Efficacy of removal of CCL viruses under enhanced coagulation conditions

The focus of coagulation as a water treatment process is shifting to accommodate recent regulatory additions that strive to balance the risks between microbial and chemical contamination of drinking water. In this work, enhanced coagulation using increased ferric chloride dose and/or pH adjustment was evaluated for removal efficacy of viruses on the United States Environmental Protection Agency (USEPA) Contaminant Candidate List (CCL), their surrogates, and dissolved organic carbon (DOC). Jar tests demonstrated that optimal DOC removal was achieved using 40 mg/L FeCl3 at a pH between 5 and 6. Under these conditions, bench-scale testing resulted in a maximum removal of 2.58 log units of adenovirus type 4, 2.50 log units of feline calicivirus, 2.32 log units of MS2, 1.75 log units of PRD1, 1.52 log units of phi-X174, 2.49 log units of fr, and 56% of DOC. The trend in virus removals (MS2 and fr > PRD1 and phi-X174) was consistent between bench- and pilot-scale testing; however, pilot-plant removals exceeded bench-scale removals. Feline calicivirus was more efficiently removed than the bacteriophages, thereby suggesting potential for the bacteriophages as suitable surrogates, with MS2 and fr being more representative and PRD1 and phi-X174 (which were removed to a lesser extent) more conservative. The bacteriophages do not appear to be appropriate surrogates for adenovirus.     

H2-selective mixed matrix membranes modeling using ANFIS,PSO-ANFIS,GA-ANFIS

The novel contribution of the current study is to employ adaptive neuro-fuzzy inference system (ANFIS) for evaluation of H₂-selective mixed matrix membranes (MMMs) performance in various operational conditions. Initially, MMMs were prepared by incorporating zeolite 4A nanoparticles into polydimethylsiloxane (PDMS) and applied in gas permeation measurement. The gas permeability of CH₄, CO₂, C₃H₈ and H₂ was used for ANFIS modeling. In this manner, the H₂/gas selectivity as the output of the model was modeled to the variations of feed pressure, nanofiller contents and the kind of gas, which were defined as input (design) variables. The proposed method is based on the improvement of ANFIS with genetic algorithm (GA) and particle swarm optimization (PSO). The PSO and GA were applied to improve the ANFIS performance. To determine the efficiency of PSO-ANFIS, GA-ANFIS and ANFIS models, a statistical analysis was performed. The results revealed that the PSO-ANFIS model yields better prediction in comparison to two other methods so that root mean square error (RMSE) and coefficient of determination (R²) were obtained as 0.0135 and 0.9938, respectively. The RMSE and R² values for GA-ANFIS were 0.0320 and 0.9653, respectively, and for ANFIS model were 0.0256 and 0.9787, respectively.     

Energy production trend in Iran and its effect on sustainable development

The purpose of this study is to review the energy production trend from different energy resources in recent decades and its effect on sustainable energy production as one of the basic axis of sustainable development in Iran.Ninety nine percent of energy production in Iran comes from oil & gas and only 1% from renewable energy resources. Since Iran has very rich fossil energy resources, little attention has been paid to explore alternative ways of energy production. Majority of country's income is from oil & gas which put extra pressure on its natural resources. Continuing with the existing trend may lead to a path away from the goals of sustainable development, set for the country. Therefore, the sustainability study should be of interest to decision-makers.     

Training based, moving digital filter method for real time heat flux function estimation

In this paper the neural networks is utilized to estimate the “filter coefficients” needed to estimate heat flux in a particular system. In developing the training phase of the network inspiration is drawn from the Burgraff's exact solution of the IHCP as well as the filter method. Thus, the estimation phase neither requires any temperature field nor the sensitivity coefficients calculations. The neural network used in this work is a 2-layer perceptron. It is shown via classical triangular heat flux test cases that the method can yield very accurate, very efficient as well as stable estimations.     

