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Autonomous Kinetic Modeling of Biomass Pyrolysis using Chemical Reaction Neural Networks [article]

Weiqi Ji, Franz Richter, Michael J. Gollner, Sili Deng
2022 arXiv   pre-print
In addition to the flexibility of neural-network-based models, the learned CRNN model is interpretable, by incorporating the fundamental physics laws, such as the law of mass action and Arrhenius law,  ...  Modeling the burning processes of biomass such as wood, grass, and crops is crucial for the modeling and prediction of wildland and urban fire behavior.  ...  Conclusions This work presents a machine learning framework based on Chemical Reaction Neural Networks for autonomously modeling the kinetics of biomass pyrolysis.  ... 
arXiv:2105.11397v2 fatcat:ccglbonq5rfdnjmerriaeydluq

Catalytic Thermal Degradation of Chlorella Vulgaris: Evolving Deep Neural Networks for Optimization

Sin Yong Teng, Adrian Chun Minh Loy, Wei Dong Leong, Bing Shen How, Bridgid Lai Fui Chin, Vítězslav Máša
2019 Bioresource Technology  
The predicted optimum conditions were reaction temperature of 900.0 o C, heating rate of 5.0 o C/min with the presence of HZSM-5 zeolite catalyst to obtain 88.3 % of Chlorella Vulgaris conversion.  ...  A Progressive Depth Swarm-Evolution (PDSE) neuro-evolutionary approach is proposed to model the Thermogravimetric analysis (TGA) data of Catalytic Thermal Degradation of Chlorella Vulgaris.  ...  Acknowledgement The authors would like to acknowledge financial support from the Ministry of Education, Youth and  ... 
doi:10.1016/j.biortech.2019.121971 fatcat:435e6ao625gsvm3i75g522hzom

Arrhenius.jl: A Differentiable Combustion SimulationPackage [article]

Weiqi Ji, Xingyu Su, Bin Pang, Sean Joseph Cassady, Alison M. Ferris, Yujuan Li, Zhuyin Ren, Ronald Hanson, Sili Deng
2021 arXiv   pre-print
models into combustion simulations and optimizing neural network models using the state-of-the-art deep learning optimizers.  ...  Differentiable programming is a promising approach for learning kinetic models from data by efficiently computing the gradient of objective functions to model parameters.  ...  Application to Model Discovery Finally, we present the application to Scientific Machine Learning (SciML) [19] by using Arrhenius.jl to develop a neural-network-based pyrolysis submodel within the HyChem  ... 
arXiv:2107.06172v1 fatcat:yk2ky46bw5fifjikmha5yvwwtq

Application of computational approach in plastic pyrolysis kinetic modelling: a review

Sabino Armenise, Syieluing Wong, José M. Ramírez-Velásquez, Franck Launay, Daniel Wuebben, Bemgba B. Nyakuma, Joaquín Rams, Marta Muñoz
2021 Reaction Kinetics, Mechanisms and Catalysis  
Despite many years and efforts to explain pyrolysis models based on global kinetic approaches, recent advances in computational modelling such as machine learning and quantum mechanics offer new insights  ...  Pyrolysis routes mapped by machine learning and quantum mechanics will gain more relevance in the coming years, especially studies that combine computational models with different time and scale resolutions  ...  Prieto for sharing their viewpoints about "in silico" modelling.  ... 
doi:10.1007/s11144-021-02093-7 fatcat:zimoweggkndhhglhbkuiofyo2u

Machine Learning Approaches to Learn HyChem Models [article]

Weiqi Ji, Julian Zanders, Ji-Woong Park, Sili Deng
2021 arXiv   pre-print
This paper proposes a machine learning approach to learn the HyChem models that can cover both high-temperature and low-temperature regimes.  ...  The approach combines lumped reaction steps for fuel thermal and oxidative pyrolysis with detailed chemistry for the oxidation of the resulting pyrolysis products.  ...  Vyaas Gururajan on the implementation of the sensBVP method and the help from Dr. Je Ir Ryu on simulating F-24 ignition with the GA-optimized HyChem model.  ... 
arXiv:2104.07875v1 fatcat:qoy7bpi4kfcwvdx67wg5sbu2wq

Applications of Artificial Intelligence‐Based Modeling for Bioenergy Systems: A Review

Mochen Liao, Yuan Yao
2021 GCB Bioenergy  
pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end-use systems, and (4) supply chain modeling and optimization.  ...  However, large-scale applications of biomass-based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability.  ...  ACKNOWLEDGEMENTS The authors thank the funding support from the Department of Forest Biomaterials at North Carolina State University, the U.S. National Science Foundation, and the Alfred P.  ... 
doi:10.1111/gcbb.12816 fatcat:ixyyk6je6rcv3jdde3p36m6hre

A review of optimisation techniques used in the composite recycling area: State-of-the-art and steps towards a research agenda

Ying Liu, Michael Farnsworth, Ashutosh Tiwari
2017 Journal of Cleaner Production  
This paper seeks to examine the applications of engineering optimisation techniques in the composite recycling and re-manufacturing processes and their relevant systems, providing an overview of state-of-the-art  ...  This means significant amount of modelling and optimisation work is required for the future research.  ...  Traditionally, the process is often done manually. The manual approach can be very time consuming, and the searching ability of it is limited and can often lead to sub-optimal solutions.  ... 
doi:10.1016/j.jclepro.2016.08.038 fatcat:6vorvy7jkza5vkalykgi2dtuxu

