The paper presents the experimental studies on the effect of the water containing micro-nano bubbles of various gases on the physico-mechanical properties of lime-cement mortars. In total, 7 types of mortars were prepared: with water containing the micro-nano bubbles of O2, O3 or CO2 as 50% or 100% substitute of ordinary mixing water (tap water) and the reference mortar prepared using tap water. In order to determine the influence of water with micro-nano bubbles of gases, the consistency of fresh mortar and the physical properties of hardened mortar, i.e., specific and apparent density, total porosity, water absorption by weight and capillary absorption, were established. The mechanical strength of the considered mortars was studied as well by conducting the tests for flexural and compressive strengths following 14, 28 and 56 days. Reduced workability and capillary absorption were observed in the modified mortars within the range of 0.9–8.5%. The mortars indicated an increase in the flexural strength after 28 days ranging from 3.4% to 23.5% and improved compressive strength in 1.2–31%, in comparison to the reference mortar. The conducted studies indicated increased flexural and compressive strengths along with the share of micro-nano bubbles of gases in the mixing water.

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The use of modern methods as well as modeling and simulation tools in the design of bioreactors allows for the analysis of the flow phenomena in a short period of time without the need of physical model preparation, and thus for the optimization of existing solutions. The article presents the simulations of the aeration process in an SBRtype bioreactor, realized by means of computational fluid dynamics (CFD) and ANSYS 12.1 software. The subject of the analysis was a diffuser of own design. The Design Modeler 12.1 module was used for the preparation of geometry representing the analyzed design, and the discretization of the continuous domain was carried out with the ANSYS Meshing 12.1 tool. The ANSYS Fluent 6.3 solver was used For model calculations. On the basis of the results obtained from the conducted simulations, it is possible to predict the parameters which will increase efficiency and effectiveness without the need to build a real set of prototype models of aeration systems. The results obtained indicate that an increase in the aeration velocity results in a decrease in the minimum Y-axis velocity for both the mixture and air. The observed differences are caused by the shape of the geometric model and the velocity of the air outlet through the openings, which affects the hydraulic process in the chamber. These processes affect both the amount of oxygen dissolved in the bioreactor and the behavior of the suspension in volume. The turbulence intensity during the aeration process is concerned mainly in the range from 3.9 to 8.7% and is comparable with the average values of turbulence degree obtained by other researchers. The air bubble diameter ranged from 0.3 to 4.5 mm, in the case of aeration velocity 5.68 cm/s, a significant part of the chamber were air bubbles with a diameter of 2.6 to 3.9 mm, i.e. they were not the limit values.

In the present work, for the first time, free vibration response of angle ply laminates with uncertainties is attempted using Multivariate Adaptive Regression Spline (MARS), Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Gaussian Process Regression (GPR), and Adaptive Network Fuzzy Inference System (ANFIS). The present approach employed 2D C0 stochastic finite element (FE) model based on the Third Order Shear Deformation Theory (TSDT) in conjunction with MARS, ANN-PSO, GPR, and ANFIS. The TSDT model used eliminates the requirement of shear correction factor owing to the consideration of the actual parabolic distribution of transverse shear stress. Zero transverse shear stress at the top and bottom of the plate is enforced to compute higher-order unknowns. C0 FE model makes it commercially viable. Stochastic FE analysis done with Monte Carlo Simulation (MCS) FORTRAN inhouse code, selection of design points using a random variable framework, and soft computing with MARS, ANN-PSO, GPR, and ANFIS is implemented using MATLAB in-house code. Following the random variable frame, design points were selected from the input data generated through Monte Carlo Simulation. A total of four-mode shapes are analyzed in the present study. The comparison study was done to compare present work with results in the literature and they were found in good agreement. The stochastic parameters are Young’s elastic modulus, shear modulus, and the Poisson ratio. Lognormal distribution of properties is assumed in the present work. The current soft computation models shrink the number of trials and were found computationally efficient as the MCS-based FE modelling. The paper presents a comparison of MARS, ANN-PSO, GPR, and ANFIS algorithm performance with the stochastic FE model based on TSDT.

Sequencing batch reactors (SBR) can be used as a fill-and draw activated sludge system for wastewater treatment with considerable operating flexibility and the possibility to conduct experiments under standard conditions and extreme case scenarios. Mathematical modeling and computer simulations provide an opportunity to implement existing wastewater processes in modeling software and evaluate different modifications at low costs and no disturbances for on-going processes of full scale WWTP. Additionally, the used model can be calibrated and validated against experimental data from laboratory scale devices. The aim of this study was to simulate the processes occurring in laboratory scale SBR under different aeration strategies. The results include the analysis of the adaptation period of the activated sludge biomass in the SBR, as well as the case of breakdown of treatment process due to stoppage of raw wastewater inflow and the interruption of the aeration and/or mixing. As a result, it can be stated that the oxygen transfer rate should be incorporated in the calibration of biological nutrient removal model in order to effectively visualize the individual contributions of each process.