scholarly journals Predicting the Electrical Energy Consumption of Electric Arc Furnaces Using Statistical Modeling

Metals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 959 ◽  
Author(s):  
Leo S. Carlsson ◽  
Peter B. Samuelsson ◽  
Pär G. Jönsson
Keyword(s):  
Statistical Modeling ◽  
Statistical Models ◽  
Linear Models ◽  
Nonlinear Models ◽  
Electrical Energy ◽  
Electric Arc ◽  
Future Research ◽  
Published Data ◽  
Industrial Setting ◽  
Input Variables

Statistical modeling, also known as machine learning, has gained increased attention in part due to the Industry 4.0 development. However, a review of the statistical models within the scope of steel processes has not previously been conducted. This paper reviews available statistical models in the literature predicting the Electrical Energy (EE) consumption of the Electric Arc Furnace (EAF). The aim was to structure published data and to bring clarity to the subject in light of challenges and considerations that are imposed by statistical models. These include data complexity and data treatment, model validation and error reporting, choice of input variables, and model transparency with respect to process metallurgy. A majority of the models are never tested on future heats, which essentially renders the models useless in a practical industrial setting. In addition, nonlinear models outperform linear models but lack transparency with regards to which input variables are influencing the EE consumption prediction. Some input variables that heavily influence the EE consumption are rarely used in the models. The scrap composition and additive materials are two such examples. These observed shortcomings have to be correctly addressed in future research applying statistical modeling on steel processes. Lastly, the paper provides three key recommendations for future research applying statistical modeling on steel processes.

scholarly journals Using Statistical Modeling to Predict the Electrical Energy Consumption of an Electric Arc Furnace Producing Stainless Steel

Metals ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 36 ◽  
Author(s):  
Leo S. Carlsson ◽  
Peter B. Samuelsson ◽  
Pär G. Jönsson
Keyword(s):  
Stainless Steel ◽  
Statistical Modeling ◽  
Model Performance ◽  
Electrical Energy ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace ◽  
Modeling Framework ◽  
Statistical Tools ◽  
Input Variables

The non-linearity of the Electric Arc Furnace (EAF) process and the correlative behavior between the process variables impose challenges that have to be considered if one aims to create a statistical model that is relevant and useful in practice. In this regard, both the statistical modeling framework and the statistical tools used in the modeling pipeline must be selected with the aim of handling these challenges. To achieve this, a non-linear statistical modeling framework known as Artificial Neural Networks (ANN) has been used to predict the Electrical Energy (EE) consumption of an EAF producing stainless steel. The statistical tools Feature Importance (FI), Distance Correlation (dCor) and Kolmogorov–Smirnov (KS) tests are applied to investigate the most influencing input variables as well as reasons behind model performance differences when predicting the EE consumption on future heats. The performance, measured as kWh per heat, of the best model was comparable to the performance of the best model reported in the literature while requiring substantially fewer input variables.

MODELING MEMORY OF ECONOMIC AND FINANCIAL TIME SERIES

2005 ◽  
Vol 50 (01) ◽  
pp. 1-8 ◽  
Author(s):  
PETER M. ROBINSON
Keyword(s):  
Time Series ◽  
Statistical Modeling ◽  
Statistical Models ◽  
Time Series Data ◽  
Financial Time Series ◽  
Nonlinear Models ◽  
Series Data ◽  
Financial Variables ◽  
Financial Time ◽  
Linear And Nonlinear

Much time series data are recorded on economic and financial variables. Statistical modeling of such data is now very well developed, and has applications in forecasting. We review a variety of statistical models from the viewpoint of "memory", or strength of dependence across time, which is a helpful discriminator between different phenomena of interest. Both linear and nonlinear models are discussed.

