Holistic Modeling Research Articles

System-level Prognostics and Health Management for Complex Industrial Systems

Prognostics and health management (PHM) has become essential to guarantee aware and safe system operation and to inform economic decision-making. However, due to the nature of detection, diagnostics, and prognostic methods, applications have mainly been limited to the component level. In practice, most industrial systems consist of multiple interacting components whose partial degradation could lead to the system's failure (or subsystems).This research addresses the limitations of traditional component-level PHM techniques by proposing a novel system-level framework. By implementing a hierarchical structure of components and subsystems, we will select an optimal method for each subsystem to aggregate its component health assessments. The overall system health can then be estimated by further combining the obtained estimates. The research considers simplified and holistic modeling techniques, margin-based methods, and hybrid graphical models. This approach aims to provide reliable system health predictions and online components' sensitivity measures to enhance maintenance decision-making. We consider an application in the context of the nuclear industry, characterized by strict safety and economic requirements. Using a SIMULINK model to approximate a Pressurized Water Reactor (PWR) with real industrial inputs, we plan to add component degradation modules and use simulated sensor data and reliability information to test the proposed framework. Initial results on artificial case studies show the feasibility of integrating component-level health predictions.

Toward a Self-consistent Evaluation of Gas Dwarf Scenarios for Temperate Sub-Neptunes

Abstract The recent JWST detections of carbon-bearing molecules in a habitable-zone sub-Neptune have opened a new era in the study of low-mass exoplanets. The sub-Neptune regime spans a wide diversity of planetary interiors and atmospheres not witnessed in the solar system, including mini-Neptunes, super-Earths, and water worlds. Recent works have investigated the possibility of gas dwarfs, with rocky interiors and thick H2-rich atmospheres, to explain aspects of the sub-Neptune population, including the radius valley. Interactions between the H2-rich envelope and a potential magma ocean may lead to observable atmospheric signatures. We report a coupled interior-atmosphere modeling framework for gas dwarfs to investigate the plausibility of magma oceans on such planets and their observable diagnostics. We find that the surface–atmosphere interactions and atmospheric composition are sensitive to a wide range of parameters, including the atmospheric and internal structure, mineral composition, volatile solubility and atmospheric chemistry. While magma oceans are typically associated with high-temperature rocky planets, we assess if such conditions may be admissible and observable for temperate sub-Neptunes. We find that a holistic modeling approach is required for this purpose and to avoid unphysical model solutions. Using our model framework, we consider the habitable-zone sub-Neptune K2-18 b as a case study and find that its observed atmospheric composition is incompatible with a magma ocean scenario. We identify key atmospheric molecular and elemental diagnostics, including the abundances of CO2, CO, NH3, and, potentially, S-bearing species. Our study also underscores the need for fundamental material properties for accurate modeling of such planets.

Ultrasonic A-Scan Signals Data Augmentation Using Electromechanical System Modelling to Enhance Cataract Classification Methods

The use of artificial intelligence in diverse diagnosis areas has significantly increased in the past few years because of the advantages it represents in clinical routine. Among the diverse diagnostic techniques, the use of ultrasounds is often preferred because of their simplicity, low cost, non-invasiveness, and non-ionizing characteristic. However, obtaining an adequate number of patients and data for training and testing machine learning models is challenging. To overcome this limitation, a novel approach is proposed for simulating data produced by ultrasonic diagnostic devices. The implemented method was based on a clinical prototype for eye cataract diagnosis, although the method can be extended to other applications as well. The proposed model encompasses the electric-to-acoustic signal conversion in the ultrasonic transducer, the wave propagation through the biological medium, and the subsequent acoustic-to-electric signal conversion in the transducer. Electrical modelling of the transducer was performed using a two-port network approach, while the acoustic wave propagation was modelled by using the k-Wave MATLAB toolbox. It was verified that the holistic modelling approach enabled the generation of synthetic data augmentation, presenting high similarity with real data.

