Improve Production Efficiency Research Articles

Research on Developing Cyclone Coupling Techniques for Heterogeneous Separators with Design Optimization of Hydrocyclone Separators

Abstract: Hydrocyclone separator is a widely used equipment in the field of liquid-solid separation due to its simple structure, convenient operation, and high separation efficiency. This paper provides a comprehensive review of the working principles, application areas, design optimization, and performance evaluation of hydrocyclone separators, while discussing their future development trends. In addition, particular attention has been given to the aspects related to patents in this study.The focus is on analyzing the structural parameters and flow field characteristics of hydrocyclone separators, as well as the different effects on separation efficiency caused by various hydrocyclone structures and adjustments in hydrocyclone size. The research analysis is conducted on the inlet, cylindrical section, overflow pipe section, cone section, hydrocyclone materials, and other parts, taking into account detailed analysis of operational conditions such as inlet flow rate, pressure drop, separation efficiency, and fluid properties. The future development trend of hydraulic hydrocyclone separators lies in refinement, intelligence, multifunctionality, integration, and energy-saving and environmental protection. In the field of liquid-solid separation, the integration of hydrocyclone separation technology and intelligent devices is becoming increasingly important, and future research will focus on developing new hydrocyclone coupling separation techniques to further improve production efficiency and convenience in daily life. The application prospects are broad, and it is expected to bring significant economic and social benefits to society. It also possesses the potential for patent protection.

Digital twin-enhanced robotic system for remote diesel engine assembly defect inspection

Purpose This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork. Design/methodology/approach This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period. Findings Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers. Originality/value The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

The LTAR Croplands Common Experiment at Upper Mississippi River Basin-Ames.

Agricultural systems evolve from the interactions of climate, crops, soils, management practices (e.g., tillage, cover crops, nutrient management), and economic risks and rewards. Alternatives to the corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] (C-S) cropping systems that dominate in the US Midwest may provide more sustainable use of resources, reduce the documented environmental impacts of current C-S systems, and improve production efficiency and ecosystem services. Innovative management practices are needed to offer producers options to increase farm resilience to variable weather conditions and offset negative environmental impacts. In response to this need, the Upper Mississippi River Basin Long-Term Agroecosystem Research network site at Ames, IA, established a cropland experiment in 2016 to investigate an alternative crop management system that includes reduced tillage, cover crops, and right source, right rate, right time, and right place (4R) nitrogen (N) management. The experimental site is located on the Iowa State University Kelley Research Farm in Boone County, IA. Crop, soil, air, and tile drainage water measurements are made throughout the year using published methods for each agronomic and environmental metric. Our goal is to provide quantitative information to farmers, consultants, agribusiness partners, and state and federal agencies to help guide decisions on the effective use of alternative management practices. Future changes in experimental treatments will adopt a knowledge co-production approach whereby researchers and stakeholders will work collaboratively to identify problems, implement research protocols, and interpret results.

Axial compression behavior of mechanized filament-wound GFRP-steel tubed concrete stub columns

Filament-wound is presently the prevailing and efficient process in the field of molding, known for its ability to achieve optimal molding effects. This technique offers numerous advantages, including high precision, a high fiber content. Traditional FRP-steel tubed concrete stub columns (FSTCSCs) are fabricated by longitudinally wrapping FRP sheet, which results in low production efficiency. This paper introduces a mechanized filament-wound FSTCSC, which can achieve overall molding and effectively improve production efficiency. This paper presents an examination of the axial compression performance of mechanically wound FSTCSC. Two variables were considered including FRP wrapping angle and concrete strength. The experimental results reveal that fiber fracture of mechanized filament-wound FSTCSC is a gradual process, with damage starting from the outermost layer. When GFRP undergoes fracture, a higher wrapping angle results in enhanced restraint stiffness but reduced ductility. Moreover, the validated finite element model is used to analyze the parameters such as the wrapping angle and the thickness of GFRP tube, and it is found that adjusting both upward and downward wrapping angles plays a role in boosting the peak load carrying capacity. Furthermore, five models for predicting ultimate strengths were evaluated using test results, followed by the provision of recommendations for practical design.

