A robust low data solution: Dimension prediction of semiconductor nanorods

Liu, Xiaoli; Xu, Youlong; Li, Jiali; Ong, Xuanwei; Ibrahim, Salwa Ali; Buonassisi, Tonio; Wang, Xiaonan

doi:10.1016/j.compchemeng.2021.107315

Cited by 12 publications

(2 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These models possess the capability to approximate the mechanism model in a reduced dimension while ensuring accuracy, thereby mitigating the complexity of many-objective problems while exhibiting strong generalization properties to ensure the efficient convergence of the optimization process. Over the course of time, a multitude of surrogate models have been gradually proposed, including the support vector machine (SVM), , Kriging model, , and artificial neural network (ANN). − An ANN is a crucial component of contemporary computer technology that aims to simulate the structure and functionality of the neural network in the human brain. The simulation comprises a network of interconnected neurons that exhibit strong data processing capabilities and the ability to adapt and learn.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Hybrid Framework Combining an Efficient Evolutionary Algorithm for Complex Many-Objective Optimization of Sustainable Triple CO₂ Feed Methanol Production

Cao,

Li,

Chang

et al. 2024

ACS Sustainable Chem. Eng.

View full text Add to dashboard Cite

Current mainstream technologies have exhibited limits in integrating global many-objective optimization methods with chemical production systems, resulting in subpar outcomes in terms of energy efficiency and environmental issues for methanol production systems. In this study, a novel deep learning hybrid framework is proposed, which involves the construction of a mechanism model with the ability to elucidate the underlying principles and interrelationships of chemistry on a macroscopic scale and a data-driven model to enhance the accuracy and dependability of predictions from available data. The efficiency and global search capability of the proposed framework are further improved through the integration of an advanced evolutionary algorithm, which incorporates many-criteria decision-making technology to provide a comprehensive set of trade-offs for the optimal solution sets. The results demonstrate that all four objective functions of carbon dioxide emissions, methane conversion rate, methanol production, and energy consumption in the triple CO 2 feed methanol production system are rapidly optimized, in which carbon dioxide emissions and energy consumption are reduced by 18.50% and 3.15%, respectively. Consequently, this considerably enhances the environment. This proposed framework holds significant potential in facilitating the efficient optimization and sustainable production of complex systems within process engineering.

show abstract

Section: Introductionmentioning

confidence: 99%

A Deep Learning Hybrid Framework Combining an Efficient Evolutionary Algorithm for Complex Many-Objective Optimization of Sustainable Triple CO₂ Feed Methanol Production

Cao,

Li,

Chang

et al. 2024

ACS Sustainable Chem. Eng.

View full text Add to dashboard Cite

show abstract

“…Liu at al. [53] created an ANN‐based regression algorithm to predict the width, length and aspect ratio of the semiconductor nanorods. They validated their model with experimental data, which showed good predicting accuracy.…”

Section: Introductionmentioning

confidence: 99%

Assessment of machine learning methods predicting the axial vibration frequencies of microbars

Tariq,

Uzun,

Deliktaş

et al. 2023

Z Angew Math Mech

View full text Add to dashboard Cite

Microbars are one of the important components of microelectromechanical systems. With the recent increase in their applications, the importance of understanding their mechanical response has become an important topic. In this study, for the first time, the mechanical behavior of microbars based on the strain gradient theory is investigated using a machine learning (ML) approach. Four distinct ML models, namely artificial neural network (ANN), support vector regression (SVR), decision tree regression (DTR), and random forest regression (RFR), are developed for microbars with clamped boundary conditions. The performance of these models is individually assessed using five different metrics: coefficient of determination (R2), Mean Absolute Error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and Nash‐Sutcliffe efficiency coefficient (NSE). The best‐performing model is selected based on these comparisons. Additionally, a semi‐analytical approach is employed to determine the natural frequencies of microbars under general elastic boundary conditions using the Fourier sine series and Stokes' transform. While the R2 value for all four models indicated a good fit of 0.999, the percentage difference in MAE and RMSE values between the training and testing data for DTR and RFR models was relatively higher as compared to ANN and SVR. The results showed that ANN and SVR models exhibit the best performance in predicting the natural frequencies on both training and testing data across all three metrics. Finally, a study on the free axial vibration frequencies of microbar under various effects was conducted.

show abstract

Machine learning‐aided process design using limited experimental data: A microwave‐assisted ammonia synthesis case study

Masud,

Araia,

Wang

et al. 2024

AIChE Journal

View full text Add to dashboard Cite

An open research question lies in how machine learning (ML) can accelerate the design optimization of chemical processes which are at very early experimental development stage with limited data availability. As an example, this article investigates the design of an intensified microwave‐assisted ammonia production reactor with 46 experimental data. We present an integrated approach of neural networks and synthetic minority oversampling technique to quantify the nonlinear input‐output relationships of this process. For ammonia concentration predictions at discrete operating conditions, the approach demonstrates 96.1% average accuracy over other ML methods (e.g., support vector regression 84.2%). The approach has also been applied for continuous optimization, identifying the optimal synthesis conditions at 597.37 K, 0.55MPa with feed flow rate of 1.67 ×10−3 m3/s kg and hydrogen to nitrogen ratio of 1 which is consistent with experimental observations. The data‐driven model enables to integrate this reactor with existing ammonia production infrastructure and benchmark with conventional techniques.

show abstract

A robust low data solution: Dimension prediction of semiconductor nanorods

Cited by 12 publications

References 42 publications

A Deep Learning Hybrid Framework Combining an Efficient Evolutionary Algorithm for Complex Many-Objective Optimization of Sustainable Triple CO₂ Feed Methanol Production

A Deep Learning Hybrid Framework Combining an Efficient Evolutionary Algorithm for Complex Many-Objective Optimization of Sustainable Triple CO₂ Feed Methanol Production

Assessment of machine learning methods predicting the axial vibration frequencies of microbars

Machine learning‐aided process design using limited experimental data: A microwave‐assisted ammonia synthesis case study

Contact Info

Product

Resources

About

A robust low data solution: Dimension prediction of semiconductor nanorods

Cited by 12 publications

References 42 publications

A Deep Learning Hybrid Framework Combining an Efficient Evolutionary Algorithm for Complex Many-Objective Optimization of Sustainable Triple CO2 Feed Methanol Production

A Deep Learning Hybrid Framework Combining an Efficient Evolutionary Algorithm for Complex Many-Objective Optimization of Sustainable Triple CO2 Feed Methanol Production

Assessment of machine learning methods predicting the axial vibration frequencies of microbars

Machine learning‐aided process design using limited experimental data: A microwave‐assisted ammonia synthesis case study

Contact Info

Product

Resources

About

A Deep Learning Hybrid Framework Combining an Efficient Evolutionary Algorithm for Complex Many-Objective Optimization of Sustainable Triple CO₂ Feed Methanol Production

A Deep Learning Hybrid Framework Combining an Efficient Evolutionary Algorithm for Complex Many-Objective Optimization of Sustainable Triple CO₂ Feed Methanol Production