Industrial Example Research Articles

Multiple Regression: Methodology and Applications

Multiple regression is one of the most significant forms of regression and has a wide range of applications. The study of the implementation of multiple regression analysis in different settings contributes to the development of relevant theories and the improvement of models. In this paper, four different kinds of regressions are discussed individually by referring to different articles. The four kinds of regressions discussed are multivariable/multiple linear regression, multivariate multiple linear regression, multinomial logistic regression, and multivariate non-linear regression. As for multivariable/multiple linear regression, examples in the manufacturing industry and medical field show that it can be applied in more fields. Multivariate multiple linear regression is more accurate than multivariable/multiple linear regression and can be used with more than a variable. Multinomial logistic regression is relatively mature and accurate, and can help people well solve the problem with non-linearity and multiple independent variables. It does not require the variables to obey a multivariate normal distribution, and is more widely used as well. Multivariate non-linear regression, however, cannot be used properly without powerful professional knowledge. This paper investigates the theoretical development and model applications of multiple regression to demonstrate the flexibility and broadness of the adoption of multiple regression analysis.

A novel approach for surface defect detection of lithium battery based on improved K-nearest neighbor and Euclidean clustering segmentation

Surface defects of lithium batteries seriously affect the product quality and may lead to safety risks. In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-nearest neighbor (KNN) and Euclidean clustering segmentation. Firstly, an improved voxel density strategy for KNN is proposed to speed up the effect for point filtering. Then, the improved clustering segmentation strategy is applied to distinguish point clouds with defect features. The outline fitting algorithm based on the least square method is applied to determine geometric features of each surface defect which are used to classify defect types. Furthermore, experimental results show that the proposed surface defect detection method reaches 99.2% accuracy and 35.3-ms average time consumption for data processing. Finally, an industrial application example of lithium battery production is demonstrated, which meets the requirements of industrial application. All these reports exhibited that the industrial visual inspection system with rapid measurement is an effective method and guarantees for accelerating industrial production and manufacturing in the future.

Measures to support small and medium-sized businesses during the crisis on the example of light industry

This article discusses the relevance of diversification in the current economic conditions, as one of the possible ways to develop small and medium-sized businesses and increase its strategic sustainability, efficiency and flexibility in today's difficult economic conditions. The authors identify a series of problems that exist today in the light industry and present possible solutions to these problems, which is reflected in the need for state support, primarily administrative, for small and medium-sized businesses in order to effectively import substitution. The main focus of the authors is on the need for close cooperation between small and medium-sized businesses and the state, since it is small and medium-sized businesses that can reduce the impact of sanctions on the Russian economy and become growth drivers. And there is a huge need for this cooperation right now, when quite a lot of foreign manufacturers have stopped production and supplies in Russia, thereby providing enough opportunities for Russian enterprises to occupy the shelf space of Russian retailers. In addition, the authors also note that small and medium-sized businesses are a good basis for using innovative technologies in the production of various products, due to their flexibility and mobility, but unfortunately, for the most part, they do not have sufficient financial opportunities for innovation, and this another way of close cooperation between small and medium-sized businesses, but with large businesses, the moderator of which should be the state.

Correction factors for the drag and pressure flows of power-law fluids through rectangular ducts.

There are many industrial examples of low Reynolds number non-Newtonian flows through rectangular ducts in polymer processing. They occur in all types of manufacturing processes in which raw polymeric materials are converted into products, ranging from screw extrusion to shaping operations in dies and molds. In addition, they are found in numerous rheological measurement systems. The literature provides various mathematical formulations for non-Newtonian flows through rectangular ducts, but-if not simplified further-their solution usually requires use of numerical techniques. Removing the need for these time-consuming techniques, we present novel analytical correction factors for the drag and pressure flows of power-law fluids in rectangular flow channels. We approximated numerical results for a fully developed flow under isothermal conditions using symbolic regression based on genetic programming. The correction factors can be applied to the analytical theory that describes the flow of power-law fluids between parallel plates to include effects of the side walls in the prediction of flow rate and viscous dissipation.

