective the implementation of a predictive maintenance decision support tool in a hydroelectric power plant. The process mapping in the plant identified four subsystems and 100 variables. With the support of the analytical workbench, all variables have been properly analyzed. All underwent a cleaning process and many had to be transformed, before being subjected to exploratory analysis. A predictive model, based on a decision tree (DT), was implemented for predictive maintenance of equipment, identifying critical variables that define the imminence of an equipment failure. This DT model was combined with a time series forecasting model, based on artificial neural networks, to project those critical variables for a future time. The real-world application showed the practical feasibility of the framework, particularly the effectiveness of the analytical workbench, for pre-processing and exploratory analysis, as well as the combined predictive model, proving effectiveness by providing information on future events leading to equipment failures.

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