System Model Research Articles

Fractional-Order Modeling and Identification for an SCR Denitrification Process

This paper presents an application of a fractional-order system on modeling an industrial process system with large inertia and time delay. The traditional integer-order model of the process system is extended to a fractional-order one in this work. To identify the parameters of the proposed fractional-order model, an output-error identification algorithm is presented. Based on the experimental step response data of the selective catalytic reduction (SCR) denitrification process in a power plant, this proposed fractional-order model shows a better fitting result compared with the typical integer-order models. An integer-order proportional–integral (PI) controller is designed for the process plant using a simple scheme according to the identified fractional-order and integer-order models, respectively. Validation tests are performed based on the obtained fractional-order and integer-order models, demonstrating the advantages of the proposed fractional-order model with the corresponding system identification approach for industrial processes with large inertia and time delay.

System Design, Modelling, Energy Analysis, and Industrial Applications of Ohmic Heating Technology

AbstractThe food and drink industry is gradually gaining sustainability pressure due to the high carbon emissions rate and limited availability of traditional energy supplies. Thus, it is essential to consider energy efficiency and decarbonisation of the ever-expanding food and drink industry. Ohmic heating technology is a highly energy-efficient and quick method of electrically heating food products. It uses the resistance of the food material to convert electrical energy into heat. However, the heating rate of ohmic heating depends on several process variables, such as electrical conductivity, voltage gradient, and voltage frequency. Therefore, it is important to study and understand the effects of these process variables to optimise the modelling and control of ohmic heating systems to obtain desirable output from food products. Hence, this study presents a review of the effects of process parameters on nutritional and organoleptic qualities, product yields, and energy efficiency of ohmic heating, along with its various industrial applications. The technology shows promising advancements in colour and nutritional quality, increasing energy efficiency, shorter processing times, and higher product yields compared to conventional heating processes.

Energy‐efficient optimal sink placement using extended pelican optimization‐based clustering with Voronoi‐based node deployment

SummaryA wireless sensor network (WSN) is a network of spatially distributed autonomous sensor nodes that collaborate to monitor physical or environmental conditions, collect data, and transmit it to a sink node. WSNs have a wide range of applications across various domains due to their ability to provide real‐time data collection, remote monitoring, and data analysis. Still, in a WSN with a fixed sink, sensor nodes closer to the sink tend to have higher traffic loads because they forward data to nodes further away. This can lead to hotspots and uneven energy consumption. Introducing a mobile sink can distribute the traffic more evenly across the network, reducing congestion and balancing the energy consumption among nodes. Hence, this research proposes a novel WSN environment with a focus on energy‐efficient routing. The network is deployed using Voronoi‐based criteria to address network coverage issues. The clustering of nodes is employed using the proposed extended pelican optimization (ExPo) algorithm to improve network lifetime and energy efficiency, critical concerns in WSNs due to limited sensor node battery capacity. Cluster heads (CHs) aggregate and process data locally, reducing the energy needed for long‐range communication. Then, an energy‐efficient optimal sink placement (EEOSP) approach is used to optimize the placement of the mobile sink. The proposed system model is evaluated based on various metrics, including average residual energy, delay, network lifetime, packet delivery ratio, and throughput and acquired the values of 0.99 J, 3.68 ms, 99.55%, 99.55%, and 81 Mbps, respectively.

Combining mathematical models and machine learning algorithms to predict the future regional-scale actual transpiration by maize

