Production Of Systems Research Articles

CREATION, MANAGEMENT, AND USE OF A DATABASE FOR THE MAINTENANCE PROCESS OF PRODUCTION EQUIPMENT IN AN INDUSTRIAL COMPANY

The paper presents the creation of a database of motion norms to support the maintenance management system in an industrial production company, implemented by a team from the Faculty of Mechanical Engineering, VSB - Technical University of Ostrava. This database is intended for filling and optimal use of maintenance modules of ERP systems and for use in the processes of preparation of maintenance and service orders. The article focuses on the methodology of creation, characteristics, use and availability of this database and its application in industrial practice.

How Are Weather and Climate Products and Decision Support Systems Used in Wildland Fire Decision Making in the US Southwest?

Abstract Wildland fire management is a challenging environment in which to utilize scientific information. Despite the robust information system that supports US wildland fire management, recent studies demonstrate that some data/information products and decision support tools available to that community are not being used to their full potential. In the US Southwest the convergence of climate change, past wildland fire policy, and an expanding wildland urban interface has increased pressure on wildland fire managers to use science-based information to improve management outcomes. This tension between the perceived need for more science in fire management and the practical challenges of using science to inform decisions led us to ask two basic questions: 1) How is information about weather and climate used in wildland fire management decision making in the Southwest? 2) How are decision support tools used in Southwest wildland fire management? We found: relatively few sources of weather and climate information are consulted by Southwest wildland fire managers; a small percentage of those sources are used to inform the specific decisions and tasks we asked about; decision support tools are commonly used to justify rather than inform decisions. We further found evidence that suggests that products and tools that are thoughtfully designed to fit within the specific information use environment of wildland fire management—rather than being produced in isolation from that environment—are more likely to be used and therefore have more impact than those that are produced in isolation from that environment.

Neuropeptides regulate shell growth in the Mediterranean mussel (Mytilus galloprovincialis)

In bivalves, which are molluscs enclosed in a biomineralized shell, a diversity of neuropeptide precursors has been described but their involvement in shell growth has been largely neglected. Here, using a symmetric marine bivalve, the Mediterranean mussel (Mytilus galloprovincialis), we uncover a role for the neuroendocrine system and neuropeptides in shell production. We demonstrate that the mantle is rich in neuropeptide precursors and that a complex network of neuropeptide-secreting fibres innervates the mantle edge a region highly involved in shell growth. We show that shell damage and shell repair significantly modify neuropeptide gene expression in the mantle edge and the nervous ganglia (cerebropleural ganglia, CPG). When the CPG nerve commissure was severed, shell production was impaired after shell damage, and modified neuropeptide gene expression, the spatial organization of nerve fibres in the ganglia and mantle and biomineralization enzyme activity in the mantle edge. Injection of CALCIa and CALCIIa peptides rescued the impaired shell repair phenotype providing further support for their role in biomineralization. We propose that the regulatory mechanisms identified are likely to be conserved across bivalves and other shelled molluscs since they all share a similar nervous system, a common mantle biomineralization toolbox, and shell structure.

Performance evaluation of solar photovoltaic system based on perturb and observe (P&O) maximum power point tracking (MPPT) algorithm

The Photovoltaic system (PV) is gaining popularity as a future energy source due to its vast, secure, essentially limitless, and widely accessible nature. However, a prevalent challenge in PV systems is the impact of solar cell radiation intensity and temperature on the output power induced in the photovoltaic module. As a result, monitoring the input source's maximum power point becomes essential for maximizing the effectiveness of the renewable energy system. In order to harvest the most power from the photovoltaic system and channel it to the load through a boost converter, which raises the voltage to the necessary level, most Power Point Tracking, or MPPT, is essential. This study uses the Perturb and Observe (P&O) method to track the photovoltaic module's Maximum Power Point (MPP) and maximize the system's overall power production to overcome this obstacle. By varying the array terminal voltage or current, the P&O approach perturbs the system and then compares the PV output power to the preceding perturbation cycle. Therefore, the main goal of this study is to simulate and assess the effectiveness of the P&O MPPT algorithm in MATLAB/Simulink when it comes to maximizing power extraction from a photovoltaic system. The simulation findings show that the suggested P&O approach is a reliable means of tracking MPP from PV panels, even when there are fluctuations in solar irradiation.

