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An implementation framework for wastewater treatment models requiring a minimum programming expertise.

J Rodríguez1, G C Premier, R Dinsdale

  • 1Sustainable Environment Research Centre, University of Glamorgan, Llantwitt Rd, Pontypridd CF37 1DL, UK. jrodrigu@glam.ac.uk

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Summary
This summary is machine-generated.

This study introduces a novel Excel and Matlab-Simulink methodology for creating and simulating mathematical models in environmental biotechnology. This approach enhances accessibility for non-programmers while maintaining flexibility for expert researchers.

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Area of Science:

  • Environmental Biotechnology
  • Computational Biology
  • Mathematical Modeling

Background:

  • Mathematical modeling in environmental biotechnology is often inaccessible to users without programming expertise due to complex implementation platforms.
  • Existing user-friendly software lacks the flexibility required for advanced research applications.

Purpose of the Study:

  • To present a flexible and accessible methodology for implementing mathematical models in environmental biotechnology.
  • To bridge the gap between user-friendliness and research-level flexibility in model implementation.

Main Methods:

  • A hybrid approach utilizing Microsoft Excel for model definition and Matlab-Simulink for simulation.
  • Models are defined in Excel, with parameters and variables easily managed.
  • Automated processing in Matlab generates model structures with minimal custom code.

Main Results:

  • Successful implementation and simulation of mathematical models for environmental bioprocesses.
  • The methodology accommodates researchers with varying programming expertise.
  • Provides a flexible platform for advanced model development, including heuristic functions and correlations.

Conclusions:

  • The proposed Excel-Matlab methodology significantly improves accessibility and flexibility in environmental biotechnology modeling.
  • It empowers both novice and expert researchers to develop and simulate complex bioprocess models efficiently.
  • This approach has been validated through successful application in multiple research studies.