scholarly journals Use of Decision Trees for the Development of Decision Support Systems for the Control of Grinding Circuits

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 595
Author(s):  
Jacques Olivier ◽  
Chris Aldrich
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Trees ◽  
Support Systems ◽  
Nonlinear Behaviour ◽  
Industrial Case Study ◽  
Variable Control ◽  
Plant Data ◽  
Grinding Circuits

Grinding circuits can exhibit strong nonlinear behaviour, which may make automatic supervisory control difficult and, as a result, operators still play an important role in the control of many of these circuits. Since the experience among operators may be highly variable, control of grinding circuits may not be optimal and could benefit from automated decision support. This could be based on heuristics from process experts, but increasingly could also be derived from plant data. In this paper, the latter approach, based on the use of decision trees to develop rule-based decision support systems, is considered. The focus is on compact, easy to understand rules that are well supported by the data. The approach is demonstrated by means of an industrial case study. In the case study, the decision trees were not only able to capture operational heuristics in a compact intelligible format, but were also able to identify the most influential variables as reliably as more sophisticated models, such as random forests.

scholarly journals Inclusive Design of Earth Observation Decision Support Systems for Environmental Governance: A Case Study of Lake Nokoué

2021 ◽  
Vol 3 ◽  
Author(s):  
Ufuoma Ovienmhada ◽  
Fohla Mouftaou ◽  
Danielle Wood
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Earth Observation ◽  
Inclusive Design ◽  
Observation Data ◽  
Climate Data ◽  
Technology Design ◽  
Data Services

Earth Observation (EO) data can enhance understanding of human-environmental systems for the creation of climate data services, or Decision Support Systems (DSS), to improve monitoring, prediction and mitigation of climate harm. However, EO data is not always incorporated into the workflow for decision-makers for a multitude of reasons including awareness, accessibility and collaboration models. The purpose of this study is to demonstrate a collaborative model that addresses historical power imbalances between communities. This paper highlights a case study of a climate harm mitigation DSS collaboration between the Space Enabled Research Group at the MIT Media Lab and Green Keeper Africa (GKA), an enterprise located in Benin. GKA addresses the management of an invasive plant species that threatens ecosystem health and economic activities on Lake Nokoué. They do this through a social entrepreneurship business model that aims to advance both economic empowerment and environmental health. In demonstrating a Space Enabled-GKA collaboration model that advances GKA's business aims, this study first considers several popular service and technology design methods and offer critiques of each method in terms of their ability to address inclusivity in complex systems. These critiques lead to the selection of the Systems Architecture Framework (SAF) as the technology design method for the case study. In the remainder of the paper, the SAF is applied to the case study to demonstrate how the framework coproduces knowledge that would inform a DSS with Earth Observation data. The paper offers several practical considerations and values related to epistemology, data collection, prioritization and methodology for performing inclusive design of climate data services.

Rule-Based Ontology Decision Model

2016 ◽  
pp. 271-291
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Process ◽  
Support Systems ◽  
Rule Based ◽  
The Matrix ◽  
Markov Decision ◽  
Minimization Technique ◽  
Complex Decision

In chapter 7, we examined some selected case study applications of some decision support systems. Those considered were the matrix-based used in determining labour cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique and Markov decision process. As earlier discussed, our focus will be on rule-based decision support systems. This is because rule-based systems are more encompassing and can easily be employed to deal with complex decision about construction activities. Hence in this chapter, an overview of rule-based decision system will be examined.

Some Selected Case Study Applications

2016 ◽  
pp. 180-270
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Process ◽  
Support Systems ◽  
Labor Cost ◽  
The Matrix ◽  
Markov Decision ◽  
Decision Support Models ◽  
Minimization Technique

Having examined the modelling principles of underpinning based decision support systems applied to construction in Chapter 6, this chapter will now demonstrate their detail applications in construction practice. Specifically, 7 decision-support systems will be examined. The choices are based on the fact that data for use in the decision support models are available. The decision-support systems considered are the matrix-based used in determining labor cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique, Markov decision process and rule-based systems.

On the Use of Abduction as an Alternative to Decision Trees in Environmental Decision Support Systems

2011 ◽  
Vol 2 (1) ◽  
pp. 63-82 ◽  
Author(s):  
Franz Wotawa
Keyword(s):  
Decision Support ◽  
Decision Tree ◽  
Decision Support Systems ◽  
Decision Trees ◽  
Support Systems ◽  
Abductive Reasoning ◽  
Effect Model ◽  
Environmental Decision Support Systems ◽  
Environmental Decision

Although decision trees are frequently used in environmental decision support systems, they have shortcomings. In the case of an available model, decision trees have to be constructed manually from the model. Moreover, not all knowledge is represented in the decision tree. To overcome this issue, the author proposes the use of abductive reasoning directly applied to the available cause-effect model. In particular the abduction problem the author introduces (i.e., the problem of finding a cause for observed effects), shows how this problem can be extended to allow distinguishing between competing explanations, and discusses the integration of testing and repair actions within the framework. The latter is especially important in case of environmental decision support systems.

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