On nonlinear K-l and K-ε models of turbulence

1987 ◽  
Vol 178 ◽  
pp. 459-475 ◽  
Author(s):  
Charles G. Speziale
Keyword(s):  
Asymptotic Expansion ◽  
Turbulent Flows ◽  
Nonlinear Model ◽  
Linear Models ◽  
Nonlinear Models ◽  
Turbulent Channel Flow ◽  
Secondary Flows ◽  
Separated Flows ◽  
Future Research ◽  
Reynolds Stresses

The commonly used linear K-l and K-ε models of turbulence are shown to be incapable of accurately predicting turbulent flows where the normal Reynolds stresses play an important role. By means of an asymptotic expansion, nonlinear K-l and K-ε models are obtained which, unlike all such previous nonlinear models, satisfy both realizability and the necessary invariance requirements. Calculations are presented which demonstrate that this nonlinear model is able to predict the normal Reynolds stresses in turbulent channel flow much more accurately than the linear model. Furthermore, the nonlinear model is shown to be capable of predicting turbulent secondary flows in non-circular ducts - a phenomenon which the linear models are fundamentally unable to describe. An additional application of this model to the improved prediction of separated flows is discussed briefly along with other possible avenues of future research.

scholarly journals Impact of chart image characteristics on stock price prediction with a convolutional neural network

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253121
Author(s):  
Guangxun Jin ◽  
Ohbyung Kwon
Keyword(s):  
Stock Price ◽  
Linear Models ◽  
Nonlinear Models ◽  
Numerical Data ◽  
Future Research ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Image Characteristics ◽  
The Subject ◽  
Accuracy Of Prediction

Stock price prediction has long been the subject of research because of the importance of accuracy of prediction and the difficulty in forecasting. Traditionally, forecasting has involved linear models such as AR and MR or nonlinear models such as ANNs using standardized numerical data such as corporate financial data and stock price data. Due to the difficulty of securing a sufficient variety of data, researchers have recently begun using convolutional neural networks (CNNs) with stock price graph images only. However, we know little about which characteristics of stock charts affect the accuracy of predictions and to what extent. The purpose of this study is to analyze the effects of stock chart characteristics on stock price prediction via CNNs. To this end, we define the image characteristics of stock charts and identify significant differences in prediction performance for each characteristic. The results reveal that the accuracy of prediction is improved by utilizing solid lines, color, and a single image without axis marks. Based on these findings, we describe the implications of making predictions only with images, which are unstructured data, without using large amounts of standardized data. Finally, we identify issues for future research.

scholarly journals Forecasting Stock Prices With Linear And Nonlinear Settings: A Comparison

2013 ◽  
Vol 17 (4) ◽  
pp. 257
Author(s):  
Dimitri Tsoukalas
Keyword(s):  
Stock Prices ◽  
Linear Models ◽  
Financial Time Series ◽  
Nonlinear Models ◽  
Future Research ◽  
Regression Splines ◽  
Forecasting Models ◽  
Non Linear ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

This paper is devoted to the application and comparison of linear (VAR) and nonlinear Multiple Adaptive Regression Splines (MARS) forecasting models, in estimating, evaluating, and selecting among linear and non-linear forecasting models for economic and financial time series. We argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic. Nonlinear models reduce nonlinearity and Gaussianity in the residuals of the linear models. Linear models, however, demonstrate better forecasts than nonlinear. However, much remains to be done. Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.

Accounting for Observed Cycle Features with a Range of Statistical Models

2016 ◽  
Author(s):  
Don Harding ◽  
Adrian Pagan
Keyword(s):  
United States ◽  
Time Series ◽  
Statistical Model ◽  
Statistical Models ◽  
Linear Models ◽  
Nonlinear Models ◽  
The United States ◽  
Multivariate Methods ◽  
Vector Autoregressions ◽  
Nonlinear Statistical Model