Towards better understanding the economic and environmental sustainability of alternative agricultural cropping production systems through integrated modelling

We explore and develop an integrated model to enhance understanding and evaluation of the economic and environmental sustainability performance of alternative agricultural cropping production systems. More specifically, our developed Environmentally Sustainable Residual Income Decomposition Modelling (ESRIDM) integrates accounting decomposition analysis, Water and Economic Sustainability Performance Measurement (WESM), system thinking, modular thinking, Environmentally Sustainable Residual Income (ESRI) theory, into a holistic modelling system influenced by Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) approaches.The ESRIDM is theorised and applied in the context of nitrogen management in Australian Cotton production through a detailed case study and comparative analysis of three nitrogen fertiliser options. We find the ESRIDM is able to: (i) provide an overall assessment of the economic and environmental sustainability performance of each scenario modelled; (ii) increase the transparency of the production system to enable the identification of key issues which influence the likely suitability of each scenario; (iii) enable an analysis of probable outcomes under various alternatives; (iv) evaluate the feasibility of regulatory option modelled. While this paper offers a novel perspective to a specific context, ESRIDM is designed to be adapted to a variety of contexts with a view to contributing to the evolution of more sustainable systems of production more generally.

Multi-agent reinforcement learning for integrated manufacturing system-process control

The increasing complexity, adaptability, and interconnections inherent in modern manufacturing systems have spurred a demand for integrated methodologies to boost productivity, improve quality, and streamline operations across the entire system. This paper introduces a holistic system-process modeling and control approach, utilizing a Multi-Agent Reinforcement Learning (MARL) based integrated control scheme to optimize system yields. The key innovation of this work lies in integrating the theoretical development of manufacturing system-process property understanding with enhanced MARL-based control strategies, thereby improving system dynamics comprehension. This, in turn, enhances informed decision-making and contributes to overall efficiency improvements. In addition, we present two innovative MARL algorithms: the credit-assigned multi-agent actor-attention-critic (C-MAAC) and the physics-guided multi-agent actor-attention-critic (P-MAAC), each designed to capture the individual contributions of agents within the system. C-MAAC extracts global information via parallel-trained attention blocks, whereas P-MAAC embeds system dynamics through permanent production loss (PPL) attribution. Numerical experiments underscore the efficacy of our MARL-based control scheme, particularly highlighting the superior training and execution performance of C-MAAC and P-MAAC. Notably, P-MAAC achieves rapid convergence and exhibits remarkable robustness against environmental variations, validating the proposed approach’s practical relevance and effectiveness.

Broaden sustainable design and optimization of decarbonized campus Energy systems with scope 3 emissions accounting and social ramification analysis

A holistic approach that considers economic, environmental, and social dimensions is crucial for informed and responsible decision-making in energy system decarbonization. Integrating techno-economic, environmental, and social perspectives, we propose a multi-objective optimization framework for the sustainable design of decarbonized campus energy systems, resonating with the tri-fold principles of Corporate Social Responsibility. This comprehensive approach reveals the techno-economic intricacies inherent in adopting renewable solutions to accommodate cooling, heat, and power demand on university campuses. Our proposed model endeavors to minimize both total annualized costs and the broader spectrum of emissions (Scope 1, 2, and 3) in pursuit of environmental justice. Meanwhile, by merging life cycle assessment (LCA) with economic input-output analysis based on established social LCA databases, we can gauge the potential social impacts associated with these sustainability initiatives. The results serve as a roadmap for designing and operating truly sustainable campus energy systems, pinpointing environmental concerns, suggesting remedial strategies, and spotlighting the resultant social consequences. To showcase the tangible applicability of this holistic modeling framework, we have used real-world data from the main campus of Cornell University, Ithaca, New York State, to develop a case study. Our results show that with a plentiful biogas supply, it is possible to reduce yearly GHG emissions by more than 70%. Even when procurement emissions are left out, Scope 3 emissions still represent 69–82% of the total GHG emissions, dominated by the transportation sector. Turning to sustainable transport options, such as electric vehicles (EVs), can markedly lower these figures. On the socio-economic front, ramping up geothermal drilling could intensify negative social consequences, such as risk of forced labor, conflicts, accidents, and natural disasters, by 29%. These are primarily due to safety issues and the labor demands of earth source heat site preparation, encompassing risks such as forced labor and accidents. As the push for energy decarbonization grows stronger, it is imperative to weigh these downsides against the broader social benefits.

