A study on the mechanical strength of Fe2O3/Cr2O3/CuO catalyst for high temperature water gas shift reaction

Journal of Porous Materials - The present study aims to investigate the effects of iron (hydr)oxide phases formed during precipitation and the addition of different binders on the mechanical and...     

Natural killer or T-lymphocyte cells: Which is the best immune therapeutic agent for cancer? An optimal control approach

Therapeutic vaccines are being developed as a promising new approach to treatment for cancer patients. There are still many unanswered questions about which kind of therapeutic vaccines are the best for the cancer treatments? In this paper we consider a mathematical model, in the form of a system of ordinary differential equations (ODE), this system is an example from a class of mathematical models for immunotherapy of the tumor that were derived from a biologically validated model by Lisette G. de Pillis. The problem how to schedule a variable amount of which vaccines to achieve a maximum reduction in the primary cancer volume is consider as an optimal control problem and it is shown that optimal control is quadratic with 0 denoting a trajectory corresponding to no treatment and 1 a trajectory with treatment at maximum dose along that all therapeutics are being exhausted. The ODE system dynamics characterized by locating equilibrium points and stability properties are determined by using appropriate Lyapunov functions. Especially we attend a parametric sensitivity analysis, which indicates the dependency of the optimal solution with respect to disturbances in model parameters.     

Polyamide 6–Cellulose Composites: Effect of Cellulose Composition on Melt Rheology and Crystallization Behavior

Melt rheology and crystallization behavior of polyamide 6 (PA 6) and microcrystalline cellulose (MCC) composites were systematically studied in this research. The incorporation of MCC into the PA 6 matrix resulted in higher complex viscosities (|η*|), storage modulus (G′), and shear viscosities than those of neat PA 6, especially at low frequencies. The orientation of rigid molecular chains in the composites introduced by the addition of MCC induced a strong shear thinning behavior with an increase in MCC loading. The non‐isothermal crystallization kinetics of PA 6 and MCC composites were investigated by differential scanning calorimetry. The Avrami and Tobin model were applied to describe the process of non‐isothermal crystallization and to determine the crystallization parameters of the composites. Analysis of the crystallization kinetics indicated that the Avrami (n_a) and Tobin exponent (n_t) was altered by the MCC. It was also found that the Avrami and Tobin equations fit the empirical data well. POLYM. ENG. SCI., 54:739–746, 2014. © 2013 Society of Plastics Engineers     

ANFIS-based prediction of the compressive strength of geopolymers with seeded fly ash and rice husk–bark ash

In the present work, compressive strength of geopolymers made from seeded fly ash and rice husk–bark ash has been predicted by adaptive network-based fuzzy inference systems (ANFIS). Different specimens, made from a mixture of fly ash and rice husk–bark ash in fine and coarse forms and a mixture of water glass and NaOH mixture as alkali activator, were subjected to compressive strength tests at 7 and 28 days of curing. The curing regimes were different: one set of the specimens were cured in water at room temperature until 7 and 28 days and the other sets were oven-cured for 36 h at the range of 40–90°C and then cured at room temperature until 7 and 28 days. A model based on ANFIS for predicting the compressive strength of the specimens has been presented. To build the model, training and testing using experimental results from 120 specimens were conducted. The used data as the inputs of ANFIS models are arranged in a format of six parameters that cover the percentage of fine fly ash in the ashes mixture, the percentage of coarse fly ash in the ashes mixture, the percentage of fine rice husk–bark ash in the ashes mixture, the percentage of coarse rice husk–bark ash in the ashes mixture, the temperature of curing, and the time of water curing. According to these input parameters in the ANFIS models, the compressive strength of each specimen was predicted. The training and testing results in ANFIS models showed a strong potential for predicting the compressive strength of the geopolymeric specimens.     

