Anaerobic digestion (AD) is the most common process for dealing with primary and secondary wastewater sludge. In the present work, four pre-treatment methods (ultrasonic, chemical, thermal, and thermo-chemical) are investigated in Al-Rustumya Wastewater Treatment plant in order to find their effect on biogas production and volatile solid removal efficiency during anaerobic digestion.
Two frequencies of ultrasonic wave were used 30 KHz and 50 KHz during the pre-treatment. Sodium hydroxide was added in different amounts to give three pH values of 9, 10 and 11 in chemical pre-treating processes. The sludge was heated at 60oC and 80oC through thermal pre-treatment experiment. Also, the sludge was treated thermo-chemically at 80 oC and pH

This work investigates the utilization of waste papers (natural and industrial) i.e (citrus aurantium and papers A4) mortars containing specified contents 0.5%, 1%, 1.5% of waste papers were prepared and cured. Mechanical characteristics such as compressive and bending strengths, hardness and water absorption were determined for the mortars mixed with the waste papers and compared with those obtained from the pure mortars. Results showed that the addition of waste paper leads to increase the hardness to (69 - 68.5) shore D for (natural and industrial) wastes materials respectively comparing with pure specimen 66 shore D. The compressed strength of the mortar cement specimen cured for 28 days from 13 MPa to (17-18) MPa for (natural and in

Metal oxide nanoparticles demonstrate uniqueness in various technical applications due to their suitable physiochemical properties. In particular, yttrium oxide nanoparticle(Y₂O₃NPs) is familiar for technical applications because of its higher dielectric constant and thermal stability. It is widely used as a host material for a variety of rare-earth dopants, biological imaging, and photodynamic therapies. In this investigation, yttrium oxide nanoparticles (Y₂O₃NPs) was used as an ecofriendly corrosion inhibitor through the use of scanning electron microscopy (SEM), Fourier transforms infrared spectroscopy (FT-IR), UV-Visible spectroscopy, X-ray diffraction (XRD), and energy dispersive X-ray spe

The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow