Sludge Dewatering Process Control Using Principal Component Analysis (PCA) and Partial Least Square (PLS)

The process control in the sludge dewatering process is to minimalize the water volume in the sludge. However, management of this process control is difficult because of its multi-variables, nonlinearity and long delay. In this paper, a control approach based on the principal component analysis (PCA) is presented. A PCA model, which incorporates time lagged variables is used. The control objective is expressed in the score space of this PCA model. A controller is designed in the model predictive control framework, and it is used to control the equivalent score space representation of the process. The score predictive model for the model predictive control algorithm is built using a partial least squares (PLS). The process control system with PLS was simulated on Matlab and the graphs showed good accuracy and stability.

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Analisis Partial Least Square Menghasilkan Output Alternatif Antarmuka Tambahan Dari Kansei Engineering

JURNAL TIARSIE ◽

10.32816/tiarsie.v17i2.78 ◽

2020 ◽

Vol 17 (2) ◽

pp. 67

Author(s):

Arief Ginanjar ◽

Awan Setiawan

Keyword(s):

Principal Component Analysis ◽

Factor Analysis ◽

Correlation Analysis ◽

Principal Component ◽

Component Analysis ◽

Partial Least Square ◽

Least Square ◽

Type I ◽

Kansei Engineering ◽

Coefficient Corrélation

Ketika menggunakan Kansei Engineering dalam mencari kandidat terbaik untuk menentukan model perancangan antarmuka website, peneliti menggunakan metode analisis Partial Least Square (PLS) yang dilakukan secara berulang hingga ditemukan elemen terbaik yang dapat diimplementasikan. PLS sebagai alat bantu untuk menentukan nilai terbaik antara elemen website. Output perbandingan yang dihasilkan akan dikelompokkan berdasarkan Kansei Word sebagaimana yang telah ditentukan dalam rencana awal implementasi Kansei Engineering, output perbandingan PLS iterasi pertama mempunyai kemungkinan mendapatkan nilai usulan terbaik jika digabung dengan melakukan iterasi kedua terhadap asimilasi dua atau tiga elemen yang mempunyai nilai tertinggi. Metodologi yang digunakan mengacu kepada Kansei Engineering Type I dengan melalui pengolahan data menggunakan Cronbach’s Alpha untuk menguji kelayakan responden, kemudian untuk mengetahui hubungan Kansei Words dapat menggunakan Coefficient Correlation Analysis (CCA), sedangkan hubungan antara Kansei Words dengan spesimen dapat menggunakan Principal Component Analysis (PCA), sedangkan mencari pengaruh Kansei Words paling kuat dapat menggunakan Factor Analysis (FA) dan analisis Partial Least Square (PLS) namun harus dilakukan iterasi proses PLS hingga variabel rekomendasi model perancangan antarmuka yang dihasilkan menjadi lebih bervariatif.

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Comparison of Principal Component Analysis and Partial Least Square Discriminant Analysis in the Classification of EEG signals

2018 IEEE International Workshop on Signal Processing Systems (SiPS) ◽

10.1109/sips.2018.8598425 ◽

2018 ◽

Cited By ~ 1

Author(s):

Abu Talha Khan ◽

Sadia Afrin ◽

Tanzilur Rahman

Keyword(s):

Principal Component Analysis ◽

Discriminant Analysis ◽

Principal Component ◽

Component Analysis ◽

Partial Least Square ◽

Least Square ◽

Eeg Signals

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Study of Concentration Dependent Curcumin Interaction with Serum Biomolecules using ATR-FTIR Spectroscopy Combined with Principal Component Analysis (PCA) and Partial Least Square Regression (PLS-R)

Vibrational Spectroscopy ◽

10.1016/j.vibspec.2021.103288 ◽

2021 ◽

pp. 103288

Author(s):

Kalindu D.C. Perera ◽

Geethika K. Weragoda ◽

Rukshani Haputhanthri ◽

Sanjeewa K. Rodrigo

Keyword(s):

Principal Component Analysis ◽

Ftir Spectroscopy ◽

Principal Component ◽

Component Analysis ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Least Square Regression

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Fault identification for chiller sensor based on partial least square method

E3S Web of Conferences ◽

10.1051/e3sconf/202123303057 ◽

2021 ◽

Vol 233 ◽

pp. 03057

Author(s):

Bang Wu ◽

Yunpeng Hu ◽

Chuanhui Zhou ◽

Guaiguai Chen ◽

Guannan Li

Keyword(s):

Principal Component Analysis ◽

Least Squares ◽

Partial Least Squares ◽

Principal Component ◽

Component Analysis ◽

Fault Analysis ◽

Partial Least Square ◽

Least Square ◽

Fault Identification ◽

Least Squares Regression

Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component analysis, its compression factor score contains more original data characteristic information, therefore, partial least squares have greater potential for fault diagnosis than the principal component analysis. However, there are few studies based on partial least squares in the field of HVAC. In order to introduce partial least squares into the field, based on the partial least squares fault detection theory, a fault analysis method suitable for this field is proposed, and the RP1403 data published by ASHARE was used to verify this method. The results show that on the basis of selecting the appropriate number of principal components, partial least squares have the ability to diagnose the fault of the chiller sensor. With the known fault source, partial least squares regression, a method with better data reconstruction accuracy than principal component analysis, is used to repair the fault. Finally, the purpose of fault identification can be achieved.

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