Plants on the land surface play a vital role in the hydrological water cycle as they transport soil water to the atmosphere through transpiration. Root water uptake (RWU) is considered a crucial step in this process as it is the first stage of transpiration, directly determining the actual transpiration (Ta) of plants. However, accurately measuring RWU or Ta in situ poses significant challenges. Here, we establish an overall approach of combining mathematical models and machine learning algorithms to obtain high-precision (500 m×500 m) regional-scale daily Ta maps for various future climate patterns. The Hydrus-1D and AquaCrop models were employed to calculate the total RWU fluxes across the entire root zone, aiming to achieve Ta at a point scale. A machine learning model was developed using the CatBoost algorithm and environmental covariates extracted from the Google Earth Engine (GEE) platform to upscale these point-scale Ta to the regional scale. Furthermore, a total of 22 CMIP6 Earth System Models (ESMs) were evaluated, and among them, ACCESS-CM2 and ACCESS-ESM1–5 were selected for simulating future climate scenarios. Based on the established machine learning model and selected ESMs, regional-scale Ta maps were generated from 2020 to 2100 for the SSP245 and SSP585 (Shared Socioeconomic Pathways) scenarios. The results indicate that near-surface specific humidity, mean near-surface air temperature, latitude, and surface downwelling shortwave radiation are the critical factors influencing regional-scale Ta. As greenhouse gas emissions intensify and temperatures rise, regional-scale Ta is enhanced, leading to an accelerated transfer of soil water to atmospheric water. Under the SSP245 scenario, Ta increases on average by 0.55–1.16 % every 20 years, with its incremental value ranging from 7.14∙10−4 to 8.65∙10−4 cm day−1, while under the SSP585 scenario, Ta increases more significantly, achieving an average increase of 0.64–1.81 % every 20 years, with its incremental value ranging from 1.595∙10−3 to 2.821∙10−3 cm day−1. This study provides a robust integrated approach to assess the future regional-scale Ta providing valuable insights into the underlying water cycle mechanisms and regional water requirements for future climate scenarios.

An efficient dynamic reliability method for maglev vehicle-bridge systems and its application in random controller parameters analysis

This article presents an efficient method for analyzing the dynamic reliability of maglev vehicle-bridge coupled systems by combining a theoretical model with an adaptive surrogate model and the probability density evolution method (PDEM). First, a refined theoretical model of a maglev vehicle-bridge coupling system is established. Next, an adaptive surrogate model of the equivalent extreme value of the system dynamic response is established by combining an adaptive sampling method with radial basis functions. Finally, the adaptive surrogate model and PDEM are combined to further improve the efficiency of the dynamic reliability analysis. In the numerical example, the theoretical model of the maglev vehicle-guideway system was first validated by comparing with the measured data from the Shanghai high-speed maglev line. Then, by treating the controller parameters as normally distributed random variables, the accuracy and efficiency of the proposed reliability method were verified through comparison with the Monte Carlo method and the one-stage sampling surrogate model. Additionally, the impact of the randomness of each controller parameter and the coefficient of variation of the controller parameters on the system’s dynamic reliability was discussed.

High-Accuracy Calibration Method of a Thermal Camera Using Two Reference Blackbodies.

Body temperature is one of the most important physiological parameters of a human being used to assess his basic vital functions. In medical practice, various types of measuring instruments are used to measure temperature, such as liquid thermometers, electronic thermometers, non-contact ear thermometers, and non-contact forehead thermometers. Such body temperature measurement techniques require the connection of appropriate sensors to a person, and non-contact thermometers operate over short distances and force a specific position of the person during the measurement. As a result, using the above methods, it is practically impossible to perform body temperature measurements of a moving human being. A thermal imaging camera can be used effectively for the purpose of the temperature measurement of moving objects, but the remote measurement of a human body temperature using a thermal imaging camera is affected by many factors that are difficult to control. Accurate remote measurement of human body temperature requires a measurement system that implements a specialized temperature determination algorithm. This article presents a model of a measurement system that facilitates the development of a highly accurate temperature measurement method. For the model, its parameters were determined on the calibration stand. The correct operation of the developed method and the effectiveness of temperature measurement have been confirmed by tests on a test stand using reference radiation sources.

Model of Sustainable Household Mobility in Multi-Modal Transportation Networks

Nowadays, urban and suburban areas face increasing environmental pressures, and encouraging sustainable transportation behaviors at the household level has become crucial. This paper presents a model of a decision support system (DSS) for promoting sustainable household mobility choices in multi-modal transport networks. The system was modeled using an enhanced Petri Net approach, allowing for the dynamic representation of complex transport networks and multi-modal journey options. The model incorporated various sustainability factors. These were combined into a single environmental impact score, which was considered alongside travel time and cost in the route optimization process. Simulation results demonstrated the DSS’s capability to guide users toward more sustainable mobility choices. The model also showed potential as a tool for policymakers to assess the impact of various sustainable transportation initiatives and infrastructure investments. This paper discussed the versatile applications of the system. It also addressed the limitations of Petri Net models in transportation systems and suggested future research directions.

