Industrial Production Data Research Articles

The trilemma between accuracy, timeliness and smoothness in real-time signal extraction

The evaluation of economic data and the monitoring of the economy is often concerned with an assessment of the mid- and long-term dynamics of time series (trend and/or cycle). Frequently, one is interested in the most recent estimate of a target signal, a so-called real-time estimate. Unfortunately, real-time signal extraction is a difficult estimation problem that involves linear combinations of possibly infinitely many multi-step ahead forecasts of a series. Here, we address the performances of real-time designs by proposing a generic direct filter approach. We decompose the ordinary mean squared error into accuracy, timeliness and smoothness error components, and we propose a new tradeoff between these competing terms, the so-called ATS-trilemma. This formalism enables us to derive a general class of optimization criteria that allow the user to address specific research priorities, in terms of the accuracy, timeliness and smoothness properties of the corresponding concurrent filter. We illustrate the new methods through simulations, and present an application to Indian industrial production data.

Broiler weight forecasting using dynamic neural network models with input variable selection

The global demand for poultry meat is predicted to increase by 18% between 2015–17 and 2027, which motivates the need for better tools for production monitoring, planning and optimization. This paper presents the first results on broiler (chicken for meat production) weight forecasting intended for production planning and monitoring using environmental broiler house conditions – such as heating, ventilation, and temperature. We investigate the dynamic impact of environmental conditions on broiler growth, which is known to be highly significant but unexplored in scientific literature. The forecasting is carried out using ensemble dynamic neural network models trained on past production data with mutual information based input variable selection. To investigate the potential of the proposed method, an extensive case study on almost 3.5 years of industrial farm scale production data from a state-of-the-art broiler house is carried out. The dynamic impact of environmental conditions on broiler growth is found to be significant and useful broiler weight forecasts are obtained – effectively providing a foundation for future research on optimization of broiler production.

Constitutive modeling for high temperature compressive deformation of non-oriented electrical steel

Abstract A material mechanical model provides basic data for numerical simulation and technological optimization during the forming process. In this paper, the flow behavior of the hot compressive deformation 0.3 wt.-% Si non-orientation electrical steel were investigated by thermal simulation tests, and a constitutive model was built based on a new modeling method. The thermal simulation tests conducted on a Gleeble 1500 thermo-mechanical simulator over a range of temperatures from 750 to 1050 °C and strain rates from 0.1 to 10 s−1. To predict the hot deformation process of the silicon steel, a constitutive model based on the multiple regression method was developed. Prediction results showed that the proposed model can track deformational behavior accurately. The model was applied to finite element (FE) simulation, and the FE calculation results matched the industry production data satisfactorily.

Performance simulation and optimization of COREX shaft furnace with central gas distribution

To improve the distribution of the reducing gas inside the shaft furnace, recently, a central gas distribution (CGD) device is installed at the bottom of the shaft furnace in Bayi Steel. In this work, a two-dimensional steady-state mathematical model, including momentum, heat and mass transfers and chemical reaction, is developed to study the performance of the furnace with CGD. The model is validated by industrial production data of COREX-3000, and then it is used to study the detailed phenomena in the furnace with CGD. The results show that, compared with the traditional COREX shaft furnace without CGD, the CGD device has a positive effect on improving the solid metallization rate (MR) and gas utilization rate (UR). The solid metallization rate (MR) increases from 0.601 to 0.628, the gas utilization rate (UR) increases from 0.3437 to 0.3519 and the standard deviation (STDEV) of solid MR decreases from 0.0820 to 0.0037, which means that the MR of the DRI from the furnace with CGD is more uniform than that without CGD. In addition, the effects of operation conditions and the CGD design on the solid MR, gas UR and STDEV of MR are discussed and the optimized conditions are suggested.

