This paper proposes a new method to tune a fractional order PID controller. This method utilizes both the analytic and numeric approach to determine the controller parameters. The control design specifications that must be achieved by the control system are gain crossover frequency, phase margin, and peak magnitude at the resonant frequency, where the latter is a new design specification suggested by this paper. These specifications results in three equations in five unknown variables. Assuming that certain relations exist between two variables and discretizing one of them, a performance index can be evaluated and the optimal controller parameters that minimize this performance index are selected. As a case study, a thir

This paper proposes a new method to tune a fractional order PID controller. This method utilizes both the analytic and numeric approach to determine the controller parameters. The control design specifications that must be achieved by the control system are gain crossover frequency, phase margin, and peak magnitude at the resonant frequency, where the latter is a new design specification suggested by this paper. These specifications results in three equations in five unknown variables. Assuming that certain relations exist between two variables and discretizing one of them, a performance index can be evaluated and the optimal controller parameters that minimize this performance index are selected. As a case study, a third order linear time

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

Green synthesis of bimetallic nanoparticles of Fe/Ni (G-Fe/Ni-NPs) and zeolite-5A supported (G-Z-Fe/Ni-NPs) as heterogeneous Fenton-like oxidation for the decolourisation of reactive red 120-dye (RR120) from the aqueous medium using green tea extract as a reducing agent. Zeolite-5A from local kaolin is prepared and characterised using the hydrothermal method and is used as a supporting material for Fe/Ni-NPs. (SEM), (EDX), (AFM), (XRD), (FT-IR), (BET). Its zeta potential were used to characterise G-Fe/Ni-NPs and G-Z-Fe/Ni-NPs. The decolourisation efficiency (Ed) of the RR120-dye using a heterogeneous Fenton-like for G-Fe/Ni-NPs and G-Z-Fe/Ni-NPs is 99.8% and 99.9%, respectively, under the optimum conditions: [H2O2] = 20 and 1 mmol/L

In the present work, the phthalic acid (phthH2) and 1.10 phenonthroline (phen), and their complexes were synthesized and isolated as [M(phth)(phen)2], Mn(II), Fe(II), Co(II), Ni(II) Cu(II), Zn(II), and Cd(II) ions. These complexes were characterized by elemental analysis, melting point, conductivity, percentage metal, UV–Vis, FT-IR, and magnetic moment measurements. The molar conductance indicates that all the metal complexes in DMSO are nonelectrolytic. phthalic acid (phtha), and 1,10-Phenanthroline (phen), behaved as bidentate, coordinating to the metal ion through their two oxygen and two pyridinyl nitrogen atoms respectively, as corroborated by. Electronic spectra, FTIR, spectroscopy amusement indicated that all the metal complexes ad

الصيغة العامة للمعقدات الجديدة [M2(BDS)Cl4] الناتجة من تفاعل الليكاند الجديد] ن1,ن4-ثنائي(1أ –بنزو]د[ اميدازول-2-يل)-ن1,ن4-ثنائي(4-ثنائي مثيل امينو) بنزيل) سكسنمايد[ (BDS) مع الايونات الفلزية الكادميوم, الكوبلت, الزئبق, النحاس والنيكل. تم اشتقاق هذا الليكاند من تفاعل المواد الثلاث 4-(ثنائي ميثيل أمينو) بنزالدهيد، 2-أمينو بنزيميدازول، وكلوريد السكسينيل. تم تشخيص المركبات باستخدام مطيافية طيف الاشعة تحت الحمراء وطيف الرن