In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, with a
good degree of accuracy reaching 97.26, 95.92 and 86.43% respectively. These ANN models could be used as a support for workers in operating the filters in water treatment plants and to improve water treatment process. With the use of ANN, water systems will get more efficient, so reducing operation cost and improving the quality of the water produced.
Background: This in vitro study compares a novel calcium-phosphate etchant paste to conventional 37% phosphoric acid gel for bonding metal and ceramic brackets by evaluating the shear bond strength, remnant adhesive and enamel damage following water storage, acid challenge and fatigue loading. Material and Methods: Metal and ceramic brackets were bonded to 240 extracted human premolars using two enamel conditioning protocols: conventional 37% phosphoric acid (PA) gel (control), and an acidic calcium-phosphate (CaP) paste. The CaP paste was prepared from β-tricalcium phosphate and monocalcium phosphate monohydrate powders mixed with 37% phosphoric acid solution, and the resulting phase was confirmed using FTIR. The bonded premolars were exp
... Show MoreIn this paper, the speed control of the real DC motor is experimentally investigated using nonlinear PID neural network controller. As a simple and fast tuning algorithm, two optimization techniques are used; trial and error method and particle swarm optimization PSO algorithm in order to tune the nonlinear PID neural controller's parameters and to find best speed response of the DC motor. To save time in the real system, a Matlab simulation package is used to carry out these algorithms to tune and find the best values of the nonlinear PID parameters. Then these parameters are used in the designed real time nonlinear PID controller system based on LabVIEW package. Simulation and experimental results are compared with each other and showe
... Show MoreThe aim of the research is to measure the length between the variable the efficiency of the tax examiner with its dimensions represented by (scientific questions, practical process (experience), training and development, impartiality and independence, ethics of the profession) and the approved variable discovering the artificial adaptation of profits, and the degree of arrangement of those dimensions its importance and priority, and the research problem has been identified In a main question that is there any effect of copying the images of the image examiner in discovering the adaptation, the financial statements and reports of the companies (X, Y) and the banks (A, B) were relied on in the interpretation of the results, t
... Show MoreSorption is a key factor in removal of organic and inorganic contaminants from their aqueous solutions. In this study, we investigated the removal of Xylenol Orange tetrasodium salt (XOTS) from its aqueous solution by Bauxite (BXT) and cationic surfactant hexadecyltrimethyl ammonium bromide modified Bauxite (BXT-HDTMA) in batch experiments. The BXT and BXT-HDTMA were characterized using FTIR, and SEM techniques. Adsorption studies were performed at various parameters i.e. temperature, contact time, adsorbent weight, and pH. The modified BXT showed better maximum removal efficiency (98.6% at pH = 9.03) compared to natural Bauxite (75% at pH 2.27), suggesting that BXT-HDTMA is an excellent adsorbent for the removal of XOTS from water. The equ
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