Optimization Of Production Process Research Articles

Reduction of rejects by combining data from the casting process and automatic X-ray inspection

Automatic inspection of castings with X-rays (radiographic and computed tomography) is widespread for parts that are relevant for safety or have high quality requirements. Examples in the automotive sector are aluminum wheels, chassis parts and new parts within the electric power train. Those parts are automatically inspected, which means that both the image acquisition and the evaluation of the images is done fully automatically. Today, in most industrial implementations, the generated data with a size up to several gigabytes per part is summarized to a simple good or bad decision, according to specification. All other data is dismissed, although this information can be valuable to optimize production processes and thus minimize rejects. This contribution gives an overview about the results of the project Cast Control, which is a collaboration of Fraunhofer Development Center for X-ray Technology EZRT, Fraunhofer Center for Applied Research on Supply Chain Services SCS and industry partner RONAL GROUP. RONAL GROUP is a major aluminum wheel manufacturer, mainly for the OEM market. Within the project we combined serial production data from the low pressure die casting process from a foundry of the RONAL GROUP with the data generated in the automatic X- ray inspection. After collecting a large base of sample data, we were able train a neural network for the prediction of error metrics obtained by X-ray inspection. We apply a combination of layer-wise relevance propagation and dimensionality reduction to find correlations between data of the casting machines (process and sensor) and the characteristics of anomalies detected during X-ray inspection. With this information, it is possible to adjust the casting process in an early stage – even before rejects are produced. This enables the foundry to reduce their rejects rate, which saves costs and energy and results in a better competitiveness.

VR Simulation and Implementation of Robotics: A Tool for Streamlining and Optimization

This article explores the significance of simulation-based analysis in understanding the effectiveness of material handling strategies. By utilizing simulation models, businesses can optimize production processes, streamline flows, and enhance overall logistics efficiency. In today’s competitive market landscape, the significance of product manipulation cannot be overstated. It directly influences consumer perception and plays a pivotal role in gaining a competitive advantage. Simulation-based analysis has emerged as a powerful tool for optimizing production processes and enhancing logistics efficiency. Robotics sorting and loading offer increased accuracy, speed, and efficiency over manual processes. Their implementation boost productivity, cuts costs, and enhances working conditions. In today’s competitive market, effective product handling shapes consumer perception and competitiveness. VR simulation-based analysis optimizes manufacturing, logistics, and robotics, driving efficiency. Through advanced VR simulation models, businesses streamline operations, adapt to market dynamics, and embrace automation, enhancing competitiveness.

Digital histological staining of tissue slide images from optical coherence microscopy

The convergence of staining-free optical imaging and digital staining technologies has become a central focus in digital pathology, presenting significant advantages in streamlining specimen preparation and expediting the rapid acquisition of histopathological information. Despite the inherent merits of optical coherence microscopy (OCM) as a staining-free technique, its widespread application in observing histopathological slides has been constrained. This study introduces a novel approach by combining wide-field OCM with digital staining technology for the imaging of histopathological slides. Through the optimization of the histology slide production process satisfying the ground growth for digital staining as well as pronounced contrast for OCM imaging, successful imaging of various mouse tissues was achieved. Comparative analyses with conventional staining-based bright field images were executed to evaluate the proposed methodology's efficacy. Moreover, the study investigates the generalization of digital staining color appearance to ensure consistent histopathology, considering tissue-specific and thickness-dependent variations.

Comprehensive Glycosylation Characterization of Recombinant Human Erythropoietin by Electron-Activated Dissociation Mass Spectrometry.

