Exhaust emission profiling of fatty acid methyl esters and NOx control studies using selective synthetic and natural additives

Clean Technologies and Environmental Policy - The present study was focused on the optimized biodiesel production using Moringa oleifera (M. oleifera) and rice bran oils, characterization, and...     

Scaling up stochastic gradient descent for non-convex optimisation

Stochastic gradient descent (SGD) is a widely adopted iterative method for optimizing differentiable objective functions. In this paper, we propose and discuss a novel approach to scale up SGD in applications involving non-convex functions and large datasets. We address the bottleneck problem arising when using both shared and distributed memory. Typically, the former is bounded by limited computation resources and bandwidth whereas the latter suffers from communication overheads. We propose a unified distributed and parallel implementation of SGD (named DPSGD) that relies on both asynchronous distribution and lock-free parallelism. By combining two strategies into a unified framework, DPSGD is able to strike a better trade-off between local computation and communication. The convergence properties of DPSGD are studied for non-convex problems such as those arising in statistical modelling and machine learning. Our theoretical analysis shows that DPSGD leads to speed-up with respect to the number of cores and number of workers while guaranteeing an asymptotic convergence rate of \(O(1/\sqrt{T})\) given that the number of cores is bounded by \(T^{1/4}\) and the number of workers is bounded by \(T^{1/2}\) where T is the number of iterations. The potential gains that can be achieved by DPSGD are demonstrated empirically on a stochastic variational inference problem (Latent Dirichlet Allocation) and on a deep reinforcement learning (DRL) problem (advantage actor critic - A2C) resulting in two algorithms: DPSVI and HSA2C. Empirical results validate our theoretical findings. Comparative studies are conducted to show the performance of the proposed DPSGD against the state-of-the-art DRL algorithms.

     

Thermal Evaluation of Diesel/Hydrogen Peroxide Fuel Blend

Thermal properties of fossil fuel are the key fundamental characteristics, which can distinguish any compound as a potential fuel. The performance of diesel fuel blend along with stability and solubility parameter designs are evaluated. The results from the experimental study indicate that the increase in hydrogen peroxide (H₂O₂) amount enhances the cetane number of diesel fuel blend significantly. However, the calorific value decreases as compared to pure diesel fuel. All values performed well according to the ASTM D‐975 diesel testing method. The thermodynamics of the prepared fuel blends also revealed that substantial solubility and diesel/H₂O₂ blend stability are provided even at lower temperatures. Such blends can be used as a feasible replacement of pure diesel fuel.     

Adsorption of U(Ⅵ) from acid solution on a low-cost sorbent:equilibrium,kinetic, and thermodynamic assessments

In this paper, waste clay was cured with ethyl acetate to obtain treated clay(TC), which was modified with gallic acid to obtain a low-cost sorbent that was characterized by EDX, SEM, and FTIR analysis. Uranium(Ⅵ) adsorption was achieved using the batch adsorption method on the TC and gallic acid-modified treated clay(GMTC). The maximum uptakes of U(Ⅵ) on TC and GMTC were 37.2 and 193.0 mg/g, respectively.The U(Ⅵ) adsorption kinetics on the TC and GMTC sorbents were well-fitted by the pseudo-second-order mechanism, and the adsorption equilibrium followed the Langmuir model. The optimum parameters were applied to El Sela leach solution for uranium recovery.     

Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA

Engineering with Computers - Air overpressure (AOp) is one of the most important undesirable effects induced by blasting operations in the mining or tunneling projects. Hence, the present precise...     

A Unified Decision-Making Technique for Analysing Treatments inPandemicContext

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before. Medical experts, on the other hand, are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection. Further, this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable. The research analysis is based on vast data gathered from professionals and research journals, making this study a comprehensive reference. To solve this challenging task, the researchers used the HF AHP-TOPSIS Methodology, which is a well-known and highly effective Multi-Criteria Decision Making (MCDM) technique. The technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries, based on the recommendations of medical practitioners and professionals. The review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as alternatives. We also perform robustness analysis to validate the conclusions of our analysis. As a result, we obtained highly corroborative results that can be used as a reference. The results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand, implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our opinion. Peepli also has the lowest priority in the estimation.     

Synthesis of a novel inorganic cation exchanger based on molybdate: Applications for removal of Pb2+, Fe3+ and Mn2+ ions from polluted water

A novel manganese phosphomolybdate exchanger was synthesized, dried at different temperatures, and evaluated for the elimination of lead, iron, and manganese ions from aqueous solutions. The chemical structure of the cation exchanger was established using Fourier-transform infrared, scanning electron microscopy, Thermo gravimetric analysis/ Differential thermal analysis, and X-ray diffraction. The adsorption performance of the heavy metals Pb²⁺, Fe³⁺, and Mn²⁺ toward the synthesized material has been studied. The obtained outcomes show that the selectivity of the cationic exchanger was descending in this order, Pb²⁺ > Fe³⁺ > Mn²⁺. The highest adsorption capacity was shown to be decreased as drying temperature of the exchanger increases from 50°C to 800°C.     

Limitations,challenges, and solution approaches in grid-connected renewable energy systems

In the modern world, only conventional energy resources cannot fulfil the growing energy demand. Electricity is a fundamental building block of a technological revolution. Today, most of the electricity demand is met by the burning of fossil fuels but at the cost of adverse environmental impact. In order to bridge the gap between electricity demand and supply, nonconventional and eco-friendly means of energy generation are considered. Renewable energy systems (RESs) offer an adequate solution to mitigate the challenges originated due to greenhouse gasses (GHG). However, they have an unpredictable power generation with specific site requirements. Grid integration of RESs may lead to new challenges related to power quality, reliability, power system stability, harmonics, subsynchronous oscillations (SSOs), power quality, and reactive power compensation. The integration with energy storage systems (ESSs) can reduce these complexities that arise due to the intermittent nature of RESs. In this paper, a comprehensive review of renewable energy sources has been presented. Application of ESSs in RESs and their development phase has been discussed. Role of ESSs in increasing lifetime, efficiency, and energy density of power system having RESs has been reviewed. Moreover, different techniques to solve the critical issues like low efficiency, harmonics, and inertia reduction in photovoltaic (PV) systems have been presented. Unlike most of the available review papers, this article also investigates the impact of FACTS technology in RESs-based power system using multitype flexible AC transmission system (FACTS) controllers. Three simulation models have been developed in MATLAB/Simulink. The results show that FACTS devices help to maintain the stability of RESs integrated power system. This review paper is believed to be of potential benefit for researchers from both the industry and academia to develop better understanding of challenges and solution techniques for REs-based power systems and future research dimensions in this area.