SIMPO – A Simplified Intelligent Modelling Platform Online for Code-free and Shareable Wastewater Treatment Process Modelling
Year: 2025
Type: article
Abstract: The expansion of knowledge of and demand for wastewater treatment has driven the increasing complexity of its models. The establishment or use of such complicated models is difficult for environmental engineers, researchers and students, which hinders model development, knowledge transformation, and practical use of the wastewater treatment process. Even for reviewers and readers, it is difficult to evaluate whether a complicated model proposed in an article is reliable without time-consuming coding work. Thus, a new wastewater treatment modelling tool that is easy to use, code free, powerful, and open for sharing is necessary. To simplify wastewater treatment modelling and enhance accessibility, this study developed a Simplified Intelligent Modelling Platform Online for wastewater process modelling (SIMPO, https://www.simpowater.org ), an open-source and free-of-charge platform. SIMPO introduces the following innovative features: 1) Intuitive drag-and-drop graphical user interfaces enable code-free modelling. 2) Dynamic adaptive algorithms (forward/improved Euler methods) optimize step size in real time to ensure computational accuracy and efficiency. 3) Balance checking of chemical stoichiometric matrix, automatic visualization of results, and Nash Sutcliffe efficiency (NSE)–based goodness-of-fit evaluation can help researchers identify subtle but impactful errors. 4) Advanced algorithms for sensitivity analysis, uncertainty assessment, and parameter estimation help researchers identify the importance of ranking parameters, understand the transmission of input uncertainty to output, and achieve a balance between parameter estimation time and accuracy in complex scenarios. 5) Open and shareable mathematical models, modification processes and calculation results promote knowledge sharing and exchange. The combination of SIMPO with artificial intelligence (AI) tools helps researchers understand and apply mathematical model papers. Validated against classic modelling tools, such as AQUASIM 2.1 (EAWAG, Zurich, Switzerland), SIMPO achieves consistent accuracy while significantly reducing barriers to its use. The calculation result of a classic case is consistent with AQUASIM and with weighted Nash Sutcliffe efficiency (WNSE) > 0.999. Case studies demonstrate its capability to detect and resolve flaws in published models (eg, stoichiometric matrix imbalances in an extended model of Activated Sludge Model 3 and poor fitting of some variables in an autotrophic denitrification model). By combining powerful algorithms with a user-centric design, SIMPO provides an efficient, reliable and open solution for wastewater treatment modelling, driving innovation and accessibility in environmental engineering research. Currently, the use of large language model (LLM) tools such as KIMI can help users understand water quality modelling more quickly. In the future, application programming interface (API) integration with more AI tools such as DeepSeek would enable the possibility of linking classic mechanism-driven modelling with AI-assisted data-driven modelling.
Source: Water & Ecology
Institutions: Sun Yat-sen University, Guangdong University of Education
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid:
OpenAlex ID: W4415018955