System Model Research Articles

A novel hybrid low-temperature thermal catalysis and radiative sky cooling system for day and night air purification and cooling

Thermal catalytic oxidation is an air purification technology that can efficiently and stably degrade volatile organic compounds. However, using thermal catalysis to purify indoor air in the summer can cause problems of indoor overheating. Radiative sky cooling is a passive cooling method that dissipates heat through reflection and radiation, which can provide sub-ambient cooling during day and night. In this study, a novel hybrid low-temperature thermal catalysis and radiative sky cooling system for day and night air purification and cooling was proposed, which combined thermal catalysis with radiative sky cooling, and used low-temperature driven thermal catalysts to solve the problem of indoor overheating in summer. A numerical model of the hybrid system was established to simulate the formaldehyde degradation and cooling performance of the system under different operating conditions. The results show that under summer conditions with an average daily temperature of 35°C and humidity levels between 70% and 80%, the system produced a total of 665.29 m³/m2 of clean air after running for an entire day. The average single-pass conversion rate of formaldehyde was 0.46, and the maximum temperature difference between indoor and outdoor air was 5°C. This provides guidance for the integration of the hybrid system with buildings.

Development of a prioritized traffic light control system for emergency vehicles

This research presents a model for an adaptive traffic signal control system aimed at improving urban traffic regulation. It dynamically adjusts signal timing based on vehicle volume at intersections, prioritizing emergency vehicles by allowing them immediate passage. Utilizing Arduino coding, the system controls traffic light intensity according to the traffic flow, enhancing road safety and efficiency. This innovative approach not only facilitates faster clearance for emergency services without human intervention but also reduces congestion and accident rates. This research creates a model for a prioritized traffic signal control system. When the vehicular volume at the intersection varies, the signal time alters autonomously. It identifies the ambulance/emergency vehicles and allows the green light for emergency vehicles like ambulances, and fire engines. This approach may be used to detect traffic accidents and infractions of automobile spiral motions. When erected on the road, the entire system allows for quick traffic clearing for rescue vehicles without requiring a policeman. The system's design eliminates the need for sensors or radio frequency identification (RFID) tags, simplifying traffic management. Simulations validate that emergency vehicle travel time is significantly reduced, proving the system's effectiveness in streamlining urban traffic flows.

A Linear Large-Signal Model for Inductive Power Transfer System

A Linear Large-Signal Model for Inductive Power Transfer System

Experiment Modelling of Automated Verification Control on Environmental Conditions During Crop Storage

The study introduces a preliminary approach to enhance the supervision of food processing operators. Specifically, it showcases a digital model of an online compliance validation system with environmental conditions designed for food processing and preservation items that are sensitive to climate factors. The technology demonstration involves four fundamental stages carried out in a complete experimental cycle, where intelligent sensors gather and transmit data through a low-power wireless wide-area network from a farm specializing in sunflower seed storage. Subsequently, six consecutive scripts are executed, five of which are coded in Python. The integration of distributed data technology brings objectivity to the process of securely storing environmental parameters and objectively verifying their compliance with recommended hygienic, bio certification standards. As a result, monitoring the food production process becomes more publicly accessible and with higher rates of objectivity in decisions, enabling prospective buyers and certification agencies to assess the quality of food products with great precision. Therefore, the suggested digital model has the potential to ensure the utmost transparency in the food processing sector.

Dynamic characteristics of pumped thermal-liquid air energy storage system: Modeling, analysis, and optimization

Pumped thermal-liquid air energy storage (PTLAES) is a novel energy storage technology that combines pumped thermal- and liquid air energy storage and eliminates the need for cold storage. However, existing studies on this system are all based on steady-state assumption, lacking dynamic analysis and optimization to better understand the system’s performance under cyclic operation. To fill this gap, the mainbody-linearized cyclic dynamic model of the PTLAES system with packed bed thermal energy storage (TES) was first developed. Then, the dynamic characteristics of the baseline system were investigated. Sensitivity analyses were carried out on TES parameters. Minimal values of levelized cost of storage (LCOS) were observed for all parameters in the range of interest. Subsequently, the TES circuit was optimized, and a triple improvement of efficiency and energy density enhancement, discharge stabilization, and cost reduction was achieved. The optimized system's round-trip efficiency and energy density increased from 61.7% to 63.1% and from 141.9 kWh/m³ to 159.2 kWh/m³, and the LCOS decreased from 163.2 $/MWh to 159.4 $/MWh. A power offset ratio lower than 3% was reached, which is the lowest value ever reported in the literature. This study provides reference for future design and operation of the PTLAES system.