Recombinant human erythropoietin (rhEPO) is a glycoprotein that acts as the main hormone involved in regulating red blood cell production to treat anemia caused by chronic kidney disease or chemotherapy, which has three N-glycosylation sites and one O-glycosylation site. It contains a variety of different glycosylation modifications, such as sialyation, O-acetylation on sialic acids, etc., which causes a big challenge for the glycosylation analysis of rhEPO. In this study, a liquid chromatography-mass spectrometry (LC-MS) method combined with electron-activated dissociation (EAD) technology was used in qualitative and quantitative characterization of rhEPO N-glycosylation and O-glycosylation in just one injection. The usage of EAD not only generated abundant MS/MS fragment ions of glycopeptides and improved the MS/MS sequence coverage but also preserved the glycan structures in the MS/MS fragment ions and the integrity of the glycosidic bond between the glycans and peptides. Three N-glycosylation sites (N24, N38, and N83) and one O-glycosylation site (S126) of rhEPO samples were successfully identified. Among them, the glycosylation ratios of N24, N38, and N83 sites were 82.7%, 100%, and 100% respectively, and 15, 10, and 12 different N-glycans could be identified at the glycopeptide level. The total average number of sialic acids, N-hydroxyacetylneuraminoic acid, and O-acetylation on sialic acid were 7.28, 4.21, and 0.66 at the intact protein level, respectively. For O-glycosylation site S126, O-glycosylation ratios analyzed at the intact protein level and the glycopeptide level were 80.2% and 80.3%, respectively, and two O-glycans were identified, including Core1_S1 and Core1_S2. This study also compared the difference of the glycans and their relative contents in batch-to-batch rhEPO samples. The results proved that the workflow using EAD fragmentation in LC-MS method could be effectively applied for characterizing the glycosylation analysis of rhEPO samples and batch-to-batch consistency analysis, which would help to reasonably guide the optimization of rhEPO production process.

Multi-objective optimization of a hydrogen production process for polymer membrane coupled with electrochemical hydrogen pump from hydrogen-doped natural gas by NSGA-II

In this work, a novel process of polymer membrane coupled with electrochemical hydrogen pump (EHP) was proposed to achieve efficient hydrogen production from hydrogen-doped natural gas (HDNG), and was simulated by UniSim Design. A hydrogen separation unit of two-stage polymer membrane was used for achieving hydrogen enrichment from low H2 concentration HDNG. Due to the hydrogen upgrade, EHP was operated at a H2 concentration much higher than HDNG, resulting in low energy consumption. Benefiting from the synergy between hydrogen upgrade and purification, the coupling process brought substantial effect while improving the hydrogen purity and recovery rate and reducing the hydrogen separation cost. The real natural gas components were considered into HDNG to ensure the effectiveness of process simulation. An optimization strategy considering both product yield and production cost was proposed to achieve the trade-off between hydrogen recovery rate and separation cost. However, the impact of process parameters on the objectives is nonlinear and difficult to describe with mathematical expressions. Therefore, an agent model of the proposed process was established based on back propagation neural network (BPNN), and multi-objective optimization of H2 recovery rate and total annual cost (TAC) of hydrogen separation unit was achieved using non-dominated sorting genetic algorithm-II (NSGA-II). Investigations with HDNG under various operating conditions (feed pressure of 8–40 bar and feed H2 concentration of 5–25 mol%) were performed, which proved that 90 % overall H2 recovery rate and 99.99 mol% H2 purity of hydrogen product can be achieved at low cost, with the lowest hydrogen separation cost of 1.00 $/kg H2 achievable at 40 bar feed pressure and 25 mol% feed H2 concentration. In summary, this coupling process can provide engineering solutions for efficient hydrogen production from HDNG and promote the development of green hydrogen.

Study of small tensile specimen thickness effects in copper oriented for IFMIF-DONES irradiation

Small specimen testing technology (SSTT) refers to the examination of tiny samples of materials to evaluate their suitability for use in fusion reactors. This includes assessing their thermal, mechanical, and radiation properties, as well as their ability to withstand extreme conditions. SSTT plays a critical role in the development of nuclear fusion technology, which holds the potential to provide a virtually limitless and clean energy source. SSTT is imperative in cases where not much material is available or must be subjected to special conditions that limit it.One of the main challenges in nuclear fusion is the development of materials that can withstand the harsh environment inside a fusion reactor. These materials must be able to withstand high temperatures, intense radiation, and mechanical stress, and be able to maintain their structural integrity for long periods. Small specimen testing allows characterizing and evaluating these properties in a controlled and efficient manner.Small specimen samples, under conditions similar to those in a fusion reactor, will provide crucial information for the design and development of fusion components when irradiated at IFMIF- DONES. The results of SSTT are used to refine materials, optimize production processes, and develop new materials with improved properties.In this work, numerous tensile tests (SSTT) have been carried out on pure copper, a material of interest for fusion with different thicknesses, analyzing the effect of the variation of this variable in the determination of mechanical properties.

