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A Combined Algorithm Using Both the MINLP Model and Approximated MILP Model for PVC Production Scheduling.

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Area of Science:

  • Chemical Engineering
  • Operations Research
  • Industrial Process Optimization

Background:

  • Polyvinyl chloride (PVC) production scheduling is critical for cost efficiency.
  • Existing scheduling models may lack speed or accuracy for practical applications.
  • Calcium carbide method is a significant route for PVC synthesis.

Purpose of the Study:

  • To design a continuous-time scheduling model for PVC production via the calcium carbide method.
  • To propose an improved mixed-integer nonlinear programming (MINLP) model to minimize total production costs.
  • To develop a combined algorithm for rapid and high-quality scheduling solutions.

Main Methods:

  • Development of a continuous-time MINLP model for PVC production scheduling.
  • Creation of a combined algorithm integrating MINLP and approximated mixed-integer linear programming (MILP) models.
  • Utilizing the MILP solution as an initial value for the MINLP model to accelerate convergence.

Main Results:

  • The proposed combined algorithm significantly accelerates the computation process for PVC production scheduling.
  • The method achieves more accurate optimal solutions compared to traditional approaches.
  • Validation through two actual case studies confirms the algorithm's effectiveness.

Conclusions:

  • The improved MINLP model and combined algorithm offer a superior approach for PVC production scheduling.
  • This method enhances both the speed and accuracy of optimization, leading to cost reductions.
  • The approach is practical and effective for real-world PVC manufacturing scenarios.