Mohammad H. Al-Beirutty

Optimization of permeate flux produced by solar energy driven membrane distillation process using central composite design approach

Membrane distillation (MD) is considered as a relatively high-energy requirement. To overcome this drawback, it is recommended to couple the MD process with solar energy as the renewable energy source in order to provide heat energy required to optimize its performance to produce permeate flux. In the present work, an original solar energy driven direct contact membrane distillation (DCMD) pilot plant was built and tested under actual weather conditions at Jeddah, KSA, in order to model and optimize permeate flux. The dependency of permeate flux on various operating parameters such as feed temperature (46.6-63.4°C), permeate temperature (6.6-23.4°C), feed flow rate (199-451L/h) and permeate flow rate (199-451L/h) was studied by response surface methodology based on central composite design approach. The analysis of variance (ANOVA) confirmed that all independent variables had significant influence on the model (where P-value <0.05). The high coefficient of determination (R(2) = 0.9644 and R(adj)(2) = 0.9261) obtained by ANOVA demonstrated good correlation between experimental and predicted values of the response. The optimized conditions, determined using desirability function, were T(f) = 63.4°C, Tp = 6.6°C, Q(f) = 451L/h and Q(p) = 451L/h. Under these conditions, the maximum permeate flux of 6.122 kg/m(2).h was achieved, which was close to the predicted value of 6.398 kg/m(2).h.

Synthesis and Characterization of Silver Nanoparticles-Filled Polyethersulfone Membranes for Antibacterial and Anti-Biofouling Application.

BACKGROUND The membrane processes are interesting and economical techniques for reuse of municipal and industrial wastewater as well as seawater desalination. However their drawbacks can be resumed in the fouling and biofouling due to the deposition and adsorption phenomenon of the components and the development of biofilm on membrane surface. Several studies have focused on the effect of the incorporation of nanoparticles in polymeric membrane matrix on the biofouling properties. Few relevant patents to the topic have been reviewed and cited. METHODS Polyethersulfone (PES) membranes filled with silver nanoparticles (AgNPs) were prepared by non-solvent induced phase separation (NIPS) process using polyvinylpyrrolidone (PVP) as additive and N-Methyl-2-pyrrolidone (NMP) as solvent. Dope solution compositions, coagulation bath (CB) compositions, time before immersion in CB and casting speed were systematically studied. Membrane structure was characterized by scanning electron microscopy, contact angle, streaming potential measurement and X-ray diffraction (XRD). RESULTS Membrane performance was assessed by pure water permeability, antifouling property, porosity and mechanical property. Silver nanoparticles (AgNPs) were prepared by the chemical reduction of silver nitrate solution with freshly prepared fructose solution, using PVP as capping agent and NaOH as accelerant and settled using acetone. The synthesized AgNPs were firstly characterized by Dynamic light scattering (DLS) technique, UV-visible spectrophotometer and X-ray diffraction spectroscopy (XRD). Then, we have selected a 15% PES mixed with 15% of PVP dope solution to prepare PESAgNPs blended membranes. CONCLUSION All the nanocomposite membranes showed superb antibacterial and anti-biofouling performances, indicating that AgNPs in the PES membranes could be an effective approach to minimize membrane biofouling.

Aerothermal Design of a Multi-Stage Axial Flow Gas Turbine with Air Cooling

The gas turbine engine is gaining acceptance throughout the world as a reliable, flexible and efficient base-load power generation plant. This is in addition to the fact that it has been the unrivalled working horse in aeroengines for decades. The more advanced the technology, the higher the turbine inlet temperature (TIT) is, which increases power, reduces weight and improves efficiency. This demands the use of advanced materials and cooling technology, and hence deeper understanding of aerothermal design becomes a premium. The study reported here shows how a simplified model for the flow in the annulus of the gas turbine can produce accurate results in relation to blade cooling. In this design, the axial, tangential and radial components of velocities are included, together with the compressibility effect and probability of introducing resonant forcing frequencies. This design exercise of finding the particulars of the geometry, gas angles, degree of reaction and cooling configuration represents the first step towards further analysis of heat transfer and overall performance. This could be carried out as a senior course project associated with a course on gas turbines and turbomachines.

Progress of Nanocomposite Membranes for Water Treatment

The use of membrane-based technologies has been applied for water treatment applications; however, the limitations of conventional polymeric membranes have led to the addition of inorganic fillers to enhance their performance. In recent years, nanocomposite membranes have greatly attracted the attention of scientists for water treatment applications such as wastewater treatment, water purification, removal of microorganisms, chemical compounds, heavy metals, etc. The incorporation of different nanofillers, such as carbon nanotubes, zinc oxide, graphene oxide, silver and copper nanoparticles, titanium dioxide, 2D materials, and some other novel nano-scale materials into polymeric membranes have provided great advances, e.g., enhancing on hydrophilicity, suppressing the accumulation of pollutants and foulants, enhancing rejection efficiencies and improving mechanical properties and thermal stabilities. Thereby, the aim of this work is to provide up-to-date information related to those novel nanocomposite membranes and their contribution for water treatment applications.

Water Desalination Fault Detection Using Machine Learning Approaches: A Comparative Study

The presence of faulty valves has been studied in the literature with various machine learning approaches. The impact of using fault data only to train the system could solve the class imbalance problem in the machine learning approach. The data sets used for fault detection contain many independent variables, where the salient ones were selected using stepwise regression and applied to various machine learning techniques. A significant test for the given regression technique was used to validate the outcome. Machine learning techniques, such as decision trees and deep learning, are applied to the given data and the results reveal that the decision tree was able to obtain more than 95% accuracy and performed better than other algorithms when considering the tradeoff between the processing time and accuracy.