Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Generative AI and process systems engineering: The next frontier
B Decardi-Nelson, AS Alshehri, A Ajagekar… - Computers & Chemical …, 2024 - Elsevier
This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …
such as large language models (LLMs), can enhance solution methodologies within process …
Machine learning in process systems engineering: Challenges and opportunities
P Daoutidis, JH Lee, S Rangarajan, L Chiang… - Computers & Chemical …, 2024 - Elsevier
This “white paper” is a concise perspective of the potential of machine learning in the
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …
[HTML][HTML] Machine learning applications in biomass pyrolysis: from biorefinery to end-of-life product management
DA Akinpelu, OA Adekoya, PO Oladoye… - Digital Chemical …, 2023 - Elsevier
The thermochemical conversion of biomass is a promising technology due to its cost-
effectiveness and feedstock flexibility, with pyrolysis being a particularly noteworthy method …
effectiveness and feedstock flexibility, with pyrolysis being a particularly noteworthy method …
Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty
Y Zheng, Z Wu - Industrial & Engineering Chemistry Research, 2023 - ACS Publications
In this work, we present a physics-informed recurrent neural network (PIRNN)-based
modeling approach for nonlinear dynamic systems with parameter uncertainty. Physics …
modeling approach for nonlinear dynamic systems with parameter uncertainty. Physics …
[HTML][HTML] Physics-informed machine learning for MPC: Application to a batch crystallization process
This work presents a framework for develo** physics-informed recurrent neural network
(PIRNN) models and PIRNN-based predictive control schemes for batch crystallization …
(PIRNN) models and PIRNN-based predictive control schemes for batch crystallization …
Data-based health indicator extraction for battery SOH estimation via deep learning
Accurately estimating the battery State of Health (SOH) is critical for the safe and stable
operation of electric vehicles. In this paper, a data-based method for SOH estimation based …
operation of electric vehicles. In this paper, a data-based method for SOH estimation based …
Adaptive model predictive control with ensembled multi-time scale deep-learning models for smart control of natural ventilation
Abstract Model predictive control (MPC) offers promising solutionsfor the smart control of
natural ventilation. However, challenges arise in constructing precise models for such …
natural ventilation. However, challenges arise in constructing precise models for such …
Model predictive control of nonlinear processes using transfer learning-based recurrent neural networks
MS Alhajeri, YM Ren, F Ou, F Abdullah… - … Research and Design, 2024 - Elsevier
Artificial neural networks (ANNs), one of the deep learning techniques that has sparked a lot
of attention recently for its exceptional modeling capabilities of nonlinear systems, are an …
of attention recently for its exceptional modeling capabilities of nonlinear systems, are an …
Reinforcement learning in process industries: Review and perspective
O Dogru, J **e, O Prakash, R Chiplunkar… - IEEE/CAA Journal of …, 2024 - ieeexplore.ieee.org
This survey paper provides a review and perspective on intermediate and advanced
reinforcement learning (RL) techniques in process industries. It offers a holistic approach by …
reinforcement learning (RL) techniques in process industries. It offers a holistic approach by …
Neural networks for fast optimisation in model predictive control: A review
C Gonzalez, H Asadi, L Kooijman, CP Lim - arxiv preprint arxiv …, 2023 - arxiv.org
Model Predictive Control (MPC) is an optimal control algorithm with strong stability and
robustness guarantees. Despite its popularity in robotics and industrial applications, the …
robustness guarantees. Despite its popularity in robotics and industrial applications, the …