Forthcoming and Online First Articles

International Journal of Simulation and Process Modelling

International Journal of Simulation and Process Modelling (IJSPM)

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International Journal of Simulation and Process Modelling (3 papers in press)

Regular Issues

  • A novel variable drive modelling approach for general framework of chemical production scheduling   Order a copy of this article
    by Yuandong Chen, Jinhao Pang, Zhen Jiang, Yuchen Gou, Dewang Chen 
    Abstract: In this paper, we present a modelling approach named as variable drive modelling (VDM) to develop general modelling framework of chemical production scheduling. VDM builds model from variable (i.e., variable-based), while the traditional method building model from rule aspect (i.e., rule-based) with constraint blocks such as allocation constraints, mass balance, capacity constraints and operation rule constraints. In this paper, we analyse three shortcomings of the traditional rule-based model building method. A novel scheduling time axis is presented, while the confusing position of variables at time axis can be avoided. A systematic method to define variables and description rules of driving functions are given. At last, a crude oil scheduling case was given out to illustrate how to implement this approach. The results show less variable numbers, less constraints, less branching nodes, and more less solution time of the proposed approach against the existing model.
    Keywords: simulation and modelling; scheduling; variable drive modelling; VDM; chemical processes; refinery.
    DOI: 10.1504/IJSPM.2024.10063225
  • A fusion model of gated recurrent unit and convolutional neural network for online ride-hailing demand forecasting   Order a copy of this article
    by Xijin Cui, Mingxia Huang, Lei Shi 
    Abstract: This paper collects and analyses the impact of weather, air quality and point of interest data on residents’ daily travel, establishes a fusion model combined the convolutional neural network based on point of interest data and gated recurrent neural network prediction model to investigate the influence of weather and air quality on the demand of online ride-hailing, uses Pearson correlation coefficient to calculate the correlation between various external factors and ride-hailing order data. Analyse the important factors affecting ride-hailing order volume through correlation analysis. In order to improve the stability of the network, a residual module is added. The results show that the models constructed in this paper has good prediction accuracy. The study shows the incorporation of multi-source data can effectively improve the prediction accuracy of the online ride-hailing prediction model.
    Keywords: online ride-hailing demand; gated recurrent unit; GRU; convolutional neural network; CNN; travel demand.
    DOI: 10.1504/IJSPM.2024.10063423
  • Lane detection method based on improved Hough transform   Order a copy of this article
    by Yimin Yang 
    Abstract: In intelligent driving, how to keep the vehicle on the road safely and accurately without deviating from the road, is an important topic. In practice, machine vision is commonly used to effectively detect lane lines, so as to alarm vehicles that deviate from lane lines. In this paper, the detection of lane lines includes image pre-processing to obtain areas of interest, histogram enhancement for low-contrast images, median filtering to remove image noise while preserving details, and Otsu threshold segmentation method to separate targets in images. After image pre-processing, the Laplacian of Gaussian operator is selected for edge detection by comparing and analysing several operators. Finally, the improved Hough transform is used to realise the lane detection within the limited parameters, reducing the computation and saving the running time. Experimental results show that the proposed algorithm can effectively detect lane lines in normal weather or under low contrast.
    Keywords: lane detect; image enhancement; edge detection; Hough transform.
    DOI: 10.1504/IJSPM.2024.10064171