Modified self-organising maps with a new topology and initialisation algorithm

Mapping quality of the self-organising maps (SOMs) is sensitive to the map topology and initialisation of neurons. In this article, in order to improve the convergence of the SOM, an algorithm based on split and merge of clusters to initialise neurons is introduced. The initialisation algorithm speeds up the learning process in large high-dimensional data sets. We also develop a topology based on this initialisation to optimise the vector quantisation error and topology preservation of the SOMs. Such an approach allows to find more accurate data visualisation and consequently clustering problem. The numerical results on eight small-to-large real-world data sets are reported to demonstrate the performance of the proposed algorithm in the sense of vector quantisation, topology preservation and CPU time requirement.     

U-shaped energy loss curves utilization for distributed generation optimization in distribution networks

We propose novel techniques to find the optimal achieve the maximum loss reduction for distribution networks location, size, and power factor of distributed generation （DG） to Determining the optimal DG location and size is achieved simultaneously using the energy loss curves technique for a pre-selected power factor that gives the best DG operation. Based on the network＇s total load demand, four DG sizes are selected. They are used to form energy loss curves for each bus and then for determining the optimal DG options. The study shows that by defining the energy loss minimization as the objective function, the time-varying load demand significantly affects the sizing of DG resources in distribution networks, whereas consideration of power loss as the objective function leads to inconsistent interpretation of loss reduction and other calculations. The devised technique was tested on two test distribution systems of varying size and complexity and validated by comparison with the exhaustive iterative method （EIM） and recently published results. Results showed that the proposed technique can provide an optimal solution with less computation.     

Optimum lipid production using agro-industrial wastewater treated microalgae as biofuel substrate

Abundant wastewater discharges from palm oil industries in tropical nations being a valuable resource of biodiesel need proper exploration. Research hinted that such wastewater as economical nutrient source or substrate can support the cultivation of microalgae. In this experiment, we have tested the growth and lipid production of five different microalgal strains in palm oil mill effluent (POME). POME as a biofuel substrate is demonstrated to be lucrative for microalgae-assisted lipids production. POME is rich in macro- and micronutrients can be used as a growth medium for algal growth in order to reduce the growth medium cost and environmental pollutions. Among the five microalgal strains tested, Chlorella sorokiniana revealed optimum biomass and lipid production. The productivity was evaluated in terms of chlorophyll content, growth rate, biomass, and lipid content, which discerned to be 0.099/day, 8.0 mg/L day and 2.68 mg/mg cell dry weight (CDW). Furthermore, in this study, an optimization study was carried out to enhance the microalgae to produce high lipid content using carbon-to-nitrogen ratio and different light/dark periods. The presence of nitrogen combined glucose (with a carbon-to-nitrogen ratio 100:7) as an alternative source to carbon displayed higher lipid production of 2.68 (mg/mg CDW) by C. sorokiniana. This study confirms that 8:16 h light/dark condition at C:TN ratio of 100:7 supported to produce high lipid content of 17 mg lipid/mg CDW. The above results revealed that POME could be a suitable growth media for the alga C. sorokiniana to improve the maximum lipid yield for biofuels production.     

A novel adaptive fuzzy predictive control for hybrid systems with mixed inputs

This paper proposes a new adaptive nonlinear model predictive control (NMPC) methodology for a class of hybrid systems with mixed inputs. For this purpose, an online fuzzy identification approach is presented to recursively estimate an evolving Takagi–Sugeno (eTS) model for the hybrid systems based on a potential clustering scheme. A receding horizon adaptive NMPC is then devised on the basis of the online identified eTS fuzzy model. The nonlinear MPC optimization problem is solved by a genetic algorithm (GA). Diverse sets of test scenarios have been conducted to comparatively demonstrate the robust performance of the proposed adaptive NMPC methodology on the challenging start-up operation of a hybrid continuous stirred tank reactor (CSTR) benchmark problem.     

Numerical investigation of effective parameters in convective heat transfer of nanofluids flowing under a laminar flow regime

This article presents a numerical investigation on heat transfer performance and pressure drop of nanofluids flows through a straight circular pipe in a laminar flow regime and constant heat flux boundary condition. Al₂O₃, CuO, carbon nanotube (CNT) and titanate nanotube (TNT) nanoparticles dispersed in water and ethylene glycol/water with particle concentrations ranging between 0 and 6 vol.% were used as working fluids for simulating the heat transfer and flow behaviours of nanofluids. The proposed model has been validated with the available experimental data and correlations. The effects of particle concentrations, particle diameter, particles Brownian motions, Reynolds number, type of the nanoparticles and base fluid on the heat transfer coefficient and pressure drop of nanofluids were determined and discussed in details. The results indicated that the particle volume concentration, Brownian motion and aspect ratio of nanoparticles similar to flow Reynolds number increase the heat transfer coefficient, while the nanoparticle diameter has an opposite effect on the heat transfer coefficient. Finally, the present study provides some considerations for the appropriate choice of the nanofluids for practical applications.     

Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics

Ultra-high-performance concrete (UHPC) is a recent class of concrete with improved durability, rheological and mechanical and durability properties compared to traditional concrete. The production cost of UHPC is considerably high due to a large amount of cement used, and also the high price of other required constituents such as quartz powder, silica fume, fibres and superplasticisers. To achieve specific requirements such as desired production cost, strength and flowability, the proportions of UHPC’s constituents must be well adjusted. The traditional mixture design of concrete requires cumbersome, costly and extensive experimental program. Therefore, mathematical optimisation, design of experiments (DOE) and statistical mixture design (SMD) methods have been used in recent years, particularly for meeting multiple objectives. In traditional methods, simple regression models such as multiple linear regression models are used as objective functions according to the requirements. Once the model is constructed, mathematical programming and simplex algorithms are usually used to find optimal solutions. However, a more flexible procedure enabling the use of high accuracy nonlinear models and defining different scenarios for multi-objective mixture design is required, particularly when it comes to data which are not well structured to fit simple regression models such as multiple linear regression. This paper aims to demonstrate a procedure integrating machine learning (ML) algorithms such as Artificial Neural Networks (ANNs) and Gaussian Process Regression (GPR) to develop high-accuracy models, and a metaheuristic optimisation algorithm called Particle Swarm Optimisation (PSO) algorithm for multi-objective mixture design and optimisation of UHPC reinforced with steel fibers. A reliable experimental dataset is used to develop the models and to justify the final results. The comparison of the obtained results with the experimental results validates the capability of the proposed procedure for multi-objective mixture design and optimisation of steel fiber reinforced UHPC. The proposed procedure not only reduces the efforts in the experimental design of UHPC but also leads to the optimal mixtures when the designer faces strength-flowability-cost paradoxes.

     

Graph attention-based collaborative filtering for user-specific recommender system using knowledge graph and deep neural networks

Knowledge and Information Systems - Collaborative filtering suffers from the issues of data sparsity and cold start. Due to which recommendation models that only rely on the user–item...     

Effect of polyethylenimine addition and washing on stability and electrophoretic deposition of Co3O4 nanoparticles

In this work, the parameters of cobalt oxide suspension such as conductivity, zeta potential, particle size, stability, and finally the electrophoretic behavior of particles in the absence and presence of polyethylenimine (PEI) in acetone medium were investigated. Also, the effects of washing on the stability and electrophoretic deposition of Co₃O₄ were studied. Characterization of the obtained layer by optical microscopy revealed that there was no deposition in the suspension without PEI, while a uniform layer was formed in the presence of PEI additive. Scanning electron microscopy (SEM) results confirmed the uniformity of layer obtained in acetone using PEI additive. Moreover, SEM results demonstrated that more porous microstructures were obtained at longer deposition durations. The difference in the porosity of the layers, as indicated by the SEM micrographs, is attributed to increase in the deposition time.     

Synthesis and characterization of alumina supported sub-nanoporous SiO2–10 wt%TiO2 membrane for nitrogen separation

Membrane of SiO₂–10TiO₂ was produced by dip coating on mesoporous-titania-coated macroporous alumina support. The amorphous SiO₂–10TiO₂ top layer with thickness of 0.9 μm was obtained as a homogenous and defect free surface. Gas permeation tests show a decrease in permeation in order of CH₄ > N₂ for the support, and N₂ > CH₄ for the multilayer membrane. The membrane delivering promised single gas separation factor for N₂/CH₄ (1.75). It was also observed that the membrane separation factor in binary gas mixture was 12–15% of the single gas separation factor, while the permeation revealed a decrease in gas mixture.     

SOLUTION OF POPULATION BALANCE EQUATIONS IN EMULSION POLYMERIZATION USING METHOD OF MOMENTS

In this work, the method of moments is used for solution of population balance equations appearing in modeling of emulsion polymerization (EP). The zero-one model without coagulation effect and the pseudo-bulk model including coagulation effect are investigated as two common approaches for modeling EP processes. The fixed quadrature method is used to close the set of moment equations, and the maximum entropy approach is applied to reconstruct the particle size distribution from a finite number of its moments. Comparing the results with those obtained by the high-precision finite volume technique indicates that, despite the low computational load of the moment method, it has an acceptable accuracy. These features support use of the moment technique for other applications such as on-line control or optimization in particulate processes.