Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model

A reliability assessment is an important tool used for processing plants, since the facility consists of many loops and instruments attached and operated based on other availability; thus, a statistical model is needed to visualize the reliability of its operation. The paper focuses on the reliability assessment and prediction based on the existing statistical models, such as normal, log-normal, exponential, and Weibull distribution. This paper evaluates and visualizes the statistical reliability models optimized using MLE and considers the failure mode caused during a simulated process control operation. We simulated the failure of the control valve caused by stiction running with various flow rates using a pilot plant, which depicted the Weibull distribution as the best model to estimate the simulated process failure.

CAPABILITY INDICES FOR CONTROL CHARTS BASED ON REGRESSION MODELS

Process capability analysis is extremely important for optimization and quality improvement. It verifies whether the process under analysis is capable of producing items within engineering and customers’ specifications. The use of capability indices when assumptions are not satisfied leads to erroneous conclusions, compromising the study and analysis of the process, jeopardizing the fulfillment of requirements from management or external customers. Aiming at filling a gap identified in the literature, the main contributions of this work are: (i) proposition of capability indices for processes monitored through control charts based on regression models, for symmetric and asymmetric specifications; and (ii) comparison of the proposed indices with traditional capability indices through a simulated process.

Multicriterial Analysis of Simulated Process of Post-Combustion Capture of Pure H2S and Mixtures of H2S and CO2 Using Single and Blended Aqueous Alkanolamines

The paper evaluates the performance of the nine selected alkanolamines, namely, monoethanolamine (MEA), diethanolamine (DEA), monomethylethanolamine (MMEA), aminoethylethanolamine (AEEA), diisopropanolamine (DIPA), triethanolamine (TEA), dimethylethanolamine (DMEA), N-methyldiethanolamine (MDEA), and piperazine (PZ) for post-combustion capture of pure hydrogen sulfide (H2S) and mixtures of hydrogen sulfide and carbon dioxide (CO2) at different solvent mass flows: 500, 750, and 1000 kg/h using Aspen Plus® Version 7.2. The objective of the paper is to select the best chemical absorbent for each different criterion: percent H2S removal, percent H2S solvent carrying capacity, percent H2S retained in the lean solvent, percent CO2 and H2S removal, percent CO2 and H2S solvent carrying capacity, percent CO2 and H2S retained in the lean solvent. Based from the obtained results, piperazine is an absorbent that has a good potential for use as a single amine or in mixtures with other amines for capture of pure H2S and mixtures of H2S and CO2.