Combining design of experiments, machine learning, and principal component analysis for predicting energy consumption and product quality of a natural gas processing plant

2020 ◽  
Author(s):  
Ladan Khoshnevisan ◽  
Farzad Hourfar ◽  
Falah Alhameli ◽  
Ali Elkamel
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Energy Consumption ◽  
Natural Gas ◽  
Design Of Experiments ◽  
Product Quality ◽  
Principal Component ◽  
Processing Plant ◽  
Gas Processing

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

scholarly journals Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Lihui Zhang ◽  
Riletu Ge ◽  
Jianxue Chai
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Tabu Search ◽  
Industrial Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Forecasting Model ◽  
Robust Principal Component Analysis ◽  
Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

scholarly journals Validation of the Arabic version of the “12-item short-form health survey” (SF-12) in a sample of Lebanese adults

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Chadia Haddad ◽  
Hala Sacre ◽  
Sahar Obeid ◽  
Pascale Salameh ◽  
Souheil Hallit
Keyword(s):  
Quality Of Life ◽  
Principal Component Analysis ◽  
Psychometric Properties ◽  
Short Form ◽  
Principal Component ◽  
Arabic Version ◽  
Short Form Health Survey ◽  
Form Health ◽  
Short Form Health

Abstract Background In clinical practice, quality of life measures can be used alongside some types of assessment to give valuable information that can identify areas that influence an individual and help the clinician make the best healthcare choices. This study aimed to investigate the psychometric properties of the Arabic version of the 12-item short-form health survey (SF-12) in a sample of Lebanese adults. Methods This cross-sectional study performed between July and November 2019 recruited 269 participants. Cronbach’s alpha was used to assess the reliability of the SF-12 questionnaire, and a factor analysis using the principal component analysis was performed to confirm its construct validity. Results The mean score for the “physical component summary (PCS-12)” was 50.27 ± 8.94 (95 % CI: 49.18–51.36) and for the “Mental component summary (MCS-12)” was 44.95 ± 12.17 (95 % CI: 43.47–46.43). A satisfactory Cronbach’s alpha was found for the two components: MCS (α = 0.707) and PCS (α = 0.743). The principal component analysis converged over a two-factor solution (physical and mental), explaining a total variance of 55.75 %. Correlations between the SF-12 scales and single items were significant, showing a good construct validity. The “physical functioning”, “role physical”, “bodily pain”, and “general health” subscales were highly associated with “PCS-12”, while the “vitality”, “social functioning”, “role emotional”, and “mental health” subscales were more associated with MCS-12. Conclusions The Arabic version of the SF-12 is a reliable, easy-to-use, and valid tool to measure health-related quality of life in the general population. Future studies using a larger sample size and focusing on questionnaire psychometric properties are necessary to confirm our findings.

Availability and Spatial Inequality of Rural Infrastructure in Jungle Mahal Blocks of Purulia District, India

2021 ◽  
pp. 232102492110082
Author(s):  
Uttam Kumar Patra ◽  
Suman Paul
Keyword(s):  
Quality Of Life ◽  
Principal Component Analysis ◽  
Regional Development ◽  
Principal Component ◽  
Spatial Inequality ◽  
Rural Infrastructure ◽  
Financial Infrastructure ◽  
Purulia District ◽  
Dimension Index

Rural infrastructure is fundamental and central to the concept of quality of life as well as human development. The major characteristic of regional development is the constant widening of regional disparity in India after different plan period. Various Finance Commissions and Planning Commissions laid emphasis on the objective of achieving balanced regional development. The article identifies a gap in terms of education, health, communication and financial infrastructure in the study of panchayats of Jungle Mahal blocks. Mapping of regional disparities can aid in effective policymaking at the preliminary stage of planning. Panchayat level inequality has been analysing using dimension index and principal component analysis (PCA). Wide disparities in the availability of rural infrastructure have been pointed out and proper recommendation has also been made to minimise the gap in spatial inequality.

Comparison of different analytical methods in the monitoring of the quality of modified atmosphere packaged broiler chicken cuts using principal component analysis

2004 ◽  
Vol 65 (2) ◽  
pp. 273-280 ◽  
Author(s):  
Jukka Vainionpää ◽  
Maria Smolander ◽  
Hanna-Leena Alakomi ◽  
Tiina Ritvanen ◽  
Tiina Rajamäki ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Analytical Methods ◽  
Broiler Chicken ◽  
Principal Component ◽  
Component Analysis ◽  
Modified Atmosphere

Effect of Amine Structure on the Corrosivity of a Carbon Steel Natural Gas Processing Plant

2020 ◽  
Author(s):  
Jessica Narku-Tetteh ◽  
Pailin M Muchan ◽  
Teeradet Supap ◽  
Raphael Idem
Keyword(s):  
Carbon Steel ◽  
Natural Gas ◽  
Processing Plant ◽  
Gas Processing ◽  
Natural Gas Processing ◽  
Amine Structure
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