Technological risk registers as an element of the integrated risk management system in food production

Technological risk registers as an element of the integrated risk management system in food production

Thermodynamic analysis of a solar-driven supercritical water gasification-oxidation system for ammonia production and heat supply from chicken manure

Faced with the dual challenges of energy shortage and environmental pollution, researchers are turning to extracting clean energy from waste resources. The coupled system of solar energy and supercritical water gasification offers a novel solution to these issues. In this study, a solar-driven supercritical water gasification-oxidation system for ammonia production and heat supply from chicken manure was simulated. Additionally, the effects of temperature of supercritical water gasification, chicken manure slurry concentration, water-dry matter ratio, and pressure of supercritical water gasification and oxidation on the system's products, efficiency, and device exergy losses were examined. Under typical working conditions (the temperature of supercritical water gasification: 750 °C, chicken manure slurry concentration: 50 wt%, water-dry matter ratio: 5, the pressure of supercritical water gasification and oxidation: 25 MPa), and the system had a treatment capacity of 5 t/h of dry chicken manure. The system yielded 2604.04 kg/h of NH3 and 23763.9 kg/h of heating steam. It demonstrated an energy efficiency of 68.38% and an exergy efficiency of 46.85%. The total exergy losses of the system amounted to 34704.73kw, with the solar collector accounting for the largest share at 49.6%. The temperature of supercritical water gasification and water-dry matter ratio significantly impacted the system efficiency. Conversely, chicken manure slurry concentration and the pressure of supercritical water gasification and oxidation exerted minimal influence. The solar collector and gasification reactor emerged as the primary contributors to exergy losses in the entire system. Notably, the lower temperatures of supercritical water gasification, chicken manure slurry concentrations, pressures of supercritical water gasification and oxidation, and water-dry matter ratios can reduce exergy losses in both the solar collector and the gasification reactor.

Thermodynamic performances of a power-to-X system based on solar concentrating photovoltaic/thermal hydrogen production

100% renewable power-to-X (fuel, power, and heat products) systems have great application potential for the achievement goal of carbon neutrality. However, the extensive chain of the power-to-X system could result in low energy utilization efficiency. A novel composite-parabolic-concentrating photovoltaic/thermal-collector-based power-to-X system utilizing 100% solar energy is proposed, which integrates proton exchange membrane electrolysis cells and proton exchange membrane fuel cells to provide heat, electricity, and hydrogen. Thermodynamic models for each subsystem are established, and energy and exergy efficiencies are assessed in the thermodynamic laws. Three operation modes are analyzed to adapt to the variations of solar radiation and a residential building is used as an application scenario to evaluate its thermodynamic and economic performances in dynamic variations of solar radiations and energy demands. The effects of key operating parameters, such as solar radiation intensity, photovoltaic coverage ratio, current density, and operating temperature, on the performances are investigated. The maximum exergy destruction of the concentrator photovoltaic/thermal collector is 93.20% at the rated operating conditions, and the system exergy and energy efficiencies are 9.11% and 48.97%, respectively. The economic results demonstrate that the levelized costs of solar power, hydrogen, and fuel cell power are 0.2936 $/kWh, 21.70 $/kg, and 2.1520 $/kWh, respectively.

Recent progress of NH2-MIL-125(Ti)-based photocatalytic system in energy production and environmental purification

With the increasing prominence of metal–organic frameworks (MOFs) in photocatalysis, NH2-MIL-125(Ti) has attracted much attention due to its typical structural and remarkable optical properties. This review furnishes a comprehensive outline of the development of NH2-MIL-125(Ti)-based nanomaterials in the field of photocatalysis. First, various conditions involved in the synthesis methods of NH2-MIL-125(Ti) and the corresponding impact on the final morphology are analyzed and discussed. Additionally, we highlight the advantages of NH2-MIL-125(Ti)-based catalysts within the photocatalytic reaction systems, further exploring and summarizing the merits and characteristics of different modification strategies for NH2-MIL-125(Ti)-based catalysts. Moreover, the review discusses the performance and benefits of NH2-MIL-125(Ti)-based photocatalysts in energy conversion, sustainable chemistry, and environmental purification. Finally, the promising prospects and current challenges facing NH2-MIL-125(Ti)-based materials have been emphasized, aiming to inspire future advancements in photocatalytic applications.