The application of big data in production and environment

Big data technology is applied to the industry, production process parameters, input cost, energy saving and water saving, environmental impact and other large amounts of data involved in the production of industry are analyzed, it established a comprehensive data analysis platform. This paper designs the micro cloud hierarchical distributed processing framework based on the research, and studies a data-intensive storage scheme based on cloud computing. Research on big data storage scheme of industrial production based on cloud computing. Industrial production data continues to grow rapidly, the pressure of data storage is more and more large, to make full use of storage resources, to meet the actual needs of industrial production data storage and efficient processing of data.

Intrusion detection and security calculation in industrial cloud storage based on an improved dynamic immune algorithm

Cloud computing is a new storage and calculation mode, which has been widely used as a tool to store and analyze users’ data. With the development of industrial intelligence and big data, it is also prevalent in industrial field to accelerate production or to discover more knowledge to make better decisions. There are three main properties in cloud computing, namely dynamic and time-variation, large-scale, and change-of-ownership. Therefore, the data security problems are different and complicated than those in conventional networks. The existing methods, such as encrypted storage, security audit, and access control, cannot solve these security problems proactively and efficiently due to their own drawbacks. Commonly, self-samples always have similar features, while non-self-samples are abnormal in their own way. This paper mainly focuses on these two sources of data, one is intrusion detection of external non-self-samples and another is security calculation of self-samples. In this case, an improved dynamic immune algorithm (IDIA) is proposed, which is composed of an improved negative selection algorithm (iNSA) with the way of shift mutation and an improved dynamic clone selection algorithm (iDCS) with random grouping strategy. The former algorithm is used to generate detectors, and the latter is used to update them dynamically and adaptively. On this basis, an automated industrial production system is designed to guarantee the data security that not been accessed by external or other users. Datasets of KDD CUP99 and real industrial production data are collected to evaluate the performance of IDIA. The experiment results show the proposed algorithm can detect the intrusive data samples and identify autologous ones efficaciously.

Predicting the impacts of escaped farmed Atlantic salmon on wild salmon populations

The escape of Atlantic salmon (Salmo salar) from aquaculture facilities can result in both negative genetic and ecological interactions with wild populations, yet the ability to predict the associated risk to wild populations has remained elusive. Here we assess the potential of a spatiotemporal database of aquaculture facility locations, production estimates, and escape events to predict the distribution of escaped farmed salmon and genetic impacts on wild populations in the Northwest Atlantic. Industry production data, reported escape events, and in-river detections of escaped farmed salmon were collected from across the Northwest Atlantic. Genetic estimates of impact were obtained using single nucleotide polymorphisms (95 loci) representing aquaculture and wild salmon throughout the region (30 populations, 3048 individuals). Both the number of escaped farmed salmon detected at counting facilities and the magnitude of genetic impacts were positively correlated with a cumulative spatial measure of aquaculture production. Our results suggest that the risk of escapees and genetic introgression from wild–farmed salmon interactions can be assessed using information on farm production characteristics. This represents a first step in predicting the impact of existing cage-based farms on wild Atlantic salmon.

Experimental and numerical investigation of silicothermic reduction process with detailed chemical kinetics and thermal radiation

This paper presents an experimental study on the intrinsic chemical dynamics mechanism of the silicothermic reduction process and a single uniform chemical reaction model which is in accord with Arrhenius' equation under the silicothermic reduction conditions was obtained. The equation of the uniform chemical reaction model can be represented by 1-(1-α)1/3=k0exp(-E/RT)τ in the temperature range 1273–1473 K (1000–1200 °C) and the apparent activation energy is a function of reaction time. Then, a three-dimensional unsteady numerical approach incorporating the chemical reaction, radiation and heat conduction models was first developed and verified by industrial production data of the magnesium production enterprises. The most appropriate radiation model was proposed and the simulations of the impact of radiation heat transfer on the magnesium reduction extent and temperature distributions were carried out utilizing this model. The analysis showed that the radiation heat transfer is an important factor in the heat transfer process in the retort. For the intensity of the radiation varies dramatically with temperature, the radiation model must not be ignored or simplified to be a heat conduction mode during the numerical calculation for the purpose of increasing the calculation accuracy. It is also concluded from the numerical results that the heat transfer efficiency is low in the initial stage, so improving the heat conduction rate could increase the magnesium production capacity, especially in the initial phase of the reduction process.