A novel geothermal system combined with fuel cell and hydrogen generation to store clean sustainable energy storage

This paper presents a thermo-economic assessment of a novel geothermal integrated system enhanced by a fuel cell. The proposed system leverages the advantages of geothermal energy resources, featuring a sustainable and environmentally friendly solution for power generation and district heating applications. Integrating a fuel cell and thermoelectric within the geothermal system aims to improve overall system performance and energy efficiency by converting waste heat into electricity. A detailed mathematical model of the integrated system is developed, incorporating various performance parameters, component efficiencies, and thermo-economic considerations. The model is applied to simulate the system's performance under different operating conditions and parameters. Additionally, sensitivity analyses are conducted to evaluate the effects of key design variables on the system's performance, cost, and environmental impacts. The results indicate that the newly developed system generates 95594 kW of electricity with a total exergy destruction rate of 4616 kW. The electricity cost rate also obtained 0.309 $/kWh. The energy efficiency of the introduced system is 24.08 % and the exergy efficiency is 26.7 %. Comparison with the basic system represents that energy efficiency shows a 15.71 % improvement.

Performance simulation and energy efficiency analysis of multi-energy complementary HVAC system based on TRNSYS

The failure rate and operating cost of the C-B (chiller-gas boiler) system, which has been in use for a long time, are increasing year by year, but there is a lack of reasonable programs and effective measures on how to retrofit the C-B system. In order to explore the prospect of ground-source heat pumps applied to the retrofit of C-B systems in HSCW (hot summer and cold winter) zone, this study designed a G-C-B (GSHP-Chiller-Boiler) system in a partial retrofit context and constructed a TRNSYS numerical simulation model of the retrofit system. Secondly, the performance and energy efficiency of the system is analyzed as well as different control strategies for the ground source heat pump cooling tower are discussed. Finally, the energy conversion efficiency of the chiller is compared based on the energy flow of the system. Studies have shown that the G-C-B system can achieve a seasonal performance factor of 5.3 during the cooling season and 4.1 during the heating season. Compared to the existing C-B system, year-round electricity consumption was reduced by 21 %, gas consumption by 92 %, and combined energy demand by 49.6 %. With the addition of GSHP, the energy conversion efficiency of the chiller has also increased by 21 %. The soil temperature of the G-C-B system increased by less than 0.5 °C after ten years of continuous operation. This paper can provide a valuable theoretical basis for the energy form modification of the C-B system in the HSCW zone.

Hydrodynamic response analysis of the aquaculture net under the combined action of irregular waves and currents

ABSTRACT The hydrodynamic response of fishing nets is crucial in aquaculture. During service, due to severe sea states and collisions with large fish, potentially resulting in net damage. Analyzing this damage is complex due to the large number of net meshes. This study establishes a mathematical model of the net system under wave-current coupling based on the rigid-body kinematics and the lumped-mass method. An analysis was conducted to investigate how various environmental conditions and types of damage affect net deformation and force. Results indicate that maximum force occurs at the connections to the rigid frame and the midpoint of the net. Among various types of net damage, the nodes motion increased exclusively around the periphery of the damaged area, while the netting twine force significantly decreased only in the direction of the damage. Notably, damage to the upper net has the greatest impact on both netting twine force and deformation.

