Francesca Capaci

ORCID: 0000-0003-0740-2531
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About
Contact & Profiles
Research Areas
  • Fault Detection and Control Systems
  • Advanced Statistical Process Monitoring
  • Advanced Control Systems Optimization
  • Manufacturing Process and Optimization
  • Mineral Processing and Grinding
  • Neural Networks and Applications
  • Operations Management Techniques
  • Reservoir Engineering and Simulation Methods
  • Control Systems and Identification
  • Complex Systems and Decision Making
  • Fuzzy Logic and Control Systems
  • Simulation Techniques and Applications
  • Social and Educational Sciences
  • Scientific Measurement and Uncertainty Evaluation
  • Spectroscopy and Chemometric Analyses
  • Scheduling and Optimization Algorithms

Luleå University of Technology
2017-2020

Engineering process control and high-dimensional, time-dependent data present great methodological challenges when applying statistical (SPC) design of experiments (DoE) in continuous industrial processes. Process simulators with an ability to mimic these are instrumental research education. This article focuses on the revised Tennessee Eastman simulator providing guidelines for its use as a testbed SPC DoE methods. We provide flowcharts that can support new users get started Simulink/Matlab...

10.1080/08982112.2018.1461905 article EN cc-by Quality Engineering 2018-04-06

Industrial manufacturing processes often operate under closed‐loop control, where automation aims to keep important process variables at their set‐points. In industries such as pulp, paper, chemical and steel plants, it is hard find production operating in open loop. Instead, control systems will actively attempt minimize the impact of disturbances. However, we argue that an implicit assumption most experimental investigations studied system loop, allowing factors freely affect responses....

10.1002/qre.2128 article EN cc-by-nc-nd Quality and Reliability Engineering International 2017-02-06

Abstract The concurrent use of statistical process control and engineering involves monitoring manipulated controlled variables. One multivariate chart may handle the all variables, but observing variables in separate charts improve understanding how disturbances controller performance affect process. In this article, we illustrate step ramp manifest themselves a single‐input–single‐output system by studying their resulting signatures is variations widely used...

10.1002/qre.2676 article EN cc-by Quality and Reliability Engineering International 2020-06-29
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