Finding and implementing the numerical solution of an optimal control problem for oscillations in a coupled objects system

Finding and implementing the numerical solution of an optimal control problem for oscillations in a coupled objects system

Novel process optimization based on machine learning: A study on biohydrogen production from waste resources

The biomass poultry litter and sewage sludge co-gasification is a good thermochemical method to produce hydrogen energy meanwhile to mitigate the consumption of fossil fuels. However, the operation optimization in the complex process system is important while computationally difficult because of high nonlinearity and many first-principle constraints. To address the optimization of this biohydrogen production process, a methodology framework for achieving the optimal operations is thus presented. The complete process system is first simulated and decomposed into upstream gasification process and downstream hydrogen purification for reducing computational complexity through the thermodynamic equilibrium-based simulation. Totally 1400 data points by pairing total 15 operating conditions with a composite sustainability index objective are generated through the random sampling and data classification strategies, which are then used for the construction of the artificial neural network (ANN)-based prediction models. ANN models of two subprocesses demonstrate the satisfactory prediction accuracy with R2 value of 0.98 and 0.99, respectively, which are then integrated with mixed-integer linear programming (MILP) for the optimization of upstream process and downstream process step-by-step. The MILP problems based on the ANN models are solved with lower optimization time of 45∼100 s compared to the heuristic algorithm optimization (3–6 h) based on the thermodynamic equilibrium-based simulation. The optimal sustainability index values of two processes are 0.80 and 0.91 which are both improved compared to the existing optimization results (0.79 and 0.84). This study emphasizes the optimization potential of the integration approach of machine learning-based modelling and mathematical programming for developing the optimal waste-to-energy processes.

Towards Sustainable Production: An Adaptive Intelligent Optimization Genetic Algorithm for Solid Wood Panel Manufacturing

Optimizing production processes to conserve resources and reduce waste has become crucial in pursuing sustainable manufacturing practices. The solid wood panel industry, marked by substantial raw materials and energy consumption, stands at the forefront of addressing this challenge. This research delves into production scheduling and equipment utilization inefficiencies, offering innovative solutions for the solid wood panel processing line aimed at achieving environmental sustainability and operational efficiency. The study is articulated through two main segments: (1) an exhaustive analysis and the development of a simulation system for the solid wood panel processing line, delineating all production elements and operational logic, furnished with a user-friendly simulation interface, and (2) a comprehensive evaluation and enhancement of various scheduling algorithms specific to the Flexible Job-Shop Scheduling Problem (FJSP) encountered in solid wood panel workshops. A significant leap forward is made with the introduction of the Adaptive Intelligent Optimization Genetic Algorithm (AIOGA), an evolved version of the standard Genetic Algorithm (GA) engineered for optimal scheduling within the solid wood panel processing line. AIOGA incorporates advanced features such as encoding strategy, population initialization, objective function setting, selection strategy, crossover operation, and mutation operation, demonstrating the methodological depth of the study. We applied AIOGA in a designed FJSP, and AIOGA substantially reduced the maximum completion time to 90 min. It evidenced an improvement of 39.60% over the conventional GA, enhancing the equilibrium of the equipment workload across the system. This research presents a multifaceted strategy to address the scheduling complications inherent in solid wood panel production and highlights the extensive applicability of adaptive intelligent optimization in diverse industrial settings. This study establishes a new paradigm in manufacturing optimization, underlining the valuable integration of sustainability and efficiency in production methodologies.

A STUDY ON QUALITY CONTROL AND WASTE MANAGEMENT PROCESS IN MODERN BREAD COMPANY

The modern bread industry faces challenges in maintaining stringent quality control standards while effectively managing waste. This study aims to explore and propose strategies to enhance quality control and waste management processes in modern bread companies, contributing to their sustainability and competitiveness. Utilizing a mixed-methods approach, data will be collected through surveys, interviews, and case studies from multiple modern bread companies. The research will investigate current quality control practices, waste management systems, and their associated challenges. Additionally, the study will identify industry best practices and technological innovations in quality control and waste management. The findings will inform the development of a comprehensive framework for improving quality control and waste management processes tailored to the specific needs of modern bread companies. This framework will encompass strategies for optimizing production processes, implementing advanced quality control measures, and adopting sustainable waste management practices. Moreover, the study will address the integration of digital technologies and data analytics to enhance monitoring and decision-making in quality control and waste management. The proposed framework aims to enhance product quality, reduce waste generation, and minimize environmental impact, thereby contributing to the overall sustainability and profitability of modern bread companies. The research outcomes will provide valuable insights and actionable recommendations for industry practitioners, policymakers, and stakeholders involved in the bread manufacturing sector. Keywords: Quality Control, Waste Management, Bread Industry, Sustainability, Digital Technologies, Production Optimization, Environmental Impact, Competitiveness.