Robot intelligent perception and control

Abstract. In recent years, with the rapid development of robotics, intelligent perception and control has become the core research direction in the field of robotics. This technology plays a key role in many fields such as industrial automation, medical assistance, and autonomous driving, significantly improving production efficiency and service quality, and also promoting the intelligent processes of various industries. On the basis of an in-depth analysis of robot intelligent perception and control technology, this paper systematically reviews the development of visual and non-visual perception technology and discusses the application of multi-modal perception in complex environments. The latest development of motion control and force control technology is analyzed, and its importance in fine operation is expounded. The future development direction of robot intelligent perception and control technology is proposed, and the necessity of enhancing real-time robustness and system integration is emphasized, which provides theoretical support and technical reference for promoting the continuous innovation of robot intelligent perception and control technology.

Methodology and Experimental Verification for Predicting the Remaining Useful Life of Milling Cutters Based on Hybrid CNN-LSTM-Attention-PSA

In modern manufacturing, the prediction of the remaining useful life (RUL) of computer numerical control (CNC) milling cutters is crucial for improving production efficiency and product quality. This study proposes a hybrid CNN-LSTM-Attention-PSA model that combines convolutional neural networks (CNN), long short-term memory (LSTM) networks, and attention mechanisms to predict the RUL of CNC milling cutters. The model integrates cutting force, vibration, and current signals for multi-channel feature extraction during cutter wear. The model’s hyperparameters are optimized using a PID-based search algorithm (PSA), and comparative experiments were conducted with different predictive models. The experimental results demonstrate the proposed model’s superior performance compared to CNN, LSTM, and hybrid CNN-LSTM models, achieving an R2 score of 99.42% and reducing MAE, RMSE, and MAPE by significant margins. The results validate that the proposed method has significant reference and practical value for RUL prediction research of CNC milling cutters.

The Impact of Globalization on World Nations: A Study on Protection, Welfare, and Producer Gain

This paper examines the effects of globalization on nations, focusing on economic, social, and cultural dimensions. It analyzes the roles of protectionism and globalization in shaping consumer welfare and producer earnings through qualitative methods and the Customs Union theory framework. The study discusses the benefits and drawbacks of globalization in the context of World Trade Organization (WTO) regulations. The findings indicate that globalization has intensified financial flows between countries, which can exacerbate economic crises. Countries with abundant human resources can capitalize on the international division of labor to specialize in high-value sectors, while those with limited resources risk falling behind in the digital landscape. This division of labor fosters specialization and improves production efficiency through targeted education. However, multinational corporations often impede cost-effective production in developing nations, underscoring the necessity to restructure research and development to facilitate technology adoption in underdeveloped areas. This restructuring can help close the technological gap and encourage equitable participation in the global economy. Jel code Classification: D6: Welfare economics, Fo1 Global outlook, F40: General F4: Macroeconomics aspects of international trade and finance

Enhanced Carbon/Oxygen Ratio Logging Interpretation Methods and Applications in Offshore Oilfields

As the development of most domestic and international oilfields progresses, many fields have entered a mature phase characterized by high water cut and high recovery, with water cut levels often exceeding 90%. Carbon/oxygen ratio logging has proven to be an indispensable tool for distinguishing oil layers from water layers in complex environments, especially where salinity is low, unknown, or highly variable. This logging method has become one of the most effective techniques for determining residual oil saturation in cased wells, providing critical insights into the oil–water interface. In this study, we evaluate two key interpretation models for carbon/oxygen ratio logging: the fan chart method and the ratio chart method. We optimize the interpretation parameters in the ratio chart model using an improved genetic algorithm, which significantly enhances interpretation precision. The optimized parameters enable a more seamless integration of logging results with reservoir and conventional logging data, reducing the influence of lithological variations and physical property differences on the measurements. This research establishes a robust theoretical foundation for enhancing the interpretation accuracy of carbon/oxygen ratio logging, which is crucial for effectively identifying water-flooded layers. These advancements provide vital technical support for monitoring oil–water dynamics, optimizing reservoir management, and improving production efficiency in oilfield development.

