scholarly journals One-Dimensional Convolutional Auto-Encoder for Predicting Furnace Blowback Events from Multivariate Time Series Process Data—A Case Study

Minerals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1106
Author(s):  
Carl Daniel Theunissen ◽  
Steven Martin Bradshaw ◽  
Lidia Auret ◽  
Tobias Muller Louw
Keyword(s):  
Principal Component ◽  
Data Driven ◽  
Positive Pressure ◽  
Process Data ◽  
One Dimensional ◽  
Event Prediction ◽  
Hazardous Event ◽  
The One ◽  
Fully Connected

Modern industrial mining and mineral processing applications are characterized by large volumes of historical process data. Hazardous events occurring in these processes compromise process safety and therefore overall viability. These events are recorded in historical data and are often preceded by characteristic patterns. Reconstruction-based data-driven models are trained to reconstruct the characteristic patterns of hazardous event-preceding process data with minimal residuals, facilitating effective event prediction based on reconstruction residuals. This investigation evaluated one-dimensional convolutional auto-encoders as reconstruction-based data-driven models for predicting positive pressure events in industrial furnaces. A simple furnace model was used to generate dynamic multivariate process data with simulated positive pressure events to use as a case study. A one-dimensional convolutional auto-encoder was trained as a reconstruction-based model to recognize the data preceding the hazardous events, and its performance was evaluated by comparing it to a fully-connected auto-encoder as well as a principal component analysis reconstruction model. This investigation found that one-dimensional convolutional auto-encoders recognized event-preceding patterns with lower detection delays, higher specificities, and lower missed alarm rates, suggesting that the one-dimensional convolutional auto-encoder layout is superior to the fully connected auto-encoder layout for use as a reconstruction-based event prediction model. This investigation also found that the nonlinear auto-encoder models outperformed the linear principal component model investigated. While the one-dimensional auto-encoder was evaluated comparatively on a simulated furnace case study, the methodology used in this evaluation can be applied to industrial furnaces and other mineral processing applications. Further investigation using industrial data will allow for a view of the convolutional auto-encoder’s absolute performance as a reconstruction-based hazardous event prediction model.

Revising the Kano model for designing an employee compensation system

2019 ◽  
Vol 32 (1) ◽  
pp. 78-91
Author(s):  
Hossein Vaez Shahrestani ◽  
Arash Shahin ◽  
Hadi Teimouri ◽  
Ali Shaemi Barzoki
Keyword(s):  
Design Methodology ◽  
Compensation System ◽  
Kano Model ◽  
Employee Compensation ◽  
Content Type ◽  
One Dimensional ◽  
Compensation Systems ◽  
Proposed Model ◽  
The One

Purpose The purpose of this paper is twofold: first, to revise the Kano model with a focus on one-dimensional attributes; and second, to use the revised model for categorizing and prioritizing various employee compensation strategies. Design/methodology/approach The Kano evaluation table has been revised and the one-dimensional attribute has been further extended to three categories of OO, OM and OA. In the next step, the literature review-based identified strategies have been categorized and prioritized according to the developed Kano model. Consequently, an employee compensation system has been proposed to a process-based manufacturing company as a case study. Findings Findings indicated that out of the 44 employee compensation strategies, typically 6 were must-be, 13 were one-dimensional, 18 were attractive and 7 were indifferent. Also, the results of the revised Kano model indicated that typically out of the 13 one-dimensional strategies, 7 were one-dimensional tending toward must-be (OM); and 6 were one-dimensional tending toward attractive (OA). Research limitations/implications The case study was limited to one company. The validity of the proposed model can be further studied in a larger population. This study provides managers with a more accurate instrument of decision making in selecting more differentiated employee compensation strategies, which, in turn, might lead to more employee satisfaction. Originality/value Theoretically, this study is different from existing studies, since almost none of the previous studies extended the Kano evaluation table for one-dimensional attributes. Practically, this study is another evidence of the application of the Kano model in the field of human resource management and in particular contributes to the design of employee compensation systems.