On-line recognition of mixture control chart patterns using hybrid CNN and LSTM for auto-correlated processes

Statistical process control (SPC), designed to detect and identify process disturbances, is an effective quality control method for ensuring process stability. Owing to the growing automation of the industry, the manufacturing process can be autocorrelated. Engineer process control (EPC) is typically used to address autocorrelation. However, process disturbances can be offset by feedback compensation, making control chart patterns (CCPs) difficult to identify. Most studies on control chart pattern recognition (CCPR) techniques are based on a single abnormal control chart pattern (CCP). However, a mixture of CCPs can occur during real-world manufacturing processes. With the development of intelligent manufacturing systems, early detection of abnormal concurrent CCPs has become an important issue. In this study, a hybrid model combining a convolutional neural network (CNN) and long short-term memory (LSTM) in an online detection system was used to recognize the concurrent CCPR problem in SPC-EPC processes. The results showed that the average accuracy of the deep learning CNN-LSTM method was 99.83%, which was significantly better than that of the machine learning method. In addition, the running time of the CNN-LSTM model was shortened. In a comparison of online monitoring between the deep learning method and the machine learning method, the CNN-LSTM model for an online monitoring system identified abnormal concurrent patterns faster. Therefore, the proposed CNN-LSTM online monitoring scheme can be applied confidently and successfully to identify mixture CCPs in an SPC-EPC process.

Energy efficiency and economic performance of a low-temperature heating system combining double-layer pipe-embedded wall and ground source heat pump

Reducing heating energy consumption in buildings is a crucial step towards a low-carbon future. The energy efficiency of heat pump heating systems can be improved by reducing the water supply temperature of terminals. However, existing terminals are limited in using lower-temperature hot water and leaves potential for energy savings. Therefore, this study proposes a double-layer pipe-embedded wall heating system coupled to a ground source heat pump. The outer pipes use shallow geothermal energy to reduce envelope load, whereas the inner pipes use water produced by the heat pump to provide heating. Energy, economic, and carbon emission analysis models of the proposed system are developed. A residential heating case study is conducted in Harbin, Shenyang, Beijing, Zhengzhou, and Shanghai to investigate the performance of the proposed system. The results show that the proposed system achieves much lower-temperature heating than the reference floor heating system. The seasonal energy savings of the proposed system in the selected cities are 12%–18%, with a SCOP of 4.0–5.8. The dynamic payback period of the proposed system is 1.6–4.3 years and the CO2 emission intensity is reduced by 1.2–3.2 kg/m2. This study provides an insight into the application of DPEW heating systems.

Comparative Time Series Analysis of SARIMA, LSTM, and GRU Models for Global SF6 Emission Management System

To maintain the balance of the atmosphere, the amount of change in greenhouse gas emissions must be under control. In order to create a management system and take forward-looking steps in this regard, there is no concrete data other than prediction models today. The success of prediction methods is better understood by comparing multiple methods. This research estimates the changes in the emissions of Sulfur Hexafluoride gas (SF6) in the atmosphere using Seasonal Autoregressive Integrated Moving Average Model (SARIMA), Long-Short Term Memory Neural Network (LSTM) and Gated Recurrent Unit (GRU) forecast models and compares their accuracies. Focusing on monthly SF6 emission values between 1998; 2023, time series analysis was performed to predict future emission figures. The actual values and forecast results were compared and evaluated with performance criteria such as R2, RMSE, NSE, MAE and MAPE%. The findings of this research highlight a continious upward trend in SF6 emissions and project that emission levels could approximately double from current levels by 2050. During the analysis process, all three methods performed well in estimating global SF6 gas emissions. The LSTM model generally outperformed SARIMA and GRU models, having the lowest MAPE (0.003%), MAE (0.0003), RMSE (0.0003), and R2 (1) values. It also exhibited very high predictive success with an NSE value of 0.9991. Therefore, it was determined to be the most suitable estimation method with the least error. The aim of this study is to contribute scientifically to the reduction strategies of SF6 emissions.