Predicting the effects of nanoparticles on compressive strength of ash-based geopolymers by gene expression programming

In the present work, the effect of SiO₂ and Al₂O₃ nanoparticles on compressive strength of ash-based geopolymers with different mixtures of rice husk ash, fly ash, nanoalumina and nanosilica has been predicted by gene expression programming. The models were constructed by 12 input parameters, namely the water curing time, the rice husk ash content, the fly ash content, the water glass content, NaOH content, the water content, the aggregate content, SiO₂ nanoparticle content, Al₂O₃ nanoparticle content, oven curing temperature, oven curing time and test trial number. The value for the output layer was the compressive strength. According to the input parameters in gene expression programming models, the data were trained and tested, and the effects of SiO₂ and Al₂O₃ nanoparticles on compressive strength of the specimens were predicted with a tiny error. The results indicate that gene expression programming model is a powerful tool for predicting the effect of nanoparticles on compressive strength of the geopolymers in the considered range.     

Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP

GEP has been employed in this work to model the compressive strength of different types of geopolymers through six different schemes. The differences between the models were in their linking functions, number of genes, chromosomes and head sizes. The curing time, Ca(OH)₂ content, the amount of superplasticizer, NaOH concentration, mold type, aluminosilicate source and H₂O/Na₂O molar ratio were the seven input parameters considered in the construction of the models to evaluate the compressive strength of geopolymers. A total number of 399 input-target pairs were collected from the literature, randomly divided into 299 and 100 sets and were trained and tested, respectively. The best performance model had 6 genes, 14 head size, 40 chromosomes and multiplication as linking function. This was shown by the absolute fraction of variance, the absolute percentage error and the root mean square error. These were of 0.9556, 2.4601 and 3.4716 for training phase, respectively and 0.9483, 2.8456 and 3.7959 for testing phase, respectively. However, another model with 7 genes, 12 head size, 30 chromosomes and addition as linking function showed suitable results with the absolute fraction of variance, the absolute percentage error and the root mean square of 0.9547, 2.5665 and 3.4360 for training phase, respectively and 0.9466, 2.8020 and 3.8047 for testing phase, respectively. These models showed that gene expression programming has a strong potential for predicting the compressive strength of different types of geopolymers in the considered range.     

Predicting compressive strength of different geopolymers by artificial neural networks

In the present study, six different models based on artificial neural networks have been developed to predict the compressive strength of different types of geopolymers. The differences between the models were in the number of neurons in hidden layers and in the method of finalizing the models. Seven independent input parameters that cover the curing time, Ca(OH)₂ content, the amount of superplasticizer, NaOH concentration, mold type, geopolymer type and H₂O/Na₂O molar ratio were considered. For each set of these input variables, the compressive strength of geopolymers was obtained. A total number of 399 input-target pairs were collected from the literature, randomly divided into 279, 60 and 60 data and were trained, validated and tested, respectively. The best performance model was obtained through a network with two hidden layers and absolute fraction of variance of 0.9916, the absolute percentage error of 2.2102 and the root mean square error of 1.4867 in training phase. Additionally, the entire trained, validated and tested network showed a strong potential for predicting the compressive strength of geopolymers with a reasonable performance in the considered range.     

Statistical analysis of the performance of a bi-functional catalyst under operating conditions of LPDME process

Liquid phase direct synthesis of dimethyl ether (LPDME™) under various operating conditions (temperature, H₂/CO molar ratio of feed) was conducted in a mechanically agitated slurry reactor system. Each run was monitored for 60 h time on stream (TOS) in order to confirm the high activity and long-term stability of a bi-functional catalytic system (CuO–ZnO–Al₂O₃/H-MFI-90). Statistical experimental design was applied for determining the optimum operating conditions under which the catalytic system shows the highest performance. A significant improvement in the performance of the bi-functional catalyst was observed when the temperature and H₂/CO molar ratio of feed were increased from 200 to 240 °C and 1 to 2, respectively at a constant pressure of 35 bar and GHSV equal to 1100 mLn/(g-cat h). CO conversion was increased from 9.1 mol% at T = 200 °C and H₂/CO = 1 to 79.6 mol% at T = 240 °C and H₂/CO = 2 and the yield and selectivity of DME also increased from 7.11% to 47.05% and 41.57% to 59.96%, (molar basis) respectively. No significant deactivation has been observed during 60 h TOS at different operating conditions. Furthermore, from the main effect plots and response table results, it was concluded that the most effective factor on activity and stability of bi-functional catalytic system is temperature.