Advanced Technologies in Food Processing—Development Perspective

Research into innovative techniques in food technology is developing dynamically. This is indicated by the significant increase in the number of scientific studies in this field. The aim of this work was to provide a comprehensive, in-depth analysis of the available scientific evidence on new techniques used in food that not only increase efficiency but also enable the creation of products with desirable sensory and nutritional characteristics. Research on techniques including cold plasma, high-pressure processing, ultrasound, pulsed electric fields, sous vide, and microwave heating aims to provide innovative methods of food processing, in the context of meeting growing consumer expectations and optimizing production processes in the food industry. Compared to traditional food processing methods, innovative techniques can provide more efficient solutions in the processing of products. Research on alternative non-thermal methods in food technology suggests their possible benefits, including enhancing sensory and nutritional quality, minimizing environmental impact, and increasing production efficiency, which are a significant challenge in the modern food industry. Despite the many benefits, it is worthwhile to continue research to further improve modern food technologies.

ОПТИМІЗАЦІЯ КИСНЕВО-КОНВЕРТЕРНОГО ПРОЦЕСУ ЗАСОБАМИ АЛГОРИТМІВ МАШИННОГО НАВЧАННЯ

Traditional methods for optimising the BOF process in steelmaking are based on experience and rules of thumb, but do not always achieve maximum efficiency. As part of the Industry 4.0 concept, the use of machine learning algorithms is becoming a promising way to optimise production processes. They allow you to analyse large amounts of data collected by a variety of different sensors that operate during an industrial process and find complex relationships between parameters to achieve the best results. The created models can work automatically, analysing data in real time and responding to changes in the process. The use of machine learning algorithms can improve the accuracy of results and the quality of steel production by optimising the BOF process with a large number of parameters and their interrelationships.

Modular Intelligent Control System in the Pre-Assembly Stage

This paper presents a novel approach to developing fully automated intelligent control systems for use within production-based organizations, with a specific focus on advancing research into intelligent production systems. This analysis underscores a prevailing deficiency in control operations preceding assembly, where single-purpose control machines are commonly utilized, thus presenting inherent limitations. Conversely, while accurate multipurpose measurement centers exist, they often fail to deliver comprehensive quality control for manufactured parts due to cost and time constraints associated with the measuring process. The primary aim in this study was to develop an intelligent modular control system capable of overseeing the production of diverse components effectively. The modular intelligent control system is designed to meticulously monitor the quality of each module during the pre-assembly phase. By integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms, this system ensures precise control over module production processes. It facilitates the monitoring of multiple parameters and critical quality features, while integrated sensors and diagnostic methods promptly identify discrepancies and inaccuracies, enabling the swift diagnosis of issues within specific modules. The system’s intelligent control algorithms optimize production processes and ensure synchronization among individual modules, thereby ensuring consistent quality and performance. Notably, the implementation of this solution reduces inspection time by an average of 40 to 60% compared to manual inspection methods. Moreover, the system enables the comprehensive archiving of measurement data, eliminating the substantial error rates introduced by human involvement in the inspection process. Furthermore, the system enhances overall project efficiency, predictability, and safety, while allowing for rapid adjustments in order to meet standards and requirements. This innovative approach represents a significant advancement in intelligent control systems for use in production organizations, offering substantial benefits in terms of efficiency, accuracy, and adaptability.

How to control product quality at an industrial enterprise?

The article discusses a coordinate measuring machine as a quality control tool in an industrial enterprise. The history of creation, structure, operating principle of coordinate measuring machines, their classification by design, controlled parameters, and areas of application in various industries are described. The factors influencing the quality of control of measured products are given. The results of measurements on coordinate measuring machines are considered from the point of view of their influence on the optimization of production processes, and arguments are also given in favor of a coordinate measuring machine as one of the profit centers of the enterprise.

