In this work the effect of choosing tri-circular tube section had been addressed to minimize the end effector’s error, a comparison had been made between the tri-tube section and the traditional square cross section for a robot arm, the study shows that for the same weight of square section and tri-tube section the error may be reduced by about 33%.

A program had been built up by the use of MathCAD software to calculate the minimum weight of a square section robot arm that could with stand a given pay load and gives a minimum deflection. The second part of the program makes an optimization process for the dimension of the cross section and gives the dimensions of the tri-circular tube cross section that have the same weight of

In this work, a functional nanocomposite consisting of multi walled carbon nanotubes combined with nanoparticles of silver and Pomegranate peel extract (MWCNTs- SNPs -NPGPE) was successfully synthesized using ultra sonic technique. The nanocomposite has been characterized using Transmission electron microscope (TEM), XRD, Energy dispersive X-ray spectroscopy (EDS) UV-Vis and FTIR. The obtained results reveal that the MWCNTs-SNPs-NPGPE nanocomposite exhibits form of nanotubes with rough surfaces and containing black spots, which are the silver nanoparticles. The dimensions of this tube are 161 nm in length and 60 nm in width with nanoparticles of silver not exceeding 20 nm. The XRD pattern of the prepared MWCNTs-SNPs-NPGPE nanocomposite s

 A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

Drilling well design optimization reduces total Authorization for Expenditures (AFE) by decreasing well constructing time and expense. Well design is not a constant pattern during the life cycle of the field. It should be optimized by continuous improvements for all aspects of redesigning the well depending on the actual field conditions and problems. The core objective of this study is to deliver a general review of the well design optimization processes and the available studies and applications to employ the well design optimization to solve problems encountered with well design so that cost effectiveness and perfect drilling well performance are achievable. Well design optimization processes include unconventional design(slimhole) co

In 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

The purpose of this work is to concurrently estimate the UVvisible spectra of binary combinations of piroxicam and mefenamic acid using the chemometric approach. To create the model, spectral data from 73 samples (with wavelengths between 200 and 400 nm) were employed. A two-layer artificial neural network model was created, with two neurons in the output layer and fourteen neurons in the hidden layer. The model was trained to simulate the concentrations and spectra of piroxicam and mefenamic acid. For piroxicam and mefenamic acid, respectively, the Levenberg-Marquardt algorithm with feed-forward back-propagation learning produced root mean square errors of prediction of 0.1679 μg/mL and 0.1154 μg/mL, with coefficients of determination of

Methods of estimating statistical distribution have attracted many researchers when it comes to fitting a specific distribution to data. However, when the data belong to more than one component, a popular distribution cannot be fitted to such data. To tackle this issue, mixture models are fitted by choosing the correct number of components that represent the data. This can be obvious in lifetime processes that are involved in a wide range of engineering applications as well as biological systems. In this paper, we introduce an application of estimating a finite mixture of Inverse Rayleigh distribution by the use of the Bayesian framework when considering the model as Markov chain Monte Carlo (MCMC). We employed the Gibbs sampler and

Bacteria strain H7, which produces flocculating substances, was isolated from the soil of corn field at the College of Agriculture in Abu-Ghrib/Iraq, and identified as Bacillus subtilis by its biochemical /physiological characteristics. The biochemical analysis of the partially purified bioflocculant revealed that it was a proteoglycan composed of 93.2 % carbohydrate and 6.1 % protein. The effects of bioflocculant dosage, temperature, pH, and different salts on the flocculation activity were evaluated. The maximum flocculation activity was observed at an optimum bioflocculant dosage of 0.2 mL /10 mL (49.6%). The bioflocculant had strong thermal stability within the range of 30-80 °C, and the flocculating activity was over 50 %. The biofloc