54 citations · 132 across the 18 of their papers we have counts for
18 papers
Simulator-Grounded Large Language Models for Industrial Causal Reasoning: Tool-Use, Structured Injection, and Plant-Portable Retrieval for Wastewater Treatment Decision Support
Gary Simethy, Daniel Ortiz Arroyo, Petar Durdevic
Wastewater operators need answers grounded in how their plant's variables interact and how fast effects propagate, not in generic pretraining text, when asking causal questions suc…
Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support
Gary Simethy, Daniel Ortiz Arroyo, Petar Durdevic
Operators of safety-critical industrial processes increasingly rely on digital twins to screen control interventions, but such simulators rarely carry certified safety guarantees.…
DART: A Vision-Language Foundation Model for Comprehensive Rope Condition Monitoring
Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic
The condition monitoring (CM) of synthetic fibre ropes (SFRs) used in offshore, maritime, and industrial settings demands more than a classifier: inspectors need continuous severit…
Imagery Dataset for Remaining Useful Life Estimation of Synthetic Fibre Ropes
Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic
Remaining useful life (RUL) estimation of synthetic fibre ropes (SFRs) is critical for safe operation in offshore-crane, wind turbine installation, and heavy-load handling applicat…
Data-Driven Open-Loop Simulation for Digital-Twin Operator Decision Support in Wastewater Treatment
Gary Simethy, Daniel Ortiz Arroyo, Petar Durdevic
Wastewater treatment plants (WWTPs) need digital-twin-style decision support tools that can simulate plant response under prescribed control plans, tolerate irregular and missing s…
STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
Incomplete sensor data is a major obstacle in industrial time-series analytics. In wastewater treatment plants (WWTPs), key sensors show long, irregular gaps caused by fouling, mai…
CLIPTime: Time-Aware Multimodal Representation Learning from Images and Text
Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic
Understanding the temporal dynamics of biological growth is critical across diverse fields such as microbiology, agriculture, and biodegradation research. Although vision-language…
FungalZSL: Zero-Shot Fungal Classification with Image Captioning Using a Synthetic Data Approach
Anju Rani, Daniel O. Arroyo, Petar Durdevic
The effectiveness of zero-shot classification in large vision-language models (VLMs), such as Contrastive Language-Image Pre-training (CLIP), depends on access to extensive, well-a…
Synthetic Fungi Datasets: A Time-Aligned Approach
A. Rani, D. O. Arroyo, P. Durdevic
Fungi undergo dynamic morphological transformations throughout their lifecycle, forming intricate networks as they transition from spores to mature mycelium structures. To support…
Application of Soft Actor-Critic Algorithms in Optimizing Wastewater Treatment with Time Delays Integration
Esmaeel Mohammadi, Daniel Ortiz-Arroyo, Aviaja Anna Hansen +4
Wastewater treatment plants face unique challenges for process control due to their complex dynamics, slow time constants, and stochastic delays in observations and actions. These…
Time Series Dataset for Modeling and Forecasting of in Wastewater Treatment
Laura Debel Hansen, Anju Rani, Mikkel Algren Stokholm-Bjerregaard +3
In this paper, we present two years of high-resolution nitrous oxide () measurements for time series modeling and forecasting in wastewater treatment plants (WWTP). The datas…
Wastewater Treatment Plant Data for Nutrient Removal System
Esmaeel Mohammadi, Anju Rani, Mikkel Stokholm-Bjerregaard +2
This paper introduces the Agtrup (BlueKolding) dataset, collected from Denmark's Agtrup wastewater treatment plant, specifically designed to enhance phosphorus removal via chemical…
Improved Long Short-Term Memory-based Wastewater Treatment Simulators for Deep Reinforcement Learning
Esmaeel Mohammadi, Daniel Ortiz-Arroyo, Mikkel Stokholm-Bjerregaard +2
Even though Deep Reinforcement Learning (DRL) showed outstanding results in the fields of Robotics and Games, it is still challenging to implement it in the optimization of industr…
Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Survey
Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic
In recent years, 3D point clouds (PCs) have gained significant attention due to their diverse applications across various fields, such as computer vision (CV), condition monitoring…
Deep Learning Based Simulators for the Phosphorus Removal Process Control in Wastewater Treatment via Deep Reinforcement Learning Algorithms
Esmaeel Mohammadi, Mikkel Stokholm-Bjerregaard, Aviaja Anna Hansen +3
Phosphorus removal is vital in wastewater treatment to reduce reliance on limited resources. Deep reinforcement learning (DRL) is a machine learning technique that can optimize com…
Design, Modelling and Control of an Amphibious Quad-Rotor for Pipeline Inspection
Petar Durdevic, Shaobao Li, Daniel Ortiz-Arroyo
Regular inspections are crucial to maintaining waste-water pipelines in good condition. The challenge is that inside a pipeline the space is narrow and may have a complex structure…
Imagery Dataset for Condition Monitoring of Synthetic Fibre Ropes
Anju Rani, Daniel O. Arroyo, Petar Durdevic
Automatic visual inspection of synthetic fibre ropes (SFRs) is a challenging task in the field of offshore, wind turbine industries, etc. The presence of any defect in SFRs can com…
Defect Detection in Synthetic Fibre Ropes using Detectron2 Framework
Anju Rani, Daniel O. Arroyo, Petar Durdevic
Fibre ropes with the latest technology have emerged as an appealing alternative to steel ropes for offshore industries due to their lightweight and high tensile strength. At the sa…