International Journal of Simulation and Process Modelling (5 papers in press)

Regular Issues

• 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 low temperature of the air supply in the pig nursery in cold areas during winter, the ventilation preheating system and control strategy are designed. The proposed system combines in house heating, a heat exchanger with 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
  Keywords: ventilation preheating; computational fluid dynamics; adaptive neuro fuzzy inference system; cold region.
  DOI: 10.1504/IJSPM.2025.10071181
   
  
• Higher coordination system for a scheduling and control integrated layer management in process industry  
  by Eugênio Costa, Mauricio 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; and (4) it is able to deal with plant disturbances and also with changes in input scenarios. A non-linear 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 minimizes the loss the perturbation may cause and able 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
   
  
• 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 Muh. Ridho Yusuf SAP 
  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 climatic conditions, pollution levels, and social dynamics, to analyze and simulate pneumonia control strategies. The model emphasizes the integration of local wisdom in shaping effective interventions. The research explores equilibrium points, stability analysis, and the basic reproduction number (R_0 ), 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 modeling 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
   
  
• 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 optimized 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 optimize 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 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