Deep Neural Network-Based Cigarette Filter Defect Detection System with FPGA Acceleration for Online Recognition

In the cigarette manufacturing industry, machine vision and artificial intelligence algorithms have been employed to improve production efficiency by detecting product defects. However, achieving both high accuracy and real-time defect detection for cigarettes with complex patterns remains a challenge. To address these issues, this study proposes a model based on RESNET18, combined with a feature enhancement algorithm, to improve detection accuracy. Additionally, a method is designed to deploy the model on a field-programmable gate array (FPGA) with high parallel processing capabilities to achieve high-speed detection. Experimental results demonstrate that the proposed detection model achieves a detection accuracy of 95.88% on a cigarette filter defect dataset with an end-to-end detection speed of only 9.38 ms.

How to enhance the resilience of domestic tourism?

Enhancing domestic tourism resilience (DTR) is crucial to achieving high-quality development of the tourism industry. However, owing to the vulnerability of the tourism industry during the COVID-19 pandemic, the enhancement pathways and associated mechanisms of DTR need to be further investigated. Therefore, this study measures the DTR of China's 31 provinces by creatively combining the simple harmonic motion and regime-switching vector autoregressive models and explores its characteristics to lay the foundation for an empirical analysis. Furthermore, the study empirically analyzes the enhancement pathways and associated mechanisms of DTR using a spatial econometric model. First, this study found that tourism agglomeration, specialization, and diversification determine the characteristics of dynamic response and spatial distributions of DTR. Second, from the perspective of spatial effects, DTR in one region positively impacts that in adjacent regions, and tourism agglomeration in a specific region positively impacts DTR in adjacent regions. Third, in terms of mechanisms, tourism agglomeration enhances DTR by optimizing industrial structure and improving production efficiency, whereas tourism specialization and diversification enhance DTR by improving production efficiency and optimizing industrial structure, respectively. These findings provide information for the enhancement of DTR and achievement of high-quality development of the tourism industry.

The Role of the Ministry of Industry as the Main Stakeholder in the Development of IKM Centers in Indonesia

Small and medium industries (SMEs) are vital sectors in Indonesia's economy, with significant contributions to Gross Domestic Product (GDP) and job creation. The IKM Center, which groups small and medium-sized business units in one location, is expected to improve production efficiency and industrial competitiveness. However, many centers are experiencing problems in management and revitalization. This research aims to explore the role of the Ministry of Industry as the main stakeholder in the development of IKM Centers in Indonesia. Using a descriptive qualitative approach, this study examines the role of the Ministry of Industry as a regulator, facilitator, catalyst, investor, and strategic planner in developing SME Centers. The results of the study show that although the IKM Center has great potential, there are still challenges in policy implementation and management that require revitalization. The government is expected to continue to strengthen its role to support the sustainability and growth of the IKM Center in the long term

Effects of poultry by-product meal and complete replacement of fish oil with alternative oils on growth performance and gut health of rainbow trout (Oncorhynchus mykiss): a FEEDNETICS™ validation study

