Novel electrospun polymer electrolytes incorporated with Keggin‐type hetero polyoxometalate fillers as solvent‐free electrolytes for lithium ion batteries

In this study, solvent‐free nanofibrous electrolytes were fabricated through an electrospinning method. Polyethylene oxide (PEO), lithium perchlorate and ethylene carbonate were used as polymer matrix, salt and plasticizer respectively in the electrolyte structures. Keggin‐type hetero polyoxometalate (Cu‐POM@Ru‐rGO, Ni‐POM@Ru‐rGO and Co‐POM@Ru‐rGO (POM, polyoxometalate; rGO, reduced graphene oxide)) nanoparticles were synthesized and inserted into the PEO‐based nanofibrous electrolytes. TEM and SEM analyses were carried out for further evaluation of the synthesized filler structures and the electrospun nanofibre morphologies. The fractions of free ions and crystalline phases of the as‐spun electrolytes were estimated by obtaining Fourier transform infrared and XRD spectra, respectively. The results showed a significant improvement in the ionic conductivity of the nanofibrous electrolytes by increasing filler concentrations. The highest ionic conductivity of 0.28 mS cm^?1 was obtained by the introduction of 0.49 wt% Co‐POM@Ru‐rGO into the electrospun electrolyte at ambient temperature. Compared with solution‐cast polymeric electrolytes, the electrospun electrolytes present superior ionic conductivity. Moreover, the cycle stability of the as‐spun electrolytes was clearly improved by the addition of fillers. Furthermore, the mechanical strength was enhanced with the insertion of 0.07 wt% fillers to the electrospun electrolytes. The results implied that the prepared nanofibres are good candidates as solvent‐free electrolytes for lithium ion batteries. © 2020 Society of Chemical Industry     

Corrigendum to “A model to control environmental performance of project execution process based on greenhouse gas emissions using earned value management” [Int. J. Proj. Manag. 36 (3) (April 2018) 397-413]

Abstract was revised as follows:In response to recent climate change, which is believed to be attributed to the release of greenhouse gas (GHG) emissions, many countries are placing CO₂ abatement programs such as carbon tax and cap-and-trade. Projects do have a significant share in GHGs and therefore their environmental performance, like their schedule and cost performance, should be monitored and controlled. Although many large projects would pass an environmental assessment in the project evaluation phase, the issue of environmental performance monitoring during the project execution phase has not been addressed in project management methodologies. The objective of this paper is to develop a model to estimate project GHG emissions, and to measure project GHG performance using the developed metrics, which can be used at any point in time over the life of a project. A comprehensive study is conducted to collect information on GHG emission factors of various project activity data (such as material use, energy and fuel consumption, transportation, etc.), and a user form interface is developed to calculate the total GHG of an activity. Also, a breakdown structure is proposed which supports managing all the project GHG accounts. The monitoring and control model is formulated based on the logic used in earned value management (EVM) methodology. The proposed model is then implemented to a work package of a real construction project. The results present the project initial GHG plan and show that the model is able to calculate project GHG variance by the reporting date and predict project final GHG based on a project GHG performance index. The method presented in this paper is general and can be applied to any type of projects in an organization that aims to reduce its carbon footprint. The same structure can be applied to monitor and control any other environmental impact associated with project execution process.     

Effect of mixer geometry and operating conditions on flow mixing of shear thinning fluids with yield stress

Flow mixing of a non‐Newtonian fluid in a stirred tank equipped with a side‐entry impeller was observed using particle image velocimetry (PIV). The effects of some geometrical parameters including the mixer shape and impeller type and position on the flow pattern were studied on velocity fields obtained at different locations inside the mixing domain. The different flow structures revealed that the ratio of inertial and viscous forces largely defines the flow pattern. Dead zones were observed inside the tank due to the rheological properties of the fluid. The size of the dynamic regions and the average velocity near the impeller were enhanced by increasing the suction area. Likewise, large pitch ratios were found to improve the active mixing zone and the axial discharge. Curves for the power and pumping numbers are reported for different axial flow impellers. © 2013 American Institute of Chemical Engineers AIChE J, 60: 1156–1167, 2014     

A variable neighborhood binary particle swarm algorithm for cell layout problem

In this paper, a new mathematical model is proposed for inter- and intra-cell layout problem in cellular manufacturing system. A binary particle swarm optimization algorithm with a new heuristic approach for satisfying the constraints of model is implemented to solve the proposed model. Also, a variable neighborhood search is applied for finding better local optimal solutions. Some sensitivity analyses are carried out to find the best values of PSO parameters. To prove the efficiency of the proposed algorithm, various numerical examples in small, medium, and large sizes in both perfect geometric shapes and random placements are generated and solved.     

