3,477 research outputs found
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Application of Computational Intelligence Techniques to Process Industry Problems
In the last two decades there has been a large progress in the computational
intelligence research field. The fruits of the effort spent on the research in the discussed
field are powerful techniques for pattern recognition, data mining, data modelling, etc.
These techniques achieve high performance on traditional data sets like the UCI
machine learning database. Unfortunately, this kind of data sources usually represent
clean data without any problems like data outliers, missing values, feature co-linearity,
etc. common to real-life industrial data. The presence of faulty data samples can have
very harmful effects on the models, for example if presented during the training of the
models, it can either cause sub-optimal performance of the trained model or in the worst
case destroy the so far learnt knowledge of the model. For these reasons the application
of present modelling techniques to industrial problems has developed into a research
field on its own. Based on the discussion of the properties and issues of the data and the
state-of-the-art modelling techniques in the process industry, in this paper a novel
unified approach to the development of predictive models in the process industry is
presented
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Review of dynamic positioning control in maritime microgrid systems
For many offshore activities, including offshore oil and gas exploration and offshore wind farm construction, it is essential to keep the position and heading of the vessel stable. The dynamic positioning system is a progressive technology, which is extensively used in shipping and other maritime structures. To maintain the vessels or platforms from displacement, its thrusters are used automatically to control and stabilize the position and heading of vessels in sea state disturbances. The theory of dynamic positioning has been studied and developed in terms of control techniques to achieve greater accuracy and reduce ship movement caused by environmental disturbance for more than 30 years. This paper reviews the control strategies and architecture of the DPS in marine vessels. In addition, it suggests possible control principles and makes a comparison between the advantages and disadvantages of existing literature. Some details for future research on DP control challenges are discussed in this paper
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