How green is manufacturing? Status and prospects of national green industrialisation. The case of Morocco

This paper reviews the concept of green industry. Our study introduces an innovative methodology to analyse green industrial development at country level by constructing an original database and looking at both green industrial production and employment data derived from UNIDO's industrial statistics database (INDSTAT). We use our methodological approach to review the status and analyse the prospects of green industrialisation in a selected set of countries. We conclude that countries have not seized yet the opportunities offered by green industry. We undertake a closer analysis of the green performance of Morocco, a country that has failed in the past to boost industrial development despite having put in place an ambitious green industrial policy. Our analysis suggests that green industrialisation offers an untapped potential that developing countries might use in the future as an alternative path to operate their structural transformation within the framework of the existing international industrial development setting.

A Scheduling Method Based on NSGA2 for Steelmaking and Continuous Casting Production Process

In this paper a new non-dominated sorting genetic algorithm with elite strategy (NSGA2) based production scheduling method is proposed for complex steelmaking and continuous casting production process which is consisted of multiple refining ways. At first, the multi-objective optimization scheduling model is established according to production process and schedule requirement. And then NSGA2 is used to solve the scheduling model. There are multiple Pareto non-inferior solutions from the solving phase, and decision maker may select one of solutions based on own preference, while it is not the most optimal. To fix this problem, the optimal decision making method is put forward, combining the fuzzy membership degree and the variance weighting. Simulation experiments are carried out with the actual industry production data, which shows that the proposed method is practical for industry production.

Sector-based vote choice: A new approach to explaining core and swing voters in Africa

This study examines economic components of the support base for each party in Ghana’s de facto two-party system. Most accounts of partisan voting in African democracies contend that some voters routinely support the same party because it rewards co-ethnics through patronage in the form of private and local community goods. A few recent studies have found that some voters vote retrospectively and sociotropically, rewarding or punishing the incumbent party on the basis of its overall performance in office. However, neither the ethno-clientelist account nor the performance assessment account addresses the possibility that African parties build their support bases around competing economic policy interests. Using a merged dataset from the Afrobarometer Survey Round 5 and district-level industrial employment and agricultural production data from Ghana, I find that it is economic interests by agricultural sub-sector that are highly predictive of parties’ issue-based platforms. Voters who do not share a common interest with any of the main parties’ key policies are most likely to switch party preferences election to election.

Multiphase equilibrium modeling of oxygen bottom-blown copper smelting process

Abstract A computational thermodynamics model for the oxygen bottom-blown copper smelting process (Shuikoushan, SKS process) was established, based on the SKS smelting characteristics and theory of Gibbs free energy minimization. The calculated results of the model show that, under the given stable production condition, the contents of Cu, Fe and S in matte are 71.08%, 7.15% and 17.51%, and the contents of Fe, SiO2 and Cu in slag are 42.17%, 25.05% and 3.16%. The calculated fractional distributions of minor elements among gas, slag and matte phases are As 82.69%,11.22%, 6.09%, Sb 16.57%, 70.63%, 12.80%, Bi 68.93%, 11.30%, 19.77%, Pb 19.70%, 24.75%, 55.55% and Zn 17.94%, 64.28%, 17.79%, respectively. The calculated results of the multiphase equilibrium model agree well with the actual industrial production data, indicating that the credibility of the model is validated. Therefore, the model could be used to monitor and optimize the industrial operations of SKS process.