An exact algorithm for calculating the minimum and feasible ranges of cycle time in periodic scheduling with shared resources

Cyclic execution of processing activities is a mode of operation frequently employed by batch manufacturers. Periodic scheduling of these cyclic activities is characterized by the cycle time, a variable that represents the time interval between the start of consecutive cycles. The cycle time determines the number of production batches that can be executed in a given time horizon and, consequently, defines the plant throughput. In this paper, an exact, exhaustive algorithm is developed for the calculation of the minimum cycle time and all feasible cycle time ranges in the context of periodic scheduling with shared resources among tasks. The algorithm is first developed for the case where multiple production tasks share a single resource and then extended for the multiple shared resources case. The implementation and efficiency of these algorithms is demonstrated with the use of illustrative and real-world industrial examples.

Dual sourcing models with stock-out dependent substitution

Companies use different criteria such as lead time, cost and quality to evaluate suppliers; often using multiple suppliers with the aim of reducing stockout risk. But in many industries there may be significant differences between the quality levels of different suppliers. Thus quality-sensitive companies may prefer an item from a primary supplier, but be forced to accept substitute products of lesser quality in case of a stock-out. Motivated by an example in the aviation industry, we introduce a Dual Sourcing problem With Stock-out dependent substitution (DSWS) which includes quality differences. Due to nonconvexity of the multi-period model, analytical characterization of the optimal policy appears intractable. To overcome this problem, we prove a relation between the optimal cost of DSWS and costs of three other problems - dual sourcing without substitution and single sourcing problems with and without backlogging. This leads us to propose the use of the dual index policy (and a variant) as heuristics for DSWS, and to develop an algorithm for parameter optimization of our heuristics. Extensive numerical experiments show that the dual index policy outperforms all other candidate solutions from the literature by at least 8%. Our experiments show that the utilization of the back-up supplier leads to substantial cost savings and service rate increase, especially in case of high differences between holding cost rates of different quality items.

Optimizing the Performance of Construction Using Demolished Waste Concrete

Abstract: In refurbishment projects a great deal of the water from existing structure is generated at the demolition stage. The study applies the waste concrete from construction and demolition waste in concrete and provide a case example for the construction industry to implement the recycle, reuse, and reduce principal to after the management policy of waste concrete. Experimental investigation was carried out of to study various engineering properties such as workability, compressive strength, qualitative and quantitative study. It highlights various combinations of recycled aggregate to match with normal grade concrete. Research project investigation the influence of different percentage of recycled coarse aggregate obtained from demolished building 30 years old on the properties of construction and demolished waste concrete. Experimental result are holds good and shows better strength as compared to normal concrete cured 28 days. This study conducts the performance analysis and optimize the overall calculation for mix design. The result indicated that larger fractal dimension of aggregate would lead to the worse appeal performance of concrete.

HUDUDLARDA TURIZM SOHASINI RIVOJLANTIRISHDA STRATEGIK MAQSADLARNI BELGILASHNING TASHKILIY USULLARI

This article describes the main strategic principles of the development of the tourism industry. This article provides, the directions of strategic development and the author's three main principles for the comprehensive development of the tourism industry in the region and the problems facing the sector are highlighted on the example of the tourism industry of Samarkand region.

Climate Services for Agriculture: Tools for Informing Decisions Relating to Climate Change and Climate Variability in the Wine Industry

Background and Aims. Australia’s changing climate is already impacting the agriculture sector and will continue to do so in the future. To help respond to these impacts, the Climate Services for Agriculture (CSA) platform presents readily accessible climate data, including future climate projections, relevant to specific agricultural commodities. This wine industry example aims to demonstrate the functionality and utility of the CSA for national use across a broad range of commodities. Methods and Results. The platform includes commodity-relevant climate indices designed in consultation with experts to ensure that they are as salient to producers as possible; the wine-grape specific indices include measures of growing season temperature, rainfall, extreme heat, and frost. Here, we describe the research behind the wine-grape specific indices and present sample outputs from the CSA platform for a site within a selected winegrowing region. We note the CSA platform has been developed through an extensive and continuing user engagement initiative, ensuring it meets the needs of the agriculture community as they grapple with how to make decisions based on longer term climate projections. Conclusions. Provision of past, seasonal outlook, and future climate information for Australia and for a range of important agricultural commodities can help improve on-farm planning and decision-making to respond to climate risks. The wine industry provides a leading example of how to use these data for decision-making, noting ongoing adjustments will be needed. Significance of the Study. The CSA platform brings together historical climate data, seasonal climate outlooks, and future climate projections to assist agricultural producers to better manage climate variability and climate change. It aims to nationalise this information for all major agricultural commodities in Australia. We use wine production as a demonstration case here.

