International Journal of Simulation and Process Modelling (10 papers in press)

Regular Issues

• Open Access

  Modelling and simulation of AI-driven operation and maintenance processes: a case study in broadcasting systems ( Free Full-text Access )

  by Ling Niu 
  Abstract: The conventional manual operation and maintenance mode has been challenging to satisfy the modern operation and maintenance needs in the radio and television sector as demand for equipment operation and maintenance efficiency and stability rises. This research thus suggests an intelligent operations and maintenance (O&M) system based on artificial intelligence (AI), hoping to increase the O&M efficiency and fault response capacity of broadcasting and television transmitters via four components. The method is based on discrete event simulation and LSTM data-driven modelling. The anomaly detection loop is implemented through isolation forest, and the large application possibility of the intelligent O&M system in terms of response timeliness and task scheduling efficiency is revealed by the trial findings. The experimental verification framework reduces response time by 38% and improves task scheduling efficiency by 42%, reflecting the value of cross domain simulation optimisation.
  Keywords: AI-driven; intelligent operations and maintenance; broadcasting and television transmitters.
  DOI: 10.1504/IJSPM.2025.10074465
   
  

• Open Access

  A simulation-based modelling framework for personalised design using an improved generative adversarial network ( Free Full-text Access )

  by Mingyu Li 
  Abstract: As a key component of the creative sector, art typeface design has progressively taken front stage given the growing demand for customised design. Manual design is common in traditional font design, which takes time and cannot rapidly adjust to individual needs. This paper presents StyleGANFont, a personalised art font design model based on improved generative adversarial network (GAN), which is capable of producing high-quality, diversified and personalised art fonts by means of multi-level style control, adaptive personalisation modelling and real-time user feedback to meet the demand for fast and tailored font design. To create typefaces, StyleGANFont has clear benefits according to the comparison and ablation experiments. At last, this work also addresses the direction of next research to support the continuous advancement of personalised art font generating technologies.
  Keywords: personalised art font generation; improved GAN; multi-level style control; user-preference modelling.
  DOI: 10.1504/IJSPM.2025.10074328
   
  
• Simulation-driven deep learning framework for early poultry disease detection using faecal image classification with optimised CNN architectures  
  by Nonita Sharma, Monika Mangla, Manik Rakhra, Baljinder Kaur, Raj Kumar Mohanta, Bijay Kumar Paikaray 
  Abstract: The present investigation aims to devise an optimized Convolutional Neural Network (CNN) framework to identify prominent poultry diseases based on faecal images. The proposed research model uses a tri-convolutional layer architecture to enhance the accuracy of poultry disease classification and improve feature extraction. Three major diseases, namely coccidiosis, salmonella, and Newcastle, have been considered. The prime objective of current research is to achieve early detection by employing advanced deep-learning techniques. The proposed model uses fecal images to identify pathological conditions using the TriConvLayer architecture accurately. In this work, custom CNN models, viz. SoloConvLayer, TriConvLayer, and FiveConvLayer models are used to achieve an accuracy of 97%, 98%, and 98%, respectively. The achieved result advocates the efficacy of the proposed approach. It thus has the potential to revolutionize early disease detection in poultry farming, a major step towards improving animal health and farm productivity.
  Keywords: deep learning; poultry disease detection; convolutional neural networks; CNN; faecal image analysis; poultry health monitoring; disease classification; simulation framework.
  DOI: 10.1504/IJSPM.2025.10073525
   
  
• Simulation and verification of binocular vision of pipeline cracks using DCW-YOLOv7 with a snake robot  
  by Man Li, Jingwei Liu, Yahui Wang 
  Abstract: This paper proposes a video processing method - DCW-YOLOv7 algorithm - based on the binocular camera of a snake robot for pipeline detection to detect cracks on the inner wall of pipelines. This method improves the accuracy of internal crack identification. The key innovation was the integration of the dynamic snake convolution module and the coordinated attention mechanism, resulting in the development of the ELAN-DSC and C3CA modules. The ELAN module in YOLOv7 has been optimised to enhance the feature extraction capabilities of the network, especially for weak and difficult-to-identify crack features. In order to reduce the influence of fuzzy images on the learning ability of the network and enhance the robustness of the model, the Wise-IoU is used to optimise the loss function. Simulation results show that DCW-YOLOv7 has superior performance compared with baseline YOLOv7 model, the experiment verifies the engineering application prospect of this algorithm.
  Keywords: crack detection; snake robot; DCW-YOLOv7; simulation studies; accuracy enhancement.
  DOI: 10.1504/IJSPM.2025.10073523
   
  
• Simulation-driven optimisation of SiO₂ aerogel thermal insulation coatings using response surface methodology  
  by Minghui Liu, Shuang Men, Li Wei 
  Abstract: This study employs response surface methodology (RSM) to optimise the composition of SiO aerogel-based thermal insulation coatings. Single-factor experiments identified SiO aerogel, glass microspheres, and fumed silica as key factors influencing thermal conductivity. The optimised composition - 9.03% SiO₂ aerogel, 76.13% glass microspheres, and 0.89% fumed silica - achieved a thermal conductivity of 0.03861 W/mK at 100°C, significantly outperforming traditional insulation materials. The simulation results using RSM were validated experimentally, showing strong agreement. The study also analyses the thermal insulation mechanisms, attributing the performance to the low density, high porosity, and nanoscale pore size of the coatings, which reduce heat transfer. These findings highlight the potential of RSM as a simulation tool for material optimisation and underscore the industrial applicability of SiO₂ aerogel coatings in energy-efficient insulation systems. The research demonstrates the effectiveness of simulation-driven design in advancing thermal insulation materials for sustainable industrial applications.
  Keywords: SiO₂ aerogel; thermal conductivity; response surface methodology; RSM; simulation optimisation; insulation coatings; process modelling.
  DOI: 10.1504/IJSPM.2025.10074855
   
