Ali Hedayati

PROCESSING OF NANO/SUBMICRON GRAINED STAINLESS STEEL 304L BY AN ADVANCED THERMOMECHANICAL PROCESS

Nano/Submicron crystalline grains of about 250 nm were obtained in a metastable austenitic stainless steel AISI304L by an advanced thermomechanical process consisting of heavy conventional cold rolling and annealing. Effects of cold thickness reduction and temperature and time of the reversion treatment on microstructure and mechanical properties of the steel were investigated. The nano-structured austenitic steel exhibited not only high strength (above 1 GPa) but also good elongation (above 50%).

Modeling of Non-catalytic Supercritical Water Oxidation of Phenol

Abstract The non-catalytic supercritical water oxidation (SCWO) of phenol was modeled using Gopalan-Savage and Thornton-Savage global and network rates. Comparison of experimental data for the phenol conversion with the numerical predictions of this study indicated very close compatibility. Applying the validated model, the phenol conversion and selectivity of various products were studied as a function of effective parameters such as feed phenol concentration, reactor residence time, feed temperature, and feed oxygen concentration. The results of modeling analysis show that an appropriate elevated temperature range (460°C < T <500°C) and long residence time (≈90 s) reduce the concentration of hazardous products (i.e., dimers, dibenzofuran, dibenzo-p-dioxin) and maximize the selectivity of environmental benign products such as water and carbon dioxide. Also, high oxygen concentration (≈0.01 mol/L) increase water and carbon dioxide yield. Moreover, high feed phenol concentrations cause a shortcoming for the SCWO system in terms of phenol conversion and selectivity of desirable environmental products. As a consequence, the feed phenol concentration of ≤2 × 10−3 mol/L is recommended as the appropriate condition.

Erosive-abrasive wear behavior of carbide-free bainitic and boron steels compared in simulated field conditions

The wear resistance of carbide-free bainitic microstructures have recently shown to be excellent in sliding, sliding-rolling, and erosive-abrasive wear. Boron steels are often an economically favorable alternative for similar applications. In this study, the erosive-abrasive wear performance of the carbide-free bainitic and boron steels with different heat treatments was studied in mining-related conditions. The aim was to compare these steels and to study the microstructural features affecting wear rates. The mining-related condition was simulated with an application oriented wear test method utilizing dry abrasive bed of 8–10 mm granite particles. Different wear mechanisms were found; in boron steels, micro-cutting and micro-ploughing were dominating mechanisms, while in the carbide-free bainitic steels, also impact craters with thin platelets were observed. Moreover, the carbide-free bainitic steels had better wear performance, which can be explained by the different microstructure. The carbide-free bainitic steels had fine ferritic-austenitic microstructure, whereas in boron steels microstructure was martensitic. The level of retained austenite was quite high in the carbide-free bainitic steels and that was one of the factors improving the wear performance of these steels. The hardness gradients with orientation of the deformation zone on the wear surfaces were one of the main affecting factors as well. Smoother work hardened hardness profiles were considered beneficial in these erosive-abrasive wear conditions.

Optimization of supercritical extraction of galegine from Galega officinalis L.: Neural network modeling and experimental optimization via response surface methodology

Supercritical CO2 extraction of galegine from Galega officinalis L. was carried out under different operating conditions of temperature (35-55 °C), pressure (10-30MPa), dynamic extraction time (30-150min), CO2 flow rate (0.5-2.5 mL/min) and constant static extraction time of 20 min. Design of experiment was by response surface methodology (RSM) using Minitab software 17. The response surface analysis accuracy was verified by the coefficient of determination (R2=93.4%) along with modified coefficient of determination (mod-R2=87.7%). The optimum operating conditions were found by using RSM modeling to be 42.8 °C, 22.7MPa, 141.5min and 2.15 mL/min, in which the maximum galegine extraction yield of 3.3932mg/g was obtained. Artificial neural network (ANN) using Levenberg-Marquardt backpropagation training function with six neurons in the hidden layer was implemented for the modeling of galegine extraction such that the coefficient of determination (R2) was 96.6%.

Microstructure analysis and mechanical properties of Low alloy High strength Quenched and Partitioned Steel

Gleeble study of the quenching and partitioning (Q&P) process has been performed on Domex 960 steel (Fe, 0.08 %C, 1.79 %Mn, 0.23 %Si, 0.184 %Ti, and 0.038 %Al). The effect of different Q&P conditions on microstructure and mechanical properties were investigated. The aim of the process is to produce a fine grained microstructure for better ductility and controlled amounts of different micro-constituents to increase the strength and toughness simultaneously. Three different quenching temperatures, three partitioning temperatures and three partitioning times have been selected to process the 27 specimens by Gleeble® 1500. The specimens were characterized by means of OM, SEM, XRD, hardness and impact tests. It was found that, fine lath martensite with retained austenite is achievable without high amount of Si or Al in the composition although lack of these elements may cause the formation of carbides and decrease the available amount of carbon for partitioning into the austenite. The hardness increases as the quenching temperature is decreased, however, at highest partitioning temperature (640◦C) the hardness increases sharply due to extensive precipitate formation.

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modeling of Supercritical CO2 Extraction of Glycyrrhizic Acid from Glycyrrhiza glabra L

Abstract In this study, the extraction of Glycyrrhizic acid (GA) from Glycyrrhiza glabra (licorice) root was investigated by Soxhlet extraction and modified supercritical CO2 with water as co-solvents and 30 min of static extraction time. The high performance liquid chromatography (HPLC) was used to identify and quantitatively determine the amount of extracted GA recovery of supercritical CO2 extraction of GA. The extraction recovery was modeled by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Different ANFIS networks (by changing the type of membership functions) were compared with evaluation of networks accuracy in GA recovery prediction and subsequently the suitable network was determined. A three-layer artificial neural network was also developed for modeling of GA extraction from licorice plant root. In this regard, different networks (by changing the number of neurons in the hidden layer and algorithm of network training) were compared with evaluation of networks accuracy in extraction recovery prediction. One-step secant back propagation algorithm with six neurons in hidden layer was found to be the most suitable network and the coefficient of determination (R2) was 98.5 %. Gaussian combination membership function (gauss2mf) using 2 membership function to each input was obtained to be optimum ANFIS architecture with mean square error (MSE) of 0.05,0.17 and 0.07 for training, testing and checking data, respectively.

Similar Tendency to Strain-Induced Martensite Transformation in Different Austenitic Stainless Steels

The formation of strain-induced martensite in metastable austenitic stainless steels depends on many parameters; the most well known and highly investigated of these is temperature. In this work, we suggest a new relationship between Md30/50, deformation temperature (DT), and martensite content based on data gathered from other studies and our experimental findings. For this purpose we rolled an AISI 304L stainless steel in different strains at 25°C, 0°C, and −15°C; then we characterized the steel via x-ray diffraction and Ferritescope studies to identify different phases and calculate their contents. According to the results, the relationship is as follows: If the delta value (DT − Md30/50) is the same for different austenitic stainless steels, they will form equal amounts of martensite under similar strain conditions. Moreover, both the delta value and the true strain have a strong effect on the formation of martensite.