Investigation of the Effect of DOP on Polyaniline and Polyaniline/Polystyrene Nanocomposites

Polyaniline nanocomposite was prepared in aqueous media by polymerization of aniline using ((NH₄)₂S₂O₈) as an oxidant in the presence of dioctyl phthalate (DOP). Also polyaniline/polystyrene (PAn/PS) nanocomposite was prepared in the aqueous solution by polymerization of styrene and aniline using potassium iodate (KIO₃) and ammonium peroxy disulfate ((NH₄)₂S₂O₈) as oxidant in the presence of dioctyl phthalate (DOP). The PAn and PAn/PS nanocomposites were characterized in terms of morphology, chemical structure and glass transition temperature. The chemical structure, morphology and glass transition temperature of product were studied by Fourier Transform Infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) and differential scanning calorimetry (DSC).     

Computational modelling of industrial pulp stock chests

Agitated pulp stock chests are the most widely used mixers in pulp and paper manufacture. Stock chests are used for a number of purposes, including attenuation of high‐frequency disturbances in pulp properties (such as mixture composition, fibre mass concentration, and suspension freeness) and are designed using semi‐empirical rules based largely on previous experience. Tests made on both laboratory and industrial‐scale pulp chests indicate that they are subject to non‐ideal flows, including channelling and creation of dead zones. In the present work, a commercial computational fluid dynamic (CFD) software (Fluent) is used to model two industrial pulp stock chests. The first chest is rectangular, agitated using a single side‐entering impeller, and feeds a mixture of chemical pulps at 3.5% mass concentration (C_m) to a papermachine. The second chest has rectangular geometry, with a mid‐feather wall used to direct suspension flow through a U‐shaped trajectory past four side‐entering impellers. This chest is used to remove latency from a C_m = 3.5% thermomechanical pulp suspension ahead of stock screening. For CFD computations, pulp rheology was described using a modified Hershel–Buckley model. Steady‐state simulations were made corresponding to process conditions during mill tests. The calculated steady‐state flows were then used to determine the dynamic response of the virtual chests and then compared with experimental measurements and found to agree reasonably well. The computed flow fields provided insight into mixing processes occurring within the chests, showing cavern formation around the impellers (which reduced the agitated volume available for mixing). Mass‐less particle tracking, using the steady‐state flow field, gave insight into the stagnant regions and bypassing zones created in the vessels. This paper discusses difficulties encountered in characterising the mixing (both experimentally and computationally) and the limitations of the industrial data.     

The importance of hydrodynamics in UV advanced oxidation reactors.

The flow field of UV reactors was characterised experimentally using particle image velocimetry (PIV) and modelled with computational fluid dynamics (CFD). The reactor flow was integrated with the radiation fluence rate and photolysis kinetics to calculate the overall conversion of photo-reactant components in annular UV reactors with an inlet parallel and perpendicular to the reactor axis. The results indicated that the fluid flow distribution within the reactor volume affects photo-reactor performance.     

Optimizing a bi-objective multi-product EPQ model with defective items,rework and limited orders: NSGA-II and MOPSO algorithms

In this paper, a bi-objective multi-products economic production quantity (EPQ) model is developed, in which the number of orders is limited and imperfect items that are re-workable are produced. The objectives of the problem are minimization of the total inventory costs as well as minimizing the required warehouse space. The model is shown to be of a bi-objective nonlinear programming type, and in order to solve it two meta-heuristic algorithms namely, the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithm, are proposed. To verify the solution obtained and to evaluate the performance of proposed algorithms, two-sample t-tests are employed to compare the means of the first objective value, the means of the second objective values, and the mean required CPU time of solving the problem using two algorithms. The results show while both algorithms are efficient to solve the model and the solution qualities of the two algorithms do not differ significantly, the computational CPU time of MOPSO is considerably lower than that of NSGA-II.     

Maximum likelihood estimation from interval censored recurrent event data

In this paper, we consider the recurrent failures of several repairable units, which can only be observed at periodic inspection times. A unit is not aging over the period between a failure and its detection. The failure times are interval censored by the periodic assessment times. The observed data consists of censoring intervals of failure times and the unobserved data are the actual ages of the units at the failure times. We formulate the likelihood function and use several iterative algorithms to find the maximum likelihood estimate (MLE) of the parameters. The complete Expectation–Maximization (EM) algorithm, the EM gradient, full Newton–Raphson (NR), and the Simplex method are used. We derive recursive equations to calculate the expected values required in the algorithms. We estimate the parameters for four failure datasets, assuming that the failures follow a non-homogeneous Poisson process (NHPP). Three datasets are obtained from a hospital for the components of general infusion pump, and the fourth dataset is simulated. Since the estimation could take a long time, we compare the performance of the algorithms in terms of the required number of iterations to converge, the total execution time, and the precision of the estimated parameters. We also use Monte Carlo and Quasi-Monte Carlo simulation as the substitutes for the recursive procedures in the Expectation step of the EM gradient and compare the results.