Artificial neural network modeling of methanol production from syngas

Ye, Jiansen

doi:10.1080/10916466.2018.1560321

Cited by 17 publications

(9 citation statements)

References 2 publications

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“…ANN has been especially effective in predicting and classifying complex gas mixtures. In [43], researchers use ANN to model fuel-gas methanol output. A comparison of the main algorithms used for gas detection has been given in table1.…”

Section: Non-linear Classification Methodsmentioning

confidence: 99%

Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

Faleh¹,

Bedoui²,

Kachouri³

2020

Adv. sci. technol. eng. syst. j.

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With the advent of the Internet of Things Technologies (IOT), smart homes, and smart city applications, E-Nose was created. Almost of gas sensors consisting the electronic nose system suffer from cross sensitivity and lack of selectivity. Coupling smart gas sensors with artificial intelligence algorithms can thus empower conventional gas sensing technologies and increase accuracy in gas detection. This study describes the overall types of smart gas sensors used in air quality control, signal pre-processing and extraction features. Also it presents pattern recognition methods used in E-nose applications including linear methods such as Linear Discriminant Analysis LDA, K-Nearest Neighbors KNN and Non-linear such as algorithms Support vector Machine SVM and Artificial Neural Network ANN and their impact on improving accuracy rate for gas detection. Finally, this paper summarizes by providing directions for about how to leverage the benefits of combining these classifiers which is known as Data fusion approach and ensemble classifiers.

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Section: Non-linear Classification Methodsmentioning

confidence: 99%

Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

Faleh¹,

Bedoui²,

Kachouri³

2020

Adv. sci. technol. eng. syst. j.

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“…ANN [91,119,120,121,122,123,124,125,126] is a universal method for prediction and analysis of complex gas which sufficient approximates the nonlinear relation and has the ability of associative learning. Casey et al [91] use low-cost sensors to quantify 4 trace gas species in an oil and gas production region, and confirm ANN can represent the complex nonlinear behavior in response to the ambient environment among multiple gas sensors, including temperature, humidity, and atmospheric chemistry.…”

Section: Gas Sensing Pattern Recognitionmentioning

confidence: 99%

“…Cheng et al [123] use ANN to study the effect of steam flow rate (SFR) and reaction temperature on gas yield (GSY) and hydrogen yield (HDY). Ye et al [124] use ANN to simulate methanol production from fuel gas derived from steam reforming of natural gas. Gao et al [125] use ANN to study the influence of equivalence ratio (ER) on gas distribution, lower caloric value (LHV) of producer gas, and performance indicators (char conversion and cold gas efficiencies).…”

Section: Gas Sensing Pattern Recognitionmentioning

confidence: 99%

Review on Smart Gas Sensing Technology

Feng

Farha

et al. 2019

Sensors

229

122

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With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper.

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“…Keyword: Cu2O-ZnO/C, CO2, PCA, Electrochemical reduction words: Keyword 1, keyword 2, number of keywords is usually 3-7, but more is allowed if deemed necessary volume of chemical compounds produced in the world [3]. Methanol is produced commercially through natural gas reform [4] or coal gasification [5].…”

Section: Introductionmentioning

confidence: 99%

Effect of Milling Time and PCA on Electrode Properties of Cu2O-ZnO/C Catalyst Alloy used on Electrochemical Reduction Method of CO2

Rohendi

Syarif

Rachmat

et al. 2022

IJIE

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Carbon dioxide is one of the greenhouse gases that cause climate change, thus the effort for reducing the concentration of CO2 necessary, for example through the conversion of CO2. The conversion of CO2 into methanol plays important role, because in addition to reducing greenhouse gas, it is also creating a future energy carrier needed in fuel cell technology. One of the CO2 conversion methods is the electrolysis method using MEA. The electrochemical CO2 conversion in this study used a Cu2O-ZnO/C composite catalyst made by milling methods at various milling times, as well as the effect of PCA utilization. The catalysts were characterized using Particle Size Analyzer (PSA), Brunauer-Emmett-Teller (BET), Cyclic Voltammetry (CV), Electrochemical Impedance Spectroscopy (EIS) and SEM-EDX analysis. The results of PSA and BET characterization showed that the longer the milling time, the smaller the particle size and the higher the relative surface area. The use of PCA increased the unoxidized Cu content as indicated by the SEM-EDX results. Based on the results of CV analysis, the catalyst that has the largest ECSA (Electrochemical surface area) value is the catalyst milled using PCA and the milling time is three hours.

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Artificial neural network modeling of methanol production from syngas

Cited by 17 publications

References 2 publications

Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring

Review on Smart Gas Sensing Technology

Effect of Milling Time and PCA on Electrode Properties of Cu2O-ZnO/C Catalyst Alloy used on Electrochemical Reduction Method of CO2

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