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Related Experiment Videos

Cleaner production for continuous digester processes based on hybrid Pareto genetic algorithm.

Fu-Jiang Jin1, Hui Wang, Ping Li

  • 1Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China.

Journal of Environmental Sciences (China)
|February 27, 2003
PubMed
Summary
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This study introduces a new modeling and optimization approach for pulp production, using genetic algorithms to reduce pollution and energy use. The method effectively cuts pollutants and boosts profits while maintaining pulp quality.

Area of Science:

  • Chemical Engineering
  • Environmental Science
  • Computer Science

Background:

  • Pulping production generates significant wastewater and pollutants, posing environmental challenges in the pulp and paper industry.
  • Implementing cleaner production strategies is crucial for mitigating the environmental impact of industrial processes.

Purpose of the Study:

  • To develop and apply advanced modeling and multi-objective genetic algorithms for optimizing the continuous digester process in pulp production.
  • To integrate environmental pollution and energy-saving factors into the process model.

Main Methods:

  • A mathematical model was established considering environmental pollution and energy saving.
  • A hybrid genetic algorithm incorporating Pareto stratum-nichecount was designed to find optimal solutions.

Related Experiment Videos

  • A novel genetic evaluation and selection mechanism was proposed for improved performance.

    • Main Results:

    • Computer simulations using real data from a pulp mill demonstrated the effectiveness of the proposed method.
    • The optimized process showed a significant reduction in pollutant levels.
    • The method successfully increased operational profit without compromising pulp quality.

    Conclusions:

    • The developed modeling and optimization technique offers a viable solution for cleaner production in the pulp and paper industry.
    • This approach effectively balances environmental protection, energy efficiency, and economic benefits.
    • The study highlights the potential of advanced algorithms in sustainable industrial practices.