The corrosion of carbon steel in single phase (water with 0.1N NaCl ) and two immiscible phases (kerosene-water) using turbulently agitated system is investigated. The experiments are carried out for Reynolds number (Re) range of 38000 to 95000 corresponding to rotational velocities from 600 to 1400 rpm using circular disk turbine agitator at 40 0C. In two-phase system test runs are carried out in aqueous phase (water) concentrations of 1 % vol., 5 % vol., 8% vol., and 16% vol. mixed with kerosene at various Re. The effect of Reynolds number (Re), percent of dispersed phase, dispersed drops diameter, and number of drops per unit volume on the corrosion rate is investigated and discussed. Test runs are carried out using two types of inhibitors: sodium nitrite of concentrations 20, 40, and 60 ppm and sodium hexapolyphosphate of concentrations 485, 970, and 1940 ppm in a solution containing 8 % vol. aqueous phase (water) mixed with kerosene (continuous phase) at 40 °C for the whole range of Re. It was found that increasing Re increases the corrosion rate and the presence of water enhances the corrosion rate by increasing the solution electrical conductivity. For two phase solution containing 8% vol. and 16% vol. of water the corrosion rate was higher than single phase (100 % vol. water). The main parameters that play the major role in determining the corrosion rate in two phase were concentration of oxygen, solution electrical conductivity, and the interfacial area between the two phases (dispersed and continuous). Sodium nitrite and sodium hexapolyphosphate were found to be efficient inhibitors in two phase solutionfor the investigated range of Re.
The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed
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XML is being incorporated into the foundation of E-business data applications. This paper addresses the problem of the freeform information that stored in any organization and how XML with using this new approach will make the operation of the search very efficient and time consuming. This paper introduces new solution and methodology that has been developed to capture and manage such unstructured freeform information (multi information) depending on the use of XML schema technologies, neural network idea and object oriented relational database, in order to provide a practical solution for efficiently management multi freeform information system.
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Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob
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The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com
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In Indonesia, cattle feces (CF) and water hyacinth (WH) plants are abundant but have not been widely revealed. The use of microorganisms as decomposers in the fermentation process has not been widely applied, so researchers are interested in studying further. This study was to evaluate the effect of the combination of CF with WH on composting by applying white-rot fungal (WRF) (Ganoderma sp) microorganism as a decomposer. A number of six types of treatment compared to R1(ratio of CF:WH)(25%:75%)+WRF; R2(ratio of CF:WH)(50%:50%)+WRF; R3(ratio of CF:WH)(75%:25%)+WRF; R4(ratio of CF:WH)(25%:75%) without WRF; R5(ratio of CF:WH)(50%:50%) without WRF; R6(ratio of CF:WH)
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By- products of corn starch industry were used to prepare media for propagation the lactic acid bacteria as a natural auxotroph. The by- products used were the corn steep water (S) and gluten extract (G) after a proper treatment to get them ready for media preparation. The results showed that it was possible to replace the peptone and meat extract by gluten extract in MRS medium. The growth was approximately similar to that obtained in standard MRS media. Corn steep water (S) was used as well and the growth enhanced by including Tween – 80 at 1% level. The later media named MZ, which was superior for growing standard and local strains and starters. The MZ medium modified by adding acetate and glacial acetic acid similarly to
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