This chapter looks at observed features of the cycle in a variety of time series. It sets out these features for the United States and a number of other countries, and then asks whether these features can be replicated by the use of a particular statistical model—a linear autoregression. For such linear models it is possible to broadly account for the observed features using moments of the series for growth rates, and this strategy is employed in the chapter. It then uses a particular nonlinear statistical model to see if it can match all the features, and further looks at two other nonlinear models first dealt with in Chapter 4. The chapter concludes with an examination of whether the binary indicators summarizing the recurrent states can be used in the context of standard multivariate methods such as vector autoregressions. This turns out not to be straightforward owing to the nature of the binary variables.

scholarly journals Emerging Energy Harvesting Technology for Electro/Photo-Catalytic Water Splitting Application

Catalysts ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 142
Author(s):  
Jianfei Tang ◽  
Tianle Liu ◽  
Sijia Miao ◽  
Yuljae Cho
Keyword(s):  
Energy Harvesting ◽  
Water Splitting ◽  
Social Impact ◽  
Electrical Energy ◽  
Future Research ◽  
Daily Lives ◽  
Energy Applications ◽  
Sustainable Environment ◽  
Energy Technologies ◽  
Splitting System

In recent years, we have experienced extreme climate changes due to the global warming, continuously impacting and changing our daily lives. To build a sustainable environment and society, various energy technologies have been developed and introduced. Among them, energy harvesting, converting ambient environmental energy into electrical energy, has emerged as one of the promising technologies for a variety of energy applications. In particular, a photo (electro) catalytic water splitting system, coupled with emerging energy harvesting technology, has demonstrated high device performance, demonstrating its great social impact for the development of the new water splitting system. In this review article, we introduce and discuss in detail the emerging energy-harvesting technology for photo (electro) catalytic water splitting applications. The article includes fundamentals of photocatalytic and electrocatalytic water splitting and water splitting applications coupled with the emerging energy-harvesting technologies using piezoelectric, piezo-phototronic, pyroelectric, triboelectric, and photovoltaic effects. We comprehensively deal with different mechanisms in water splitting processes with respect to the energy harvesting processes and their effect on the water splitting systems. Lastly, new opportunities in energy harvesting-assisted water splitting are introduced together with future research directions that need to be investigated for further development of new types of water splitting systems.

scholarly journals Assessing the indirect effects of COVID-19 on healthcare delivery, utilization and health outcomes: a scoping review

2021 ◽  
Author(s):  
Charlotte M Roy ◽  
E Brennan Bollman ◽  
Laura M Carson ◽  
Alexander J Northrop ◽  
Elizabeth F Jackson ◽  
...  
Keyword(s):  
Health Outcomes ◽  
Health Effects ◽  
Scoping Review ◽  
Cardiovascular Health ◽  
Healthcare Delivery ◽  
Final Analysis ◽  
High Income ◽  
Future Research ◽  
Published Data ◽  
Local Health

Abstract Background The COVID-19 pandemic and global efforts to contain its spread, such as stay-at-home orders and transportation shutdowns, have created new barriers to accessing healthcare, resulting in changes in service delivery and utilization globally. The purpose of this study is to provide an overview of the literature published thus far on the indirect health effects of COVID-19 and to explore the data sources and methodologies being used to assess indirect health effects. Methods A scoping review of peer-reviewed literature using three search engines was performed. Results One hundred and seventy studies were included in the final analysis. Nearly half (46.5%) of included studies focused on cardiovascular health outcomes. The main methodologies used were observational analytic and surveys. Data were drawn from individual health facilities, multicentre networks, regional registries, and national health information systems. Most studies were conducted in high-income countries with only 35.4% of studies representing low- and middle-income countries (LMICs). Conclusion Healthcare utilization for non-COVID-19 conditions has decreased almost universally, across both high- and lower-income countries. The pandemic’s impact on non-COVID-19 health outcomes, particularly for chronic diseases, may take years to fully manifest and should be a topic of ongoing study. Future research should be tied to system improvement and the promotion of health equity, with researchers identifying potentially actionable findings for national, regional and local health leadership. Public health professionals must also seek to address the disparity in published data from LMICs as compared with high-income countries.

