Using Special Filter with Membership Function in Biomass Combustion Process Control

Mižáková, Jana; Piteľ, Ján; Hosŏvský, Alexander; Kolarčík, Martin; Ratnayake, Madhawa

doi:10.3390/app8081279

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…Control of the vibrational signal from the perforated basket is carried out using the GDS-820S oscilloscope. The change of technological characteristics during the dispersion process is realized by measuring the flow rate and frequency with the consequent processing by means of the Matlab software [38,39].…”

Section: Methodsmentioning

confidence: 99%

Effect of Superimposed Vibrations on Droplet Oscillation Modes in Prilling Process

et al. 2020

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This article was aimed to solve an urgent problem of ensuring quality for prilling processes in vibrational prilling equipment. During the research, the need for the application of vibrational prilling to create a controlled impact on the process of jet decay on droplets with the proper characteristics was substantiated. Based on the experimental and theoretical studies of the process of decay of a liquid jet into drops, axisymmetric droplet oscillation modes for the different frequencies were observed. Frequency ranges of transition between modes of decay of a jet into drops were obtained. As a result, the mathematical model of the droplet deformation was refined. The experimental research data substantiated this model, and its implementation allowed determining the analytical dependencies for the components of the droplet deformation velocity. The proposed model explains the existence of different droplet oscillation modes depending on the frequency characteristics of the superimposed vibrational impact. Based on an analytical study of the droplet deformation velocity components, the limit values of the characteristics defining the transition between the different droplet oscillation modes were discovered. Analytical dependencies were also obtained to determine the diameter of the satellites and their total number.

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Section: Methodsmentioning

confidence: 99%

Effect of Superimposed Vibrations on Droplet Oscillation Modes in Prilling Process

et al. 2020

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“…While (6) resolves the issue of various lenght of data, it still suffers from various lengths of feature vectors n x that BFSA can principally find for various datasets. Thus, we propose to resolve the non-unique feature-vector-length problem by compressing (columnwise) the matrix of state vectors X as in (1) using Principal Component Analysis (PCA) into a customized number of components n c , i.e., the matrix X is compressed to a constant number of columns (new features), and the MSFNA is then evaluated for state vectors of the same length n c .…”

Section: Multiscale False Neighbours Analysis (Msfna)mentioning

confidence: 99%

“…Computational intelligence approaches with machine learning have been attractive to study for combustion processes in the last decades, e.g., [5], where multilayer feedforward neural network with error back-propagation learning was used for approximation of measured CO/lambda biomass combustion dependence and that shows a significant variance in data that can be seen as a kind of uncertainty, and the neural network is used to extract the prevailing dependence in data; further example of filtration with membership function design can be found in [6]. Further computational intelligence techniques based on immune systems and applied to biomass combustion and that highlights the nonlinearity and complexity of the process can be found in [7].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Uncertainty Analysis for Bubbling Fluidized Bed Oxy-Fuel Combustion Data

Marzova

Bukovský

2021

Processes

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This paper presents a novel feature extraction and validation technique for data-driven prediction of oxy-fuel combustion emissions in a bubbling fluidized bed experimental facility. The experimental data were analyzed and preprocessed to minimize the size of the data set while preserving patterns and variance and to find an optimal configuration of the feature vector. The Boruta Feature Selection Algorithm (BFSA) finds feature vector’s configuration and the Multiscale False Neighbours Analysis (MSFNA) is newly extended and proposed to validate the BFSA’s design for emission prediction to assure minimal uncertainty in mapping between feature vectors and corresponding outputs. The finding is that the standalone BFSA does not reflect various sampling period setups that appeared significantly influencing the false neighborhood in the design of feature vectors for possible emission prediction, and MSFNA resolves that.

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“…Researchers and engineers very often face the challenges of predicting the behaviour of certain systems or processes in order to control them and what it is possible to achieve through mathematical models [1,2] and numerical simulations [3]. Although numerical simulations usually provide a good prediction of the behaviour of a certain system and its properties [4], initially, the best choice of many solution alternatives is unknown [5,6]. As research activities are aimed at finding an alternative with the best properties, engineers and researchers eventually enter the field of engineering optimization based on a mathematical approach, the field of optimal control [6,7].…”

Section: Introductionmentioning

confidence: 99%

Selected Mathematical Optimization Methods for Solving Problems of Engineering Practice

2022

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Engineering optimization is the subject of interest for many scientific research teams on a global scale; it is a part of today’s mathematical modelling and control of processes and systems. The attention in this article is focused on optimization modelling of technological processes of surface treatment. To date, a multitude of articles are devoted to the applications of mathematical optimization methods to control technological processes, but the situation is different for surface treatment processes, especially for anodizing. We perceive their lack more, so this state has stimulated our interest, and the article contributes to filling the gap in scientific research in this area. The article deals with the application of non-linear programming (NLP) methods to optimise the process of anodic oxidation of aluminium using MATLAB toolboxes. The implementation of optimization methods is illustrated by solving a specific problem from engineering practice. The novelty of this article lies in the selection of effective approaches to the statement of optimal process conditions for anodizing. To solve this complex problem, a solving strategy based on the design of experiments approach (for five factors), exploratory data analysis, confirmatory analysis, and optimization modelling is proposed. The original results have been obtained through the experiment (performed by using the DOE approach), statistical analysis, and optimization procedure. The main contribution of this study is the developed mathematical-statistical computational (MSC) model predicting the thickness of the resulting aluminium anodic oxide layer (AOL). Based on the MSC model, the main goal has been achieved—the statement of optimal values of factors acting during the anodizing process to achieve the thickness of the protective layer required by clients, namely, for 5, 7, 10, and 15 [μm].

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Using Special Filter with Membership Function in Biomass Combustion Process Control

Cited by 8 publications

References 10 publications

Effect of Superimposed Vibrations on Droplet Oscillation Modes in Prilling Process

Effect of Superimposed Vibrations on Droplet Oscillation Modes in Prilling Process

Feature Selection and Uncertainty Analysis for Bubbling Fluidized Bed Oxy-Fuel Combustion Data

Selected Mathematical Optimization Methods for Solving Problems of Engineering Practice

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