BackgroundAquaculture, traditionally a form of biotechnology, has evolved to integrate innovative biotechnological applications, such as advanced feed formulations, aimed at improving the growth performance and health of farmed fish species. In the present study, the effects of feeding rainbow trout with novel feed formulations were investigated. Fish growth, gut and liver morphology, the concentration of fatty acids in the fillet, and volatile fatty acids in the gut were assessed. The study also validated scenarios from in vivo experiments using a nutrient-based model called FEEDNETICS™. This globally used model serves as a tool for data interpretation and decision support in the context of precision fish farming.MethodsAlternative protein and oil sources, including poultry by-product meal (PBM) and natural algae oil, were explored as sustainable replacements for fishmeal (FM) and fish oil (FO). A 90-day feeding trial was conducted using rainbow trout, comparing two isoproteic, isolipidic and isoenergetic diets. The control diet contained 15% FM, 5% PBM, and 8% FO, while the test diet replaced FM with 15% PBM and 5% feather meal hydrolysate (FMH), and fully substituted FO with VeraMaris® natural algae oil and rapeseed oil.ResultsPBM successfully replaced FM protein without negatively affecting feed intake, growth performance or feed utilization in trout. The combination of PBM and natural algae oil was well tolerated by the trout and showed no negative effects on gut health. A detailed analysis of fatty acids in the fillet revealed that PUFAs of the n3 and n6 series were significantly higher in the PBM group than in the FM group. Values of fatty acid-related health indexes, including atherogenicity index, and thrombogenicity index, confirmed the high nutritional value of trout filet, thus representing a healthy product for human. In addition, the predictions using the FEEDNETICS™ indicated that the tested novel alternative formulations are economically viable. The validation of the model for fish growth resulted in a Mean Absolute Percentage Error (MAPE) of 8%.ConclusionsThe FEEDNETICS™ application enhances our ability to optimize feeding strategies and improve production efficiency in the aquaculture industry. VeraMaris® algae oil and PBM could serve as viable and sustainable raw materials for fish feed, promoting environmentally friendly aquaculture practices.

Improvement of Power Production Efficiency Following the Application of the GD InC Maximum Power Point Tracking Method in Photovoltaic Systems

This paper proposes a new maximum power point tracking (MPPT) method based on machine learning with improved power production efficiency for application to photovoltaic (PV) systems. Power loss occurs in the incremental conductance (InC) method, depending on the size of the voltage step used to track the maximum power point. Additionally, the size of the voltage step must be specified by the initial user; however, an appropriate size cannot be determined in a rapidly changing environment. To solve this problem, this study presents a gradient descent InC (GD InC) method that optimizes the size of the voltage step by applying an optimization method based on machine learning. The effectiveness of the GD InC method was verified and the optimized size of the voltage step was confirmed to produce the largest amount of power. When the size of the voltage step was optimized, a maximum difference of 4.53% was observed compared with the case when the smallest amount of power was produced. The effectiveness of the GD InC method, which improved the efficiency of power production by optimizing the size of the voltage step, was verified. Power can be produced efficiently by applying the GD InC method to PV systems.

Maltose gradient-induced biosensor-based high-throughput screening for directed evolution of maltogenic amylase from Bacillus stearothermophilus

Maltogenic amylase is a starch-hydrolyzing enzyme commonly used in bread baking and high-concentration maltose syrup production. However, low catalytic activity limits its industrial application. Improving catalytic activity based on molecular modification and directed evolution requires a High-Throughput Screening (HTS) method. In this study, a maltose gradient-induced (MaGI) biosensor was designed and applied for the directed evolution of maltogenic amylase AmyM, showing a good positive correlation between enzyme activity and fluorescence. The MaGI biosensor detected maltose and maltogenic amylase activity efficiently and specifically. Two mutants, Q440N and S442N/Q661L, were identified through the screening of 3000 mutants using the MaGI biosensor, showing a significant increase in catalytic activity of 35.56 % and 24.51 %, respectively, compared to the wild-type. Meanwhile, the t1/2 of Q440N and S442N/Q661L at 60 °C increased by 58.53 % and 66.66 %, respectively. In industrial applications, the enhancement of catalytic activity and stability is conducive to improving production efficiency and reducing costs. MD simulation has found that when modifying multidomain enzymes, distal mutations can enhance catalytic activity. In conclusion, the developed MaGI biosensor is a promising tool for high-throughput and specific detection of maltose.