Fermatean hesitant fuzzy rough aggregation operators and their applications in multiple criteria group decision-making

The precise selection of suppliers to fulfill production requirements is a fundamental component of all manufacturing and process industries. Due to the increasing consumption levels, green supplier selection (GSS) has been one of the most important issues for environmental preservation and sustainable growth. The present work aims to develop a technique based on Fermatean hesitant fuzzy rough set (FHFRS), a robust fusion of Fermatean fuzzy set, hesitant fuzzy set, and rough set for GSS in the process industry. On the basis of the operational rules of FHFRS, a list of innovative Fermatean hesitant fuzzy rough weighted averaging operators has been established. Further, several intriguing features of the proposed operators are highlighted. To cope with the ambiguity and incompleteness of real-world decision-making (DM) challenges, a DM algorithm has been developed. To illustrate the applicability of the methodology, a numerical example for the chemical processing industry is presented to determine the optimum supplier. The empirical findings suggest that the model has a significant application of scalability for GSS in the process industry. Finally, the improved FHFR-VIKOR and TOPSIS approaches are employed to validate the proposed technique. The results demonstrate that the suggested DM approach is practicable, accessible, and beneficial for addressing uncertainty in DM problems.

Smart optimization with PPCR modeling in the presence of missing data, time delay and model-plant mismatch

In this paper, a data-driven smart optimization algorithm is presented to identify the optimal operating point of the process and the path from the current operating condition to the optimal operating condition. To model the plant dynamics, a locally weighted semi-supervised probabilistic principal component regression (PPCR) model that is robust to missing data and can handle the time-invariant delay is developed in this paper. Being a locally weighted linear model, a nonlinearity index is utilized to control the accuracy of approximation of the local model. In addition, an updating strategy on the delay range is proposed to reduce the computational effort of real-time modeling. To account for the model–plant mismatch, a penalty term in the form of a robust Gaussian process regression is incorporated into the optimization problem. Finally, to ensure that the algorithm reaches different regions of search space and avoid convergence to local optimum, an exploration step with the aid of acquisition functions is utilized. The performance of the developed approach is illustrated through an experimental case study on a hybrid tank system, and the practical applicability of the algorithm is demonstrated through an industrial example.

Detrending and Denoising of Industrial Oscillation Data

Industrial oscillation recordings are often corrupted by underlying nonstationary trend and noisy artifacts, which can occlude features of interest and complicate subsequent oscillation detection and diagnosis. However, there are considerably fewer techniques available in the literature for systematically removing both trend and noise terms from the oscillation measurements. To cater for the general detrending and denoising needs of oscillatory signals, this paper proposes an integrated framework featuring the following steps: (i) The ensemble empirical mode decomposition is first adopted to decompose the industrial single-loop data into several Intrinsic Mode Functions (IMFs). (ii) To eliminate the trend term, a surrogate-based nonstationarity testing algorithm is implemented to automatically identify and remove the requisite IMFs. (iii) By applying canonical correlation analysis on the remained IMFs, the noise-dependent components can be further isolated, which finally yields the detrended and denoised oscillation data. We conducted performance comparison study through extensive simulations and industrial examples. The results demonstrate that the proposed work is a promising tool for industrial oscillation data preprocessing.

Technology Focus: Natural Gas Processing and Handling (April 2023)

