Search Results - "Computers & chemical engineering"

A review On reinforcement learning: Introduction and applications in industrial process control by Nian, Rui, Liu, Jinfeng, Huang, Biao

“…•An overview of reinforcement learning with tutorials for industrial practitioners on implementing RL solutions into process control applications.•An…”
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Advances in surrogate based modeling, feasibility analysis, and optimization: A review by Bhosekar, Atharv, Ierapetritou, Marianthi

“…•State of the art review of surrogate modeling techniques is provided.•Best choice of the surrogate model depends on the type of problem at hand.•Recent…”
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Deep convolutional neural network model based chemical process fault diagnosis by Wu, Hao, Zhao, Jinsong

“…•A deep convolutional neural network model based fault diagnosis method is proposed for chemical processes.•A deep convolutional neural network model is…”
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Process systems engineering – The generation next? by Pistikopoulos, E N, Barbosa-Povoa, Ana, Lee, Jay H, Misener, Ruth, Mitsos, Alexander, Reklaitis, G V, Venkatasubramanian, V, You, Fengqi, Gani, Rafiqul

“…•Perspective of the field of Process Systems Engineering.•Circular Economy Systems Engineering.•Recent trends in process design, control, optimization and…”
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Advances and opportunities in machine learning for process data analytics by Qin, S. Joe, Chiang, Leo H.

“…In this paper we introduce the current thrust of development in machine learning and artificial intelligence, fueled by advances in statistical learning theory…”
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Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming by Ning, Chao, You, Fengqi

“…•An introduction to optimization under uncertainty is presented.•Recent advances in data-driven optimization under uncertainty are reviewed.•Future perspective…”
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Machine learning: Overview of the recent progresses and implications for the process systems engineering field by Lee, Jay H., Shin, Joohyun, Realff, Matthew J.

“…•Recent advances in deep learning and reinforcement learning (RL) are reviewed.•Motivation, early problems and recent resolutions of deep learning are…”
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A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery by Houssein, Essam H., Hosney, Mosa E., Oliva, Diego, Mohamed, Waleed M., Hassaballah, M.

“…Cheminformatics has main research factors due to increasing size of the search space of chemical compound databases and the importance of similarity…”
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Design of computer experiments: A review by Garud, Sushant S., Karimi, Iftekhar A., Kraft, Markus

“…•Modern DoE techniques are comprehensively reviewed.•A detailed classification and chronological evolution of the modern DoE research is presented.•Our…”
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Recent trends on hybrid modeling for Industry 4.0 by Sansana, Joel, Joswiak, Mark N., Castillo, Ivan, Wang, Zhenyu, Rendall, Ricardo, Chiang, Leo H., Reis, Marco S.

“…•Hybrid modeling has been attracting the interest of the scientific community for almost 30 years.•Big data and the industry 4.0 bring opportunities for new…”
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Reinforcement learning for batch bioprocess optimization by Petsagkourakis, P., Sandoval, I.O., Bradford, E., Zhang, D., del Rio-Chanona, E.A.

“…Bioprocesses have received a lot of attention to produce clean and sustainable alternatives to fossil-based materials. However, they are generally difficult to…”
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A review on superstructure optimization approaches in process system engineering by Mencarelli, Luca, Chen, Qi, Pagot, Alexandre, Grossmann, Ignacio E.

“…•Critical review of state-of-the-art in superstructure-based process synthesis.•Tools and methods for superstructure generation.•Superstructure representation…”
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A deep belief network based fault diagnosis model for complex chemical processes by Zhang, Zhanpeng, Zhao, Jinsong

“…•An improved Deep Belief Network is proposed to extract fault features.•A new fault diagnosis model based on DBN is proved and applied.•An average fault…”
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Reinforcement Learning – Overview of recent progress and implications for process control by Shin, Joohyun, Badgwell, Thomas A., Liu, Kuang-Hung, Lee, Jay H.

“…This paper provides an introduction to Reinforcement Learning (RL) technology, summarizes recent developments in this area, and discusses their potential…”
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Green hydrogen for industrial sector decarbonization: Costs and impacts on hydrogen economy in qatar by Kazi, Monzure-Khoda, Eljack, Fadwa, El-Halwagi, Mahmoud M., Haouari, Mohamed

“…•A strategic framework for the design of a multi-sectors hydrogen supply chain network.•Investigating the potential of industrial decarbonization via green…”
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Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems by Ajagekar, Akshay, Humble, Travis, You, Fengqi

“…•Novel quantum computing based hybrid solution strategies are developed.•The proposed hybrid techniques leverage both quantum and classical…”
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Formation lithology classification using scalable gradient boosted decision trees by Dev, Vikrant A., Eden, Mario R.

“…•Scalable gradient boosting systems, XGBoost, LightGBM and CatBoost compared for formation lithology classification.•Hyperparameter tuning, training and model…”
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Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges by Yue, Dajun, You, Fengqi, Snyder, Seth W.

“…This article describes the key challenges and opportunities in modeling and optimization of biomass-to-bioenergy supply chains. It reviews the major energy…”
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Distributed model predictive control: A tutorial review and future research directions by Christofides, Panagiotis D., Scattolini, Riccardo, Muñoz de la Peña, David, Liu, Jinfeng

“…In this paper, we provide a tutorial review of recent results in the design of distributed model predictive control systems. Our goal is to not only…”
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A deep reinforcement learning approach for chemical production scheduling by Hubbs, Christian D., Li, Can, Sahinidis, Nikolaos V., Grossmann, Ignacio E., Wassick, John M.

“…This work examines applying deep reinforcement learning to a chemical production scheduling process to account for uncertainty and achieve online, dynamic…”
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