A New Method and Device for Temperature Measurement Using One-Dimensional Heat Equation

1994 ◽  
Author(s):  
Nicolae A. Damean
Keyword(s):  
Heat Equation ◽  
Temperature Measurement ◽  
Low Temperatures ◽  
Principal Component ◽  
The Other ◽  
New Method ◽  
Gradient Algorithm ◽  
One Dimensional ◽  
The One ◽  
The Mathematical Model

Abstract A new method and device for temperature measurement are presented. The method reduces the measurement of the unknown temperature to the solving of an optimal control problem, using a numerical computer. The device consists of a hardware part including some conventional transducers and a software one. The problem of temperature measurement, according to this method, is mathematically modelled by means of the one-dimensional heat equation, describing the heat transfer through the device. The principal component of the device is a rod. The variation of the temperature which is produced near one end of the rod is determined using some temperature measurements in the other end of the rod, the mathematical model and a type of gradient algorithm. This device works as an attenuator of high temperatures and as an amplifier of low temperatures.

Air Temperature Distribution Inside of a Longitudinal Highway Tunnel: A Case Study

2012 ◽  
Vol 433-440 ◽  
pp. 6384-6389 ◽  
Author(s):  
Xing Han ◽  
Xu Zhang
Keyword(s):  
Steady State ◽  
Temperature Distribution ◽  
Air Temperature ◽  
State Theory ◽  
One Dimensional ◽  
Highway Tunnel ◽  
Temperature Enhancement ◽  
The One ◽  
General Method

With the development of tunneling technology and the increase of transportation, the mobiles are discharging more and more heat into the tunnel nowadays, which will cause the temperature enhancement. In this paper, general method of calculating the heat discharge is studied, and temperature distribution in the tunnels, which use different ventilation systems, is studied according to the one-dimensional steady state theory. One tunnel is taken for example to calculate the temperature distribution. The result can b e used in the relevant design and research.

scholarly journals A computational method to solve for the heat conduction temperature field based on data-driven approach

2021 ◽  
pp. 165-165
Author(s):  
Kun Li ◽  
Shiquan Shan ◽  
Qi Zhang ◽  
Xichuan Cai ◽  
Zhou Zhijun
Keyword(s):  
Temperature Field ◽  
Heat Conduction ◽  
Steady State ◽  
Transient State ◽  
Absolute Error ◽  
Computational Method ◽  
Data Driven ◽  
One Dimensional ◽  
Data Driven Approach ◽  
The One

In this paper, a computational method for solving for the one-dimensional heat conduction temperature field is proposed based on a data-driven approach. The traditional numerical solution requires algebraic processing of the heat conduction differential equations, and necessitates the use of a complex mathematical derivation process to solve for the temperature field. In this paper, a temperature field solution model called HTM (Hidden Temperature Method) is proposed. This model uses an artificial neural network to establish the correspondence relationship of the node temperature values during the iterative process, so as to obtain the "Data to Data" solution. In this work, one example of one-dimensional steady state and three examples of one-dimensional transient state are selected, and the calculated values are compared to those obtained by traditional numerical methods. The mean-absolute error(MAE)of the steady state is only 0.2508, and among the three transient cases, the maximum mean-square error(MSE) is only 2.6875, indicating that the model is highly accurate in both steady-state and transient conditions. This shows that the HTM simulation can be applied to the solution of the heat conduction temperature field. This study provides a basis for the further optimization of the HTM algorithm.

Physical modelling of industrial multistage centrifugal compressors for monitoring and simulation

2015 ◽  
Vol 229 (18) ◽  
pp. 3433-3448 ◽  
Author(s):  
Matteo Cicciotti ◽  
Dionysios P Xenos ◽  
Ala EF Bouaswaig ◽  
Nina F Thornhill ◽  
Ricardo F Martinez-Botas
Keyword(s):  
Physical Modelling ◽  
Industrial Applications ◽  
Data Driven ◽  
Variable Speed ◽  
One Dimensional ◽  
Simulation And Optimization ◽  
The Common ◽  
Using Data ◽  
Multistage Centrifugal Compressor

Currently, industrial applications to monitoring, simulation and optimization of compressors employ empirical models that are either data-driven or based on the manufacturer performance maps. This paper proposes the use of one-dimensional aerodynamic models for industrial applications such as simulation and monitoring. The physical model establishes causality relationships among input and output variables that are tuned to match the real compressor by using operation data. The application of the method is shown using data from an industrial multistage centrifugal compressor with interstage coolers and variable inlet guide vanes. This is a more complex but more relevant case study for process industry, as opposed to the single-stage variable speed compressors, which is the common example in the literature.