RescuePY: Simulation-Based Rescue Response Impact Assessment

Mobility in metropolitan regions is changing. The distribution of space in cities, the design of transport modes, and the organization of mobility are being re-thought. However, no matter the changes and innovations on the way to a more sustainable future, essential constants must be upheld: In the event of minor, regionally limited emergencies, medical assistance must reach those in need quickly. When dealing with large-scale emergencies, the ability to evacuate the area promptly must be ensured. The impact analysis of mobility innovations on emergency services within urban areas so far has been based purely on empirical observations using existing data. Currently, it is only possible to analyze what-if considerations in a limited way. Nevertheless, due to the increasingly rapid changes in mobility, a comprehensive and interlinked analysis will be necessary. This is the key contribution of rescuePY: rescuePY is a simulation suite based on the mesoscopic and microscopic simulation environment hybridPY. It allows holistic and microscopic transport modeling of rescue infrastructure to quantify the impact of the mobility transition towards higher sustainability on the performance of rescue services. The main features of this software are: Rescue system assessment for strategic, long-term planning Mobility-influence studies for operative, mid-term planning Activity-based urban evacuation modeling The capabilities of rescuePY are demonstrated by two applications: a simulation- based, mesoscopic system analysis of emergency services in Munich compared to real-world data and microscopic modeling of emergency vehicles (EMVs) in different road architectures. Ongoing developments aim to improve the evaluation methodology for the aggregated impact analysis of mobility innovations on rescue response services.

Spark plasma sintering of boron carbide (B4C): From characterisation to finite element modeling of sintering

Boron carbide (B4C), known for its unique properties, presents challenges in reaching its full density with sintering process due to low diffusion coefficients. This study explores the application of Spark Plasma Sintering (SPS) to overcome these challenges and enhance the understanding of its impact on pure B4C ceramics. The study outlines a comprehensive methodology for developing an SPS sintering model. Starting with a summary of conducted studies, powder oxidation analysis, characterization methods, and dilatometry curves processing, the article details the derivation of constitutive parameters based on the Skorohod-Olevsky theory. Isothermal profiles at different temperatures, variation in heating regimes, and a stepwise approach for SPS pressure application form the basis of the derivation methods. A grain growth model is developed for a comprehensive simulation of sintering, incorporating microstructural analysis and parameter derivation. Finally, the article addresses the integration of this mechanical sintering model with thermal and electrical considerations into a finite element method (FEM) software for a holistic SPS modeling. Thermo-electrical considerations, including the Peltier effect, are also computed, providing a well-rounded understanding of the SPS process for B4C ceramics. This study contributes valuable insights to optimizing the SPS parameters to achieve enhanced densification and microstructure control in B4C ceramics.

Model‐Based Systems Engineering Approach for Designing an Artificial Magnetic Field Generator System for Spacecraft Radiation Protection

AbstractThe hazardous environment of space, dominated by cosmic and solar radiation, poses a significant threat to spacecraft and their occupants. Traditional radiation shielding methods, like passive materials, have limitations in weight and effectiveness. An artificial magnetic field generator system emerges as a promising solution to replicate Earth's magnetosphere, providing a protective magnetic shield against harmful radiation. (Dick, Launius, 2007) This paper presents a Model‐Based Systems Engineering (MBSE) approach to the holistic modeling and design of such a system.Utilizing the MBSE approach, this study models the system's components, their interactions, and the offered services, incorporating Radiation Monitoring, Magnetic Shielding, Power Management, System Health & Diagnostics, and Crew Communication services. The conceptual data model captures key entities and their relationships, ensuring a coherent integration of the system's parts. The activity diagram illustrates the operational flow, providing clarity on the system's dynamic behavior under varying radiation conditions and power reserves. A services taxonomy is developed to hierarchically categorize and ensure the comprehensive functionality of the system.The application of MBSE methodology provides numerous advantages, including a unified visualization of the system's complexities, enhanced stakeholder communication, and a streamlined validation and verification process. Furthermore, the flexibility inherent in the MBSE approach ensures that the system can be easily updated or scaled based on advancements in technology or changing mission requirements. (ECLIPSE Suite, 2022) The MBSE approach's application to the design of an artificial magnetic field generator system for spacecraft presents a robust and systematic method to ensure optimal performance, safety, and adaptability in the perilous realm of space.

Reverse engineering neuron type-specific and type-orthogonal splicing-regulatory networks using single-cell transcriptomes.

