Combustion Efficiency Optimization and Virtual Testing: A Data-Mining Approach

Kusiak, Andrew; Song, Zhe

doi:10.1109/tii.2006.873598

Cited by 69 publications

(22 citation statements)

References 20 publications

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“…Shong describe the existence of three different DM-based approaches to optimization of combustion efficiency: analytical models based on thermodynamics and chemistry, soft computing and hybrid systems [66]. Optimization DM-based models for improvement of a boiler-turbine system performance are formulated in [67] and [68].…”

Section: Kdd Techniques Applied To Manufacturing Processesmentioning

confidence: 99%

A knowledge discovery in databases approach for industrial microgrid planning

Gamarra

Guerrero

Montero

2016

Renewable and Sustainable Energy Reviews

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Section: Kdd Techniques Applied To Manufacturing Processesmentioning

confidence: 99%

A knowledge discovery in databases approach for industrial microgrid planning

Gamarra

Guerrero

Montero

2016

Renewable and Sustainable Energy Reviews

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“…Kusiak and Z. Shong describe the existence of three different approaches to optimization of combustion efficiency: analytical models based on thermodynamics and chemistry, soft computing and hybrid systems [25]. Optimization data-mining based models for improvement of a boiler-turbine system performance are formulated in [26] and [27].…”

Section: About To Smart Industriesmentioning

confidence: 99%

Innovative planning synergies between manufacturing processes and microgrids

Gamarra¹,

Ortega²,

Montero³

et al. 2016

REPQJ

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Abstract. Industrial equipment is evolving towards data gathering and communication, following the Internet of things approach. During last years, different management systems and related standards have been developed, in order to raise the performance of manufacturing processes. But a complete optimization of a manufacturing process requires a holistic approach, and adopting microgrid architectures can actually empower the optimization of a manufacturing process. In this paper, synergies about microgrids and manufacturing processes planning will be pointed out. Thus, a suitable approach to MG planning for manufacturing companies from a Knowledge Discovery in Databases (KDD) point of view is described.

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“…A testing procedure is needed to first, collapse by real-time data acquisition the huge retrospective uncertainty of risk associated with each individual component, and second, validate and/or guide optimization of recovery of an individual component as intended for any specific mitigation procedure. The variable, unpredictable,and complex data anticipated for use in this testing process will require test procedures that are inherently flexible and selfteaching so that autonomous decisions can be made in selection and applications of available algorithms for riskassessment and risk-management (Kusiak and Song, 2006). This is the province of data mining.…”

Section: Bidirectional Data Miningmentioning

confidence: 99%

Risk-Assessment for Equipment Operating on the Lunar Surface

Richmond

Kusiak

Ramachandran³

2008

AIP Conference Proceedings

Self Cite

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Abstract.Particle-size distribution of lunar dust simulant is evaluated using scanning electron spectroscopy in order to consider approaches to evaluating risk to individual mechanical components operating on the lunar surface.Assessing component risk and risk-mitigation during actual operations will require noninvasive continuous data gathering on numerous parameters.Those data sets would best be evaluated using data-mining algorithms to assess risk, and recovery from risk, of individual mechanical components in real-time.

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Combustion Efficiency Optimization and Virtual Testing: A Data-Mining Approach

Cited by 69 publications

References 20 publications

A knowledge discovery in databases approach for industrial microgrid planning

A knowledge discovery in databases approach for industrial microgrid planning

Innovative planning synergies between manufacturing processes and microgrids

Risk-Assessment for Equipment Operating on the Lunar Surface

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