AN IMPROVED DIAGNOSTIC TOOL FOR CONTROL VALVE STICTION BASED ON NONLINEAR PRINCIPLE COMPONENT ANALYSIS AND AUTOCOVARIANCE

Control valves suffer from wear and aging when opening and closing the valves which causes stiction nonlinearity. The existing stiction detection techniques are only applicable to loops from a certain type of processes or a certain amount of dataset. The nonlinear principle component analysis (NLPCA) method requires many ensemble runs per data, a large quantity of raw data and shows uncertain results when applied to loops from integrating processes

Removal of Heavy Metal Ions using Amine-Functionalized Mesoporous Adsorbent

The capability of adsorption of heavy metals such as Cu (II) and Cr (VI) ions from an aqueous solution using amine-functionalized MCM-41 mesoporous silica was investigated in this study. In the present work, 20% of secondary and tertiary amine functional groups namely, N-(2-aminoethyl)-3-aminopropyltrimethoxysilane (AAPTMS) and 3-[2-(2-aminoethylamino)ethylamino]propyltrimethoxysilane (AEPTMS) were loaded into MCM-41, respectively, via co-condensation synthesis method. The structure and properties of the amine-functionalized MCM-41 absorbents were characterized by Fourier transform infrared (FT-IR) spectrometry and scanning electron microscope (SEM)

Removal of Heavy Metal Ions using Amine-Functionalized Mesoporous Adsorbent

The capability of adsorption of heavy metals such as Cu (II) and Cr (VI) ions from an aqueous solution using amine-functionalized MCM-41 mesoporous silica was investigated in this study. In the present work, 20% of secondary and tertiary amine functional groups namely, N-(2-aminoethyl)-3-aminopropyltrimethoxysilane (AAPTMS) and 3-[2-(2-aminoethylamino)ethylamino]propyltrimethoxysilane (AEPTMS) were loaded into MCM-41, respectively, via co-condensation synthesis method. The structure and properties of the amine-functionalized MCM-41 absorbents were characterized by Fourier transform infrared (FT-IR) spectrometry and scanning electron microscope (SEM)

AN IMPROVED DIAGNOSTIC TOOL FOR CONTROL VALVE STICTION BASED ON NONLINEAR PRINCIPLE COMPONENT ANALYSIS AND AUTOCOVARIANCE

Control valves suffer from wear and aging when opening and closing the valves which causes stiction nonlinearity. The existing stiction detection techniques are only applicable to loops from a certain type of processes or a certain amount of dataset. The nonlinear principle component analysis (NLPCA) method requires many ensemble runs per data, a large quantity of raw data and shows uncertain results when applied to loops from integrating processes