Optuna-DFNN: An Optuna framework driven deep fuzzy neural network for predicting sintering performance in big data

Sintering performance index can reflect the quality of sintered ore, and the level of sintered ore performance directly affects the stability of blast furnace production. Accurate prediction of sinter performance metrics is essential to optimise sinter quality, improve production efficiency and reduce energy consumption. Based on the sintering big data, this paper proposes the Optuna-DFNN model by combining the fuzzy neural network algorithm, the deep neural network algorithm and the Optuna framework to address the problems of difficulty in tuning the parameter, lack of self-learning ability, and poor generalisation of the model of the traditional method when faced with the input of multiple parameters. The paper predicts the four prediction indexes of yield rate, return fines ratio, drum index and RDI+3.15 respectively. Among them, the relative error of the prediction of the yield rate is within 1.3 %, the relative error of the prediction of the return fines ratio is within 2.8 %, the relative error of the prediction of the drum index is within 0.32 %, and the relative error of the prediction of the RDI+3.15 is within 1.4 %. The results indicate that the Optuna-DFNN model has better performance in predicting sintering performance metrics. Based on the Optuna-DFNN sintered ore performance prediction model, the quality of sintered ore can be accurately predicted, the sintering process parameters can be adjusted in a timely manner, the sintering process can be optimized, and the utilization coefficient of sintering can be improved. Provide a reference for the optimisation of production processes, with a positive impact on cost reduction, environmental protection and increased sustainability of production.

Biogas Production Optimization in the Anaerobic Codigestion Process: A Critical Review on Process Parameters Modeling and Simulation Tools

Many operational parameters, either discretely or collectively, can influence the biodegradation performance towards enhancing biogas yield and quality. Among the operating parameters, organic loading rate (OLR), inoculum-substrate ratio, and carbon-nitrogen ratio (C/N) are the most critical parameters in the optimization and enhancement of biogas yield. Optimization of the biogas production processes depends on the ability of anaerobic microorganisms to respond to variations in operational parameters such as pH, redox potential, and intermediate products to enhance the biogas yield. This review article focuses on the role of process parameters, kinetic models, artificial intelligence, Aspen Plus (AP), and anaerobic digestion model no. 1 (ADM1) in optimizing biogas yield via an anaerobic codigestion (AcoD) process. The review showed that biomaterials codigestion upgraded biogas yield to the extent of 400%, and organic removal efficiency reached up to 90% compared to a single substrate. In addition, the current work has verified that the kinetic model is the most effective tool for signifying that the hydrolysis phase is the rate-limiting step, whereas AP is the most effective tool in the design and optimization of the AcoD process parameters. The reviewed kinetic and AI models show strong correlation values ranging from 0.931 to 0.9991 and 0.8700 to 0.9998, respectively. The AcoD system involves complex chemical reactions, but AP might have limitations in representing such complex chemical processes with nonideal behavior and complicated reaction mechanisms. The design and optimization of AcoD with reliable input parameters are highly limited or nonexistent. The AcoD process design with AP opens fresh research opportunities, including improved efficiency, finding appropriate retention time, and saving time, as well as finding the optimum biogas yield. This review article gives an insightful understanding of AcoD process parameter optimization and valuable strategies for policy development enhancing sustainability in the biogas sector.

IOT Based Water Quality Detection for Sugar Factory

This paper proposes an IoT-based water quality detection system tailored for sugar factories. Employing sensors and microcontrollers, it continuously monitors crucial parameters like pH, turbidity, and dissolved oxygen levels. Data is transmitted wirelessly to a central server for real-time analysis and alerts. This system enhances operational efficiency by enabling timely interventions to maintain water quality standards, ensuring optimal production processes.