Cell type-specific alternative splicing (AS) enables differential gene isoform expression between diverse neuron types with distinct identities and functions. Current studies linking individual RNA-binding proteins (RBPs) to AS in a few neuron types underscore the need for holistic modeling. Here, we use network reverse engineering to derive a map of the neuron type-specific AS regulatory landscape from 133 mouse neocortical cell types defined by single-cell transcriptomes. This approach reliably inferred the regulons of 350 RBPs and their cell type-specific activities. Our analysis revealed driving factors delineating neuronal identities, among which we validated Elavl2 as a key RBP for MGE-specific splicing in GABAergic interneurons using an in vitro ESC differentiation system. We also identified a module of exons and candidate regulators specific for long- and short-projection neurons across multiple neuronal classes. This study provides a resource for elucidating splicing regulatory programs that drive neuronal molecular diversity, including those that do not align with gene expression-based classifications.

Sustainable supply chains – Designing a requisite holistic model

AbstractOur study explored the relationships between supply chain (SC) members and their local and global stakeholders in achieving sustainability goals based on requisite holistic analysis and system dynamics modeling, which goes beyond the previous attempts to improve SC sustainability. In this research, we first developed a model that addresses the basic holistic treatment of SC sustainability; in a second model of sustainable SC (SSC), we then considered requisite holistic interactions among its stakeholders. We used the theory‐based viable system model to diagnose the proposed holistic model of SSC. The results show that especially the low‐tier SC members require supporting frameworks to develop and maintain the capacity for sustainable interactions with nature and society. These supportive frameworks should be provided by the regulatory bodies, society, and nature representatives to surpass the limited SC profit‐oriented incentives and structures. The implications of the proposed models are aimed at developing supportive SC sustainable policies and improving the capacities of SC members to coexist with local social and natural environments. Regulators, society, and the natural environment representatives can apply the proposed models to establish new policies for sustainable interactions between SC members and other societal stakeholders.

Transformative, Transdisciplinary, Transcendent Digital Education: Synergy, Sustainability and Calamity

Dynamic transformation of the knowledge economy, enhanced by Industry 4.0/5.0 development and rise of the networked society in the Digital Age, emergency digitization of all social communicative spheres due to pandemic measures have imposed dramatic changes onto transdisciplinary overlap in different areas of human knowledge and experience, induced by the cross-sectorial job market demands of university level education, curriculum design and learning outcomes. The global pandemic and subsequent warfare in Ukraine induced amplified digitalization measures in the higher education sphere. This end-to end digital shift in the educational processes (communication, content, outcomes and outputs, skills) heralded the introduction of meta-disciplinary dimensions of learning – digital, hybrid and, blended. These meta-disciplinary dimensions can be considered conduits of vertical (endocentric) and horizontal (exocentric) transdisciplinary of digital education as a sustainable dynamic system. Applied trans-disciplinary lens contributes to the solution of holistic modeling of processes and results of updating models and mechanisms of the highly dynamic communication system of education in the digital environment as a whole and its individual formats in the emergency digitization measures of different types.

Algorithm for steady contaminant distribution in ventilation systems with air recirculation: Contaminant release in ventilation ducts

When contaminants are released naturally or deliberately in ventilation ducts, they can contaminate the supply air and rooms through the return air recirculation of the ventilation and air conditioning system. Few studies have considered the impact of contaminant transport through air recirculation on the non-uniform indoor contaminant distribution. In this study, a method for calculating the steady-state contaminant distribution in a ventilation system with air recirculation when there is a contaminant release from the ventilation duct is presented. The ventilation ducts are considered as a virtual room and modelled separately from the rooms, both using linear superposition expressions based on accessibility indicators to obtain a relationship between contaminant concentrations at the outlet and inlet. A total of four sub-methods are proposed considering different treatments for the return and supply air ducts connected to each air handling unit (AHU). The proposed methods (Methods 1–3) considering the non-uniform characteristics of the ducts are shown to have an accuracy comparable to that of CFD (computational fluid dynamics) simulations, with an average prediction deviation of less than 5%, whereas the use of well-mixed assumption in the ducts (Method 4) results in an average prediction deviation of up to 45% in one of the rooms when the contaminant source is close to the diversion position of the supply air duct. The proposed method avoids the need for holistic modelling and iterative simulation of complex ventilation and air-conditioning system and multiple rooms, and allows for rapid prediction of contaminant distribution.