A fractional adaptive type-2 fuzzy structural control system: Theoretical/experimental study

A new modified sliding mode control (SMC) method based on type-2 fuzzy neural networks (T2FNNs) is introduced for the active mass damper (AMD) systems. An adaptive T2FNN is used in the switching part, and another adaptive T2FNN is used to estimate the uncertainty of the AMD system. T2FNNs are adopted to predict the system’s uncertainties and establish a dynamic model of the AMD system, independent of the mathematical model. The chattering phenomenon is also taken into account and analyzed. The stability is studied by using the Lyapunov approach to derive training rules for both T2FNNs in dynamic modeling and switching part. Numerical simulation and experimental verifications confirm the feasibility and effectiveness of the designed T2FNN based SMC. The results reveal that the designed controller outperforms the conventional controllers in reducing the vibration peak and reducing the root mean square (RMS) value of structural displacement and acceleration. It also exhibits good robustness against external disturbances and structural dynamic perturbations. The suggested algorithm combines the advantages of adaptive control and fuzzy logic, addressing the issue of chattering in control and overcoming the lack of self-learning capability in conventional SMCs.

A novel heat load prediction model of district heating system based on hybrid whale optimization algorithm (WOA) and CNN-LSTM with attention mechanism

Machine learning models, particularly long short-term memory (LSTM) networks, have been extensively employed for heat load prediction in district heating systems (DHS). Nevertheless, the over-reliance on default hyperparameter settings in most methods hinders further enhancement of prediction accuracy. A novel load prediction model is presented, which integrates the whale optimization algorithm (WOA) to refine the hyperparameters of an LSTM model bolstered by an attention mechanism (ATT) and convolutional neural network (CNN). Three hybrid models (WOA-CNN-ATT-LSTM, PSO-CNN-ATT-LSTM and GA-CNN-ATT-LSTM) are constructed by comparing WOA with particle swarm optimization (PSO) and genetic algorithm (GA). The proposed hybrid models are evaluated against traditional LSTM models using an 1100-hour dataset from a real DHS. The outcomes reveal that the WOA-CNN-ATT-LSTM model surpasses both the PSO-CNN-ATT-LSTM and GA-CNN-ATT-LSTM models in heat load prediction accuracy, achieving improvements of 1.9% and 3.2% respectively, and attaining the highest prediction accuracy (R2=0.9962, MSE=0.0001, MAE=0.0082). Moreover, the WOA-CNN-ATT-LSTM model demonstrates superior performance across various time scales (half-day, one-day, three-days, and one-week), highlighting its robustness in heat load prediction. This novel model adaptively adjusts its hyperparameters to identify the optimal configuration, thereby significantly augmenting the overall predictive capabilities of the model.

Enhancing power quality in wireless DC power supplies through active power filtering: A computer simulation approach

This paper presents a computer simulation model for a high-power factor wireless DC power supply system, integrating an active power filter (APF) at the rectifier stage on the transmitter side using a rectifier boost technique. The APF, employing a MOSFET switch regulated by active pulse width modulation (APWM) within a current control loop, addresses pulsating and distorted AC supply currents caused by non-linear loads. A robust closed-loop control mechanism, including a subtractor circuit, proportional-integral (PI) controller, and comparator, ensures the generation of a continuous sinusoidal waveform synchronized with the supply voltage. The model utilizes a high-frequency inverter to convert DC to AC, which is then wirelessly transmitted via wireless power transfer (WPT) technology and converted back to DC by a high-frequency rectifier. MATLAB/Simulink simulation results show a significant reduction in total harmonic distortion (THD) of the AC supply current, complying with IEEE 519 standards. Selected results are presented to verify the proposed method's effectiveness in reducing harmonic distortions and enhancing power quality. This study highlights the advantages of WPT in scenarios where traditional wired connections are impractical and underscores the potential of this system for real-world applications, particularly in developing high-power factor wireless DC power supply systems.