Surface wave isolation by variable depth infilled trenches

Excessive environmental vibrations generated by urban traffic pose adverse effects on nearby structures and residents. These vibrations are predominantly carried by surface waves, which are localized within the surface layer of soil. The isolation of surface waves through the embedding of periodic wave barriers in soils between the source and the receiver has gained significant attention in recent years. In this paper, a novel approach is proposed for isolating surface waves induced by urban traffic through the use of variable depth infilled trenches. This innovative design not only achieves efficient surface wave isolation but also minimizes the consumption of structural materials. Based on the measured dominant frequency range of rail transit and the available soil parameters, variable depth infilled trenches are designed with suitable dimensions. The eigenvalue equation is solved using the finite element method to derive the dispersion relations and bandgap of identical regularly spaced trenches. To study the efficacy of the proposed structure, a finite element model of the soil-infilled trench system is developed using COMSOL. The mechanism underlying the isolation of surface wave is elucidated, and the effect of variable angle α on the isolation efficiency within 40–50 Hz η40−50Hz of surface waves is studied. The results of this study reveal that for variable angle α of 15°, the surface wave isolation efficiency within 40–50 Hz η40−50Hz is 90.9 % and 92.5 % for uniformly increasing depth infilled trenches and uniformly decreasing depth infilled trenches, respectively. Although the surface wave isolation efficiencies predicted for the variable depth infilled trench arrangements are only 93.8 % and 95.5 % of those predicted for the regularly spaced identical infilled trenches, the variable depth arrangements result in a remarkable 34 % reduction in material usage. These findings highlight the potential of the proposed variable depth infilled trenches as a cost-effective and efficient solution for surface wave isolation.

Artificial Neural Network and Adaptive Neuro Fuzzy Inference System Hybridized Models in the Sustainable Integration of Language and Mathematics Skills: The Case of Singapore and Hong Kong

The four basic language skills, listening, speaking, reading, and writing play, a crucial role in the development of an individual’s skills in other disciplines. The current study aims to underpin the relationship between language skills and mathematics skills by focusing on the language and mathematics curricula of two consistently high-achieving countries, Hong Kong and Singapore, in the Program for International Student Assessment (PISA) rankings. In the current study, the convergent parallel mixed method was utilized that qualitative and quantitative data were composed together. Primarily, the outcomes of four language skills were determined in the native language teaching curricula of the two countries. The topics and themes related to four basic language skills were determined from the two mathematics curricula. The curricula were examined by document analysis from qualitative research methods. The analysis was conducted by examining the native language teaching and the mathematics curricula of both countries by the content analysis method. Later, the findings of the document analysis were used to develop machine learning models to find a possible positive relationship between language skills and the PISA scores. Although a number of previous studies have found a reasonable relationship between language skills and mathematics skills, the current study results were contradictory to the ones performed previously in the literature, and considering the curricula no positive relationship between the language and mathematics skills was found. The findings of the current study were further supported by the artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) model performance metrics. Compared to an acceptable level of 0.80, significantly low R2 values of 0.35 and 0.39 for the ANN and ANFIS models, respectively, indicated very little relationship between the language and mathematics skills.

Heat and Drought Have Exacerbated the Midday Depression Observed in a Subtropical Fir Forest by a Geostationary Satellite

Recently, increasing heat and drought events have threatened the resilience of Chinese fir forests. Trees primarily respond to these threats by downregulating photosynthesis including through stomatal limitation that causes a drop in productivity at noon (known as the midday depression). However, the effects of these events on midday and afternoon GPP inhibition are rarely analyzed on a fine timescale. This may result in negligence of critical responses. Here, we investigated the impact of climatic events on the midday depression of photosynthesis at a subtropical fir forest in Huitong from 2016 to 2022 using data from the Himawari 8 meteorological satellite and flux tower. Our results indicated that the highest number of midday depression occurred in 2022 (126 times) with the highest average temperature (29.1 °C). A higher incidence of midday depression occurred in summer and autumn, with 48 and 34 occurrences, respectively. Compound drought, heat, and drought events induced increases in midday depression at 74.3%, 66.0%, and 47.5%. Thus, trees are more likely to adopt midday depression as an adaptive strategy during compound drought and heat events. This study can inform forest management and lead to improvements in Earth system models.