Barium cobaltite nanoparticles: Sol-gel synthesis and characterization and their electrochemical hydrogen storage properties

The study of the effect of different chelating agents in the Pechini method on the morphology has been a promising strategy that can be used for practical tuning of the nanoparticle's morphology and hence the electrochemical hydrogen storage capacity. In the current study, the conventional Pechini sol-gel approach was used to prepare the Ba₂Co₉O₁₄ nanoparticles as a novel hydrogen storage material. The X-ray diffraction investigation approved the formation of Ba₂Co₉O₁₄ with a Hexagonal crystal structure for all of the synthesized samples. The scanning electron microscopy (SEM) revealed when citric acid was used as a chelating agent, nanoparticles with finer and more uniform morphology were obtained rather than other chelating sources. The transmission electron microscopy (TEM) showed in the presence of citric acid; the size of the synthesized nanoparticles was between 14 and 24 nm. According to the Diffuse Reflectance Spectroscopy (DRS) analysis, the calculated bandgap of synthesized nanoparticles was approximately 3.2 eV, which indicates that synthesized nanoparticles were semiconductors in essence. The electrochemical hydrogen adsorption/desorption results showed that the sample synthesized by the citric acid has an enhancement in electrochemical hydrogen storage of approximately 800 mAh/g after 15 cycles.     

Effects of passive vibration absorbers on the mechanisms having clearance joints

Multibody System Dynamics - In the current study, the dynamic behavior of two planar mechanisms with revolute joints, in the presence of clearances is investigated. Subsequently, a control scheme...     

Pythagorean fuzzy combined compromise solution method integrating the cumulative prospect theory and combined weights for cold chain logistics distribution center selection

The evaluation and selection of cold chain logistics distribution centers are of vital importance for third-party logistics companies which want to build green cold chain logistics networks. To select distribution centers, the conflicts among multiple criteria should be considered. The combined compromise solutions (CoCoSo) method can help enterprises make a structural decision; however, in the original CoCoSo method, the evaluation information was expressed by crisp numbers. Nevertheless, in many cases, because of the imprecision and incompleteness of information, it may be more flexible for evaluators to provide imprecise and fuzzy values rather than crisp numbers. In addition, the judgment values are often expressed based on decision-makers' psychological expectations. The evaluation criteria of alternatives have relevance to some extent, which would influence the evaluation results. Based on these concerns, this study presents a modified CoCoSo method in the Pythagorean fuzzy environment in which evaluators can express psychological expectations on alternatives. To achieve this goal, the cumulative prospect theory is introduced to obtain the Pythagorean fuzzy prospect weights. Then, an objective weight determination method of criteria under the Pythagorean fuzzy environment is proposed to eliminate the influence of homogeneity of criteria. Based on the Pythagorean fuzzy prospect weights and the combined weights, the original CoCoSo method is extended to the Pythagorean fuzzy environment. A case of selection logistics distribution center is investigated to demonstrate the practicality of the proposed method. The advantages of the proposed method are verified by comparative analysis.     

Evaluation of Clustering Algorithms in Ad Hoc Mobile Networks

Ad hoc mobile networks are free of any infrastructures and their nodes are not aware of the connections of the network locating in, since the structure of these networks is dynamic. To send data to other nodes, each node should detect the sending path and then save it. Due to their dynamic nature, these types of networks face design complexity and limitations such as a lack of specific infrastructure and the change of the infrastructure with passing the time, the limitation of energy, bandwidth, and the considerations of quality and security. Therefore, bandwidth optimization, power and energy control and an improvement in transmission quality are challenges of these types of networks in routing. To meet these challenges, the node clustering methods were welcomed for less energy consumption and longer network lifetime. In this paper, we deal with a systematic literature review of different clustering methods and propose a general categorization for them. Furthermore, we compare the performance the methods as well as the related algorithms and their strengths and weaknesses. Finally, we rank the algorithms regarding the four parameters of transmission range, mobility speed, battery, and connectivity degree using the multi-criterion decision-making and analytical hierarchical process techniques.