Methodology сomparative statistical analysis of Russian industry based on cluster analysis

The article is devoted to researching of the possibilities of applying multidimensional statistical analysis in the study of industrial production on the basis of comparing its growth rates and structure with other developed and developing countries of the world. The purpose of this article is to determine the optimal set of statistical methods and the results of their application to industrial production data, which would give the best access to the analysis of the result. Data includes such indicators as output, output, gross value added, the number of employed and other indicators of the system of national accounts and operational business statistics. The objects of observation are the industry of the countrys of the Customs Union, the United States, Japan and Erope in 2005-2015. As the research tool used as the simplest methods of transformation, graphical and tabular visualization of data, and methods of statistical analysis. In particular, based on a specialized software package (SPSS), the main components method, discriminant analysis, hierarchical methods of cluster analysis, Ward’s method and k-means were applied. The application of the method of principal components to the initial data makes it possible to substantially and effectively reduce the initial space of industrial production data. Thus, for example, in analyzing the structure of industrial production, the reduction was from fifteen industries to three basic, well-interpreted factors: the relatively extractive industries (with a low degree of processing), high-tech industries and consumer goods (medium-technology) sectors. At the same time, as a result of comparison of the results of application of cluster analysis to the initial data and data obtained on the basis of the principal components method, it was established that clustering industrial production data on the basis of new factors significantly improves the results of clustering. As a result of analyzing the parameters of data partitioning into clusters using k-means and hierarchical methods using different distances, it was determined that the best result is obtained when using a combination of these methods, when in the first stage the number of clusters is determined by analyzing the visualization of hierarchical algorithms (dendrogram construction) , On the basis of which the division by the method of k-means is made. At the same time, a significant improvement in the quality of the partition is achieved by eliminating the emissions in the clustered data, and then including them in the analyzed set using discriminant analysis. The application of this approach to the data of the structure of industrial production ensured good results. The resulting clusters are uniform in composition and meaningfully interpreted: the first cluster includes countries with low rates of output of the extractive industry relative to the cumulative output of the economy, with a sufficiently high value of this indicator in other sectors. In general, this group can be designated as a country with a developed industrial production of a high-tech type. The second group of countries with respect to other groups is characterized by a generally low share of industry in the economy, and in particular by lower rates of extractive industries. The third group of countries includes countries with a high resource base, which is characterized by a high share in the output of extractive industries.

Sustainability of insect use for feed and food: Life Cycle Assessment perspective

The emergence of insect production as a source of sustainable protein, aimed to substitute traditional feeds and foods, indicates the need to perform their environmental impact assessment with a holistic approach and on a fair functional basis. The study aims at life cycle assessment of insect production at industrial level and further insect biomass processing for feed and food purposes. In order to identify a relative sustainability state of insect-based products, the results of life cycle assessment are compared to the appropriate benchmarks (protein feeds, whey protein powder, and chicken meat). Current study includes an analysis based on industrial insect production data, with further scenarios modelling based on data from industries, databases and literature. It is indicated that environmental performance of insect based feed could be beneficial, provided that suitable diet is selected. Protein diets are responsible for high environmental impacts and high insect yields, while the utilization of manure is environmentally beneficial, but does not result in high yields. Low value food processing by-products (distilled grains) and high-impacting waste streams use for insect growing are confirmed to be among the best strategies for sustainable feed production. The production of insect-based protein powder and meat substitute, based on food by-products, is 2–5 times more environmentally beneficial than that of traditional products. The need for extended studies of different insect species use in combination with identified beneficial options is highlighted.

Multi-model soft measurement method of the froth layer thickness based on visual features

The flotation froth layer thickness is an important factor for production performance. Influenced by the harsh environment of the production site, the flotation froth layer thickness is difficult to measure accurately. This paper proposes a multi-model soft measurement method of the froth layer thickness based on the visual features. In this method, the froth layer thickness is established by the kernel extreme learning machine (KELM) according to different working conditions. The membership of the current froth image for each of the working conditions is obtained based on the combined similarity coefficient and is used as the combination of weights of multiple sub-models to realize the global soft measurement model. The performance of the soft measurement model is verified by the industry production data. Based on the KELM global model, the root mean square error and the average relative error of predicting the thickness of the froth layer in the experiment are 3.01 and 3.98%, respectively, which show that the model has high prediction accuracy and strong generalization performance. This method provides a new way for froth layer thickness measurement and the basis for controlling flotation process.

