Model Of Production System Research Articles

Topological Scheme and Analysis of Operation Characteristics for Medium-Voltage DC Wind Turbine Photovoltaic Powered Off-Grid Hydrogen Production System

Renewable energy has high volatility in the traditional off-grid AC hydrogen (H2) production system, which leads to low reliability of the system operation. To address this issue, this paper designs the topology scheme of wind-photovoltaic generation powered off-grid H2 production system. Firstly, a DC off-grid system topology scheme with the wind turbine (WT) and photovoltaic (PV) is connected to the medium voltage DC bus by two-stage conversion is proposed. The power fluctuation of WT and PV generation systems and the power-adjustable characteristics of electrolyzers are taken into consideration. Meanwhile, the scheme of distributed access of energy storage (ES) to the WT side and PV side to provide the voltage support for the system is proposed. Secondly, the operating characteristics of DC microgrids and AC microgrids under abnormal operating conditions, such as the fault of the source side, the fault of the load side, and communication interruption, are analyzed in this paper. Finally, the electromagnetic transient simulation model of the DC off-grid H2 production system and the traditional AC off-grid H2 production system is established. The effectiveness of the proposed topology scheme is verified by simulation of typical operating conditions.

Using fuzzy cognitive mapping to assess the sustainability impacts of transitioning to pasture-fed production in the UK beef sector

Abstract One hundred percent pasture-fed beef production has been suggested as a promising approach for sustainable ruminant farming, due to the potential benefits that can accrue across a range of sustainability domains. This study aimed to investigate the impacts across the four domains of sustainability of a wholesale switch from conventional to 100% pasture-fed beef production in the UK. We used fuzzy cognitive mapping (FCM) as a method for extracting knowledge from multiple stakeholders to create representative systems models of both conventional and pasture-based beef production systems. We then conducted a scenario analysis to assess how a switch to a pasture-fed system could affect components of sustainability in the UK beef sector. The FCMs indicated that vegetation quality, grass use efficiency, and soil health were central components of the pasture-fed approach, while economic and regulatory aspects, and climate change targets were more central to mainstream production approaches. The most marked changes under the 100% conversion scenario were an increase in income from subsidies (27.3%) in line with ‘public money for public goods’, a decrease in ability to export beef (unless advice to reduce consumption of animal protein is followed) (23.5%), a decrease in land used for farming vs other uses (e.g., natural capital) (11.23%), and a decrease in the use of feed from agricultural co/byproducts (7.5%), freeing up these feed sources for more sustainable monogastric production. Therefore, the mapping and scenario analysis suggests that while upscaling the pasture-fed approach may reduce productivity, it would likely increase public goods provision and reduce feed–food competition in the UK.

Flow shop manufacturing layout as low carbon approach for application in bonding wire drawing industry

Modern manufacturing environments are yet to fulfil the evergrowing competitive supply and demand due to the limitation of conventional production approaches. A low-carbon approach incorporating the change from process to product-centric manufacturing systems could conserve energy while fulfilling supply and demand requirements in a competitive market. Current study had conducted a case study in a bonding wire factory that operates on process-oriented system with job shop layout. Preliminary data collection had assessed the existing practices on the factory's shop floor, and simulation model using WITNESS software was developed using this data. A job shop to flow shop conversion framework was developed with the integration of factors such as shop floor layout, order release, and dispatch rule play crucial roles in the conversion process of the production system. Based on the application of the conversion framework encompassing six future production system models, CL-SPT model which uses flow shop as layout, CONLOAD as order release and shortest processing time as the dispatch rule is selected as the best performing and economical production system. This model enhanced the output and average flow time by 2.7% and 27.7%, respectively, in tandem with low-carbon requisites while retaining machine utilization performance equivalent to the conventional system.

Synergizing ergonomic principles with lean philosophy model towards productivity improvement in the shoes manufacturing industry