Log-Linear Models with Dependent Spatial Data

1983 ◽  
Vol 15 (6) ◽  
pp. 801-813 ◽  
Author(s):  
B Fingleton
Keyword(s):  
Spatial Data ◽  
Statistical Models ◽  
Linear Models ◽  
Square Lattice ◽  
Categorical Variables ◽  
Systematic Sampling ◽  
Pairwise Correlations ◽  
Independent Observations ◽  
Intersection Points ◽  
Log Linear

Log-linear models are an appropriate means of determining the magnitude and direction of interactions between categorical variables that in common with other statistical models assume independent observations. Spatial data are often dependent rather than independent and thus the analysis of spatial data by log-linear models may erroneously detect interactions between variables that are spurious and are the consequence of pairwise correlations between observations. A procedure is described in this paper to accommodate these effects that requires only very minimal assumptions about the nature of the autocorrelation process given systematic sampling at intersection points on a square lattice.

CRISPR/Cas9 has introduced new gene therapy possibilities for muscular dystrophies

2021 ◽  
Author(s):  
Moataz Dowaidar
Keyword(s):  
Satellite Cells ◽  
Muscular Dystrophies ◽  
Future Research ◽  
Published Data ◽  
End Joining ◽  
Track Record ◽  
Effective Homology ◽  
Vector Targeting ◽  
Shape Of Nanoparticles

Advancements in using CRISPR/Cas9 have introduced a host of new therapy possibilities for muscular dystrophies (MDs). There is a definite feeling of hope in the industry, but other barriers lay ahead, and they will define the future of MD gene editing. The ambiguity surrounding AAV transduction of satellite cells in vivo must be explained so that, if required, effort may be focused on optimizing vector targeting. Although the satellite cell correction needs are evident, it must be determined experimentally if high muscle turnover has a deleterious effect on CRISPR approaches. Another issue with muscular HDR is its low editing efficiency. Even outside the MD, exogenous, effective DNA integration would open up a slew of new possibilities.Either conventional HDR must be upgraded, or alternative techniques must be developed. The fact that both myotubes and latent satellite cells are post-mitotic means the latter are the most effective. Homology-independent targeted integration (HITI), homology-mediated end joining (HMEJ) and prime editing are three novel potentials. Duplication removal is another technique to restore full-length proteins. Duplications are the second most frequent DMD mutation, and a single sgRNA technique was used to restore dystrophin. To date, CRISPR/Cas9-mediated duplication removal has only been evaluated in DMD patient cells and must be tested in vivo. Because of their demonstrated track record in in vivo research and clinical trials, AAVs are expected to be employed in early generations of MD CRISPR therapy. Currently, AAVs may be the biggest choice, but future drugs will almost probably require a different delivery approach. It may take the shape of nanoparticles, which may carry a large range of transiently expressed payloads, while being very variable. If satellite cells can not be repaired, their capacity to escape immune reactions is crucial. To decrease the effects of muscle turnover, re-administration of nanoparticles may be utilized to treat MD throughout one's life. However, effective nanoparticle dosing for CRISPR in vivo editing has yet to be established in the muscle. Because this was not an AAV problem, the focus should be on new compositions of nanoparticles rather than improving the CRISPR/Cas9 system. The lack of published data suggests that nanoparticles' systemic muscle transport remains a considerable challenge. Due to muscle volume in the human body and the need to target muscles within the thoracic cavity, local intramuscular injections are not practical. Future research will focus primarily on developing an effective, muscle-specific nanoparticle that can be administered through circulation. The challenges ahead are tremendous, but with the appropriate focus and resources, answers will emerge, bringing therapeutic genome editing closer to the clinic than ever. While this research focused on DMD, the mentioned principles and methodology may and will undoubtedly be extended to several other MDs.

Sign in / Sign up

Export Citation Format

Share Document