Optimization through Response Surface Methodology of a Reactor Producing Methanol by the Hydrogenation of Carbon Dioxide

Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. The artificial intelligence algorithms demonstrate the correlation between the number of vacant staff jobs and both the frequency of license event reports each year and the ratio of contract employees to regular employees in the administrative domain. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective machine learning neural network models are derived from generative adversarial network (GAN) algorithms and proposed for administrative, operational and maintenance loops of nuclear reactor control unit.

Presence of activated carbon particles from waste walnut shell as a biosorbent in monoethanolamine (MEA) solution to enhance carbon dioxide absorption

Greenhouse effects are a natural phenomenon that plays a high role in shaping the climate system. In this research, MEA solution was used for CO₂ capture in presence of activated carbon particles from waste walnut shells as a biosorbent. The process parameters including temperature, pressure, MEA concentration, and activated carbon were used in the central composite design (CCD) model. The absorption experiments were carried out in a laboratory setup at operational conditions including temperature in range of 20-60 °C, pressure in range of 3.5-9.5 bar, MEA concentration in range of 2.5-8.5 wt%, and active carbon amount in range of 0.3-0.9 g/L. The process responses including CO₂ loading, the amounts of CO₂ absorption, and absorption percentage were obtained in the range of 0.444-0.720 mol_CO2/mol_MEA, 0.294-0.687 mol/L, and 19.32-52.25%, respectively. The optimal value of CO₂ loading was obtained at temperature of 30 °C, pressure of 5.19 bar, activated carbon of 0.75 g, and MEA concentration of 7.00 wt%. The optimum values of responses were obtained 0.531, 0.609 mol/L and 50.04% for maximum loading, absorption amount, and absorption percentage, respectively. From the results, carbon dioxide loading in MEA solution increases in presence of activated carbon particles.