PurposeThis study aims to address the gap in integrating ergonomic principles with lean principles in the shoe product manufacturing process. The objective is to develop a customized model that effectively combines and synergizes ergonomics and lean principles.Design/methodology/approachPrimary data was collected through on-site observations, interviews and assessment of whole-body discomfort to evaluate the implementation of lean and ergonomic principles in each section of the shoe production process. The collected data were analysed using descriptive statistical tools, content analysis and software such as Microsoft Excel.FindingsThe findings of the study indicate that the case company’s underperformance, achieving only 26.96% of its designed capacity and 50.19% of its planned capacity, is primarily attributed to the poor integration of ergonomic principles with the lean philosophy model. The high prevalence of work-related musculoskeletal disorders (WMSDs) in the shoe production process significantly contributes to a loss of productivity due to increased absenteeism. The factory experiences a labour absenteeism rate of 5.59%, resulting in overtime and additional costs. To address these issues, the study proposes the adoption of 8S principles and an ergo-lean production system model. This model, conceptualized as a building construction structure, effectively eliminates waste in the shoe production process.Practical implicationsThe study’s findings will greatly enhance productivity in the shoe manufacturing sector by establishing a work environment that prioritizes employee needs and well-being. This will have significant practical implications for improving overall productivity in the industry.Originality/valueThis study stands out as it investigates the proactive integration of ergonomics and lean principles in the shoe manufacturing industry, an area that has not been previously explored. By bridging the gap between these two principles, the research contributes to the existing knowledge base.

Study of control methods to reduce off-grid faults and improve system frequency stability in multi-energy systems based on impedance modeling of electric hydrogen production systems

Renewable energy coupling hydrogen production technology can overcome the weakness of randomness and intermittence for renewable energy to a certain extent. However, due to the long-distance and reverse distribution of renewable energy generating units and the main network. There will be a risk of oscillation instability when a high proportion of power electronic hydrogen production system is interconnected with the power grid. Firstly, the impedance model of the electric hydrogen production system is established. The oscillation characteristics of hydrogen production system interconnected with power grid are analyzed. Secondly, the influence of electrolytic hydrogen system on the stability of multi-energy system is analyzed. The instability problems caused by input power fluctuation and hydrogen production rate change are studied. Then, an oscillation suppression strategy for renewable energy hydrogen production system based on power allocation is proposed. It is used to enhance the stability of the multi-energy system of the electrolytic water hydrogen production system. Finally, through the establishment of renewable energy electrolysis hydrogen experimental simulation system. The system frequency stability under different renewable energy output fluctuations and different impedance conditions of the system is verified. The simulation results show that the proposed multi-energy hydrogen production control method based on power allocation can ensure the stability of the system under the fluctuation of renewable energy output.

Process Simulation, Thermodynamic and System Optimization for the Low-Carbon Alcohols Production via Gasification of Second-Generation Biomass

Biomass-to-liquid fuels technology offers a promising method for high-value biomass conversion, addressing environmental toxicity and fossil fuel non-renewability. However, challenges such as low system efficiency and identifying efficiency losses persist. In this study, a comprehensive process model of a low-carbon alcohols production system via biomass gasification was developed, based on the first demonstration project in China. The innovation of this study lies in its detailed experimental validation, as the model simulation was performed using laboratory data and verified with pilot-scale platform data ensuring high accuracy. Additionally, the study conducted a thorough sensitivity analysis of system parameters, energy, and exergy assessments to find the proper operating conditions, including equivalent ratio, biomass type, and reactor temperature and pressure. The simulation results demonstrated an energy efficiency of 34.67% and an exergy efficiency of 31.24%. Through operating parameters and heat recovery measures, these efficiencies increased by 11.66% and 8%, respectively. This research not only obtains improved operating parameters for the pilot-scale platform but also provides actionable insights for enhancing the yields of target products and upgrading low-grade energy utilization. These findings offer valuable guidance for the commercialization of bio-syngas alcohols production systems, highlighting significant advancements in efficiency and system performance.

Application of Max-Plus Algebra for Time Optimization of Wooden Furniture Production System “Berkah Usaha” Jepara Regency

This research aims to provide a periodic schedule on the Berkah Usaha wood furniture production system in Jepara using Max-Plus Algebra. The data required in the research is data on the flow and processing time of each project work unit. In this research, the main focuses on the Berkah Usaha wooden furniture dining table production system whose processing stages consist of raw material preparation, measurement, wood cutting, wood smoothing, measurement of puruses and holes, table floor manufacturing, puruses and holes manufacturing, table floor smoothing, assembling, sanding, and finishing. The techniques of data collection used in this research are interviews and field observations. Then, a directed graph and model of the Berkah Usaha wood furniture production system were compiled based on the flow data, processing time for each project work unit, and the rules for applying Max-Plus Algebra to the scheduling system. Analysis of the Max-Plus Algebra model of the Berkah Usaha wooden furniture production system using the Power Algorithm with Scilab software assisted. The results of the analysis obtained the production time period of the Berkah Usaha wooden furniture for 253 minutes and the optimal time to start production on each processing unit are 0, 11, 46, 92, 115, 117, 127, 137, 155, 195, 223 time units (in minutes).