_ Regardless of which side of the aisle you are on, the critical role that natural gas will play in the energy evolution (or energy transition, as some would say) is undeniable. Natural gas as a fuel is an important bridge toward this evolution and doubles as a reliable partner for renewable energy sources in providing support for wind, solar, and hydroelectric generation by helping to match supply and demand. Renewed interest in, and high-level discussions regarding, the expansion in necessary natural gas infrastructure, the growth in energy consumption, and the expansion of liquefied natural gas business are all strong indicators for technical improvements in the handling and processing of natural gas in this new phase of the energy economy. The first of the case-study-oriented papers in this section highlights a coupled geomechanical and dynamic flow simulation work flow that forms an effective decision-making tool for the process optimization of underground natural gas storage operations in southwest China. Such a multidisciplinary approach allows for the optimization of several key variables to develop strategies for increasing withdrawal rates, meeting the needs of future peak gas demands, and supporting clean-energy initiatives. Indeed, the larger context of underground gas storage plays a key role in addressing supply and demand dynamics as natural gas consumption grows in the future in response to the energy evolution and cleaner-energy initiatives. Another key focus of the energy-evolution discussion is the reduction of carbon emissions through the framework of the circular carbon economy. A case study is presented on the mitigation of routine gas flaring as part of strategic initiatives of the Saudi oil and gas industry. The lessons learned from this case study can be scaled up to the broader industry. In the third highlighted paper, the focus is on the management of methane emissions across the natural gas value chain and a case study on the deployment of methane detection and quantification technologies of the national operator of the Malaysian oil and gas industry. Methane-emissions reduction directly affects advocacy for natural gas as a low-carbon fuel for energy-evolution stakeholders. In the paper, specific methods are developed to quantify methane from fugitive leaks, loss of primary containment, venting and flaring of hydrocarbons, stationary combustion, compressor seals, pneumatic control devices, and pumps. Other than the specific papers highlighted, it is quite encouraging to see cost-effective carbon-emission reduction initiatives at the local operator scale in the sphere of natural gas handling and processing. One such standout that rightfully serves as an example for the entire industry is the 100% elimination of natural-gas-powered pneumatic devices of the largest natural gas producer in the United States, EQT. This meaningful milestone sharply reduced this operator’s annual carbon footprint and, in the wider sense, provided a practical example of aggressively addressing methane emissions through exceptional teamwork and strategy implementation. Recommended additional reading SPE 210890 Strategies for Reducing and Monetizing Fugitive Methane Emissions From Natural Gas Infrastructure by Stefan Diezinger, Siemens Energy SPE 210446 What’s the Best Way To Stabilize Oil in the Permian? An Examination of Different Facilities Layouts by Isabel Chan, TechnipFMC, et al. OTC 31891 A New Approach to Future Floating Liquefied Natural Gas for Offshore Gas Monetization by Xiaogang Liu, Wison Offshore and Marine, et al.

Framework and Classification of Battery System Architectures

In this paper, battery system architectures are methodologically derived in order to find the key type differences. In a first step, the system levels are identified and distinguished. In order to be able to completely cover the solution space of battery system architectures, a distinction is also made between mono- and multifunctional materials. Based on the system levels, a framework for possible architectures is derived. Four system architecture generations with a total of eight different types are identified and analyzed in the dimensions “Nomenclature”, “Approach”, “Omitted Components” and “Industry Examples”. In this way, upcoming system architectures, such as cell-to-pack and cell-to-chassis, can be clearly differentiated. Finally, fundamental product characteristics for the four system generations are derived and compared.

Cross Market Monetization Strategies Using Google Mobile Ads

In the rapidly evolving digital landscape, effective monetization strategies are crucial for businesses seeking to maximize revenue across diverse markets. This paper explores cross-market monetization strategies utilizing Google Mobile Ads, focusing on how businesses can leverage this platform to optimize their revenue streams. Google Mobile Ads offers a versatile range of tools and solutions designed to target and engage users across different geographical and demographic segments. By employing these tools, businesses can enhance their ad campaigns' effectiveness, ensuring they reach the right audience with relevant content. The study examines various approaches to integrating Google Mobile Ads into broader monetization frameworks, including localized ad targeting, dynamic ad placements, and performance analytics. Emphasis is placed on the strategic use of data-driven insights to tailor ad content and delivery, thereby increasing user engagement and conversion rates. Additionally, the research highlights best practices for balancing global and local market demands, optimizing ad spend, and improving overall campaign ROI. Through a comprehensive analysis of case studies and industry examples, this paper provides actionable recommendations for businesses looking to implement cross-market monetization strategies effectively. The findings underscore the importance of leveraging Google Mobile Ads' features to achieve a competitive edge in diverse markets while driving sustainable growth and profitability.