A holistic analysis of Chinese sponge city cases by region: Using PLS-SEM models to understand key factors impacting LID performance

Sponge city is a national sustainable stormwater management program proposed by the Chinese government to deal with urban water issues (e.g., flooding, poor water quality) brought on by climate change and urbanization. Various strategies for sponge city construction are required since environmental constraints differ regionally across the country. To identify regional variations, reveal the inner links between externalities and design elements in sponge city construction, and offer practical suggestions, efforts in two directions are made based on the data of 68 sponge city cases around China: 1) discussing design parameters of four Low Impact Development (LID) facilities, including bioretention cell, permeable pavement, grass swale, and sunken green space, under four regionalization maps of hydrologic, climatic, landform and soil texture factors, and 2) building a holistic Partial Least Squares-Structural Equation Modelling (PLS-SEM) model illustrating the relationship between local characteristics, LID system design, and LID system performance in sponge city construction. The results show that: 1) rainfall and landform factors have great impact on LID facilities design, as their depths tend to be higher in water rich areas and coastal areas. 2) LID types and areas are positively influenced (+0.745) by the total area and permeable portion of a project, while the LID system performance (water quantity and quality control) is negatively impacted (−0.407) by the rainfall amount and clay fraction. 3) comprehensive LIDs choice instead of single LID facility shows more significant effect on improving LID system performance. It is recommended to establish different design standards and assessment indexing systems tailored to local environment when constructing sponge city projects.

Dynamic control of district heating networks with integrated emission modelling: A dynamic knowledge graph approach

This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a previously devised methodology, and couple it with dispersion simulations for induced airborne pollutants to provide automatic insights into air quality implications of various heat sourcing strategies. We create cross-domain interoperability in the nexus of energy and air quality via newly developed ontologies and semantic software agents, which can be chained together via The World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we integrate the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and renewable generation potential to foster strategic analyses and scenario planning. Underlying calculations use building and weather data from the knowledge graph in place of inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.

A coupled PSR-based framework for holistic modeling and flood resilience assessment: A case study of the 2022 flood events in five southern provinces of China

In the context of global climate change, the increasing frequency of extreme weather events has led to increasingly severe disaster situations. Traditional disaster management approaches are inadequate for addressing these challenges; thus, the concept of resilience has gradually been integrated into disaster management strategies. This study addresses this gap by developing a semi-quantitative evaluation framework for flood resilience on a large, regional scale. This framework combines the Pressure-State-Response (PSR) model with a dynamic representation of flood resilience processes, incorporating pre-disaster resistance, during-disaster absorption, and post-disaster recovery. A comprehensive regional flood resilience index system (RFRI) is constructed to provide a holistic assessment of flood resilience throughout the disaster cycle. This study quantified flood resilience and utilized Geographic Information System spatial analysis to evaluate the spatial patterns of flood resilience at the municipal level. The evaluation framework was applied to a real flood disaster that affected five southern provinces of China in 2022. The results indicate that: (1) The overall flood resilience levels in the five southern provinces were relatively low, with only 6% of the units having a Flood Resilience Index (FRI) exceeding 0.6; and (2) in terms of pre-disaster resistance, 74% of municipalities in the five southern provinces of China exhibited a moderate or higher level of resilience. However, the overall performance of these provinces is relatively poor in terms of absorption during disasters and post-disaster recovery; (3) The development of flood resilience systems in the five provinces was severely imbalanced, with only two municipal-level cities having an FRI above 0.8, both of which are located in Guangdong Province. The evaluation framework established in this study enables rapid and accurate quantitative assessment of flood resilience at the municipal level. It aids in identifying deficiencies in the flood resilience systems of various regions, thereby strengthening efforts to build flood resilience.

Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir.