A testing and data processing method of projectile explosion position based on three UAVs’ visual spatial constrain mechanism

To accurately obtain the position of the projectile explosion relative to the attack target, this paper propose a new method for measuring the explosive position of projectile using three UAVs’ visual spatial constrain, this method is based on the platform of the UAV equipped with a high-speed camera, and uses the UAV to control the imaging angle of the high-speed camera to form the visual rendezvous mechanism of multiple high-speed cameras. Based on obtaining the pixel coordinate position of projectile explosion in each high-speed camera, we establish the measurement geometric calculation model of the projectile explosive position, and give the coordinates calculation function of projectile explosive that relative to the attack target. To determine the projectile explosion real position, we put forward a measurement data fusion processing method to obtain the real projectile explosion position, and give the data fusion processing algorithm based on projectile explosion position data under the pairwise UAVs visual rendezvous. we also established the engineering uncertainty error calculation model of the test system. Under the testing range of 150 m × 150 m × 30 m, we verify the feasibility and correctness of the proposed test method, the results show that the method proposed in this paper is not only suitable for three-direction coordinate measurement of projectile explosive in the high altitude, but also its measurement error is less than 0.3 m compared with multi-screen optical method.

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.

Modeling and Construction of Underwater Single Wire Power Transfer System

Modeling and Construction of Underwater Single Wire Power Transfer System

Circular economy-based integrated closed-loop farming system: A sensitivity analysis for profit optimization

This study proposes and constructs a model for a closed-loop farming system (CLFS) for pig manure waste reuse under circular economy principles (CEPs) to optimize economic benefits. Pig farming produces waste that harms the ecological environment in Taiwan. However, few studies have focused on pig farming waste within the context of CEPs, where the resource utilization of pig manure waste and CLFS are the main concerns. This study introduces a CLFS with a farming production plan that enhances systematic efficiency and builds a framework for resource reuse, supporting a local living circle. The resource utilization of pig manure waste is integrated into the CLFS, ensuring that the entire pig raising production process operates within the system, maximizing waste utilization to increase economic benefits. A sensitivity analysis is also presented. The results confirm that all counties and municipalities in Taiwan achieved self-sufficiency in pork production, reaching a 100% satisfaction rate for pork demand, and feed prices did not impact the overall CLFS.

Human–Machine coupled modeling of mandibular musculoskeletal multibody system and its application in the designation of mandibular movement function trainer

Many patients suffering from oral and maxillofacial tumors present trismus in the six months following mandibulectomy. Traditional mandibular movement function trainers (MMFT) cannot fulfill patient-specific targeted training, but mandibular musculoskeletal modeling can reveal patient-specific muscle recruitment patterns. This study proposed a digital rehabilitation framework for patients via mandibular musculoskeletal simulations. A flexible MMFT consisting of a soft intraoral airbag and a cable-driven extraoral trainer was designed. A human–machine coupling model was established to estimate the patient-specific muscle activations. Here, the intraoral trainer was modeled as a force vector, and the extraoral trainer was discretized by the flexible cable elements via an arbitrary Lagrangian–Eulerian description. Dynamic optimizations were performed to determine the patient-specific auxiliary forces, and the obtained values were utilized to design a quantitative rehabilitation plan. The effectiveness of the MMFT at increasing the magnitude of mandibular opening was validated with in vitro measurements. Numerical predictions for eight patients demonstrated that the proposed rehabilitation framework could improve the patient's jaw opening magnitude by an average of 3.8 ± 2.0 mm, highlighting the importance of subject-specific musculoskeletal modeling in mandibular rehabilitation.

Modeling microbial growth based on time-dependent kinetic mechanisms of digestion and passage in the ruminoreticulum

The original Cornell Net Carbohydrate and Protein System (CNCPS) model, which was developed in 1992 and revised in 2004, included a rumen submodel with a set of equations that were algebraically programmed in a spreadsheet to predict bacterial N flow to the caudal tract of cattle. In this study, we propose a modification to the original CNCPS rumen submodel based on mathematical concepts that simulate the flow paths of solids and liquid in the rumen, which are known to be essential factors affecting rumen fermentation and microbial growth. This modification allows the quantification of the impact of aging mechanisms on the availability of both soluble and insoluble substrates for microbial growth in the rumen. Additionally, a correction has been proposed on peptide uptake by rumen bacteria. Literature data were used to evaluate the model adequacy of CNCPS versions 1992 and 2004, along with the proposed model, to determine their ability to predict bacterial N flow. The proposed model more accurately predicted the variable of interest, but there was an overall underprediction bias. Despite the rightful critics of the ability of mechanistic models to predict microbial crude protein flow from the rumen, the 1992 and 2004 versions of the CNCPS and the proposed modifications implemented within the CNCPS framework led to predictions that agreed with observational data at a discordance rate of 0.05, tolerance probabilities <0.001 for versions 1992 and 2004, and a tolerance probability equal to 0.13 for the proposed model given a sample size n=39. Therefore, the proposed modifications might improve the ability of CNCPS-based models to predict the bacterial N flow from the rumen, thereby resulting in improved predictive performance compared to the original CNCPS model. Thus, it seems that models built on time-dependent kinetics of food particles and fluid are more fitting than steady-state ones to predict bacterial N flow to the caudal tract.