  
• Higher coordination system for a scheduling and control integrated layer management in process industry  
  by Eugênio Pacceli Costa, Maurício Figueiredo 
  Abstract: A higher coordination system is designed to manage the scheduling and control integrated layer architecture in process industries. Among its most important features are: 1) it may be used considering any combination of usual strategies adopted for scheduling and process control; 2) it is flexible and scalable; 3) it makes the scheduling and process control tasks synergistically integrated; 4) it is able to deal with plant disturbances and also with changes in input scenarios. A nonlinear chemical process is considered for analysis and comparison purposes according to several experiments. The system is able to identify, if any, a new schedule that minimises the loss the perturbation may cause and to replace the schedule generated initially. Simulation results show that the system manages the layer integration leading to a better performance than the case in which the layers operate in a segregated way.
  Keywords: process industry; process control; scheduling and control layer integration; enterprise integration.
  DOI: 10.1504/IJSPM.2025.10072780
   
  
• Discrete element method simulation of particle size ratios and fill factors in rotating drum  
  by Lin Wang, Meng-Cheng Li, Jin-Cai Chang 
  Abstract: The motion behaviour of multi-sized particles in a rotary drum significantly impacts heat and mass transfer rates, yet studies on ternary particle mixtures remain limited. This study employs the discrete element method (DEM) to simulate the motion of particle materials under varying filling factors and particle size ratios. Results indicate that for uniform particle sizes, a higher rotational speed increases active layer thickness when the drum-to-particle diameter ratio is 100. At a rotational speed of 2 rpm, a filling factor close to 0.25 achieves optimal particle mixing. For mixtures with stable mass, larger average particle sizes result in greater average total forces and reduced translational velocity fluctuations. However, a particle size ratio of 3:1:6 is not recommended, as it promotes sedimentation patterns unfavourable for heat transfer when large particles dominate in mass but are numerically fewer.
  Keywords: rotating drum dynamics; discrete element method simulation; granular material flow; particle segregation; multi-sized particle interaction.
  DOI: 10.1504/IJSPM.2025.10075061
   
  
• Modelling the impact of pneumonia influenced by air pollution and climate change: a numerical approach in South Sulawesi  
  by Suwardi Annas, Syafruddin Side, Muhammad Ansarullah S. Tabbu, Ahmadin Ahmadin, Andi Muhammad Ridho Yusuf Sainon Andi Pandjajangi 
  Abstract: Pneumonia remains a pressing health issue in Indonesia, particularly in South Sulawesi Province, influenced by environmental factors like air pollution and climate change. This study introduces the SEILRQ-D model, a novel framework incorporating key variables such as climate conditions, pollution levels, and social dynamics, to analyse and simulate pneumonia control strategies. The model emphasises the integration of local wisdom in shaping effective interventions. The research explores equilibrium points, stability analysis, and the basic reproduction number (R₀), demonstrating the potential for controlling pneumonia outbreaks. Simulations using advanced numerical methods highlight the model's applicability to diverse scenarios beyond the specific case of South Sulawesi. Findings indicate that community-based strategies significantly enhance disease management efforts, offering a blueprint for broader applications in similar environmental and epidemiological contexts. This work advances the modelling and simulation of infectious diseases, providing actionable insights for global public health planning.
  Keywords: pneumonia; homotopy perturbation 8th order; climate change; simulation; optimisation.
  DOI: 10.1504/IJSPM.2025.10073122
   
  
• Technical and economic feasibility analysis based on statistical modelling and simulation of thermal power plant projects  
  by Eduardo S. Piropo, Adonias M.S. Ferreira, Anastácio P.G. Filho 
  Abstract: This paper proposes a techno-economic assessment methodology based on financial simulations and statistical models to identify the most suitable alternative among those analysed for the implementation of a thermal power plant. To this end, a database was constructed comprising more than 1,300 data points generated through simulations in financial spreadsheets, covering 98 different scenarios. This database was subsequently processed using specialized software, which enabled the development of statistical models and graphical representations essential for analysing the results. The findings demonstrate that the most advantageous project features lower capital expenditure (CAPEX), a higher unit variable cost (CVU), and higher revenue. Therefore, the methodology developed differs from the traditional approach by enabling a more comprehensive and robust analysis, offering enhanced support for decision-making. Additionally, it has potential applications in other industrial sectors, contributing to the advancement of the energy industry.
  Keywords: thermal power plants; technical and economic feasibility; statistical modelling; simulation-based analysis; energy project optimisation; RStudio for process modelling.
  DOI: 10.1504/IJSPM.2025.10074399
   
  
• Design of ventilation preheating system for pig nursery based on ANFIS  
  by Zhidong Wu, Kaixiang Xu, Yanwei Chen, Meiqi Liu, Yonglan Liu 
  Abstract: Aiming at the problem that the temperature of the air supply in the pig nursery in cold areas during winter is too low, the ventilation preheating system and control strategy are designed. The proposed system combines in-house heating, a heat exchanger and a ventilation heater. The adaptive neuro fuzzy inference system (ANFIS) is established by learning sample data, and the ventilated heater controller is designed. The results of simulations and field tests show that the proposed system based on the ANFIS algorithm can efficiently raise the temperature of the air supply to 20 ± 0.5°C. The temperature inside the pig nursery is maintained in the range of 22°C to 25°C. The temperature inside the pig nursery is suitable for the healthy growth of pigs.
  Keywords: ventilation preheating; computational fluid dynamics; CFD; adaptive neuro fuzzy inference system; cold region.
  DOI: 10.1504/IJSPM.2025.10071181