Analyzing the determinants of carbon emissions from transportation in European countries: the role of renewable energy and urbanization

Clean Technologies and Environmental Policy - The continuous growth of transport sector and the increase in carbon emissions from transportation attract the attention of policy makers in...     

Modeling and simulation of a fully air swept ball mill in a raw material grinding circuit

A raw material grinding circuit was modeled using plant data. Samples were collected from around the circuit and, following a crash stop, from inside the mill. The size distributions of the samples were determined down to a few microns. Using the data from inside the mill a modeling approach, based on perfect mixing, was developed. The modelling approach implicitly assumes that the mixture of feed materials broken is homogenous from the breakage point of view. The air classification around the circuit was modeled using the efficiency curve approach. In order to measure the success of the method the circuit performance was predicted by simulation studies while it was operating at different conditions. The results were then compared with the measured data. It is concluded that modeling gives a useful quantitative indication of what may occur in fully air swept mills.     

Vibrations of stretched damped beams under non-ideal boundary conditions

A simply supported damped Euler-Bernoulli beam with immovable end conditions are considered. The concept of non-ideal boundary conditions is applied to the beam problem. In accordance, the boundaries are assumed to allow small deflections and moments. Approximate analytical solution of the problem is found using the method of multiple scales, a perturbation technique.     

An experimental study on the performance parameters of an experimental CI engine fueled with diesel-methanol-dodecanol blends

The effects of using diesel-methanol-dodecanol blends including methanol of various proportions on a CI engine performance are experimentally investigated. The methanol concentration in the blend has been changed from 2.5% to 15% with the increments of 2.5%, and 1% dodecanol was added into each blend to solve the phase separation problem. Experimental study has been conducted on a single-cylinder, water-cooled CI engine. The engine has been operated at different compression ratios (19, 21, 23 and 25) and the engine speed was varied from 1000 to 1600 rpm at each compression ratio. The performance parameters such as torque, effective power, specific fuel consumption and effective efficiency for each blend at various conditions are calculated depending on the experimental data. It was concluded that among the different blends, the blend including 10% methanol (DM10) is the most suited one for CI engines from the engine performance point of view. Improvements obtained up to 7% in performance parameters with this blend without any modification to engine design and fuel system are very promising.     

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.