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Related Experiment Video

Updated: May 10, 2026

Measuring Biomethane Potential of Food Scrap Waste Anaerobically Co-Digested with Waste-Activated Sludge Using Respirometry
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Published on: April 26, 2024

System dynamics model for hospital waste characterization and generation in developing countries.

Derar Eleyan1, Issam A Al-Khatib, Joy Garfield

  • 11Information Systems, Birzeit University, West Bank, Palestine.

Waste Management & Research : the Journal of the International Solid Wastes and Public Cleansing Association, ISWA
|June 8, 2013
PubMed
Summary

Predicting medical solid waste generation is crucial for effective management. System Dynamics modeling offers a novel approach to forecast waste trends and composition, even with limited historical data.

Keywords:
Hospital wasteSystem Dynamicscharacterizationdeveloping countriesgeneration rate

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Published on: July 13, 2012

Area of Science:

  • Environmental Science
  • Public Health
  • Waste Management Engineering

Background:

  • Accurate prediction of medical solid waste (MSW) quantity and composition is essential for effective waste management strategies, including treatment, recycling, and disposal.
  • Policy makers often struggle with predicting future MSW due to scarcity of historical data, exacerbated by budget constraints and limited management capacity.
  • This scarcity hinders long-term planning and short-term expansion programs for MSW management systems.

Purpose of the Study:

  • To introduce and validate a novel System Dynamics (SD) modeling technique for predicting MSW generation in a developing urban area.
  • To address the challenge of limited historical data in forecasting MSW trends and composition.
  • To provide a tool for better planning and design of MSW management systems.

Main Methods:

  • Development and application of a System Dynamics model using limited sample data from hospitals in Jenin District, Palestine.
  • Utilizing the SD model to simulate and forecast the generation trends of MSW and its various components.
  • Comparing the capabilities of the SD approach with traditional statistical methods, such as least-squared regression, in handling data limitations and uncertainties.

Main Results:

  • The developed System Dynamics model successfully presents the projected trends of medical solid waste generation.
  • The model provides insights into the changing composition of medical solid waste over time.
  • The findings demonstrate the model's ability to track uncertainties inherent in forecasting, outperforming traditional methods in scenarios with sparse data.

Conclusions:

  • System Dynamics modeling provides a robust and adaptable technique for forecasting medical solid waste generation, particularly in data-scarce environments.
  • This approach offers a valuable tool for waste management policy makers in developing urban areas to plan and design sustainable MSW management systems.
  • The SD model's capacity to handle uncertainties makes it a superior alternative to traditional statistical methods for complex waste forecasting challenges.