Enhancing the interpretability of spatially variable N2O model predictions with soft sensors during wastewater treatment

• URL: http://arxiv.org/abs/2605.04082v1
• Date: Wed, 15 Apr 2026 09:48:49 GMT
• Title: Enhancing the interpretability of spatially variable N2O model predictions with soft sensors during wastewater treatment
• Abstract summary: We analysed how machine learning models predict disturbances to wastewater treatment plants.<n>Monitoring campaigns for nitrous oxide (N2O) emissions were simulated with a plant-wide mechanistic model.<n>We argue that N2O soft sensor model predictions are limited to the measuring location and the methodological uncertainty of the dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Model-based solutions for nitrous oxide (N2O) emissions from wastewater treatment plants (WWTP) are informed by operational datasets designed to control nutrient levels in liquid waste, coupled with dedicated campaigns for N2O measurements. We analysed how machine learning (ML) models predict disturbances to WWT operation and spatially variable N2O emissions. A real dataset was investigated to validate the modelling framework from N2O emissions predicted by four ML models (R2 = 0.79 - 0.89). Monitoring campaigns for N2O were simulated with a plant-wide mechanistic model to include additional sensors, site-level N2O datasets, and wastewater disturbances (n = 16). ML models were highly accurate (0.97 +- 0.02, n = 80), but the feature importance depended on the model, the scenario and the N2O measurement scale (reactor vs. WWTP). We argue that N2O soft sensor model predictions are limited to the measuring location and the methodological uncertainty of the dataset, which affect the interpretability of the model. Lastly, the analysis of the mechanistic model structure exposed interactions between autotrophic and heterotrophic pathways over nitric oxide which can overestimate aerobic nitrite production and bias the N2O pathway contributions.

