Recent advances in sago (Metroxylon sagu) fibres, biopolymers, biocomposites, and their prospective applications in industry: A comprehensive review.

Escalating petroleum depletion and environmental crises linked to conventional plastics have fueled interest in eco-friendly alternatives. Natural fibres and biopolymers are garnering increasing attention due to their sustainability. The sago palm (Metroxylon sagu), a tropical tree, holds potential for such materials, with cellulose-rich fibres (42.4-44.12 %) showcasing strong mechanics. Extracted sago palm starch can be blended, reinforced, or plasticised for improved traits. However, a comprehensive review of sago palm fibres, starch, and biocomposites is notably absent. This paper fills this void, meticulously assessing recent advancements in sago palm fibre, cellulose and starch properties, and their eco-friendly composite fabrication. Moreover, it uncovers the latent prospects of sago palm fibres and biopolymers across industries like automotive, packaging, and bioenergy. This review presents a crucial resource for envisaging and realising sustainable materials.

Techno-economic optimization and Nox emission reduction through steam injection in gas turbine combustion chamber for waste heat recovery and water production.

Considering the persistent human need for electricity and fresh water, cogeneration systems based on the production of these two products have attracted the attention of researchers. This study investigates a cogeneration system of electricity and fresh water based on gas turbine (GT) as the prime mover. The wasted energy of the GT exhaust gases is absorbed by a heat recovery steam generator (HRSG) and supplies the superheat steam required by the steam turbine (ST). In order to produce fresh water, a multi-effect desalination (MED) system is applied. The motive steam required is provided by extracting steam from the ST. In order to reduce the environmental pollution of this cogeneration system, the steam injection method is proposed in the GT's combustion chamber (CC). This system is optimized by a multi-objective optimization tool based on the Genetic Algorithm (GA). The design variables include pressure ratio of compressor (CPR), inlet temperature of gas turbine (TIT), steam injection mass flow rate in the CC, HRSG operating pressure, HRSG evaporator pinch point temperature difference (PPTD), steam pressure of the MED ejector, ejector motive steam flow rate, number of MED effects, and return effect. The goals are to minimize the total cost rate (TCR), which includes the cost of initial investment and maintenance of the system, the cost of consumed fuel, and the cost of disposing of CO and NO pollutants, as well as maximizing the exergy efficiency. In the end, it is observed that the steam injection in the CC leads to the reduction of the mentioned pollutant index, and it is proposed as a suitable solution to reduce the pollution of the proposed cogeneration system.

Comprehensive review of environmental factors influencing the performance of photovoltaic panels: Concern over emissions at various phases throughout the lifecycle.

Recently, solar photovoltaic (PV) technology has shown tremendous growth among all renewable energy sectors. The attractiveness of a PV system depends deeply of the module and it is primarily determined by its performance. The quantity of electricity and power generated by a PV cell is contingent upon a number of parameters that can be intrinsic to the PV system itself, external or environmental. Thus, to improve the PV panel performance and lifetime, it is crucial to recognize the main parameters that directly influence the module during its operational lifetime. Among these parameters there are numerous factors that positively impact a PV system including the temperature of the solar panel, humidity, wind speed, amount of light, altitude and barometric pressure. On the other hand, the module can be exposed to simultaneous environmental stresses such as dust accumulation, shading and pollution factors. All these factors can gradually decrease the performance of the PV panel. This review not only provides the factors impacting PV panel's performance but also discusses the degradation and failure parameters that can usually affect the PV technology. The major points include: 1) Total quantity of energy extracted from a photovoltaic module is impacted on a daily, quarterly, seasonal, and yearly scale by the amount of dust formed on the surface of the module. 2) Climatic conditions as high temperatures and relative humidity affect the operation of solar cells by more than 70% and lead to a considerable decrease in solar cells efficiency. 3) The PV module current can be affected by soft shading while the voltage does not vary. In the case of hard shadowing, the performance of the photovoltaic module is determined by whether some or all of the cells of the module are shaded. 4) Compared to more traditional forms of energy production, PV systems offer a significant number of advantages to the environment. Nevertheless, these systems can procure greenhouse gas emissions, especially during the production stages. In conclusion, this study underlines the importance of considering multiple parameters while evaluating the performance of photovoltaic modules. Environmental factors can have a major impact on the performance of a PV system. It is critical to consider these factors, as well as intrinsic and other intermediate factors, to optimize the performance of solar energy systems. In addition, continuous monitoring and maintenance of PV systems is essential to ensure maximum efficiency and performance.

Additive Manufacturing of Polymer-Fiber Composites.

Additive Manufacturing of Polymer-Fiber Composites is a newly open Special Issue of Materials, which aims to publish original and review papers on new scientific and applied research, and make great contributions to the finding and understanding of the fabrication of fiber-reinforced polymer composites using current advanced additive manufacturing techniques [...].