A multidimensional holistic sustainability model for production systems

ABSTRACT Manufacturing companies face a volatile environment with multiple sustainability challenges threatening their competitiveness. To meet these challenges, they need to adapt quickly to the changing demands and environment to improve their sustainability in production. Therefore, this contribution presents a holistic and multidimensional model for sustainable production systems based on three levels: elements, overarching elements, and dimensions. First, a systematic literature review (SLR) was conducted to capture the state of research. Second, two studies were conducted to identify and cluster the relevant sustainability elements. One of the studies is based on the SLR findings, which were verified by an expert survey. The other is based on a document analysis of sustainability reports. Finally, both studies are compared, and the results are presented. In total, 40 elements, such as energy efficiency, and eight overarching elements, such as efficiency, can be identified in addition to the three dimensions of economic, social, and environmental sustainability.

Feasibility Analysis of Green Hydrogen Production Systems for Decarbonized Heating Applications: A Dynamic Modelling Approach on Simulink

Abstract This paper presents a dynamic model of a hydrogen production system aimed at residential heating applications, utilizing MATLAB® and Simulink® softwares. The global model is divided into different macro-sections, each covering a specific role in the hydrogen production and distribution process. These include the photovoltaic field, the electrolyzer, the short-term and long-term storage sections and the end-users. The graphical interface of Simulink is employed, with particular reliance placed on the Simscape library for the modelling and simulation of physical lines. The control logic that governs the operation of the system is implemented using the Simulink tool Stateflow, thereby ensuring efficient management of hydrogen production, storage, and distribution phases. Two distinct scenarios are analysed, each considering different weather data, thermal demand distribution and infrastructure configuration. For each Scenario, simulation results provided a detailed trend of PV power production and electrolyser power consumption, hydrogen production, variations in storage tank pressures, as well as power consumption by auxiliary systems. A comparative analysis with fossil fuel sources is then assessed by calculating the overall reduction of CO2 emissions due to green hydrogen production. In conclusion, this paper provides a comprehensive framework for modelling, simulating, and evaluating green hydrogen production systems, offering insights into their potential as sustainable energy solutions for residential heating decarbonization.

Simulation Experiment Research on the Production of Large Box Girders

This paper introduces system simulation technology into large-scale beam field production and uses the simulation software Arena (14.0) to construct a simulation model of the beam field production system considering the randomness of the actual beam field production process operation time. The relationship between the production efficiency of the beam yard and the working time system was studied. In this paper, the improvement in beam-making efficiency in the existing beam field that is achieved by the commonly used reinforcement pre-binding method in the existing beam field is analyzed and calculated, and the improvements in the production efficiency in the ordinary beam field and the intelligent beam field are quantitatively calculated and compared. The results show that (1) when the working time system is increased from 8 h/d to 12 h/d, the average occupancy time of the traditional beam-making pedestal is shortened by 11.5 h when the working time is extended by 1 h per day; (2) with the extension of the working time system, the advantages of the pre-binding method of reinforcement gradually decrease; and (3) the application of intelligent technology not only improves the production efficiency of the beam yard but also makes the beam yard’s production more flexible and more resistant to risks.

Amplified temperature sensitivity of extreme precipitation events following heat stress

This study investigates global extreme precipitation events (EPEs) during warm seasons, with a particular focus on EPEs preceded by extreme heat stress (EPE-Hs) and a comparative analysis with those not (EPE-NHs). Using reanalysis product and Earth System Model data, the spatiotemporal characteristics and temperature sensitivities of EPEs are analyzed. Results show that EPE-Hs, while less frequent, have longer duration and greater magnitude compared to EPE-NHs, particularly in high latitude regions. In the future, a significant increase is projected in the characteristics of EPE-Hs, in contrast to the stable duration and magnitude of EPE-NHs. EPE-Hs demonstrate substantially higher temperature sensitivity than EPE-NHs, especially in low latitudes. The precipitation-temperature scaling relationships diverge markedly between EPE-Hs and EPE-NHs, with notable regional variations. These insights are pivotal for crafting region-specific early warning and adaptation strategies to mitigate the risks associated with extreme precipitation under the backdrop of global warming.

