Neural network based corrosion modeling of Stainless Steel 316L elbow using electric field mapping data.

Stainless steel (SS) is widely employed in industrial applications that demand superior corrosion resistance. Modeling its corrosion behavior in common structural and various operational scenarios is beneficial to provide wall-thickness (WT) information, thus leading to a predictive asset integrity regime. In this spirit, an approach to model the corrosion behavior of SS 316L using artificial neural networks (ANNs) is developed, whereby saline water at different concentrations is flown through an elbow structure at different flow rates and salt concentrations. Voltage, current, and temperature data are recorded hourly using electric field mapping (EFM) pins installed on the elbow surface, which serve as training data for the ANNs. The performance of corrosion modeling is verified by comparing the predicted WT with actual measurements obtained from experimental tests. The results show the exceptional performance of the proposed single ANN model to predict WT. The error is calculated by comparing the estimated WT and actual measurement recorded, where the maximum error for each setting is range from 0.5363 to [Formula: see text]. RMSE and MAE values of each pin in every setting are also computed such that the maximum values of RMSE and MAE are 0.0271 and 0.0266, respectively. Moreover, a concise account of the observed scale formation is also reported. This comprehensive study contributes to a better understanding of SS 316L corrosion and offers valuable insights for developing efficient strategies to prevent corrosion in industrial environments. By accurately predicting WT loss using ANNs, this approach enables proactive maintenance planning, minimizing the risk of structural failures and ensuring the extended sustainability of industrial assets.

Comparative study of regular and smart grids with PV for Electrification of an academic campus with EV charging.

The current global greenhouse gas (GHG) emission rates will increase the average global temperature by 1.5 °C by 2050. This will be detrimental for organisms and ecosystems, as well as human well-being. Many countries have pledged to halve their emissions by 2030 and reach net zero targets by 2050. Optimum generation of electricity from sustainable green sources and its use to charge electric vehicle (EV) batteries will solve this problem to greater extent. The best places to capture solar energy, use it to meet load demand and charge EV batteries are the large open car parking areas near retail stores, academic institutes, industrial areas, and offices. This study targets the open parking areas of an academic campus (King Saud University, Riyadh) to meet the load demand of 25,000 kWh/day with a peak load of 4180 kW and charging the batteries of parked EVs. Four system designs, simple grid, standalone photovoltaic (PV), simple grid and PV, and smart grid and PV, are compared. Currently, the cost of energy (COE) of the grid is US $ 0.085 in KSA. In comparison, the COE of standalone PV is almost 4.5 folds higher and in simple grid with PV, it is around 58% higher. However, a renewable penetration of 53.8% is achieved. In the third option, smart grid with PV, the COE is 24% lower compared to the base case. A 54.3% of the total energy produced is sold back to the grid, and the total renewable penetration of 77.7% is achieved. To observe the effect of energy sale limit on project parameters, the sensitivity analysis is performed. It can be observed that with a 1 MW increase in the limit, the COE decreases by around 20% and net present cost (NPC) by around 6%. The proposed models for the solar car parks can be used elsewhere with similar climatic conditions.

Wind and wind power characteristics of the eastern and southern coastal and northern inland regions, South Africa.

The objective of this work is to understand the fluctuating nature of wind speed characteristics on different time scales and to find the long-term annual trends of wind speed at different locations in South Africa. The hourly average mean wind speed values over a period of 20 years are used to achieve the set objective. Wind speed frequency, directional availability of maximum mean wind speed, total energy, annual energy yield and plant capacity factors are determined for seven locations situated both inland and along the coast of South Africa. The highest mean wind speed (6.01 m/s) is obtained in Port Elizabeth and the lowest mean wind speed (3.86 m/s) is obtained in Bloemfontein. Wind speed increased with increasing latitudes at coastal sites (Cape Town, Durban, East London and Port Elizabeth), while the reverse trend was observed at inland locations (Bloemfontein, Johannesburg and Pretoria). Noticeable annual changes and relative wind speed values are found at coastal locations compared to inland sites. The energy pattern factor, also known as the cube factor, varied between a minimum of 1.489 in Pretoria and a maximum of 1.858 in Cape Town. Higher energy pattern factor (EPF) values correspond to sites with fair to good wind power potential. Finally, Cape Town, East London and Port Elizabeth are found to be good sites for wind power deployments based on the wind speed and power characteristics presented in this study.