Related papers

• Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures [66.44589802420899]
  We introduce a new ab initio approach to simulate semiconductor devices that integrates scalable machine-learned (ML) electronic structure models with an advanced quantum transport (QT) solver.<n>The framework enables 10,000X speedups over density functional theory to produce the Hamiltonian matrix of devices made of >20,000 atoms.
  arXiv  Detail & Related papers  (2026-08-27T15:33:39Z)
• Physics-Informed Neural Networks for Predicting Nitrous Oxide Flux [0.0]
  Nitrous oxide (N$$O) is the dominant ozone-depleting substance emitted in the 21st century.<n>More than 70% of N$O emissions occur as a result of agricultural processes.<n>Current approaches to predicting N$O flux emissions include process-based models, as well as classical AI models.
  arXiv  Detail & Related papers  (2026-07-26T22:53:02Z)
• A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications [46.07145155913717]
  We propose a scalable operator-learning framework based on the Karhunen-Loeve Deep Neural Network (KL-DNN)<n>The model is trained on a dataset comprising 100 samples of large-scale simulations in a three-dimensional domain with 1.7 million cells and 50 time steps.
  arXiv  Detail & Related papers  (2026-06-26T18:15:24Z)
• SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction [46.645872209565574]
  We present a specialized large-scale foundation model for virtual cell perturbation prediction.<n>First, we build a BioNeMo-based training and inference framework that substantially improves data throughput.<n>Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture.<n>Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics.
  arXiv  Detail & Related papers  (2026-03-18T05:51:08Z)
• Estimating Dense-Packed Zone Height in Liquid-Liquid Separation: A Physics-Informed Neural Network Approach [35.66975292316149]
  Separating liquid-liquid dispersions in gravity settlers is critical in chemical, pharmaceutical, and recycling processes.<n>Dense-packed zone height is an important performance and safety indicator but it is often expensive and impractical to measure.<n>We propose to estimate phase heights using only inexpensive volume flow measurements.
  arXiv  Detail & Related papers  (2026-01-26T11:57:28Z)
• Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models [100.28111930893188]
  Some of today's best generative models still require hundreds to thousands of neural function evaluations to compute a single likelihood.<n>We present fast flow joint distillation (F2D2), a framework that simultaneously reduces the number of NFEs required for both sampling and likelihood evaluation by two orders of magnitude.<n>F2D2 is modular, compatible with existing flow-based few-step sampling models, and requires only an additional divergence prediction head.
  arXiv  Detail & Related papers  (2025-12-02T10:48:20Z)
• Lightweight ML-Based Air Quality Prediction for IoT and Embedded Applications [0.0]
  This study investigates the effectiveness and efficiency of two variants of the XGBoost regression model.<n>The full XGBoost model achieved superior predictive accuracy for both pollutants, while the tiny model, though slightly less precise, offered substantial computational benefits.<n>This makes the tiny XGBoost model suitable for real-time air-quality monitoring in IoT and embedded applications.
  arXiv  Detail & Related papers  (2025-11-26T19:31:20Z)
• Interactive Atmospheric Composition Emulation for Next-Generation Earth System Models [0.0]
  Interactive simulations in Earth System Models (ESMs) are computationally expensive as they transport numerous gaseous and aerosols at each timestep.<n>We extend the ModelE NINT tracer version using machine learning (ML) to create SmartINT, which emulates interactive emissions.<n>Smart NINT interactively calculates concentrations using ML with surface emissions meteorological data as inputs, avoiding full physics parameterizations.
  arXiv  Detail & Related papers  (2025-10-12T15:18:37Z)
• Neurons for Neutrons: A Transformer Model for Computation Load Estimation on Domain-Decomposed Neutron Transport Problems [48.35237609036802]
  We propose a Transformer model with a unique 3D input embedding, and input representations designed for domain-decomposed neutron transport problems.<n>We demonstrate that such a model trained on domain-decomposed Small Modular Reactor (SMR) simulations achieves 98.2% accuracy while being able to skip the small-scale simulation step entirely.
  arXiv  Detail & Related papers  (2024-11-05T18:17:51Z)
• Graph Neural Networks for Pressure Estimation in Water Distribution Systems [44.99833362998488]
  Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. We combine physics-based modeling and Graph Neural Networks (GNN), a data-driven approach, to address the pressure estimation problem. Our GNN-based model estimates the pressure of a large-scale WDN in The Netherlands with a MAE of 1.94mH$$O and a MAPE of 7%.
  arXiv  Detail & Related papers  (2023-11-17T15:30:12Z)
• Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano-particles in a contaminated aquifer [0.0]
  Engineered Nano-particles (ENPs) have emerged as an efficient reactive agent for the in-situ degradation of groundwater contaminants. The complex transport and retention mechanisms of ENPs hinder the development of an efficient remediation strategy. This work uses a dynamic, weight-enabled Physics-Informed Neural Network (dw-PINN) framework to model the nano-particle behavior within an aquifer.
  arXiv  Detail & Related papers  (2022-10-25T07:55:20Z)
• Spatiotemporal Estimation of TROPOMI NO2 Column with Depthwise Partial Convolutional Neural Network [0.17590081165362778]
  Satellite-derived measurements are negatively impacted by cloud cover and surface reflectivity. This paper expands the application of a partial convolutional neural network (PCNN) to incorporate depthwise convolution layers. The deep learning system is trained with the Community Multiscale Air Quality model-simulated tropospheric column density of Nitrogen Dioxide (TCDNO2) to impute TROPOspheric Monitoring Instrument TCDNO2.
  arXiv  Detail & Related papers  (2022-04-12T16:23:36Z)
• Evaluating the Adversarial Robustness for Fourier Neural Operators [78.36413169647408]
  Fourier Neural Operator (FNO) was the first to simulate turbulent flow with zero-shot super-resolution. We generate adversarial examples for FNO based on norm-bounded data input perturbations. Our results show that the model's robustness degrades rapidly with increasing perturbation levels.
  arXiv  Detail & Related papers  (2022-04-08T19:19:42Z)
• MoEfication: Conditional Computation of Transformer Models for Efficient Inference [66.56994436947441]
  Transformer-based pre-trained language models can achieve superior performance on most NLP tasks due to large parameter capacity, but also lead to huge computation cost. We explore to accelerate large-model inference by conditional computation based on the sparse activation phenomenon. We propose to transform a large model into its mixture-of-experts (MoE) version with equal model size, namely MoEfication.
  arXiv  Detail & Related papers  (2021-10-05T02:14:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.