Mathematical analysis of hybrid mediated blood flow in stenosis narrow arteries.

In this paper the behavior of flow of blood under stenosis suppositions is studied. Nanoparticles of Ag and Cu are being used with blood as base fluid. The problem governing equations are modeled into PDE's, which are transformed into set of ODE's with the help of useful similarity transformation. We investigated the solution numerically for various parameters on temperature and velocity distribution and shown in the form of tables and graphs. It is found that the velocity of blood increases while the temperature curve goes down by increasing the concentration of nanoparticles and also temperature curve decreases by increasing the values of gamma and Prandtl number. Furthermore, the calculated results shows that increment in flow parameter gamma caused an increase in velocity values. In the field of biomedicine, the important approach of nanotechnology is the use of nanoparticles in chemotherapy.

Artificial neural network prediction of performance and emissions of a diesel engine fueled with palm biodiesel.

Increasing of energy consumption, depletion of petroleum fuels and harmful emissions have triggered the interest to find substitute fuels for diesel engines. Palm ethyl ester was synthesized from palm oil through transesterification process. The physicochemical properties of palm biodiesel have been measured and confirmed in accordance with ASTM standards. The aim of the paper is to show the effect of different diesel-palm biodiesel blends on performance, combustion and emissions in diesel engine at engine load variation. Artificial Neural Network was used for the prediction of engine performance, exhaust emission and combustion characteristics parameters. Palm ethyl ester and diesel oil were blended in 5, 10, 15 and 20 by volume percentage. The maximum decreases in thermal efficiency, fuel-air equivalence ratio for B20 were 1.5, 3.5, 6 and 8% but the maximum increases in BSFC, exhaust gas temperature and NOx emission for B20 at full load about diesel fuel were 9, 8 and 10%, respectively. The highest decreases in CO, HC and smoke emissions of B20 about diesel oil at full load were 2, 35 and 18.5% at full load, respectively. Biodiesel blend B20 achieved the maximum declines in peak HRR, cylinder temperature and combustion duration about diesel fuel. The results of ANN were compared with experimental results and showed that ANN is effective modeling method with high accuracy. Palm biodiesel blends up to 20% showed the highest enhancements in engine performance, combustion and emission reductions compared to diesel fuel.

Natural Fiber-Reinforced Thermoplastic ENR/PVC Composites as Potential Membrane Technology in Industrial Wastewater Treatment: A Review.

Membrane separation processes are prevalent in industrial wastewater treatment because they are more effective than conventional methods at addressing global water issues. Consequently, the ideal membranes with high mechanical strength, thermal characteristics, flux, permeability, porosity, and solute removal capacity must be prepared to aid in the separation process for wastewater treatment. Rubber-based membranes have shown the potential for high mechanical properties in water separation processes to date. In addition, the excellent sustainable practice of natural fibers has attracted great attention from industrial players and researchers for the exploitation of polymer composite membranes to improve the balance between the environment and social and economic concerns. The incorporation of natural fiber in thermoplastic elastomer (TPE) as filler and pore former agent enhances the mechanical properties, and high separation efficiency characteristics of membrane composites are discussed. Furthermore, recent advancements in the fabrication technique of porous membranes affected the membrane's structure, and the performance of wastewater treatment applications is reviewed.

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment.

We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model's training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.

Numerical assessment of the influence of helical baffle on the hydrothermal aspects of nanofluid turbulent forced convection inside a heat exchanger.

This study is devoted to the numerical assessment of the influence of helical baffle on the hydrothermal aspects and irreversibility behavior of the turbulent forced convection flow of water-CuO nanofluid (NF) inside a hairpin heat exchanger with 100 mm length, 10 mm inner tube internal diameter, and 15 mm outer diameter internal diameter. The variations of the first-law and second-law performance metrics are investigated in terms of Reynolds number (Re = 5000-10,000), volume concentration of NF ([Formula: see text]) and baffle pitch (B = 25-100 mm). The results show that the NF Nusselt number grows with the rise of both the Re and [Formula: see text] whereas it declines with the rise of B. In addition, the outcomes depicted that the rise of both Re and [Formula: see text] results in the rise of pressure drop, while it declines with the increase of B. Moreover, it was found that the best thermal performance of NF is equal to 1.067, which belongs to the case B = 33.3 mm, [Formula: see text]=2%, and Re = 10,000. Furthermore, it was shown that irreversibilities due to fluid friction and heat transfer augment with the rise of Re while the rise of B results in the decrease of frictional irreversibilities. Finally, the outcomes revealed that with the rise of B, the heat transfer irreversibilities first intensify and then diminish.