Efficient Fault Warning Model Using Improved Red Deer Algorithm and Attention-Enhanced Bidirectional Long Short-Term Memory Network

The rapid advancement of industrial processes makes ensuring the stability of industrial equipment a critical factor in improving production efficiency and safeguarding operational safety. Fault warning systems, as a key technological means to enhance equipment stability, are increasingly gaining attention across industries. However, as equipment structures and functions become increasingly complex, traditional fault warning methods face challenges such as limited prediction accuracy and difficulties in meeting real-time requirements. To address these challenges, this paper proposes an innovative hybrid fault warning method. The proposed approach integrates a multi-strategy improved red deer optimization algorithm (MIRDA), attention mechanism, and bidirectional long short-term memory network (BiLSTM). Firstly, the red deer optimization algorithm (RDA) is enhanced through improvements in population initialization strategy, adaptive optimal guidance strategy, chaos regulation factor, and double-sided mirror reflection theory, thereby enhancing its optimization performance. Subsequently, the MIRDA is employed to optimize the hyperparameters of the BiLSTM model incorporating an attention mechanism. A predictive model is then constructed based on the optimized Attention-BiLSTM, which, combined with a sliding window approach, provides robust support for fault threshold identification. The proposed algorithm’s efficacy is demonstrated through its application to real-world gas-fired power plant equipment fault cases. Comparative analyses with other advanced algorithms reveal its superior robustness and accuracy in efficiently issuing fault warnings. This research not only provides a more reliable safeguard for the stable operation of industrial equipment but also pioneers a new avenue for the application of metaheuristic algorithms.

AI-Driven Quality Control in PCB Manufacturing: Enhancing Production Efficiency and Precision

This paper investigates the application of Artificial Intelligence (AI) in enhancing quality control within Printed Circuit Board (PCB) manufacturing processes, focusing on how AI-driven technologies improve production efficiency and precision. The research addresses traditional quality control methods, such as manual inspection and Automated Optical Inspection (AOI), highlighting their limitations in keeping up with the complexities and demand for high-precision PCBs in modern electronics. The study delves into how AI technologies, particularly machine learning, computer vision, and predictive analytics, are being leveraged to overcome these limitations. By automating defect detection, improving accuracy, and enabling real-time analysis, AI systems not only streamline the quality control process but also significantly reduce human error and production costs. AI-driven quality control systems are shown to increase defect detection rates, reduce inspection times, and enhance overall production throughput. The paper includes a comparative analysis between traditional and AI-based quality control methods, revealing a notable improvement in both detection accuracy and production speed when AI systems are employed. Additionally, cost efficiency is explored, demonstrating how AI systems reduce waste, minimize rework, and lower operational costs. The potential challenges of AI implementation, such as the high initial costs, data requirements, and integration with legacy systems, are also discussed. The paper concludes that despite these challenges, AI offers a transformative solution to the growing demands of the PCB manufacturing industry, with the ability to scale effectively and adapt to future technological advancements. Ultimately, this research underscores the significant role of AI in revolutionizing PCB quality control by providing a more efficient, precise, and cost-effective approach, paving the way for further innovation in the electronics manufacturing sector.

Field production efficiency investigation of broilers immunized with a turkey herpesvirus vector vaccine expressing hemagglutinin from H9N2 subtype avian influenza virus

Turkey herpesvirus (HVT) vector vaccine expressing hemagglutinin from the H9N2 AIV, namely HVT-H9, were demonstrated to block H9N2 AIV infection and transmission in chickens. In this study, we evaluated the protection efficiency and production performance of broilers in HVT-H9 field trials in the presence or absence of the H9N2 AIV natural infection. HI titers against H9N2 AIV in broilers harboring maternal antibodies were successfully induced by HVT-H9. In the presence of H9N2 AIV natural infection, immunization with HVT-H9 blocked H9N2 AIV infection and reduced the mortality rate. Importantly, HVT-H9 vaccination slightly increased broiler weight and decreased the feed conversion rate in the absence of the H9N2 AIV natural infection but significantly reduced mortality rates and increased production efficiency during the H9N2 AIV natural infection. In summary, HVT-H9 immunization might block H9N2 AIV infection and improve production efficiency in the field, especially in the presence of H9N2 AIV natural infection.