scholarly journals Topological measurement of deep neural networks using persistent homology

2021 ◽  
Author(s):  
Satoru Watanabe ◽  
Hayato Yamana
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Persistent Homology ◽  
Topological Data Analysis ◽  
Data Sets ◽  
One Dimensional ◽  
Novel Approach ◽  
The One ◽  
Fully Connected ◽  
Fully Connected Networks

AbstractThe inner representation of deep neural networks (DNNs) is indecipherable, which makes it difficult to tune DNN models, control their training process, and interpret their outputs. In this paper, we propose a novel approach to investigate the inner representation of DNNs through topological data analysis (TDA). Persistent homology (PH), one of the outstanding methods in TDA, was employed for investigating the complexities of trained DNNs. We constructed clique complexes on trained DNNs and calculated the one-dimensional PH of DNNs. The PH reveals the combinational effects of multiple neurons in DNNs at different resolutions, which is difficult to be captured without using PH. Evaluations were conducted using fully connected networks (FCNs) and networks combining FCNs and convolutional neural networks (CNNs) trained on the MNIST and CIFAR-10 data sets. Evaluation results demonstrate that the PH of DNNs reflects both the excess of neurons and problem difficulty, making PH one of the prominent methods for investigating the inner representation of DNNs.

scholarly journals Analysis of One-Dimensional Consolidation for Unsaturated Soils under Piecewise Cyclic Loading

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yuanchun Huang ◽  
Sidong Shen ◽  
Lei Wang ◽  
Tianyi Li ◽  
Xianlei Fu
Keyword(s):  
Cyclic Loading ◽  
Unsaturated Soils ◽  
Laplace Domain ◽  
One Dimensional ◽  
Ground Treatment ◽  
Parametric Studies ◽  
The One ◽  
Semianalytical Method ◽  
Excess Pore Pressures

This paper studies the one-dimensional (1D) consolidation behavior for unsaturated stratum subjected to piecewise cyclic loading. Combined with the widely accepted consolidation theory of unsaturated soils, a semianalytical method was employed to investigate the consolidation of unsaturated foundation considering piecewise cyclic loading in the Laplace domain. Furthermore, the reduced solutions were produced to perform the verification work accompanied by the results in the existing literature. Finally, a case study was conducted to explore the consolidation characteristics under piecewise cyclic loading (i.e., triangular and trapezoidal cyclic loadings). Parametric studies were carried out by variations of excess pore pressures and settlement against the ratio of air-water permeability coefficients, depth, and loading parameters. The research proposed in this paper can provide theoretical basis for the ground treatment of unsaturated soils, especially for rationally accelerating consolidation or avoiding sudden settlement.

scholarly journals KNOWLEDGE BASED EMPLOYMENT PROCESS – DATA DRIVEN RECRUITMENT

2020 ◽  
Author(s):  
Jelena Vemić Đurković ◽  
◽  
Ivica Nikolić ◽  
Slavica Siljanoska ◽  
◽  
...  
Keyword(s):  
Data Driven ◽  
Quality Data ◽  
Process Data ◽  
High Quality Data ◽  
Knowledge Based ◽  
Special Part ◽  
Using Data ◽  
Recruitment System ◽  
The One ◽  
Further Development

The purpose of this paper is to highlight the main benefits and challenges of using a data-driven recruiting system in enterprises. The trend of increasing digital presence in all fields requires new knowledge and skills of employees. Sustainable development of enterprise is increasingly based on human capital and investment in it. Precisely in these conditions of business, on the one hand, there is increasing pressure to attract and hire the highest quality employees more efficiently, which implies large investments in the recruitment processes and on the other hand to justify those investments. The high-quality data-driven recruitment system provides a way to measure the contribution of recruiting process to the success, to adequately manage existing recruitment programs, and to justify investments in their further development. A special part of this paper will be consecrated to the trends and challenges of using data-driven recruitment in the context of the global crisis of the coronavirus COVID - 19 pandemic.

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