Prediction of solubility of N-alkanes in supercritical CO2 using RBF-ANN and MLP-ANN

Abdi-Khanghah, Mahdi; Bemani, Amin; Naserzadeh, Zahra; Zhang, Zhien

doi:10.1016/j.jcou.2018.03.008

Cited by 115 publications

(37 citation statements)

References 42 publications

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“…A similar study on predicting CO 2 thermodynamic properties is shown in Reference [49], where the inputs of blend concentration, temperature, and CO 2 operating partial pressure can be used as inputs and specifically predict the CO 2 solubility, density, and viscosity of a solution. Similar studies for mining the gas capture and separation can be found in References [50,51]. In addition to the use of ANNs, Günay et al used a decision tree model to evaluate the important factors of the reaction activity and selectivity of catalysts during CO 2 electro-reduction process ( Figure 2) [52].…”

Section: Mining the Trends And Properties In Chemistry And Materialsmentioning

confidence: 99%

Data-Mining for Processes in Chemistry, Materials, and Engineering

Zhang

Zhao

2019

Processes

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With the rapid development of machine learning techniques, data-mining for processes in chemistry, materials, and engineering has been widely reported in recent years. In this discussion, we summarize some typical applications for process optimization, design, and evaluation of chemistry, materials, and engineering. Although the research and application targets are various, many important common points still exist in their data-mining. We then propose a generalized strategy based on the philosophy of data-mining, which should be applicable for the design and optimization targets for processes in various fields with both scientific and industrial purposes.

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Section: Mining the Trends And Properties In Chemistry And Materialsmentioning

confidence: 99%

Data-Mining for Processes in Chemistry, Materials, and Engineering

Zhang

Zhao

2019

Processes

Self Cite

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“…Once the pattern is established the machine is able to solve similar problems [34]. ANNs are one of the most popular modeling methods used in predictive applications (such as [35][36][37][38][39][40][41][42]), due to their power flexibility and ease of use. In general, ANNs obtain their knowledge from the learning process and then use interneuron connection strengths (known as synaptic weights) to store the obtained knowledge [43,44].…”

Section: Predictive Modeling (Using Ibm Spss)mentioning

confidence: 99%

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things

Vaňuš

Fiedorova

Kubíček

et al. 2020

Sensors

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The operating cost minimization of smart homes can be achieved with the optimization of the management of the building’s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.

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“…There are many types of neural networks. The multi-layer feed-forward neural network is one of the most commonly-used networks at the moment [36][37][38][39][40]. It is composed of many simple processing units, called neurons or nodes, that are arranged in layers.…”

Section: Neural Networkmentioning

confidence: 99%

An Integrated Turning Movements Estimation to Petri Net Based Road Traffic Modeling

Riouali

Benhlima

Bah

2019

JSAN

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The tremendous increase in the urban population highlights the need for more efficient transport systems and techniques to alleviate the increasing number of the resulting traffic-associated problems. Modeling and predicting road traffic flow are a critical part of intelligent transport systems (ITSs). Therefore, their accuracy and efficiency have a direct impact on the overall functioning. In this scope, a new approach for predicting the road traffic flow is proposed that combines the Petri nets model with a dynamic estimation of intersection turning movement counts to ensure a more accurate assessment of its performance. Thus, this manuscript extends our work by introducing a new feature, namely turning movement counts, to attain a better prediction of road traffic flow. A simulation study is conducted to get a better understanding of how predictive models perform in the context of estimating turning movements.

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Prediction of solubility of N-alkanes in supercritical CO2 using RBF-ANN and MLP-ANN

Cited by 115 publications

References 42 publications

Data-Mining for Processes in Chemistry, Materials, and Engineering

Data-Mining for Processes in Chemistry, Materials, and Engineering

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things

An Integrated Turning Movements Estimation to Petri Net Based Road Traffic Modeling

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