Study on recirculating aquaculture system (RAS) in organic fish production

Abstract The growth of conventional aquaculture has created environmental issues due to excessive feeding, low dissolved oxygen level etc into the water body. Organic aquaculture is a recent development as a solution to these issues. It is a clean alternative to reduce pollution and to produce safer consumable food. In the past few decades, recirculating aquaculture systems (RAS) were introduced to maintain pond water quality through lesser water exchange, by focusing on water reuse after treatment. In this study, the technical viability of RAS was analyzed for introduction in organic aquaculture systems. Indian major carps (IMCs) were cultured through conventional methods in the three tanks of conventional system, while the same species was grown organically in another three tanks in organic system. The stocking density and physical conditions were kept same for both cultured systems. The RAS consisted of fish culture tank with an average volume of each tank was 165 m3, a screen filter, foam fractionator and trickling filter. The various water quality parameters, i.e., solid size distribution in water, and removal efficiencies in biological treatment of both systems were compared. The present study, particle size distribution of solids in water body was measured by filtration, followed by weighing of dry residue solids. Pore sizes of 1000μ, 100μ, 20μ and 3μ were employed for filtration. For organic tanks, a majority of solids are of size between 1 mm and 100 μm. The larger sized particles (> 30 μm) constitute nearly 70 % of the solids and the trait differs from the generalized conclusions of that a major part of sediments will be of sizes less than 20 μm. For the conventional tanks, majority of solids are of size between 3 mm and 20 μm and TAN shows higher removal efficiencies for effluents from organic system rather than conventional system. The results showed that organic aquaculture causes lesser pollution load per weight of fish. The particle size distribution of organic water was better compatible to screening and sedimentation than conventional water. Also the filtration efficiencies in nitrifying trickling filters of both water bodies were comparable, with that of organic water slightly on the higher side. Thus recirculating water treatment systems are introduced in organic aquaculture.

Enviro-economic and optimal hybrid energy system: Photovoltaic–biogas–hydro–battery system in rural areas of Pakistan

To satisfy the electricity needs of a village in Tangi, northwest Pakistan, the present research can design and evaluate the environmental and economical aspects of an optimal hybrid photovoltaic-biogas-hydropower-battery energy sustainable system (PV-BG-HP-BESS). This framework integrates various renewable energy sources, delivering a modern, efficient approach to sustainable energy solutions. The HOMER Pro software is utilized to optimize the most economical and effective hybrid energy system. The results showed that the proposed hybrid system comprising 91.4 kWp PV modules, 19.6 kW hydropower, a 50 kW biogas generator (BG), 36 batteries, and a 60.6 kW converter was the most economical choice. This system, which used the cyclic charging (CC) method, had a cost of energy (COE) of 0.0728 $/kWh and a total net present cost (NPC) of $152,242. The suggested hybrid energy system for rural areas of Pakistan includes photovoltaic (PV), biogas (BG), hydro, and battery components to provide a dependable and sustainable power supply. This system minimizes the need for expensive fossil fuels while simultaneously minimizing environmental impact by lowering pollutants and greenhouse gas emissions. The system's annual electricity production is 294,782 kWh, with PV leads at 59.4%, BG at 6.02%, and hydro at 34.6%, ensuring uninterrupted power generation even in remote areas. The unmet load, extra electricity, and capacity shortage illustrate the reliability of the system and make it possible to address rural electrification challenges while supporting sustainable development and economic growth. Moreover, the outcomes of the proposed hybrid system dominate the previous studies in multiple objectives, including cost and sensitivity analysis, when compared.

Enhancing Autonomous Vehicle Technology with YOLOv8

The system's products and features form the basis of the product search process. By splitting up and recording huge photos of low-quality images in high resolution, its performance may be easily balanced As machine learning advances quickly, powerful tools are capable of to take on more intricate, sophisticated, or profound features to address issues with legacy tools.. This project offers a new way to detect vehicles, pedestrians and traffic signs using only publicly available data. Because research requires long-term photographs (such as images shot in direct sunlight), it is challenging to incorporate research into the data, and confidence training is uncommon due in part to the nature of the data. We presents modification of the YOLOv8 model for training to improve accuracy. In that model, a number of constants and lossy components were employed. The reason behind this is that YOLOv8 works well utilizing mobile gadgets and requires less RAM management. Unity also provides additional support to simplify the conversion process.