ENERGY TECHNOLOGY OF DAIRY PRODUCTS QUALITY ASSESSMENT USING IMPEDANCEMETRY

The article examines the peculiarities of milk quality research using the impedance method. Impedance is a complex parameter that characterises the interaction of the electric current with milk. The authors have developed a methodology based on the specified method to assess the quality of dairy products and have created a hardware and software complex that makes it possible to analyse the quality of dairy products. The research, conducted using the developed hardware and software complex, revealed several regularities characterising different types of milk. These regularities demonstrate the relationship between the impedance signal and the milk fat index. Analysis of the obtained data allowed us to identify differences in impedance between samples with varying fat content and origin from different manufacturers. The developed experimental setup prototype enabled the following research stages: checking the change in voltage during the passage of an alternating electric current through milk. This work used the AFS Sensor DAQ data acquisition system, designed for sensor control projects and which runs with NI LabVIEW software, for data output. The authors tested the installation while investigating the impedance of dairy products with different fat content and from multiple manufacturers. During the experiment, we measured the impedance and capacitance of the samples, and the obtained data became available on a PC for further analysis. The aim was to study the electrical properties of dairy products and their dependence on the composition and manufacturer. The obtained results represent valuable information for dairy product production and quality control. It is possible to use them to optimise production processes, ensure stable product quality, and improve consumer characteristics of dairy products. The obtained data contribute to a deeper understanding of the relationship between the impedance signal and the composition of dairy products, which can lead to the development of new methods of analysis and evaluation of the quality of food products. In addition, these studies have implications for food science and nutrition. Keywords: dairy products, milk quality, impedancemetry, electrical measurements, hardware and software complex.

Effect of Echinacea purpurea Herb Defatting on the Extraction of Hydroxycinnamic Acids

INTRODUCTION. Currently the development of novel resourceand cost-effective extraction techniques for herbal medicinal products is a priority for pharmaceutical technology. A unique position among these herbal medicinal products belongs to the medicinal products of Echinacea purpurea, which stimulate the innate immune system.AIM. The study aimed to investigate the effect of E. purpurea herb defatting on the extraction of hydroxycinnamic acids (HCAs) from the herbal drug and to evaluate the suitability of this treatment for the production of E. purpurea tinctures.MATERIALS AND METHODS. On the basis of previous experimental findings, heptane was chosen as the defatting solvent. The production of E. purpurea tinctures included an additional step of herbal drug heating. The content of total HCAs was determined using a pharmacopoeial method, while the content of individual HCAs was determined using high-performance liquid chromatography. The residual defatting solvent was quantified by gas chromatography. The experiment used reference standards for HCAs.RESULTS. The study showed that a single one-hour defatting treatment of the herbal drug with heptane at a 1 g to 5 mL ratio increased the yield of HCAs extracted from Е. purpurea herb. The optimal production process for E. purpurea tinctures involved grinding the herbal drug to 500–2000 μm, macerating the powder with 60% ethanol (v/v) at a 1 g to 10 mL ratio for 7 days, mechanically stirring the mixture for 60 min, and settling the crude extract for 3 days. Heating the herbal drug prior to extraction resulted in the highest yield of HCAs.CONCLUSIONS. The newly developed production technology for E. purpurea tinctures with a preliminary defatting step increases the HCA content in the tinctures. This technology can be used for the industrial production of tinctures high in HCAs.

Poincaré’s method of normal forms in the study of factors of Production

As the main factors of production, the paper considers physical capital and labor resources. The study of the behavior of the main factors of production is based on the analysis of the volume of investment in them, the depreciation of physical capital and changes in the volume of labor resources. Most of the studies on this topic do not address issues related to distortions in financing and their possible consequences for the main factors of production. To assess these consequences, formulas were derived that include partial derivatives of investment volumes for each of the main factors. The use of these formulas helps to develop an investment algorithm that contributes to the sustainable growth of the main factors of production. The method of Poincaré normal forms was chosen as the main research method, which allows us to simplify the initial problem and reduce it to an analysis in a linear form. A classification of possible variants of eigenvalues, the resulting linear form was carried out. The behavior of the main factors of production depending on the found eigenvalues is determined. This method allows you to get more understandable results and draw more accurate conclusions. The results obtained were tested in a number of federal districts of Russia and a number of EU countries. Testing showed a qualitative match with the real state of the main factors of production. This suggests that the developed investment algorithm can be applied in practical conditions and contribute to the achievement of stable growth in various regions of Russia. This study is important for understanding and optimizing production processes and economic development. It provides a basis for developing effective investment and decision-making strategies that promote both a balanced and directed development of the main factors of production.