Legal Regulation of a Market Transport Economics as a Legal Discipline

The article is a review of the textbook «Legal regulation of the market economics (on the example of the transport industry)» for students of transport universities studying in master’s programs, prepared by the team of the Department of Transport Law of the Law Institute of Russian University of Transport under the general editorship of the Honoured Scientist of the Russian Federation, D.Sc. (Law), professor A. I. Zemlin. The author notes not only the relevance of topics in the content of the textbook, but also multidimensionality of their coverage: from theoretical aspects to the practices of their implementation. The depth of the scientific approach, demonstrated by the authors, ensures a complete and comprehensive assimilation of the material, ensuring practical significance and potential relevance of the textbook. The authors managed to determine the optimal amount of sufficiency of didactic material in the textbook for formation of legal competencies among highly qualified specialists in the field of transport, as well as employees of public agencies and corporate governance bodies, necessary for successful performance of their duties in the market economy.

H κατοικία στην επικράτεια της Κορίνθου από την Αρχαϊκή έως και την Ελληνιστική περίοδο. Μια πρώτη κριτική προσέγγιση

H κατοικία στην επικράτεια της Κορίνθου από την Αρχαϊκή έως και την Ελληνιστική περίοδο. Μια πρώτη κριτική προσέγγιση

Nonlinear process monitoring based on generic reconstruction-based auto-associative neural network

A significant concern with statistical fault diagnosis is the large number of false alarms caused by the smearing effect. Although the reconstruction-based approach effectively solves this problem, most of them only focus on linear rather than nonlinear systems. In the present work, a generic reconstruction-based auto-associative neural network (GRBAANN) is proposed that uses the reconstruction-based approach to isolate simple and complex faults for nonlinear systems. Nevertheless, in GRBAANN, it is challenging to acquire a trivial solution for the reconstruction-based index, which is equivalent to a complex vector fixed-point problem. In this regard, the Steffensen method is employed to deal with this problem with an accelerated iterative process, which is appropriate for both single and multiple variable faults. The variable selection procedure is time-consuming but imperative for reconstruction-based approaches, with no exception to the proposed method. In order to ensure the real-time diagnosis for large-scale systems, the Sequential floating forward selection method with memory is proposed to minimize the computation time of the variable selection procedure. The effectiveness of the proposed GRBAANN scheme is illustrated through a validation example and an industrial example. Comparisons with the state-of-art methods are also presented.

Synthesis and characterization of new low molecular weight aluminum organometallic complexes using amino/imine type ligands with possible uses in Atomic Layer Deposition processes

The driver of development and innovation in the microelectronics industry is the miniaturization of the components that make up each technological node. This miniaturization has given rise to new challenges for traditional materials and the need for new fabrication methods with sub-nanometer precision.Based on this, numerous methods have emerged for the fabrication of this type of nanometer materials, among which chemical deposition processes stand out. An industrial example of this type of chemistry is the so-called ALD (Atomic Layer Deposition), which generates deposition of nanometric metal layers, using precursors in a gaseous state.The use of metallic precursors in gaseous state, generates that the process has as a key factor the correct molecular design of the inorganic/organometallic material, since the method imposes strict requirements to the chemical and physical properties of these compounds, such as thermal stability, volatility, synthetic scaling, among others. With this in mind, in this research, new aluminum complexes were synthesized using TMA and imino/amine type ligands with low molecular weight substituents (isopropyl, tertbutyl), to generate stable and volatile liquid complexes in inert atmosphere at room temperature.Finally, four liquid complexes are obtained, stable in nitrogen atmosphere, with yields of 70%. Due to the mentioned characteristics, these complexes could be used as precursors for the deposition of Al and Al2O3 films. In addition, it is demonstrated that the use of TMA in this type of ligands can generate a methylation in the chain of this molecule, which opens a new field in the development of aluminum organometallic complexes.

Variational Bayesian State Space Model for dynamic process fault detection

Industrial processes are subject to various noise disturbances that lead to the stochastic nature of the modeled system and the uncertainty of the model parameters. In this paper, a variational Bayesian State Space Model (VBSSM) model is developed for dynamic process monitoring, allowing the uncertainty of parameters to be described by probabilities. The conjugate prior allows the posterior distribution of the parameters to be estimated iteratively through the Rauch Tung Striebel (RTS) smoothing estimation with the VB framework. The next state can be better predicted even in the presence of noisy perturbations, and imperceptible correlations among multiple variables can be found. By maximizing the lower bound of the objective function to approximate the true posterior distribution of the parameters, the fault detection index is constructed by the corresponding residual. Finally, the effectiveness and superiority of the proposed method is verified by numerical simulations, Tennessee Eastman process and hot rolling industry examples.