Performance of a direct-expansion solar-assisted heat pump for domestic hot water production in Algeria

The focus of this study is to investigate the energy performance of a direct-expansion solar-assisted heat pump water heating system (DX-SAHPWH). The system consists of an unglazed solar collector-evaporator, which can absorb heat from solar energy and air ambient simultaneously, a condenser in the form of a coil immersed in a hot water storage tank, a thermostatic expansion valve and a hermetic reciprocating compressor. The performance of the heat pump system is evaluated using a developed mathematical model under Matlab code. The modelling method is based on lumped and distributed parameter approach of different system components. Numerical calculations were carried-out to study the influence of different parameters, such as ambient temperature, solar radiation intensity and polytropic index on the system performance. Additionally, in order to evaluate the long-term system performance, the system’s model was applied on a case study of a single-family building located in Djelfa (Algeria), which represents the coldest arid region of the country. The results showed that the solar radiation intensity and ambient temperature have significant effects on the heat pump performance. A COP of 5.9 and a collector-evaporator efficiency of 1.9 were obtained at high solar radiation of 850 W/m2 resulting in lower heating time (29 min). In addition, results revealed that the system can operate even at lower ambient temperature due to its ability to take advantage of heat from the ambient air. The results from the case study gave a COP ranging from 2.3 to 3.8, which enhance the promising adoption of this system in domestic hot water production to respond to people daily life needs with clean, abundant and renewable energy.

Descriptive epidemiology of spotty liver disease in Australian cage-free brown egg layer chicken flocks with a scratch area

Spotty Liver Disease (SLD), caused by Campylobacter hepaticus or C. bilis infection in adult female chickens continues to emerge as a major disease problem in cage-free production systems. Free range production has become the predominant system in Australian egg production and SLD is widespread in these farms. Previous studies have identified having a scratch area as a key determinant for SLD occurrence. An Australia-wide survey of egg production flocks with scratch areas was conducted regarding SLD including 48 individual flocks. Descriptive information on the facilities and flock management practices was reported. The incidence of SLD, age of first outbreak, initial mortality rate, duration of elevated mortality, and magnitude and duration of any associated egg production decline are described. Recurrence of SLD in the same flock was also reported and discussed. Therapies applied were recorded and assessed across SLD severity and duration. SLD occurred in 66.7% of layer flocks whose facility included a scratch area. Recurrent SLD outbreaks occurred in 31% of flocks experiencing SLD. Antibiotic medication reduced duration of mortality and egg production decline. Antibiotic therapy was associated with reduced duration of mortality and a less severe and shorter duration of egg production drops compared to untreated flocks. PCR detection of C. hepaticus in cloacal swabs and house dust samples and a serological ELISA test were compared and evaluated as diagnostic aids or as possible predictors of SLD outbreaks. The ELISA showed substantial agreement with detection of C. hepaticus in cloacal swabs by PCR. Examining composite house dust samples by PCR for C. hepaticus DNA appeared to be the most convenient and cost-effective aid to diagnosis and as a putative predictor for SLD outbreaks.

Model Prediction XGBoost for Perspective of Closing Cernavoda Reactor 1 and the Impact on the Romanian Energy System

Abstract Employing artificial intelligence (AI) and machine learning (ML) technologies to analyze the progression of energy markets is imperative, as these methodologies facilitate the modeling of diverse domain-specific data to forecast developments in an exceedingly volatile market. Utilizing machine learning algorithms to model data concerning the Romanian energy sector proves beneficial, particularly in circumstances where significant changes necessitate adaptation to internal consumption demands, while mitigating substantial fluctuations in electricity prices. Such fluctuations can profoundly impact crucial national economic indicators.. In this paper, we aim to simulate such a situation for the moment when Romania will be obliged to shut down and modernize Reactor 1 at Cernavodă due to reaching the standard operating time for the CANDU model. We will use an XGBoost for Regression algorithm in which we will input data about the national energy system (consumption, energy quantities according to the energy system's production structure by resource types) from the period 2020-2023, and then we will simulate the shutdown of the reactor to analyze how the national energy market is influenced. Our findings not only reveal the indispensable role of predictive analysis through ML/AI models in anticipating market changes, but also provide practical insights for decision-makers aiming to optimize the national energy system so that when Reactor 1 at Cernavodă is shut down, the domestic market is affected as little as possible, considering that this reactor has supplied the national system with approximately 700 MWe/h over the past 20 years.