Antarctic meltwater reduces the Atlantic meridional overturning circulation through oceanic freshwater transport and atmospheric teleconnections

The Atlantic meridional overturning circulation is an important component of the climate system because of its role in the heat transport. Its strength is sensitive to the surface density but mechanisms of the effect of Southern Ocean freshwater anomalies are relatively unknown. Here, we investigate the impact of Antarctic ice sheet meltwater on the Atlantic meridional overturning circulation using an earth system model of intermediate complexity. The meltwater over the Pacific sector of the Southern Ocean is transported to the east and, after passing the Drake Passage, travels northward reaching the North Atlantic. Furthermore, Southern Ocean cooling induces a northward shift of the Intertropical Convergence Zone, leading to more precipitation in the tropical Atlantic. Consequently, the reduced salinity weakens the Atlantic meridional overturning circulation. Additional experiments, in which the duration period of freshwater hosing was varied while keeping its total amount constant, indicate that the amplitude and the duration of the meltwater play crucial roles in determining the degree of reduction in Atlantic meridional overturning circulation.

Explanatory frameworks in complex change and resilience system modelling

Abstract Heterogenous flows across system boundaries continue to pose significant problems for efficient resource allocation especially with respect to long term strategic planning and immediate problems about allocation to address particular resource shortages. The approach taken here to modelling such flows is an engineering change prediction one. This enables margin modelling by producing system models in dependency matrices with different linkage types. Change prediction approaches from engineering design can analyse where these bottlenecks in integrated systems would be so that resources can be deployed flexibility to avoid them and address them when they occur. Current state of the art of margin research can be furthered by identifying margins on multiple levels of system composition. It can usefully be complemented by a category theory based approach which allows representation of variable and constant properties of models under changing conditions, and the identification of flows within models. Category theory is useful for formalising such explanatory frameworks as it can both structure systems and permit analysis of their applications in a complementary way.

Public Awareness: Understanding Register Of First Information Report Through Critical Discourse Analysis

The purpose of this study is to explore the linguistic aspects of police FIR using Michael Halliday’s system of transitivity and three-dimensional model of CDA, encompassing the macro-sociological analysis of social practice. FIR is a genre of discourse, referring to a report prepared by the police, which reaches the police station first after the commission of a cognizable offence. FIR needs thorough and careful reading, and very difficult to understand by the people of a particular community. In this regard, the Transitivity system is a medley of six different processes realizing ideational meaning which is further strengthened by CDA, which also helps the people of a community to comprehend complex discourses. In accordance with this study, the researchers used the qualitative approach as a paradigm. They used close reading technique as a strategy and relying on purposive sampling in the selection of data. Further textual analysis is used as a method of analysis. The researcher took five FIRs which were analyzed with respect to the transitivity system. This study reveals that the FIRs conform to five processes out six processes of the transitivity system and the three-dimensional model of CDA. Moreover, this study can be extended to other police reports and common people may easily expand their awareness to understand the language and context of FIRs.

Fuzzy inference system enabled neural network feedforward compensation for position leap control of DC servo motor

To improve dynamic performance and steady-state accuracy of position leap control of the direct current (DC) servo motor, a fuzzy inference system (FIS) enabled artificial neural network (ANN) feedforward compensation control method is proposed in this study. In the method, a proportional-integral-derivative (PID) controller is used to generate the baseline control law. Then, an ANN identifier is constructed to online learn the reverse model of the DC servo motor system. Meanwhile, the learned parameters are passed in real-time to an ANN compensator to provide feedforward compensation control law accurately. Next, according to system tracking error and network modeling error, an FIS decider consisting of an FI basic module and an FI finetuning module is developed to adjust the compensation quantity and prevent uncertain disturbance from undertrained ANN adaptively. Finally, the feasibility and efficiency of the proposed method are verified by the tracking experiments of step and square signals on the DC servo motor testbed. Experimental results show that the proposed FIS-enabled ANN feedforward compensation control method achieves lower overshoot, faster adjustment, and higher precision than other comparative control methods.