Predicting the hotspots of antimicrobial resistance (AMR) in aquatics is crucial for managing associated risks. We developed an integrated modeling framework toward predicting the spatiotemporal abundance of antibiotics, indicator bacteria, and their corresponding antibiotic-resistant bacteria (ARB), as well as assessing the potential AMR risks to the aquatic ecosystem in a tropical reservoir. Our focus was on two antibiotics, sulfamethoxazole (SMX) and trimethoprim (TMP), and on Escherichia coli (E. coli) and its variant resistant to sulfamethoxazole-trimethoprim (EC_SXT). We validated the predictive model using withheld data, with all Nash-Sutcliffe efficiency (NSE) values above 0.79, absolute relative difference (ARD) less than 25%, and coefficient of determination (R2) greater than 0.800 for the modeled targets. Predictions indicated concentrations of 1-15 ng/L for SMX, 0.5-5 ng/L for TMP, and 0 to 5 (log10 MPN/100 mL) for E. coli and -1.1 to 3.5 (log10 CFU/100 mL) for EC_SXT. Risk assessment suggested that the predicted TMP could pose a higher risk of AMR development than SMX, but SMX could possess a higher ecological risk. The study lays down a hybrid modeling framework for integrating a statistic model with a process-based model to predict AMR in a holistic manner, thus facilitating the development of a better risk management framework.

Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization

Modern recommender systems lie at the heart of complex recommender ecosystems that couple the behavior of users, content providers, vendors, advertisers, and other actors. Despite this, the focus of much recommender systems research and deployment is on the local, myopic optimization of the recommendations made to individual users. This comes at a significant cost to the long-term utility that recommender systems generate for their users. We argue that modeling the incentives and behaviors of these actors, and the interactions among them induced by the recommender systems, is needed to maximize value and improve overall ecosystem health. Moreover, we propose the use of economic mechanism design, an area largely overlooked in recommender systems research, as a framework for developing such models. That said, one cannot apply “vanilla” mechanism design to recommender ecosystem modeling optimization out of the box—the use of mechanism design raises a number of subtle and interesting research challenges. We outline a number of these in this talk (and paper), emphasizing the need to develop nonstandard approaches to mechanism design that intersect with numerous areas of research, including preference modeling, reinforcement learning and exploration, behavioral economics, and generative AI, among others.

Disentangling coastal groundwater level dynamics in a global dataset

Abstract. Groundwater level (GWL) dynamics result from a complex interplay between groundwater systems and the Earth system. This study aims to identify common hydrogeological patterns and to gain a deeper understanding of the underlying similarities and their link to physiographic, climatic, and anthropogenic controls of groundwater in coastal regions. The most striking aspects of GWL dynamics and their controls were identified through a combination of statistical metrics, calculated from about 8000 groundwater hydrographs, pattern recognition using clustering algorithms, classification using random forest, and SHapley Additive exPlanations (SHAPs). Hydrogeological similarity was defined by four clusters representing distinct patterns of GWL dynamics. These clusters can be observed globally across different continents and climate zones but simultaneously vary regionally and locally, suggesting a complicated interplay of controlling factors. The main controls differentiating GWL dynamics were identified, but we also provide evidence for the currently limited ability to explain GWL dynamics on large spatial scales, which we attribute mainly to uncertainties in the explanatory data. Finally, this study provides guidance for systematic and holistic groundwater monitoring and modeling and motivates a consideration of the different aspects of GWL dynamics, for example, when predicting climate-induced GWL changes, and the use of explainable machine learning techniques to deal with GWL complexity – especially when information on potential controls is limited or needs to be verified.

Wastewater generation model to predict impacts of urine separation on wastewater treatment plants.

Wastewater treatment plants (WWTPs) are under increasing pressure to enhance resource efficiency and reduce emissions into water bodies. The separation of urine within the catchment area may be an alternative to mitigate the need for costly expansions of central WWTPs. While previous investigations assumed a spatially uniform implementation of urine separation across the catchment area, the present study focuses on an adapted stochastic wastewater generation model, which allows the simulation of various wastewater streams (e.g., urine) on a household level. This enables the non-uniform separation of urine across a catchment area. The model is part of a holistic modelling framework to determine the influence of targeted urine separation in catchments on the operation and emissions of central WWTPs, which will be briefly introduced. The wastewater generation model is validated through an extensive sampling and measurement series. Results based on observed and simulated wastewater quantity and quality for a catchment area of 366 residents for two dry weather days indicate the suitability of the model for wastewater generation and transport modelling. Based on this, four scenarios for urine separation were defined. The results indicate a potential influence of spatial distribution on the peaks of total nitrogen and total phosphorus.