Surface Modification of Biochar for Dye Removal from Wastewater

Lalit Goswami, Anamika Kushwaha, Saroj Raj Kafle, Beom-Soo Kim
2022 Catalysts  
Meanwhile, a framework for artificial neural networking and machine learning to model the dye removal efficiency of biochar from wastewater is proposed even though such studies are still in their infancy  ...  The present review article recommends that smart technologies for modelling and forecasting the potential of such modification of biochar should be included for their proper applications.  ...  Application of Machine Learning and Artificial Neural Networks into Biochar-Facilitated Wastewater Remediation The process initiates from BC production.  ... 
doi:10.3390/catal12080817 fatcat:2rf6l2uqh5cwjnaue4pbyesww4

Mapping and characterization of LCA networks

Anders Bjørn, Mikołaj Owsianiak, Alexis Laurent, Christine Molin, Torbjørn Bochsen Westh, Michael Zwicky Hauschild
2012 The International Journal of Life Cycle Assessment  
of the European Commission in the framework of the FP7 Collaborative project Advanced Technologies for the Production of Cement and Clean Aggregates from Construction and Demolition Waste (C2CA), Grant  ...  Acknowledgement: This study is funded by the support programme of National 12 th five-year science and technology (NO. 2011BAC04B003) Acknowledgements: This abstract is realized through the financial support  ...  its application for the management of LCA data networks.  ... 
doi:10.1007/s11367-012-0524-6 fatcat:hvpd6ius2ngbxnseecyedpgyim

Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Iftikhar Ahmad, Adil Sana, Manabu Kano, Izzat Iqbal Cheema, Brenno C. Menezes, Junaid Shahzad, Zahid Ullah, Muzammil Khan, Asad Habib
2021 Energies  
The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions.  ...  Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution.  ...  The capability of ANN modeling significantly reduced the processing time required for control of the process. Nair et al.  ... 
doi:10.3390/en14165072 fatcat:mydds6vo45dthbqkodeybdnopm

Autonomous discovery in the chemical sciences part I: Progress

Klavs F. Jensen, Connor W Coley, Natalie S Eyke
2019 Angewandte Chemie International Edition  
These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modeling.  ...  In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems.  ...  This work was supported by the Machine Learning for Pharmaceutical Discovery and Synthesis Consortium and the DARPA Make-It program under contract ARO W911NF-16-2-0023.  ... 
doi:10.1002/anie.201909987 pmid:31553511 fatcat:yfg4jnixhvfgdmnr4p6mbyolpe

A Review of Process Systems Engineering (PSE) Tools for the Design of Ionic Liquids and Integrated Biorefineries

Nishanth G. Chemmangattuvalappil, Denny K. S. Ng, Lik Yin Ng, Jecksin Ooi, Jia Wen Chong, Mario R. Eden
2020 Processes  
The development and advances of novel computational tools and optimization approaches in recent years have enabled these applications with practical results.  ...  Significant improvements in computational efficiency have made it possible to provide more reliable data for optimal system design, minimize the production cost of ionic liquids, and reduce the environmental  ...  has enabled the application of ionic liquids in the chemical process industry.  ... 
doi:10.3390/pr8121678 fatcat:dlchhuiy5zebvozszaghxnqj2m

Sustainability assessment of biomethanol production via hydrothermal gasification supported by artificial neural network

Dániel Fózer, András József Tóth, Petar Sabev Varbanov, Jiří Jaromír Klemeš, Péter Mizsey
2021 Journal of Cleaner Production  
The relationship between the elemental composition of the feedstock, HTG reaction conditions (380 °C-717 °C, 22.5 MPa-34.4 MPa, 1-30 wt.% biomass-towater ratio, 0.3 min-60.0 min residence time, up to 5.5  ...  In this study, the environmental and economic performances of biomethanol production are examined using artificial neural networks (ANNs) for the modelling of catalytic and noncatalytic hydrothermal gasification  ...  The boundary and applicability of the machine learning model are in the 380-717 °C, 22.5-34.4 MPa, 1-30 wt.% biomass-to-water ratio, 0-5.5 wt.% CSR with NaOH catalyst load, and 0.3-60.0 min residence time  ... 
doi:10.1016/j.jclepro.2021.128606 fatcat:dtffr5jnframfbluojonq35vci

Progress in the Application of Machine Learning in Combustion Studies

Zhi-Hao Zheng, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China, Xiao-Dong Lin, Ming Yang, Ze-Ming He, Ergude Bao, Hang Zhang, Zhen-Yu Tian, University of Chinese Academy of Sciences, Beijing 100049, China, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China, University of Chinese Academy of Sciences, Beijing 100049, China, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China (+8 others)
2020 ES Energy & Environment  
ML is used to reduce the cost of CFD, including reducing the scale of combustion mechanism, saving the memory storage of the probability density function table and optimizing Large Eddy Simulation.  ...  In the past decades, machine learning (ML), as a branch of artificial intelligence, has attracted increasing interests, especially in the combustion field.  ...  Supporting information Not applicable  ... 
doi:10.30919/esee8c795 fatcat:ezb4yc7iwvdjfmpsjy3p6djvma

ChemNODE: A Neural Ordinary Differential Equations Approach for Chemical Kinetics Solvers [article]

Opeoluwa Owoyele, Pinaki Pal
2021 arXiv   pre-print
However, due to the nonlinearities and multi-scale nature of combustion, the predicted solution often diverges from the true solution when these deep learning models are coupled with a computational fluid  ...  This has motivated the use of fully connected artificial neural networks to predict stiff chemical source terms as functions of the thermochemical state of the combustion system.  ...  , and perform publicly and display publicly, by or on behalf of the Government.  ... 
arXiv:2101.04749v3 fatcat:3aesqbugcze4tojfhsz6xff44y
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