Features
- SIMPO is initiated by JiangLab (a research team in the School of Environmental Science and Engineering, Sun Yat-sen University, China). It is an intelligent computing SaaS platform specifically designed for wastewater treatment process modelling.
- SIMPO integrates advanced algorithms with error-proofing mechanisms, offering a user-friendly interface to lower the learning curve for model development, simulation and evaluation. Its open and collaborative features enhance accessibility and innovation, making it a transformative tool for environmental engineering research.
- SIMPO's innovations address critical limitations of traditional platforms:
- SIMPLE
- EFFICIENCY
- PREVENTION
- ADVANCED
- COLLABORATION
![[object Object]](https://i.postimg.cc/bvV8GgKj/The-Easiest-Way.gif)
Interaction and visualization of GUI provide a user-friendly modelling approach:
- Intuitive tools, such as candidate boxes and color coding, visually assist in correctly inputting variables and parameters in a complex matrix.
- The drag-and-drop GUI simplifies the design of complex wastewater treatment processes to the point of simply adding and connecting tanks on a webpage.
- The automatic drawing display helps users quickly, comprehensively, and intuitively understand the simulation and evaluation results.
![[object Object]](https://i.postimg.cc/cHN584Qz/Figure-6a.png)
Dynamic algorithm ensures the accuracy and efficiency of modelling:
- SIMPO adopts self-adjusting solvers to ensure computational efficiency without affecting accuracy, which is evaluated by weighted Nash Sutcliffe efficiency (WNSE).
- Dynamic real time step size optimization can alleviate common pitfalls such as negative concentration, which is a frequent problem in traditional tools.
![[object Object]](https://i.postimg.cc/prSfzqwd/Figure-7c.png)
Automatic error-proofing and intuitive display help to quickly identify errors:
- Balance checking can help users review the rationality of stoichiometry matrices from a theoretical perspective.
- NSE intuitively reflects the goodness-of-fit of various variables, and the automated and interactive display of results exposes the problem of certain variables being obscured by graphical scales.
![[object Object]](https://i.postimg.cc/Z5XVNK8S/Advanced.gif)
Advanced evaluation algorithms bring comprehensive and systematic understanding of models:
- Sensitivity analysis identifies sensitive parameters and their correlations, reducing the number of parameters to enhance computational efficiency.
- Uncertainty assessment evaluates the uncertainty of each parameter, determines the predicted range of model outputs, and refines parameter ranges for more accurate simulations.
- Parameter estimation identifies the local optima of parameters that best fit the measured data, ensuring robust and reliable model calibration.
![[object Object]](https://i.postimg.cc/Gp2K6TbG/Collaboration.gif)
Collaborative science facilitates the dissemination, exchange, and development of models:
- SIMPO supports version-controlled models, data and results sharing, provides public/private repositories, and advocates a new type of peer review workflow for modelling papers, in line with the principles of open science.
Algorithms
SIMPO's core algorithms:

Simulation
SIMPO provides accurate and efficient ODE solvers, negative value response strategy to cater to diverse modelling needs. The WNSE is employed as the goodness-of-fit criterion for model evaluation.

Sensitivity
Sensitivity analysis identifies the most sensitive parameters that have the greatest impact on outcomes, and improve computational efficiency by reducing the number of parameters.

Uncertainty
Uncertainty assessment evaluates the uncertainty, distributions and equifinality of parameters, determine the predicted range distribution of model outputs, and refine the range of parameter values.

Estimation
Parameter estimation, which is performed using Genetic Algorithms (GA), obtains the local optimal solutions of the parameters by approximating the maximum value of the WNSE.
Resourses
Let's build and use the Paper Resource together.
![[object Object]](https://i.postimg.cc/HL2FXpF6/resource.png)
Everyone is welcome
- Join us in building and enhancing Paper Resource with the help of powerful AI tools. Your contributions can significantly enhance and expand the collection, making it more valuable for the entire community.
- We appreciate your support and collaboration, fork and pull request are welcome in: simpo-home
Publications
The following papers have adopted SIMPO as their experimental tool.