Phytoplankton as CO2 Sinks: Redirecting the Carbon Cycle

Since the Industrial Revolution, nearly 700 Gt of carbon (GtC) have been emitted into the atmosphere as CO2 derived from human activities, of which 292 GtC remain uncontrolled. By the end of this century, the atmospheric CO2 concentration is predicted to surpass 700 ppm. The effects of this sudden carbon release on the worldwide biogeochemical cycles and balances are not yet fully understood, but global warming and climate change are undeniable, with this gas playing a starring role. Governmental policies and international agreements on emission reduction are not producing results quickly enough, and the deadline to act is running out. Biological CO2 capture is a fast-acting carbon cycle component capable of sequestering over 115 GtC annually through photosynthesis. This study analyses a hypothetical scenario in which this biological CO2 capture is artificially enhanced through the large-scale cultivation of phytoplankton in partially natural photobioreactors (PBRs). To develop this approach, the current figures of the carbon cycle have been updated, and the key aspects of phytoplankton cultivation technology have been analysed. Our results show that a global increase of 6.5% in biological capture, along with the subsequent stabilization of the produced biomass, could counteract the current CO2 emission rate and maintain atmospheric levels of this gas at their current levels. Based on a review of the available literature, an average production rate of 17 g/m2·day has been proposed for phytoplankton cultivation in horizontal PBRs. Using this value as a key reference, it is estimated that implementing a large-scale production system would require approximately 2.1 × 106 km2 of the Earth’s surface. From this, a production system model is proposed, and the key technological and political challenges associated with establishing these extensive cultivation areas are discussed.

Calibration and validation of the AquaCrop model for forage cactus production systems under different management interventions in the semi-arid region of Brazil

Simulation models are useful tools for estimating plant response and contributing to the development of management strategies and yield predictions in crops. This study aimed to calibrate and evaluate the AquaCrop-FAO model for the forage cactus imposed on different cropping systems in the semi-arid region of Brazil. Four experiments were conducted: cactus-sorghum intercropping system with five spacings between plants (0.10, 0.20, 0.30, 0.40 and 0.50 m); cactus-sorghum intercropping system with five spacings between rows (1.00, 1.25, 1.50 and 1.75 m); single cactus with four levels of mulch (0, 5, 10 and 15 Mg ha−1); and single forage cactus with four levels of nitrogen fertilisation (50, 150, 300 and 450 kg of N ha−1). The performance of the model was evaluated using the following parameters: root mean squared error (RMSE); normalised RMSE (NRMSE), coefficients of determination (R2), Nash-Sutcliffe model efficiency coefficient (ME) and the Willmott index of agreement (d). During the calibration stage, the statistical indices pointed to the good performance of the AquaCrop model in estimating dry biomass in most of the cropping systems, with 10 < NRMSE <20; R2 > 0.96 and ME > 0.95. The AquaCrop model showed the best performance simulating systems under different levels of nitrogen fertilisation. Among the intercropping systems, the denser arrangements showed the highest values for simulated biomass.

Development of a Simulation Model to Improve the Functioning of Production Processes Using the FlexSim Tool

One of the goals of Industry 4.0 is to increase the transparency of the value chain through modern tools in production processes. This article aims to discuss the possibility of increasing the efficiency of a production system by modernizing it with the use of computer modelling tools. This article describes a method for the simulation modelling of a selected production system using the specialized FlexSim 2023 software in a 3D environment. The results and benefits of the practical application of the object-oriented modelling are presented, as well as the possibilities of collecting simulation data used to optimize production processes. The analyses were conducted at a selected production plant in a case study. The research assessed the effectiveness of the existing system and determined the impact of process changes in the event of the introduction of a new design solution. The simulation identified bottlenecks in the material flow. The basis for creating the simulation model was the analysis of the technological process. A simulation model for a real situation was created, and a simulation model was designed to identify and indicate a solution to eliminate the detection of the bottleneck. The problem area identified using visualization in the technological process slowed down the entire production process and contributed to time and economic losses. Thus, the authors confirmed the thesis that the simulation modelling of production systems using the FlexSim program can help eliminate bottlenecks and increase the efficiency of human resource use. At the same time, the use of this tool can lead to increased efficiency, reduced costs and improved sustainability and other performance indicators important for modern production environments as part of the promoted Industry 4.0 idea. A noticeable result of these changes was an increase in production from about 80–90 units. In addition, it was noticed that the condition of the machines preceding the stand changed.