Preparation of novel nanoparticle demulsifier and its demulsification for surfactant-stabilized crude oil emulison

The application of nanoparticle (NP) demulsifiers in processing the emulsified crude oil production fluid is of great significance for improving production efficiency and reducing energy consumption in crude oil extraction. In this work, a novel NP demulsifier (PPA@KAO) was prepared by chemically modifying the mineral kaolinite (KAO) nanoparticles using γ-Methacryloyl propyltrimethoxysilane (MPS) and polyether-polyquaternium (PPA). The morphology and structure of the PPA@KAO demulsifiers were characterized by FT-IR, SEM, TG, XPS and Zeta potential. The contact angle measurements, interfacial tension (IFT) characterization, and the distribution of nanoparticles in the oil-water phase showed that PPA@KAO have good amphiphilicity and interfacial activity. The demulsification performance of PPA@KAO demulsifiers for a sodium dodecyl benzene sulfonate (SDBS) stabilized crude oil emulsion was studied with bottle method. It was found that the oil-water separation could be completed within 20 min, with a demulsification efficiency of 96.75 % at a dosage of 3 g L−1，pH of 6 and temperature of 30°C. Such a result indicates that the PPA@KAO NP demulsifier can achieve rapid and efficient demulsification for the surfactant-stabilized crude oil emulsion at room temperature. The demulsification mechanism of PPA@KAO for the SDBS-stabilized crude oil emulsion was well revealed, which provides an important synthetic strategy of how obtaining an effective NP demulsifiers by regulating the amphiphilicity and surface functional groups of the nanoparticles.

Research and Application of Euler Cutting Path Based Optimisation Algorithm with Greedy-RRT Algorithm

With the development of social economy, mould processing enterprises have an increasingly strong demand for reducing costs and improving economic efficiency. The optimisation of steel plate cutting path is one of the key links to improve production efficiency. In this paper, the optimal cutting path problem of steel plate under different cutting tasks is investigated, and the optimal cutting path under complex geometries is found through various optimisation algorithms. For cutting task N3, the cutting path is divided into outer contour cutting, inner hole cutting and inner hole part cutting. According to the problem, all rectangular parts inside the ellipse need to be cut before the ellipse. Through the Eulerian cutting path optimisation algorithm, we solve the air-travel problem between the inner holes and the outer contour as well as the air-travel problem between the inner holes of the ellipse and the parts inside the ellipse. The total length of the finally determined optimal air-travel path is 97 units. By combining the greedy algorithm and the RRT algorithm, we determine the number and location of the bridges and design the optimal cutting path starting from the lower right corner of the steel plate. The final clearance of the bridges is 4 units, the total length of the combined optimal path clearance is 35.56 units, and the number of bridges is 8, which are located between adjacent rectangles.

Improving production sequencing in the age of Industry 4.0: A mathematical launching model approach

Purpose: To enhance enterprise efficiency, this study examines the pivotal role of operations management in optimizing the use of materials, technology, equipment, and personnel, especially within the transformative framework of Industry 4.0.Design/methodology/approach: This article investigates operational preparation in the context of Industry 4.0. Through questionnaires and interviews with stakeholders, problems related to operational preparation were identified. To address these problems, a launching model was developed for production systems. The model considers several parameters important for the production process, including business goals, customer needs, and environmental conditions that impact enterprise performance and profit.Findings: The model determines the importance of jobs to be performed in a certain order to optimize production sequence. The proposed parameters are included in a mathematical-algorithmic launching model based on categorical levels related to the production process, such as profit, delivery times, processing times, total number of technological operations, product types, materials, required quality, product complexity, and resource use. The model was developed based on observations and investigations conducted in Kosovo's enterprises on operations research, and it has the potential to significantly improve production efficiency and profitability in the Industry 4.0 era.Practical implications: The findings of this study have practical implications for operations management within the Industry 4.0 framework, proposing a launching model designed to optimize production processes and enhance efficiency.Originality/value: The originality and value of this research lie in the development of a mathematical-algorithmic launching model that addresses operational preparation in the Industry 4.0 context, taking into account various crucial parameters.