Information Modeling Implementation in PAO “Rosneft” as Challenge of Digitization in Construction Industry

Key goals of construction industry modernization and quality improvement are the introduction of information modeling which is an effective tool for increasing the enterprise competitiveness through the quality of design and construction, resource optimization and efficiency of interaction between process participants throughout the entire life cycle.The article presents information modeling (3D design) in PAO “Rosneft” with respect to the tasks of ensuring adaptation to design procedure in the company and the transition to full-scale 3D design. The choice of software is justified, real examples of completed pilot projects using information modeling are described, and information modeling implementation are presented. The experience in information modeling implementation helps companies to choose one or another software, and become familiar with the process at PAO “Rosneft”. As a result of the digital transformation of design processes, a model of the life cycle management system of capital construction project is developed, the basis of which is its information model. In addition, the main problems that require solutions for the successful information modeling implementation are identified, i.e., shortcomings of domestic software, shortage or absence of highly qualified personnel, unwillingness of customers to work with technical information, efficiency decrease of the organization due to the introduction of a new approach to project implementation into existing processes, resistance from employees, customers and contractors of construction, and installation works.The information modeling introduction in the PAO “Rosneft” allow to achieve goals for im[1]port substitution of technologies and prepare a worthy response to global geopolitical challenges.

Research on the Intelligent System of English Teaching from the Perspective of Intercultural Education Based on Deep Learning

In today’s globalized world, cross-cultural communication skills are crucial for English learners. However, traditional English teaching often overlooks cultural differences among students, leading to difficulties in practical communication. The development of deep learning technology has provided new possibilities for English teaching, which can analyze students’ cultural background and learning needs through intelligent systems, and provide more personalized teaching plans. This study focuses on the application of intelligent system in English teaching from the perspective of intercultural education. By analyzing the characteristics of cross-cultural education and the advantages of deep learning technology, an intelligent system model of English teaching based on deep learning is proposed. The model can provide individualized English teaching programs according to students’ cultural backgrounds, learning needs and learning progress. A recommendation algorithm based on a cross-cultural education model comparison is proposed. For each student, retrieve similar teaching cases from the database based on their cultural background and learning needs. Compare the similarity between current students and cases in the database using similarity calculation algorithms. Based on the sustainable development teaching model of college English, the MAP algorithm is analyzed. The experimental results show that the intelligent English teaching system based on deep learning has a remarkable effect on improving students’ English achievement and intercultural communication ability. The model is trained and tested by the measured data. The error of the test results is less than 5%. The results of the experiment prove that the construction of a Chinese college English sustainable development education model can effectively enhance China’s cultural soft power.

A System for Countering Possible Threats from the Use of Dangerous Robotic Systems

Based on the generalization and analysis of data from foreign and domestic studies, the article discusses the position that in some cases robotization poses threats proportional to its benefits. The problems and risks caused by the introduction of robotics and their manifestation in various spheres of human activity are outlined. It is noted that the downside of the successful development and use of robotics is the emergence of new types of hazards caused by robots getting out of control or intentionally using them for illegal purposes. The main problems associated with the use of robots are the following: conducting industrial, domestic and other types of espionage, violating traffic safety (air, automobile, marine, etc.), preparing and carrying out terrorist acts, as well as conducting other types of activities dangerous to people and the environment. The structure of the system for countering dangerous robotic complexes for various purposes (RTK VP) and the composition of its main components are proposed, the close relationship of tasks is highlighted, on the one hand, to ensure protection, and on the other, to organize counteraction to RTK VP at the stage of forming tactical and technical requirements for samples of newly created promising robotics products and determining their technical appearance. The analysis of RTK VP as objects of counteraction is given and the main elements of their vulnerability, including the vulnerabilities of groups of robots, are clarified. The probable variants of the illegal use of RTK VP, the causes of their occurrence, the features of their manifestation and the expected consequences are analyzed. The classification of possible ways and means of countering dangerous RTK VP is carried out, and the rational sequence of their implementation is reasoned. A formalized description of the task of countering dangerous RTK VP and a model of the counteraction system are presented. As an indicator of the effectiveness of the counteraction system, it is proposed to use the correspondence of actual and required damage to the object of counteraction in the dynamics of change. Based on the analysis of the features of the counteraction process, a methodological approach to creating a model of a promising anti-RTK VP system is proposed.