Using predictive analytics systems to resolve a legal dispute

Increasingly, in the mass media, we hear about examples of using predictive analytics systems to obtain solutions to legal disputes. However, from the viewpoint of legal regulation, the question arises: Can we consider a solution proposed by the system to be final and legally significant, or just one of a possible set of solutions? A parallel with legal principles is drawn in the scientific literature analyzing the prospects for such systems application. Researchers come to disappointing predictions about possible risks to human rights and freedoms if the solutions proposed by predictive systems are approved without human participation. In our study, we came to the following conclusions. Firstly, at the moment of technological development, intelligent systems cannot explain why they make certain decisions. Secondly, because the system’s decision-making is not transparent, it is incorrect to assume that programmers or developers replace the judge. The role of programmers and developers of an intelligent system model is very important but purely technical. Thirdly, the problem of inaccuracy in the system’s decisions refers only to the stage of system training. The higher the quality of the datasets and the more data sets there are, the more accurate the decision made by this technology will be. That is why forming correct datasets is an independent and challenging technological task.

A method for the core accident chain based on fuzzy-DEMATEL-ISM: An application to aluminium production explosion

The occurrence of accidents is often influenced by many factors, which result in the complexity of the causes of accidents. Therefore, clarifying the accident chain of the explosion accident is of great significance for the daily management of safety. In this study, a novel method of the core accident chain based on the traditional fuzzy-DEMATEL-ISM method was proposed. This method can clarify the importance of each accident chain and rank them accordingly by calculating their weighted centralities. Then, the explosion accident caused by the contact of molten aluminium and water, which has caused heavy casualties and property losses, is analyzed systematically. In order to clarify the impact relationships and hierarchical structure of the various factors in the complex explosion accident systems, and establish the accident system models, twenty-two factors were summarized, and the evolution process of explosion accident were obtained by the fuzzy-DEMATEL-ISM approach. The results reveal that equipment damage or failure has the most direct impact to the explosion accidents. The core accident chain that national policy (aluminium market demand)→incomplete safety management system→disordered on-site management→insufficient lighting→physical discomfort→improper operation→equipment damage or failure is obtained, which has the most significant influence on the occurrence of explosion accidents. The result analysis is employed to give some advice. This research is helpful for safety practitioners to develop accident prevention strategies and make aluminum production safer.

The Role of Sea Ice Insulation Effects on the Probability of AMOC Transitions

Abstract Recent simulations performed with the Community Earth System Model (CESM) have suggested a crucial role of sea ice processes in Atlantic meridional overturning circulation (AMOC) hysteresis behavior under varying surface freshwater forcing. Here, we further investigate this issue using additional CESM simulations and a novel conceptual ocean–sea ice box model. The CESM simulations suggest that the presence of sea ice gives rise to the existence of statistical equilibria with a weak AMOC strength. This is confirmed in the conceptual model, which captures similar AMOC hysteresis behavior as in the CESM simulation and where steady states are computed versus freshwater forcing parameters. In the conceptual model, transition probabilities between the different equilibrium states are determined using rare event techniques. The transition probabilities from a strong AMOC state to a weak AMOC state increase when considering sea ice insulation effects and indicate that sea ice promotes these transitions. On the other hand, sea ice insulation effects strongly reduce the probabilities of the reverse transition from a weak AMOC state to a strong AMOC state and this implies that sea ice also limits AMOC recovery. The results here indicate that sea ice effects play a dominant role in AMOC hysteresis width and influence transition probabilities between the different equilibrium states. Significance Statement We develop a novel conceptual ocean–sea ice box model to explain AMOC hysteresis behavior recently found in a modern complex climate model (the Community Earth System Model) and determine how sea ice insulation effects influence the hysteresis width and the probability of AMOC transitions.