In this paper a dynamic behavior and control of  a jacketed continuous stirred tank reactor (CSTR)  is developed using different control strategies, conventional feedback control (PI and PID), and neural network (NARMA-L2, and NN Predictive) control. The dynamic model for CSTR process is described by a first order lag system with dead time.

The optimum tuning of control parameters are found by two different methods; Frequency Analysis Curve method (Bode diagram) and Process Reaction Curve using the mean of Square Error (MSE) method. It is found that the Process Reaction Curve method is better than the Frequency Analysis Curve method and PID feedback controller is better than PI feedback controller.

The results s

In the present work, the behavior of thick-walled cylinder of elasto-plastic material (polymeric material) has been studied analytically. The study is based on modified Von-Mises yield criterion (for non metallic material). The equations of stress distribution are obtained for the cylinder under general cases of elastic expansion, plastic initiation and elastic-plastic expansion.

        A computer program is developed for evaluating the stress distribution. The solution is carried out for worst boundary conditions when the cylinder is subjected to the combination of pressure load, inertia load, and temperature gradient.

        The results are presente

Depletion of fossil fuel is one of the main reasons why the bioethanol has become popular. It is a renewable energy source. In order to meet the great demand of bioethanol, it is best that the bioethanol production is from cheap raw materials. Since the golden shower fruit is not being utilized and is considered as waste material, hence, this study was conducted to make use of the large volume of the residue as feedstock to test its potential for bioethanol extraction.The main goal of this study is to obtain the most volume of bioethanol from the golden shower fruit liquid residue by the factors, days of fermentation (3, 5, and 7 days) and sugar concentration (15, 20 and 25 brix) of the liquid residue. Also, part of the study is to compu

Wastewater treatment plants operators prefer to make adjustments because they are more cost effective, to use the existing tank instead of building new ones. In this case an imported materials would be  used as bio-loads to increase biomass and thus maintain efficiency as the next organic loading increases.In the present study, a local substance "pumice stone" was used as a biological carrier in the aeration tank, and the experiments were carried out in five stages: without biological carriers, filling ratio of 4%,10%,20%, and25% with pumice stone, the maximum organic loading at each stage (1.1884, 1.2144, 1.9432, 2.7768, 3.3141)g BOD /l.d respectively.Other experiments were carried out to determine the best filling ratio, the SS remova

In this work laser detection and tracking system (LDTS) is designed and implemented using a fuzzy logic controller (FLC). A 5 mW He-Ne laser system and an array of nine PN photodiodes are used in the detection system. The FLC is simulated using MATLAB package and the result is stored in a lock up table to use it in the real time operation of the system. The results give a good system response in the target detection and tracking in the real time operation.

Four rapid, accurate and very simple derivative spectrophotometric techniques were developed for the quantitative determination of binary mixtures of estradiol (E2) and progesterone (PRG) formulated as a capsule. Method I is the first derivative zero-crossing technique, derivative amplitudes were detected at the zero-crossing wavelength of 239.27 and 292.51 nm for the quantification of estradiol and 249.19 nm for Progesterone. Method II is ratio subtraction, progesterone was determined at λmax 240 nm after subtraction of interference exerted by estradiol. Method III is modified amplitude subtraction, which was established using derivative spectroscopy and mathematical manipulations. Method IIII is the absorbance ratio technique, absorba

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

The use of artificial intelligence (AI) technology is rapidly expanding in nursing and society. However, its use in healthcare comes with a number of challenges and concerns. The authors of this article use the sociotechnical model to consider the expanding use of AI in nursing and healthcare from a global perspective. Select references from the literature are used to support this important discussion for nurses and other healthcare professionals. Artificial intelligence is a major innovation that, if used properly, can reduce errors and improve efficiency and healthcare quality. It has also been shown to increase patient support, healthcare access and patient care. Here the authors address some of the limitations and challenges of