Modeling and Simulation of Petroleum Coke Calcination in Pot Calciner Using Two-Fluid Model

The aim of this work was to establish a mathematical model for the analysis of calcining process of petroleum coke in a 24-pot calciner via computational fluid dynamics (CFD) numerical simulation method. The model can be divided into two main parts (1) heterogeneous reacting flow of petroleum coke calcination in the pot was simulated using a two-fluid model approach where the gas and solid phase are treated as a continuous phases; and (2) the standard turbulence equations combined with the finite rate/eddy-dissipation combustion model and discrete ordinates model were solved for the turbulent gas reacting flow in the flue. The model of the calcining process was implemented in ANSYS Fluent 15.0 (commercial CFD software) and validated by industrial production data. After the validation research, the model has been applied to inspect the distribution features of the temperature field in the furnace, the concentration field of residual moisture and volatiles in the petroleum coke, and the vector velocity field of gas and solid phases. This research can provide a theoretical basis for optimizing the structure and improving the automatic control level of a pot calciner.

A contribution to the chronology of turning points in global economic activity (1980–2012)

We propose a novel chronology of global business cycles based on industrial production data for a sample of 85 countries at a monthly frequency from 1980 until 2012 and the Bry and Boschan (1971) algorithm. We assess the methodology with statistical tools of signal detection theory and against existing chronologies of country-specific turning points. Finally, we conclude by introducing and evaluating a logit model of the probability of a global downturn occurring up to 12 months ahead. This model uses the macro and financial indicators tracked by the Database of Global Economic Indicators (DGEI) of the Federal Reserve Bank of Dallas (see Grossman et al., 2014) to offer advanced warning of upcoming turning points of the global cycle.

Series de días hábiles en Colombia: una aplicación al ajuste del crecimiento industrial

Si bien muchos economistas son conscientes del efecto de los días hábiles sobre la medición de la producción, aún es frecuente encontrar que estas cifras se analizan sin el correspondiente ajuste. En general, el número de días hábiles en un mes cambia de un año a otro, lo cual es cierto para todos los meses, no solo para los que contienen la Semana Santa. Este trabajo recopila la serie de días hábile mensuales tomando en cuenta los festivos actuales y anteriores a la Ley Emiliani, y la utiliza para ajustar la serie de producción manufacturera del DANE. Para algunos meses, la diferencia entre la cifra de crecimiento industrial reportada por el DANE y la cifra ajustada por días hábiles puede acercarse a los 12 puntos porcentuales, en cualquier dirección.

Multi-step virtual metrology for semiconductor manufacturing: A multilevel and regularization methods-based approach

In semiconductor manufacturing, wafer quality control strongly relies on product monitoring and physical metrology. However, the involved metrology operations, generally performed by means of scanning electron microscopes, are particularly cost-intensive and time-consuming. For this reason, in common practice a small subset only of a productive lot is measured at the metrology stations and it is devoted to represent the entire lot. Virtual Metrology (VM) methodologies are used to obtain reliable predictions of metrology results at process time, without actually performing physical measurements. This goal is usually achieved by means of statistical models and by linking process data and context information to target measurements. Since semiconductor manufacturing processes involve a high number of sequential operations, it is reasonable to assume that the quality features of a given wafer (such as layer thickness and critical dimensions) depend on the whole processing and not on the last step before measurement only. In this paper, we investigate the possibilities to enhance VM prediction accuracy by exploiting the knowledge collected in the previous process steps. We present two different schemes of multi-step VM, along with dataset preparation indications. Special emphasis is placed on regression techniques capable of handling high-dimensional input spaces. The proposed multi-step approaches are tested on industrial production data.

Trade and Uncertainty

We offer a new explanation as to why international trade is so volatile in response to economic shocks. Our approach combines the idea of uncertainty shocks with international trade. Firms order inputs from home and foreign suppliers. In response to an uncertainty shock firms disproportionately cut orders of foreign inputs due to higher fixed costs. In the aggregate, this leads to a bigger contraction in international trade flows than in domestic activity, a magnification effect. We confront the model with newly compiled US import and industrial production data. Our results help to explain the Great Trade Collapse of 2008–2009.