Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2208.04373

Soft Sensors and Process Control using AI and Dynamic Simulation

Shumpei Kubosawa·Takashi Onishi·Yoshimasa Tsuruoka
View original source ↗Read source document ↗Export metadata ↓

Abstract

During the operation of a chemical plant, product quality must be consistently maintained, and the production of off-specification products should be minimized. Accordingly, process variables related to the product quality, such as the temperature and composition of materials at various parts of the plant must be measured, and appropriate operations (that is, control) must be performed based on the measurements. Some process variables, such as temperature and flow rate, can be measured continuously and instantaneously. However, other variables, such as composition and viscosity, can only be obtained through time-consuming analysis after sampling substances from the plant. Soft sensors have been proposed for estimating process variables that cannot be obtained in real time from easily measurable variables. However, the estimation accuracy of conventional statistical soft sensors, which are constructed from recorded measurements, can be very poor in unrecorded situations (extrapolation). In this study, we estimate the internal state variables of a plant by using a dynamic simulator that can estimate and predict even unrecorded situations on the basis of chemical engineering knowledge and an artificial intelligence (AI) technology called reinforcement learning, and propose to use the estimated internal state variables of a plant as soft sensors. In addition, we describe the prospects for plant operation and control using such soft sensors and the methodology to obtain the necessary prediction models (i.e., simulators) for the proposed system.

Explore related subjects

cs.AIcs.SYeess.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shumpei Kubosawa, Takashi Onishi, Yoshimasa Tsuruoka. 2022-08-08. Soft Sensors and Process Control using AI and Dynamic Simulation. https://doi.org/10.1252/kakoronbunshu.48.141

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Collab-Solver: Collaborative Solving Policy Learning for Mixed-Integer Linear Programming

Mixed-integer linear programming (MILP) has been a fundamental problem in combinatorial optimization. Conventional MILP solving mainly relies on carefully designed heuristics embedded in the branch-and-bound framework. Driven by the strong capabilities of neural networks, recent research is exploring the value of machine learning alongside conventional MILP solving. Although learning-based MILP methods have shown great promise, existing works typically learn policies for individual modules in MILP solvers in isolation, without considering their interdependence, which limits both solving efficiency and solution quality. To address this limitation, we propose Collab-Solver, a novel multi-agent-based policy learning framework for MILP that enables collaborative policy optimization for multiple modules. Specifically, we formulate the collaboration between cut selection and branching in MILP solving as a Stackelberg game. Under this formulation, we develop a two-phase learning paradigm to stabilize collaborative policy learning: the first phase performs data-communicated policy pretraining, and the second phase further orchestrates the policy learning for various modules. Extensive experiments on both synthetic and large-scale real-world MILP datasets demonstrate that the jointly learned policies significantly improve solving performance. Moreover, the policies learned by Collab-Solver have also demonstrated excellent generalization abilities across different instance sets.

cs.AI↗
arXiv2026

Fact Grounded Attention: Eliminating Hallucination in Large Language Models Through Attention Level Knowledge Integration

"The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge." Large Language Models have conquered natural language but remain prisoners of their own probabilistic nature--confidently hallucinating facts they never truly knew. We present Fact Grounded Attention (FGA), a novel architectural modification that transforms unreliable language models into deterministic truth tellers by injecting verifiable knowledge directly into the attention mechanism. Unlike existing approaches that patch hallucinations after generation or prepend retrieved text, FGA intervenes at the mathematical heart of the transformer--the pre-softmax attention scores--creating a model that cannot hallucinate when facts exist in its knowledge base. Our experiments across 1,107 technical queries spanning smartphones, laptops, and electric vehicles demonstrate a transformation from 6.3% accuracy in vanilla Llama 3.2 to 99.7% accuracy with FGA. More critically, knowledge updates occur in under one second without retraining, compared to hours for parameter editing approaches. FGA doesn't just reduce hallucination--it eliminates it entirely for verifiable facts, marking a fundamental shift from probabilistic approximation to deterministic precision in neural language generation.

cs.AI↗
arXiv2026

MemeLens: Multilingual Multitask VLMs for Memes

Memes are a dominant medium for online communication and manipulation because meaning emerges from interactions between embedded text, imagery, and cultural context. Existing meme research is distributed across tasks (e.g., \textit{hate, misogyny, propaganda, sentiment, humour}) and languages, which limits cross-domain generalization. To address this gap, we propose \textsc{MemeLens}, a unified multilingual, multitask explanation-enhanced Vision-Language Model (VLM) for meme understanding. We consolidate $38$ public meme datasets, filter and map dataset-specific labels into a shared taxonomy of $20$ tasks spanning harm, targets, figurative/pragmatic intent, and affect. We present a comprehensive empirical analysis across modeling paradigms, task categories, and datasets. Our findings suggest that robust meme understanding requires multimodal training, varies substantially across semantic categories, and remains sensitive to over-specialization when models are fine-tuned on individual datasets rather than trained in a unified setting. We make the experimental resources (https://github.com/MohamedBayan/MemeLens), model (https://huggingface.co/QCRI/MemeLens-VLM) and datasets (https://huggingface.co/datasets/QCRI/MemeLens) publicly available to the community.

cs.AI↗