Hydrogen Production using PEM Electrolyzer

Abstract: This paper presents a simulation study of alkaline electrolysis for H2 production using Aspen Plus. The simulation model incorporates reactor, separators, mixer, and heat exchanger units to accurately represent the AWE process. The model is designed to evaluate the performance of an AWE system under different operating conditions and aims to achieve a reference H2 production rate of 0.179663 kg/hr. The simulation results demonstrate that the AWE system can successfully produce H2 at the specified rate. The model provides a detailed analysis of the system's behavior and H2 production rate, under varying operating conditions. The findings of this study provide valuable insights into optimizing the design and operation of AWE systems for large-scale H2 production

Performance of a recirculating aquaponic system in vegetable production using river fish (Nile tilapia)

Performance of a recirculating aquaponic system in vegetable production using river fish (Nile tilapia)

Investigation of the catalytic activity of Ag(0) nanoparticles supported into anionic hydrogel networks for hydrogen production process

In the presented study, the hydrogen production from the hydrolysis of the morpholine-borane (MB) with hydrogel supported Ag(0) nanoparticle catalyst system was reported. For this purpose, nanosized p(sulfopropyl acrylate potassium salt-co-itaconic acid)@Ag composite material, (p-SIH@Ag), was prepared and used as catalyst. In this study, the effects of base, catalyst amount, MB substrate concentration and temperature were investigated for the MB hydrolysis reaction. p-SIH@Ag catalyzed MB hydrolysis reaction at 30 °C, the activation energy (Ea) was determined as 24.3 kjmol−1 and the turn over frequency (TOF) as 37.52 mol H2(mol Ag(0).h)−1. In reusability studies, the catalyst preserved 72% of its activity even after the 5th catalytic cycle despite the sensitivity of Ag metal to oxidation. Our study is the first in the literature to use hydrogel supported Ag metal catalyst system in hydrogen production via MB hydrolysis.

Network security AIOps for online stream data monitoring

In cybersecurity, live production data for predictive analysis pose a significant challenge due to the inherently secure nature of the domain. Although there are publicly available, synthesized, and artificially generated datasets, authentic scenarios are rarely encountered. For anomaly-based detection, the dynamic definition of thresholds has gained importance and attention in detecting abnormalities and preventing malicious activities. Unlike conventional threshold-based methods, deep learning data modeling provides a more nuanced perspective on network monitoring. This enables security systems to continually refine and adapt to the evolving situation in streaming data online, which is also our goal. Furthermore, our work in this paper contributes significantly to AIOps research, particularly through the deployment of our intelligent module that cooperates within a monitoring system in production. Our work addresses a crucial gap in the security research landscape toward more practical and effective secure strategies.

A knowledge-guided Estimation of Distribution Algorithm for energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem

The assembly line serves as a fundamental system in discrete production. To address the challenges in balancing robotic assembly lines, timely part feeding, and the need for sustainable manufacturing, this paper studies an energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem (JRALB-FP) with the criteria of minimizing both cycle time and total fuel consumption cost. Considering the complexity of the multi-problem and multi-objective optimization, a knowledge-guided Estimation of Distribution Algorithm (KEDA) is proposed to solve energy-efficient JRALB-FP. First, a probability model of EDA for task-workstation allocation paired with a heuristic method-based sampling mechanism is created. Using this probability model, a specific encoding mechanism is designed for part-trailer allocation, and good initial solutions are produced. Second, several properties of the bi-objective problem are analyzed to guide the design of local search operators for both objectives optimization. Third, the updating mechanism of the probability model is designed to learn from the elite solutions. Fourth, two knowledge-guided local search operators are designed and implemented to exploit better non-dominated solutions sufficiently. A design of experiment is carried out to determine the parameters. Extensive computational tests and comparisons with the state-of-the-art multi-objective algorithms are carried out, which verify the effectiveness of the knowledge-guided local search operators, the problem-oriented heuristic-based sampling mechanism, and the special designs of the KEDA in solving the energy-efficient JRALB-FP.

Quality Management System in Production of Reference Materials

Quality Management System in Production of Reference Materials