Development of machine learning regression models for the prediction of tensile strength of friction stir processed AA8090/SiC surface composites

Friction Stir Processing is a state-of-the-art technology for microstructure refinement, material property enhancement, and fabrication of surface composites. Machine learning approaches have garnered significant interest as prospective models for modeling various production systems. The present work aims to develop four machine learning models, namely linear regression, support vector regression, artificial neural network and extreme gradient boosting to predict the influence of FSP parameters such as tool rotational speed, tool traverse speed and groove width on ultimate tensile strength of friction stir processed AA8090/SiC surface composites. These models were developed through Python programming and the original dataset was divided into 80% for the training phase and 20% for the testing phase. The performance of the models was evaluated by root mean squared error, mean absolute error and R2. Based on the results and graphical visualization, it was observed that the XGBoost model outperformed other models with high accuracy in predicting UTS of AA8090/SiC surface composites.

Threshold-impeded stochastic production: how noise interacts with disruptive thresholds to affect the production output in fluctuating environments

Introduction: Production systems are bound to operate in stochastic conditions. Prominent sources of performance-reducing uncertainty are constituted by machine failures, decision errors, and fluctuating supplies. This article offers a novel approach to uncertainty through modelling and simulation of nonlinear production systems. In particular, the authors consider production systems where the output is drastically reduced when a resource of fluctuating input values reaches an upper threshold.Methods: The article introduces minimal models of such hreshold-impeded stochastic production (TISP) systems and the system performance (i.e., the output) is analyzed as a function of system parameters (e.g., the type of nonlinearity) and noise input features (e.g., the distribution width or time correlations). Applications to steel manufacturing via continuous casting and power generation through wind turbines are discussed in detail.Results and Discussion: The simulation experiments illustrate that especially the extent of the input fluctuations affects the output performance which is why the authors recommend that TISP system operators counterbalance such fluctuations if possible.

Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind–solar complementary, and wind–solar–storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation.

Evaluation of CRU-JRA gridded meteorological dataset for modeling of wheat production systems in Iran.

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates.

Research on Fast Frequency Response Control Strategy of Hydrogen Production Systems

With the large-scale integration of intermittent renewable energy generation presented by wind and photovoltaic power, the security and stability of power system operations have been challenged. Therefore, this article proposes a control strategy of a hydrogen production system based on renewable energy power generation to enable the fast frequency response of a grid. Firstly, based on the idea of virtual synchronous control, a fast frequency response control transformation strategy for the grid-connected interface of hydrogen production systems for renewable energy power generation is proposed to provide active power support when the grid frequency is disturbed. Secondly, based on the influence of VSG’s inertia and damping coefficient on the dynamic characteristics of the system, a VSG adaptive control model based on particle swarm optimization is designed. Finally, based on the Matlab/Simulink platform, a grid-connected simulation model of hydrogen production systems for renewable energy power generation is established. The results show that the interface-transformed electrolytic hydrogen production device can actively respond to the frequency disturbances of the power system and participate in primary frequency control, providing active support for the frequency stability of the power system under high-percentage renewable energy generation integration. Moreover, the system with parameter optimization has better fast frequency response control characteristics.

Optimization and evaluation of a neural network based policy for real-time control of construction factory processes

This paper focuses on the development, optimization, and evaluation of an intelligent real-time control system for the fabrication of precast reinforced concrete components. The study addresses the unique challenges associated with real-time control in the construction manufacturing industry, including high customization, uncertain work demand, and limited stockpiling opportunities. A production system model is built based on a real construction manufacturing factory to simulate real-world precast reinforced concrete component fabrication, and acts as the basis for the development and validation of the control system. A review of alternative decision-making techniques is presented to identify the most suitable for the control of construction manufacturing factories. Ultimately, an artificial neural network approach trained using a reinforcement learning strategy is selected as a promising technique for effective real-time control. The controller is developed and validated, and its performance is optimized using sensitivity analysis, which takes into account both the structure of the artificial neural network and the parameters of the reinforcement learning algorithm. The ANN-based control policy is applied to the sequencing of precast reinforced concrete component production, while a rule-of-thumb policy is used as a benchmark for comparison. The study demonstrates that the optimized ANN-based control policy significantly outperforms the standard rule-of-thumb policy. The paper concludes by providing suggestions for further advancement of the ANN-based approach and potential avenues to increase the control policy's scope of application in construction manufacturing.